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1、2024STATE OF EDGE AI REPORTExploring the dynamic world of Edge AI applications across industriesAbout the Contributors IntroductionChapter I:Edge AI Market Analysis and TrendsEdge AI Market LandscapeIndustry Adoption and Trends of Edge AIChapter II:Healthcare and Medical ApplicationsReal-Time Patien

2、t Monitoring A Fast,Cost-Effective,and Reliable Way to Add Smart Features to Healthcare DevicesOn-Device Medical Imaging and Smart RehabilitationClinical Trials with Real-World DataPrediction,Detection,and Tracking of Disease OutbreaksChapter III:Industrial IoT and ManufacturingEnabling Predictive M

3、aintenance with Edge AIInnovate Predictive Maintenance with Arduinos Open-Source Edge AI SolutionsReal-Time Quality ControlReinforcing Quality Control with Edge AI Tools from RenesasFacilitating Supply Chain OptimizationTransforming Supply Chain Organization:OKdos Edge AI Solutions in ActionHuman-Ro

4、bot Collaboration and Worker Safety and TrainingChapter IV:Smart Cities and Urban Infrastructure Navigating the Future of Traffic ManagementLeopard Imaging Pedestrian Detection Solutions for Smart City Powered by Sony AITRIOSAdvancing Digital Infrastructure and Smart Buildings Edge AI Vision Sensor

5、for Buildings with Rapid AI Model DevelopmentEnsuring More Sustainable Cities with Edge AISafeguarding Public Health and SafetyChapter V:Retail and Customer ExperienceInventory Management PerfectedScaling E-Commerce with Vision-Based AI for Inventory Management and AutomationCustomer Behavior Analys

6、is:Personalization at ScaleQualcomm Technologies:The Power to Transform Retail with On-Device Generative AICheckout Automation for Reduced Waiting TimesChapter VI:Energy Efficiency and SustainabilitySmart Energy Monitoring for Consumer Awareness and Cost SavingsInnovations in Renewable Energy Integr

7、ation Powered by Edge AIProactive Smart Grid Management through Edge AI SolutionsTransforming the Conventional Energy Sector with Edge AIChapter VII:Agriculture and Food ProductionImproved Crop Monitoring and Analytics for Maximizing YieldStreamlining Livestock Management with Edge AIFood Quality As

8、surance4 78911141516171718202122242426272830 313234353637383940424344464748495052535455Chapter VIII:Automotive and TransportationAutonomous Vehicles:Enhancing Safety and Efficiency on the RoadReal-Time Traffic Management and Smart ParkingElectric Vehicles:Enhancing Battery Management and Charging In

9、frastructure OptimizationFleet Management and Traffic Sign RecognitionChapter IX:Generative AI at the EdgeLLMs at the Edge:The ChallengesEnabling LLMs and Edge Computing ConvergenceExamples of Real-Life Edge LLM SystemsGenerative AI Meets Axeleras In-Memory Computing at the EdgeLLM at the Edge:Trans

10、forming Multiple Industries at OnceScaling Generative or Multi-modal AIChapter X:Edge AI Challenges and Real-World MitigationsTackling Edge AIs Key ChallengesOvercoming the Challenges of Edge AINavigating Resource-Constrained Environments with Edge AIEdge AI on Embedded Low-Power Devices Will Transf

11、orm IoTAI Adoption Challenges:Where Are We on the Human Front?Chapter XI:The Future of Edge AIEmbracing the Latest Innovations in Edge AIFuture of Edge AI:A New World of Efficiency and SustainabilityEdge AI Adoption Levels Shaping the FutureShaping the Future of Industry with WiFi-enabled Edge AIRep

12、ort PartnertinyML FoundationSponsorsRenesasSynapticsArduinoNordic SemiconductorSyntiantMouser ElectronicsAxeleraOKdoBrainchipRelay2ImagimobAdditional ContributorsEta ComputeQualcomm SonyLeopard ImagingAbout Wevolver565758595960616263646566686972737475767780818386868888 909294969810010110210310410610

13、6 10610710710845About the ContributorsHow this report came togetherThis technology report came to fruition thanks to the collaborative efforts of multiple contributors.Samir Jaber,the editor-in-chief,alongside John Soldatos and Ravi Rao,the supporting authors,have played integral roles in researchin

14、g,developing,and editing the report content.The insights provided by our sponsors and contributors have significantly enriched the content.Special acknowledgment goes to Jessica Miley,the content director at Wevolver,for her consistent support and leadership throughout the process.Each contributors

15、dedication and input are deeply valued,highlighting the collective effort that has gone into creating this report.Samir Jaber,Editor-in-ChiefLeipzig,GermanySamir Jaber is an editor,writer,and industry expert on technology,science,and engineering topics.He is an online content specialist with an acad

16、emic background in mechanical engineering,nanotechnology,and scientific research.Samir has comprehensive experience working with major engineering and technology companies as a writer,editor,content manager,and digital marketing consultant.He is a featured author in 30+industrial magazines with a fo

17、cus on Artificial Intelligence(AI),the Internet of Things(IoT),3D printing,Autonomous Vehicles(AV),nanotechnology,materials science,and sustainability.Samir is also an award-winning engineering researcher in the fields of nanofabrication and microfluidics.John Soldatos,Co-authorAthens,GreeceHonorary

18、 Research Fellow at the University of Glasgow John Soldatos holds a Ph.D.in Electrical&Computer Engineering from the National Technical University of Athens(2000)and is currently an Honorary Research Fellow at the University of Glasgow,UK(2014-present).He was Associate Professor and Head of the Inte

19、rnet of Things(IoT)Group at the Athens Information Technology(AIT),Greece(20062019),and Adjunct Professor at the Carnegie Mellon University,Pittsburgh,PA(20072010).He has significant experience working closely with large multi-national industries(e.g.,IBM,INTRACOM,INTRASOFT International)as an R&D c

20、onsultant and delivery specialist while being a scientific advisor to various high-tech startup enterprises.Dr.Soldatos is an expert in Internet-of-Things(IoT)and Artificial Intelligence(AI)technologies and applications,including IoT/AI applications in smart cities,finance(Finance 4.0),and industry(

21、Industry 4.0).Ravi Rao,Co-authorAhmedabad,IndiaRavi is a software engineer at one of the worlds largest IT consulting firms.He works on digital transformation projects that help streamline operations for customers.Ravis education in Power Electronics Engineering and his professional experience have

22、equipped him with expertise in electrical,electronics and computer engineering.Ravi has authored two IEEE research papers:one exploring the integration of AI and IoT in irrigation systems and another assessing the implementation of a curriculum to stimulate innovation among engineering students.Addi

23、tionally,Ravi has earned a patent for a project aimed at enhancing independence for individuals with mobility challenges.Outside work,Ravi is passionate about writing on technology,gadgets,and gaming.He also loves engaging in conversations about the latest advancements in technology.7IntroductionIn

24、the midst of all the technological advancements happening today,one term has taken over almost every discourse about technology:artificial intelligence(AI).Once a thrilling notion for Sci-Fi movies,AI is now an indisputable reality,and it has taken multiple industries by storm.From Large Language Mo

25、dels(LLMs)enabling fantastic chatbots like ChatGPT to Internet-of-Things(IoT)devices bringing about Industry 4.0,AI has found applications and use cases across almost every modern industry.While the majority of AI implementations have taken place centrally on cloud servers,there has been a strong pu

26、rsuit for intelligence to be deployed locally at the edge.Artificial intelligence at the edge is referred to as Edge AI,and it is the convergence of AI model deployment with edge computing.By bringing computational power closer to the data source,Edge AI offers real-time data processing on edge devi

27、ces with reduced latency,higher bandwidth efficiency,enhanced reliability,and more robust security and privacy.This has the potential to propel AI capabilities beyond its current centralized,cloud-based structure.As the adoption of Edge AI continues to grow almost exponentially,we will witness a par

28、adigm shift in how businesses deploy and utilize AI,especially with edge computing decentralizing data processing.Industries that require real-time data processing capabilities are the primary industries in Edge AIs scope of impact and are expected to experience a radical transformation as a result.

29、Embracing this dramatic shift is essential for businesses that seek to stay ahead of the competition and leverage the benefits of real-time,context-aware decision-making.But knowing that Edge AI impacts every industry differently,how can a business in a specific industry embrace this technology to e

30、nsure it keeps pace with its rapid evolution?Accordingly,Wevolver has partnered with several major companies and thought leaders in this space to produce this holistic report on the state of Edge AI in 2024 and its applications in every relevant industry,from healthcare to industrial IoT and manufac

31、turing,smart cities,retail,energy,agriculture,and automotive.Each industry is covered by a dedicated chapter that provides industry-specific insights,descriptions,and examples,complemented by featured sections of real-world case studies.The report also provides a peek into the exciting generative AI

32、 technology and its convergence with edge computing before delving into the challenges still hindering Edge AI today.Finally,it gives a glimpse into the future of Edge AI and how it will develop in the years to come.This is the second in-depth report on Edge AI by Wevolver after its inaugural report

33、 in 2023,which focused primarily on the technological side of Edge AI.This report explores Edge AIs application side and its impact on the various industries mentioned above.Will Edge AI be one of the worlds fundamental technologies going forward?Will it continue to reshape our industries and become

34、 ubiquitous?If so,how?Lets find the answers in the chapters below.Samir JaberEditor-in-Chief89Edge AIs growing momentum over recent years has not been a stroke of luck or an unexpected turn of events.Its been brewing in the research spheres for quite a while now.In fact,the way AI influences technol

35、ogy has been a discussion since the mid-1900s,ever since Alan Turing set the standards for the“thinking machine,”and Christopher Strachey wrote the first successful AI computer program,followed by Arthur Samuel of IBM pioneering machine learning.From then on,AI went on a long rollercoaster ride of r

36、ises and dips,going through hype cycles and significant interest and funding all the way down to not one but two“AI winters”before the turn of the millennium.Nonetheless,in recent years,especially going into the 2020s,AI has taken yet another leap,but this time,it has taken off.With major developmen

37、ts in machine learning and the advent of technologies like cloud computing,edge computing,and fog computing,AI has significantly benefited from the data processing capabilities brought about by these technologies.Cloud computings ability to process massive amounts of data simultaneously and edge com

38、putings ability to process data locally and in real time are both crucial for AIs mass adoption.This helps AI make informed decisions in fractions of the time that it would take humans,thus improving processes and systems across different industries and creating new and optimal ways of working.As a

39、result,AI has witnessed significant growth in adoption rate across various large organizations,reaching 42%in 2023,according to the IBM Global AI Adoption Index 2023,with as many as 40%of organizations actively exploring the use of AI in their business operations.AI has once again captured the spotl

40、ight and this resurgence has brought about a new wave of possibilities and opportunities to explore.Chapter I:Edge AI Market Analysis and TrendsToday,a lot of data processing is being decentralized from large cloud data centers to smaller localized data centers and edge devices.This has enabled the

41、emergence of Edge AI,which processes data at or near the source of data generation.Many organizations are deploying edge functionalities,resulting in energy-efficient,low-latency applications with real-time performance.Edge AI offers significant data protection and security benefits,making it an att

42、ractive proposition for organizations across sectors to use edge computing features for various use cases.In the following sections,we take a look at the Edge AI market to see how its been responding to the technologys potential,and we explore how different industries are adopting Edge AI into their

43、 workflows and systems.Edge AI Market LandscapeFor a technology that is moving quickly on the Gartner hype cycle for AI,Edge AI is constantly finding new use cases in various industries,and its adoption is expected to grow further and faster.In fact,Gartner analysts predict that edge computing techn

44、ologies will gain traction and maturity in 2024,especially with the significant drop in the cost of developing and deploying edge systems thanks to technical innovation in this space.Such improvements in the technology have enabled the Edge AI market to witness remarkable growth over recent years.Ac

45、cording to Market.US research,the global Edge AI market is expected to surpass the USD 140 billion mark by 2032,a considerable rise from just over USD 19.1 billion in 2023.That is a compound annual growth rate(CAGR)of almost 26%across nine years.The growth of the Edge AI market reflects the increasi

46、ng integration of technology into various aspects of modern life.With the proliferation of IoT devices across industries,from manufacturing to healthcare,vast volumes of data are being generated continuously.This data holds significant potential for insights and optimization,but traditional centrali

47、zed processing methods often struggle to handle real-time data processing without having to deal with latency issues.Edge AI addresses this challenge by bringing AI and machine learning algorithms closer to where the data The Edge AI Market is expected to reach USD 143.6 Billion by 2032,an exponenti

48、al rise from its 2023 value of USD 19.1 Billion(Credit:market.us)“Simplicity is key in the tech world:a solution is only as successful as it is widely accepted,adopted,and applied.Thats why Arduinos mission is to democratize technologies like Edge AI,making it an accessible option for people with di

49、fferent backgrounds and in all industries to solve problems,create value,and grow.”Fabio Violante,CEO of Arduino1011is generated,at the“edge”of the network.This localized approach allows for real-time processing and analysis,minimizing latency,reducing bandwidth usage,and enabling quicker decision-m

50、aking.For instance,within the realm of autonomous vehicles,split-second responses to changing road conditions are crucial for safety.Edge AI enables these vehicles to process sensor data onboard,ensuring faster reaction times.Moreover,advancements in semiconductor technology have played a crucial ro

51、le in enabling more powerful and energy-efficient edge computing devices.These devices are capable of handling complex AI algorithms while remaining efficient enough to operate in resource-constrained environments,such as remote industrial sites or within wearable devices.The rollout of 5G technolog

52、y further amplifies the capabilities of Edge AI solutions.With its significantly enhanced connectivity and data transfer speeds,5G facilitates seamless communication between edge devices and central systems,enabling faster data transmission and response times.This is particularly beneficial in scena

53、rios such as healthcare monitoring,where timely analysis of patient data can have life-saving implications.In essence,the growth of the Edge AI market represents a response to the evolving demands of industries and engineers in a data-driven world.Its about leveraging innovation to optimize processe

54、s,enhance efficiency,and ultimately improve the way industries operate.Industry Adoption and Trends of Edge AI“Under the radar thus far,edge is set to become a ubiquitous lever of scale and reinvention as artificial intelligence(AI)including generative AIdriven applications become pervasive in enter

55、prise functions and operations.”This is what researchers at Accenture articulated in their 2023 study on Leading with Edge Computing.This statement sums up the current state of Edge AI in a nutshell.With edge computing permeating a lot of industries and application areas,it is reaching a level of ub

56、iquity that renders its implementation almost a necessity.In fact,Accentures survey uncovered that 83%of executives across multiple industries think that in order to stay competitive in the future,edge computing will be essential.And many are fearing missing out on all of Edge AIs benefits if they d

57、o not act quickly and incorporate it into their workflows,products,and services.However,the adoption of Edge AI is not uniform.While some companies regard edge as a key differentiator to bring AI into their core business,others still struggle with fully leveraging the technologys benefits,mostly due

58、 to them considering it a standalone technology and using it in ad-hoc projects.Adopting edge computing strategically and integrating it with existing cloud strategies has shown the best outcomes:Advanced users of edge are four times more likely to achieve accelerated innovation,nine times more like

59、ly to increase efficiency,and seven times more likely to reduce costs(Accenture,2023).Edge moves computing closer to users and devices at the edge of the network,where it is the closest possible to data sources(Credit:Accenture)How organizations have been using AI in the past two years(Credit:IBM)12

60、Looking at Edge AI from the industry level,we see a similar distribution,with some industries already utilizing the technology almost ubiquitously and others still exploring its potential.The manufacturing industry seems to take the lions share of revenue with approximately 31%thanks to the integrat

61、ion of automation and real-time insights,capitalizing on Edge AIs benefits in defect detection,reduced latency,real-time decision-making,cost efficiency,and data security.Following it is the automotive and transportation sector,especially with the recent ACES trends(autonomous driving,connectivity,e

62、lectric vehicles,and shared mobility)leading the industry,all of which require Edge AI,albeit at varying levels.Furthermore,traffic management is seeing significant developments by integrating Edge AI into its sensors,cameras,and traffic management systems(TMS).While other industries like healthcare

63、,retail,energy,and agriculture acknowledge the potential of Edge AI,they are yet to adopt it at the same level as manufacturing and automotive.That being said,Edge AI is seemingly showing clear patterns and trends that are influencing the future of data science and machine learning(DSML)across many

64、industries.As Gartner outlined in 2023,one trend that is leading the way is Edge AI as a promise of responsiveness.Edge AI promises quicker decision-making by executing AI algorithms locally,bypassing the need for the Cloud or remote data center connections.This reduces latency and enhances system r

65、esponsiveness.Converging AI and edge computing leads to more efficient and potentially energy-saving solutions.Gartner has forecasted that,by 2025,more than half of data analysis by deep neural networks will occur at the point of capture in an edge system,a significant increase from single-digit per

66、centage points in 2021.This shows the significance of Edge AI in the years to come and how its implementation will continue to grow and penetrate various systems and workflows.1415Edge AI in Healthcare In recent years,the emergence of Edge AI has played a significant role in the digital transformati

67、on of the healthcare sector.With the shift of AI capabilities closer to the source of data generation,Edge AI can enable real-time,data-driven clinical decisions,enhance accuracy,and boost privacy and data protection in various healthcare applications where sensitive data are used.There is a multitu

68、de of Edge AI use cases in medical and clinical settings,including use cases that improve existing capabilities and others that enable functionalities that were hardly possible before the advent of AI at the edge.These capabilities fulfill different objectives and serve the needs of various stakehol

69、ders,as outlined in the Edge AI use cases presented below.Chapter II:Healthcare and Medical ApplicationsEdge AI is transforming the medical industry by enabling digital diagnosis and remote patient monitoring(Credit:Jenny Wang,Seeed Studio)Real-Time Patient MonitoringContinuos patient monitoring is

70、crucial to ensuring timely detection of warning signs and providing appropriate interventions.Edge AI plays a vital role in patient monitoring use cases by enabling real-time analysis of data collected from various data sources like wearable devices,sensors,and electronic health records(EHR).Based o

71、n data processing at the edge,patient monitoring systems can instantly identify anomalies and trigger appropriate actions,such as alerting healthcare providers or autonomously adjusting medication dosages.Hence,real-time patient monitoring powered by Edge AI can significantly improve patient outcome

72、s and reduce the risk of adverse events.1617A Fast,Cost-Effective,and Reliable Way to Add Smart Features to Healthcare DevicesThe healthcare industry has seemingly infinite vital signs and symptoms to track;finding ways to turn these signals into medically useful information can mean game-changing i

73、mprovements for the industry and its patients.Edge AI offers an exciting opportunity to propel medical care and healthful living to an impactful new level.It is particularly suitable for the privacy requirements of this industry because data can be kept on the device instead of being sent to the clo

74、ud.Among those testing the way forward is Imagimob,a company intent on making it easier to deploy machine learning on edge devices.With their collection of off-the-shelf models that can be added to healthcare and wearable products with ease and an end-to-end development platform for solving problems

75、 with custom Edge AI models,Imagimobs tools can change healthcare for the better.The Fastest Way to Launch Smart AI FeaturesA major barrier to creating smarter medical devices is the typically extensive development process.With Imagimobs Ready Models,AI features can easily be deployed onto existing

76、products without the significant time,cost,or machine learning expertise required for custom development.Ready Models currently available for use in healthcare include audio-based models for coughing and snoring detection.And this is just the beginning additional Ready Models under development inclu

77、de models based on Radar,IMU,and Capacitive Sensing,which can be used for presence detection,fall identification,and more.Developing Sharper Models in Real Time Imagimobs machine learning development platform,Imagimob Studio,covers the entire workflow from data collection to quick deployment in a he

78、althcare product.Their recently launched Graph UX interface helps produce even higher quality models by letting engineers see them working in real-time.Take,for instance,a model that identifies coughing in a healthcare setting.In a scenario where coughs are being under-identified,an engineer can pin

79、point which data the model is failing to classify and make direct improvements.“Graph UX makes models more robust as you have greater visibility and can identify problems fast,”says Alexander Samuelsson,Imagimobs CTO.“This is a great advantage for the healthcare industry,where the ability to adapt q

80、uickly to new scenarios and information can be critical.”The possibilities are limitless.Learn more about how Imagimobs Edge AI solutions are helping to build the intelligent products of the future with applications in healthcare and beyond at .On-Device Medical Imaging and Smart RehabilitationTradi

81、tional medical imaging techniques require transferring large amounts of data to remote servers for processing,which creates concerns about latency and privacy.With Edge AI,medical imaging can be performed directly on the device,such as ultrasound machines and MRI(Magnetic Resonance Imaging)scanners.

82、This ensures enhanced accuracy and reduced diagnosis time while increasing data protection.In particular,Edge AI algorithms can detect abnormalities in scans and provide immediate feedback to radiologists,enabling more efficient diagnoses.Furthermore,Edge AI addresses privacy concerns by minimizing

83、the transmission of sensitive patient data over the network.Technology trends like TinyML,embedded machine learning,and on-device neuromorphic computing are likely to increase the number and variety of embedded medical imaging applications in the years to come.On-device inference for medical decisio

84、n-making is set to become smaller yet faster,smarter,and more privacy-friendly.Similarly,rehabilitation processes can benefit significantly from real-time feedback in order to optimize therapy routines and improve patient outcomes.Edge AI empowers smart rehabilitation devices with on-device inferenc

85、e,allowing them to analyze sensor data and provide real-time feedback to patients during rehabilitation sessions.For instance,an Edge-AI-powered prosthetic limb could adjust its movements based on the patients gait analysis toward a more natural and personalized experience.Based on Edge AI,rehabilit

86、ation devices can adapt to individual needs in real time,which enhances the effectiveness of therapies and reduces the need for constant supervision.Clinical Trials with Real-World DataReal-time medical adherence monitoring is essential in clinical trials to ensure accurate data collection and proto

87、col compliance.Edge AI enables the integration of real-time monitoring devices that track medication intake,adherence to treatment plans,and patient vitals.By leveraging Edge AI algorithms,researchers can assess,in real time,whether participants are following the prescribed protocols.Such functional

88、ities can provide valuable insights into the effectiveness of treatments while reducing the burden of manual data collection.Most importantly,they are key to ensuring that clinical trials adhere to the prescribed protocols,which increases their credibility.Edge AI on real-world data is,therefore,a t

89、echnology that will be increasingly adopted and used by Contract Research Organizations(CROs)in a variety of clinical trials worldwide.1819Prediction,Detection,and Tracking of Disease OutbreaksProcessing local data at the edge is particularly valuable for predicting and tracking disease outbreaks.Ba

90、sed on the analysis of medical records,wearables data,and environmental factors in real-time at the edge,healthcare organizations can detect early signs of potential outbreaks.This is fundamental to enabling proactive measures to prevent their spread.Edge AI can also ensure privacy and data protecti

91、on by minimizing the reliance on centralized data repositories,which mitigates the risk of unauthorized access to sensitive healthcare information.Many healthcare applications can also benefit from decentralized learning models at the edge,such as federated learning.Federated learning is a technique

92、 in which global models are developed by combining locally trained models.It is already used in various disease detection and prediction use cases to enhance accuracy.The latter stems from the fact that global models turn out to be more accurate than local models,as they are,in principle,trained on

93、more data.In the scope of a federated learning approach,edge devices(e.g.,smartphones or IoT sensors)locally train AI models using data from individual patients or individual care service providers(e.g.,care centers).Encrypted model updates are then sent to a central server,where they are combined t

94、o create a global model without compromising the privacy of individual data.This approach has been successfully applied by organizations like the World Health Organization(WHO)during the COVID-19 pandemic,where local models trained on edge devices contribute to a comprehensive global model that aide

95、d in disease detection and prediction.Decentralized learning paradigms will be increasingly adopted as the number of connected medical devices and other healthcare data sources grows rapidly.Managing huge numbers of data sources in a centralized manner increases data protection risks and creates lat

96、ency and power efficiency concerns.This is why it has to be avoided whenever possible and substituted by decentralized systems powered by Edge AI.Overall,Edge AI applications in healthcare offer numerous benefits,ranging from real-time patient monitoring and on-device medical imaging to clinical tri

97、als with real-world data and smart rehabilitation devices.Based on Edge AI paradigms,healthcare organizations can achieve real-time performance,enhanced accuracy,and improved patient outcomes.Moreover,privacy and data protection are prioritized due to the reduced transmission of sensitive informatio

98、n over networks,minimizing the attack surface of the healthcare application.In the coming years,Edge AI technologies will empower healthcare professionals to work faster and more effectively,delivering tangible benefits to millions of patients worldwide.Artificial intelligence-enabled remote patient

99、 monitoring architectures(Credit:Shaik,T.et al.,WIREs)2021Edge AI Enabling Industry 4.0 The advent of Industry 4.0 is gradually revolutionizing industrial production based on the digitization of physical processes and the introduction of cyber-physical production systems on the manufacturing shop fl

100、oor.A significant number of Industry 4.0 applications are based on the integration of Artificial Intelligence(AI)and the Internet of Things(IoT).Edge AI is,nowadays,one of the most in-demand implementations of this AI-IoT combination,bringing AI capabilities closer to the network edge and enabling r

101、eal-time data processing and analysis on edge devices.Edge AI improves the performance,timeliness,and security of various use cases of the manufacturing sector,including predictive maintenance,real-time quality control,and supply chain optimization.These use cases deliver tangible benefits to key st

102、akeholders such as manufacturers,maintenance teams,quality control departments,supply chain managers,plant operators,and workers.Chapter III:Industrial IoT and ManufacturingEnabling Predictive Maintenance with Edge AIPredictive maintenance is critical to ensuring continuous machine functionality and

103、 preventing costly downtime.Today,most predictive maintenance applications deploy machine learning models within cloud infrastructures to predict asset parameters,such as Remaining Useful Life(RUL).Edge AI can be vital in enhancing these use cases to minimize downtime and optimize maintenance.In thi

104、s direction,it is possible to leverage real-time calculations of Remaining Useful Life(RUL)and End of Life(EOL)to provide real-time insights into the health and performance of machinery and other industrial assets.For instance,it is possible to use Edge AI to detect anomalies and deviations from exp

105、ected behaviors within very short timescales,i.e.,almost in real time.Based on signals about these deviations,maintenance teams can make timely and informed decisions to proactively address potential failures and optimize the maintenance schedules for key assets.Edge AI also reduces the attack surfa

106、ce of predictive maintenance and intelligent asset management systems;ML models for RUL calculation can be executed within edge clusters or devices instead of within cloud data centers.This is the basis for increasing the security of predictive maintenance and intelligent asset management solutions.

107、An example of applying Edge AI for predictive maintenance of a Forge Edge robot by Ready Robotics(Credit:Dominic Pajak).“Edge AI isnt just a technology;its the driving force behind smarter,more efficient,and more responsive industrial ecosystems.By deploying AI capabilities at the network edge,we en

108、able engineers to pioneer the forefront of innovation,leveraging real-time insights and tangible advancements to elevate industrial processes to new heights.”Richard Curtin,SVP Technology OKdo&RS Group2223Innovate Predictive Maintenance with Arduinos Open-Source Edge AI SolutionsThe convergence of I

109、oT and Edge AI is crucial to address the limitations of traditional cloud-based systems by enabling real-time decision-making closer to the source of data generation something particularly relevant in manufacturing,where split-second responses can prevent costly machinery failures.Today,relying on p

110、owerful products like Arduino Pros Opta and Portenta Machine Control represents the most robust and user-friendly catalyst to embrace Edge AI and revolutionize predictive maintenance in factories big and small.Opta is gaining huge traction in the industrial world as the innovative micro PLC that is

111、quick and easy to use because it supports PLC standard languages in addition to the Arduino programming experience.Portenta Machine Control is a fully centralized,low-power industrial control unit that several businesses have successfully chosen to empower new and existing machinery with IIoT capabi

112、lities,easily adapting it to a wide range of applications thanks to its modular design.For example,AROL a leading provider of capping machines has paired the Portenta Machine Control with Arduino Pros Nicla Sense ME modules to integrate monitoring and predictive maintenance capabilities into the equ

113、ipment they sell,leveraging efficient data processing and wireless communication to significantly enhance the value they offer customers.Spanish engineering company Engapplic chose Arduinos Portenta Machine Control to monitor air compressors efficiency for a demanding client in the automotive field,

114、allowing for the timely detection and even prediction of any anomalies.The result is a cost-effective and future-proof PoC that reduces downtime and saves energy.While predictive maintenance is a great reason to add Edge AI capabilities to your machines,its not the only one.Incorporating the brains

115、of the Portenta Machine Control into the installed base think professional kitchen appliances or office printers and copier machines allows manufacturers to not only improve user experience and customer service but also access entirely new business models,such as usage-based rental contracts.Arduino

116、s commitment to the open-source philosophy brings additional advantages to industrial clients investing in advanced predictive maintenance solutions:No Vendor Lock-In:The companys open-source approach ensures that you have the flexibility to program,customize,and scale their solutions independently,

117、avoiding vendor lock-in.Shorter Learning Curve:The user-friendly nature of Arduinos products allows engineers to quickly grasp and implement solutions,even with a limited programming background,facilitating efficient adoption by existing teams.Complete Customization:You can access,upgrade,and modify

118、 Arduino solutions freely,with the companys support,ensuring a seamless process.This flexibility is particularly valuable for companies looking to innovate and adapt to changing requirements.Integrating IoT and Edge AI for predictive maintenance in manufacturing is a trans-formative journey one that

119、 Arduinos robust products and open-source approach can help navigate with ease and no limits to innovation.Arduino Nicla Sense MEArduino Opta WiFiArduino Portenta Machine Control2425Real-Time Quality ControlMaintaining high product quality is crucial for manufacturers to meet customer expectations a

120、nd comply with industry standards.Edge AI enables real-time quality control by utilizing on-device inference for anomaly and defect detection.Based on AI model deployment directly on edge devices,manufacturers can analyze sensor data in real time,identify anomalies or defects,and trigger immediate a

121、ctions to rectify issues.This approach significantly reduces latency in quality control processes.Hence,it enables manufacturers to promptly detect and address quality-related issues to reduce waste,foster sustainability,and ensure high-quality products in the market.Reinforcing Quality Control with

122、 Edge AI Tools from RenesasEdge AI is gaining attention in quality control due to its ability to run AI/ML algorithms on edge devices,enabling scalable,real-time solutions.This approach,compatible with Renesass diverse MCU and MPU edge devices,is ideal for large-scale manufacturing environments.It o

123、ffers advantages such as real-time analysis,reduced latency,improved data safety,and cost savings by eliminating the need for extensive data storage and communication infrastructure.Moreover,Edge AI systems can adapt to dynamic manufacturing processes,accommodating variations efficiently.In a typica

124、l manufacturing line for welding,for example,multiple robots perform various automated tasks.Here,end-of-line testing is crucial for quality control,particularly for manufacturing-dedicated components.The challenge is to detect porosity and burn-through in the welding process using conventional meth

125、ods.The need,therefore,is to reduce the dependency on human inspections and enable low-cost solutions with real-time anomaly detection and high accuracy.Strict quality control ensures no faulty components are misclassified as good,avoiding costly recalls for automotive OEMs(though some tolerance exi

126、sts for misclassifying good components as bad,which are discarded before shipment).Accordingly,the core tasks are identifying,collecting,and analyzing the data set(s),then developing unification of the performance(inference),footprint,and accuracy in the best-fit AI/ML model.To ensure liable and hig

127、h-quality data set(s),selecting the sensors and their positioning within the system is crucial.Renesas Reality AI Tools provides the users with an automatic exploration of the sensor data and performs the analytics to find the best sensor(or combination of sensors)and the best placement.With the AI-

128、driven feature Discovery in Reality AI Tools,users have an advanced automatic exploration of the Sensor Data execution that generates optimized AI/ML Models at the end based on the explored data set(s).To accelerate the development and deployment of dedicated Edge AI/ML solutions,Reality Al Tools he

129、lps by using a machine-learning-guided process to explore the data and create a set of custom transformations(feature spaces)that define anomalies or maximize separation of classes/correlation to a target variable.The user can inspect the feature spaces and generate a time-frequency heatmap to show

130、the most important structure for model accuracy.Achieving the highest rate of accuracy while meeting 0%False Negatives is a result of increasing the high level of manufacturing efficiency and productivity as a standard.The Renesas scalable product portfolio comprising 16Bit,32Bit,and 64Bit MCUs and

131、MPUs,together with the rich ECO system and development infrastructure for embedded systems and Edge AI/ML solutions,is a perfect match for system solution packages.It targets and enables fast prototyping,evaluation,development,and deployment of your next embedded edge AI/ML solution.Reality AI Tools

132、(Credit:Renesas)2627Facilitating Supply Chain OptimizationEfficient inventory management and logistics are crucial for maintaining a streamlined manufacturing process.Manufacturers are constantly trying to identify supply chain challenges in order to take remedial actions and implement optimizations

133、.With Edge AI,they have opportunities to identify and confront issues faster than ever before.In particular,Edge AI can revolutionize supply chain optimization by enabling timely detection of real-time events.Specifically,data collection and analysis from various sensors and devices enable Edge AI s

134、ystems to generate real-time insights into inventory levels,demand fluctuations,and transportation conditions.Manufacturers can use these insights to make data-driven decisions,such as optimizing stock levels,adjusting production schedules,or rerouting shipments.This can greatly improve overall supp

135、ly chain efficiency,reduce costs,and ensure timely delivery of products.The Renesas product portfolio(Credit:Renesas)Transforming Supply Chain Organization:OKdos Edge AI Solutions in ActionPrecision,efficiency,and safety are paramount in supply chain organization,which necessitates integrating cutti

136、ng-edge technology.OKdo offers a wide range of AI-embedded products that drive faster and better decisions through data collection and analysis at the edge.Useful Sensors Tiny Code Reader:A Giant Leap in Inventory ControlTiny Code Reader from Useful Sensors is a ground-breaking solution in supply ch

137、ain,equipped with an onboard processor,camera,and AI accelerator.With its compact form factor,Tiny Code Reader featuring Qwiic connectivity seamlessly integrates into diverse systems,making it ideal for streamlined stock management.This miniature marvel,with a size akin to a coin,maximises spatial e

138、fficiency in warehouses,appealing to engineers who value flexible layouts.Its cost-effective design doesnt compromise technical prowess,offering a comprehensive developer guide with example codes for popular systems.Envision a warehouse with unparalleled accuracy.Tiny Code Reader reduces errors and

139、transforms inventory management for engineers at the forefront of supply chain innovation.OStreams ROCK 5 AIO:Empowering Predictive Maintenance&Safety PrecisionPredictive maintenance is crucial for preventing downtime and ensuring the smooth flow of the supply chain.The ROCK 5 AIO,with its integrate

140、d 3 TOPS AI acceleration and pre-integrated 91 open-source AI models,brings a new level of sophistication to this arena.Real-time equipment analysis predicts failures and prevents downtime whilst mitigating associated costs.Its AI capabilities extend to monitoring conveyor systems,detecting anomalie

141、s,vibrations,and wear indications,facilitating predictive maintenance scheduling to ensure uninterrupted goods flow and staff downtime.Furthermore,its robust AI processing enables real-time recognition for worker safety,ensuring compliance with safety equipment rules by monitoring clothing and movem

142、ent,thus enhancing overall workplace safety.2829Radxa ROCK 5A:Real Time Supply Chain Management At Your FingertipsIn todays hyperconnected world,supply chain success relies on real-time data processing.The Radxa ROCK 5A,powered by the advanced Rockchip RK3588S SoC,ensures exceptional processing effi

143、ciency with its Octa-core Arm DynamIQ CPU and Arm Mali G610MC4 GPU.Enhanced AI capabilities,driven by the onboard 6TOPS NPU,improve digital display interactivity and intelligence,particularly in computer vision and image processing.HDMI outputs support resolutions up to 8Kp60 for seamless real-time

144、inventory updates,while predictive analytics are displayed via dual micro HDMI ports.With 40 pin GPIO interface and versatile USB Type C and HDMI connectivity,the ROCK 5A seamlessly integrates with sensors,cameras,and monitors,offering a unified platform for data analysis.Supporting various operatin

145、g systems including Android 12 and Debian/Ubuntu Linux,it provides flexibility for tailored digital display solutions,advancing supply chain technology.OKdo Leading the Charge in Supply Chain InnovationOKdos Edge AI solutions are actively reshaping conventional practices.The Tiny Code Reader,ROCK 5

146、AIO,and ROCK 5A signify the practical application of AI,transforming inventory control,predictive maintenance,and ensuring peoples safety.Design engineers in the manufacturing sector find a valuable ally in these advanced technologies,offering a gateway to heightened efficiency,unwavering reliabilit

147、y,and enhanced safety protocols within the supply chain.Human-Robot Colla-boration and Worker Safety and TrainingWith the rise of automation in manufacturing,human-robot collaboration(HRC)has become an essential aspect of optimizing operational efficiency.Edge AI is an effective contributor to enabl

148、ing successful HRC.Deploying AI models on edge devices as part of Edge AI systems provides real-time feedback from the robot to humans and vice versa,facilitating seamless collaboration between human workers and robots.Real-time feedback improves synchronization,enhances safety measures,and allows e

149、fficient task allocation.Overall,this Edge-AI-powered,collaborative approach enables manufacturers to leverage the strengths of both humans and robots,leading to increased productivity and improved operational outcomes.On the safety front,Edge AI can significantly boost worker safety by leveraging A

150、I models to detect hazardous situations in real time.The continuous monitoring of data from sensors and devices enables Edge AI systems to identify potential risks,such as machine malfunctions,abnormal movements,or hazardous conditions,in a timely manner.Early warnings and notifications can be sent

151、to workers or supervisors,allowing them to take immediate action and prevent accidents.In this direction,Edge AIs ability to analyze data on edge devices ensures minimal latency in detecting safety-related issues,which keeps workers safe in real time.It is also positive that this increased An engine

152、er using the help of a Kuka robot in a smart manufacturing setting(Credit:Zenoot)safety comes with improved privacy,as there is no need to transmit workers personal data outside the factory(e.g.,to a cloud data center).Furthermore,at the core of adapting to technological advancements and improving w

153、orkforce efficiency is upskilling workers.Edge AI can contribute effectively by enabling real-time feedback during training tasks.With the integration of augmented reality(AR)and virtual reality(VR)applications,Edge AI can provide interactive training experiences,allowing workers to learn and practi

154、ce in simulated environments.Moreover,real-time feedback based on AI models helps workers understand their performance,identify areas for improvement,and adjust their actions accordingly.This iterative training approach enhances worker skills and knowledge,ultimately leading to increased productivit

155、y.Overall,the emergence and rise of Edge AI in the manufacturing sector offers a wide range of use cases that can significantly improve operational efficiency,product quality,supply chain management,worker safety,and workforce skills.Most of these improvements stem from Edge AIs ability to process a

156、nd analyze data at the edge,leading to real-time performance,low latency,and enhanced security.3031Chapter IV:Smart Cities and Urban InfrastructureEdge AI Transfor-ming Urban Areas Urban environments are grappling with complex challenges across transportation,infrastructure,energy,waste management,a

157、nd public safety,underscoring the pressing need for adaptation in our rapidly evolving cities.For instance,traditional traffic control measures,including infrastructure expansion and static traffic signals,fall short of resolving the complexities of modern urban congestion.The static nature of these

158、 systems,which rely on predetermined timing for traffic signals,fails to adapt to real-time traffic conditions,resulting in inefficiencies and unnecessary delays.Similarly,challenges in energy efficiency and sustainability in smart buildings are made worse by outdated systems,leading to excessive co

159、nsumption.Meanwhile,waste management systems lag behind in handling urban refuse volumes,and public safety in dense environments calls for more advanced surveillance and responsive emergency services.Recognizing these limitations,there is a growing emphasis on leveraging Edge AI to pioneer intellige

160、nt solutions.Navigating the Future of Traffic ManagementEdge AI enhances urban mobility through technologies like AI-powered traffic prediction models that analyze vast amounts of data from cameras and sensors.This data helps optimize public transport routes and schedules,leading to more efficient s

161、ervices.For example,AI-enabled buses equipped with sensors can adjust routes in real time based on traffic conditions,reducing delays and improving passenger experiences.Its integration into public transportation systems has also led to significant advancements in the predictive maintenance of vehic

162、les and effective passenger flow management.By harnessing real-time data,these systems offer commuters updated information,ensuring a more seamless and efficient travel experience.However,the integration of Edge AI extends beyond just route optimization.A core component of intelligent traffic manage

163、ment is the optimization of traffic signal sequences.Edge AI devices like adaptive traffic lights use real-time data from traffic cameras and sensors to optimize signal timing,reducing congestion.These systems can adjust green light durations based on real-time traffic flow,significantly improving t

164、raffic conditions.Edge AI can also contribute to broader urban planning and daily commutes.The introduction of Edge AI in traffic management has brought about substantial improvements in commute times and overall urban mobility,bringing about reduced congestion,which in turn leads to lower emissions

165、,thus aligning with environmental sustainability goals.Furthermore,AI-based traffic simulation models can predict the impact of new construction on traffic flow,aiding in more informed urban planning decisions.These advancements aid urban planners in developing more efficient and responsive city lay

166、outs,further enhancing the quality of life for urban residents.Edge AI can be utilized to collect,visualize,and make decisions based on real-time weather and traffic data.(Credit:Department for Transport(DfT),UK,CC License,no changes made)“Edge AI is the architect of change for our cities.As enginee

167、rs,we hold the power to reshape our cities,infusing intelligence into their very foundations.We can build a future where cities seamlessly adapt,optimize,and protect,where innovation meets necessity,and where every line of code shapes the landscape of progress.“Richard Curtin,SVP Technology OKdo&RS

168、Group3233Leopard Imaging Pedestrian Detection Solutions for Smart City Powered by Sony AITRIOSTraffic safety is a top concern for big cities.Accidents tend to happen both at and between intersections,so a scalable pedestrian detection system is required at an affordable cost.Traffic congestion manag

169、ement is another key issue for the city.To solve these two challenges,Leopard Imaging,a global leader in embedded vision design and manufacturing,collaborates with Sony AITRIOS to develop“Dolphin”advanced intelligent imaging solutions powered by Sonys IMX500 smart sensors.This state-of-the-art solut

170、ion has won first place in the 2023 Pedestrian Safety Challenge sponsored by the tinyML Foundation and The City of San Jos in 2023.Sonys IMX500 series smart sensor is Sonys latest smart imaging technology to enable smart camera implementation.The smart sensor has built-in image processing and AI acc

171、eleration,thus reducing the need for a high-power-consuming processor inside the camera.Leopard Imagings Dolphin intelligent solution is created for optimized cost and performance,low maintenance and installation costs,small form factor,and different available system design configurations to meet va

172、rious applications,price points,and use cases.By taking advantage of Sonys smart sensors unique capabilities and AITRIOS backbone,Leopard Imagings latest Dolphin intelligent vision solutions can address many challenges facing smart city infrastructure proliferation.By training the model with 11 giga

173、bytes of data,Leopard Imaging has achieved an average of 90%accuracy in detecting pedestrians,bicyclists,and vehicles.Besides the increase in accuracy,Leopard Imaging has also achieved:Tailoring the YOLOV8n model to fit in the IMX500 Obtaining a large Field of View(FOV)Scanning image Region of Inter

174、est(ROI)to improve accuracy Implementing self-cleaning lensingThe proposed solution also considers making the installation as easy as possible,leading to TCO reduction.The AI camera can be integrated with existing streetlight management systems by connecting a camera to a network lighting controller

175、 IoT gateway with a single cable.Thanks to this integration,users can install cameras on the LED lighting pole without having any power and data network installation.This approach helps municipalities not only protect existing investments but also add value to existing infrastructure.Additionally,th

176、is solution is very flexible and can be adapted to various use cases in the city.The same system can be used for pedestrian detection,traffic counting,curbside parking occupancy detection,flood/snow detection for road management,and dynamic lighting control by simply changing the AI model from a rem

177、ote site.In conclusion,the partnership between Leopard Imaging and Sony AITRIOS represents a transformative stride in the evolution of smart city technology.Leopard Imagings vision and detection solution“Dolphin”in-action (Credit:Leopard Imaging)3435Advancing Digital Infrastructure and Smart Buildin

178、gsOne of the keystones of smart urban infrastructure is energy optimization in buildings,where Edge AI plays a pivotal role.Edge AI enables the real-time management of energy consumption by integrating temperature,motion,and light sensors,along with advanced actuators for heating,ventilation,and air

179、 conditioning(HVAC)control.This system fine-tunes energy use based on occupancy,temperature,and lighting conditions,exemplified by adaptive lighting systems that adjust according to time,occupancy,and environmental factors.Such innovations not only reduce energy waste but also contribute to a more s

180、ustainable urban ecosystem.Beyond energy management,Edge AI is instrumental in managing the flow of pedestrians within complex structures like offices,malls,and train stations.By monitoring and analyzing movement patterns,these systems ensure efficient and safe pedestrian traffic.In cases of emergen

181、cy or security threats,the systems can guide occupants to safety in real time,showcasing Edge AIs critical role in public safety.The deployment of Edge AI in building management marks a new era of efficiency and operational excellence.With capabilities like predictive maintenance,these intelligent s

182、ystems preemptively address potential issues,minimizing downtime and extending the lifespan of building infrastructure.Sensors detect early signs of equipment failure,and AI algorithms predict when maintenance is needed,preventing downtime and extending the lifespan of building infrastructure.Transi

183、tioning from building efficiency to broader urban practices,the application of Edge AI in waste management exemplifies another leap toward sustainability.Smart waste collection systems,powered by Edge AI,optimize routes and schedules for waste collection based on real-time data from waste sensor-equ

184、ipped bins.This approach streamlines the process and supports the broader goal of reducing the carbon footprint and sustainable urban living.Integrating Edge AI in waste management also has a notable impact on public health and safety.By ensuring more efficient and timely waste collection,these syst

185、ems contribute to cleaner urban environments,reducing the risk of health hazards associated with accumulated waste.This is particularly important in crowded urban areas,where effective waste management is key to maintaining public health and hygiene standards.Dustbins can use sensors and Edge AI to

186、check garbage accumulation and predict when they have to be emptied next.Edge AI Vision Sensor for Buildings with Rapid AI Model DevelopmentImagine easily setting up battery-powered sensors in your existing commercial building,safely gathering valuable insights into building usage without a disrupti

187、ve installation process.That was the ambition for this innovative,Edge-AI-powered,vision-based sensor project by Eta Compute,powered by the latest low-power inference semiconductors and cutting-edge software tools to speed the otherwise time-consuming development of optimized ML models for accurate

188、people counting.Why People-Counting MattersUnderstanding how buildings are used optimizes resources and enhances experiences:Optimize Resource Allocation:Adjust real estate portfolio levels,identify underutilized areas,and streamline layout based on actual measured occupancy,reducing waste and costs

189、.Improved Sustainability:Optimizing resource allocation,HVAC settings,and energy consumption based on real-time occupancy data contributes to more sustainable building usage.Boosted Safety and Security:Trigger alerts for overcrowding or unauthorized access to safeguard people and property.Edge AI:Th

190、e Power of On-device IntelligenceOur vision sensor leverages low-power Edge AI for on-device people-counting,delivering key advantages:Privacy-First:Keep sensitive vision data local,ensuring privacy compliance and project acceptance.Reduced Bandwidth:Minimize network traffic and congestion while low

191、ering power requirements.Real-time Insights:Make immediate decisions based on near-instantaneous data.3637Easy Installation:The Game ChangerThe most revolutionary aspect of this solution is its battery-powered,wireless design.Wired sensor installations requiring power and data have been the bane of

192、IoT in buildings,limiting deployments to proof-of-concept trials and blocking widespread rollouts across building portfolios.Our sensor provides up to a 3-year battery life and offers:Minimal Disruption:Mount anywhere in minutes;no drilling,wires,or professional skills needed.Scalability:Add or remo

193、ve sensors as your needs evolve without complex infrastructure adjustments.Better Data:Place sensors in previously inaccessible areas for richer insights.Aptos Edge ML:Unleashing AI for EveryoneDeveloping low-power AI models has been a slow,manual process requiring unicorn expertise in both AI and e

194、mbedded systems.To overcome the challenges,we developed a radical new Edge AI toolkit tailored for creating such models,and it is called Aptos.Now commercially available,Aptos provides:No-code Tools:Embedded with the knowledge of capabilities and constraints of target silicon devices and compilers,i

195、t automatically builds optimal ML models.Neural Network Algorithmic Search and Optimization:Automatically explores neural network architectures and hyperparameters for optimal power,latency,and accuracy for your chosen low-power semiconductor.Model Quantization:Dramatically reduces the models footpr

196、int and computational demand for efficient edge device operation.By combining Edge AI,low-power technology,and the ease of ML development using Aptos,this battery-powered vision sensor project unlocks the full potential of commercial spaces and mindful use of resources.Ensuring More Sustainable Citi

197、es with Edge AIEdge AI is crucial for real-time monitoring of urban environmental factors,such as air quality and noise levels.By continuously analyzing data from various sensors spread across the city,these intelligent systems provide valuable insights into the environmental health of urban areas.T

198、his information is vital for city administrations to make informed decisions regarding pollution control and urban planning,leading to healthier living conditions for residents.Edge AI also offers innovative solutions for monitoring and managing water resources.These systems can detect leaks,predict

199、 usage patterns,and optimize water distribution,ensuring efficient and sustainable water management.This technology is fundamental in densely populated cities,where water demand is high,and resource management is vital to sustainability.Safeguarding Public Health and SafetyEdge AI devices like smart

200、 cameras and drones are used for real-time surveillance,quickly identifying and responding to safety threats.By processing data on the spot,these systems rapidly identify potential safety threats or criminal activities,enabling swift response from law enforcement and emergency services.Edge AI also

201、plays a pivotal role in disseminating real-time information to the public through smart digital signage.This technology is crucial in a variety of scenarios,from providing daily updates to broadcasting crucial information during disasters or emergencies.These dynamic digital platforms enhance the ci

202、tys ability to communicate effectively with its residents,fostering a well-informed and prepared community.Integrating Edge AI into surveillance and information systems not only enhances urban safety but also significantly strengthens community trust and cohesion by fostering a more secure and infor

203、med urban environment.The deployment of Edge AI in public safety initiatives has profound implications for community trust and safety.By enhancing surveillance capabilities and improving information dissemination,these technologies make cities safer and bolster the publics trust in urban infrastruct

204、ure and governance.This,in turn,contributes to a more secure and cohesive urban community.Throughout this chapter,weve seen Edge AIs multifaceted impact from revolutionizing traffic management and building operations to advancing waste management and enhancing public safety.As we look to the future,

205、the potential of Edge AI in urban environments continues to expand.With ongoing technological advancements,we can anticipate even more sophisticated applications that will further enhance the quality of urban life.These advancements promise to streamline city operations and forge deeper connections

206、between the urban landscape and its residents,fostering a more responsive,adaptive,and harmonious living environment.The journey of smart,Edge-AI-powered cities is just beginning,and its full potential is yet to be realized in the quest for smarter,more sustainable urban futures.Leveraging Edge AI,s

207、mart digital signage can adapt content based on real-time data and audience demographics,ensuring tailored and impactful communication for diverse urban populations.(Credit:Kriesten objekt design GmbH,CC License)3839Chapter V:Retail and Customer ExperienceEdge AI Redefining Retail The retail sector

208、is undergoing a significant transformation driven by Edge AI,responding to mounting margin pressures and evolving consumer expectations.According to a report by McKinsey&Company,retailers,from grocery to specialty stores,face margin pressures ranging from 100 to 500 basis points,highlighting a criti

209、cal need for efficiency gains.Edge AI offers a solution to improve margins by 300 to 500 basis points through technologies like self-checkout systems and digital shelves.By leveraging the capabilities of Edge AI,retailers are not only addressing the immediate challenges of margin pressure but also g

210、etting ready for a future where technology-driven solutions redefine the shopping experience.Inventory Manage-ment PerfectedEfficient inventory management is a balancing act of critical importance.Retailers are constantly grappling with the twin challenges of stockouts and overstock each carrying it

211、s own set of repercussions.Stockouts can result in lost sales and erode customer trust and satisfaction,leading to potential long-term implications on brand loyalty.Conversely,overstock situations tie up capital,increase storage costs,and lead to wastage,especially for perishable goods.These invento

212、ry misalignments directly impact a retailers revenue and customer experience,making effective inventory management a foundation of successful retail operations.In response to these challenges,the integration of Edge AI in inventory management has emerged as a transformative solution.For instance,sma

213、rt shelves equipped with weight sensors and RFID tags can automatically monitor stock levels.These shelves relay real-time data to Edge AI systems,which then analyze and predict stock requirements,minimizing the occurrences of stockouts and overstock.Another example is the use of AI-powered cameras

214、that track inventory movement,providing instant data on stock levels and customer preferences.Edge AI also enables retailers to seamlessly integrate online and offline retail experiences.This technological advancement hinges on Edge AIs capability to provide real-time updates on inventory levels,ens

215、uring accurate and current information is available to customers regardless of their shopping channel.Such real-time synchronization is essential in todays omni-channel retail landscape,where a smooth transition between online browsing and in-store purchasing is expected.For instance,advanced predic

216、tive analytics and machine learning algorithms are employed to anticipate stock requirements,while digital price tags updated via Edge AI systems maintain price consistency across platforms.By utilizing these Edge AI-driven solutions,retailers effectively reduce the occurrence of stockouts and overs

217、tock,enhancing inventory management efficiency and ensuring a seamless and satisfying shopping journey for the customer.Shelves with digital displays for price tags(Credit:MediaTile)“Edge AI transforms retail from transactional to experiential,revolutionizing customer interaction.Its not merely abou

218、t selling products;its about crafting personalized journeys that engage shoppers,fostering loyalty and driving growth.”Samir Jaber,Editor-in-Chief4041Scaling E-Commerce with Vision-Based AI for Inventory Management and AutomationRetail and e-commerce center around customer satisfaction,reflected thr

219、ough online product reviews and ratings.While scaling retail or e-commerce,hidden pitfalls like inefficient warehouse utilization and inaccurate inventory affect their seller rating by customers,siphon profits,and stifle growth.This poses a challenge for online retailers that sell the same goods on

220、multiple websites.The importance of omnichannel for e-commerce means inventory needs to be accurately reflected across all sales channels,instantly.When its not,order cancellations or wrongly shipped items can decrease seller ratings,resulting in deprioritization of the vendors product listings,a lo

221、ss of sales influence of up to 80%,or even vendor suspensions until issues are addressed.The e-commerce boom brought FrioCONNECT challenges:inventory was inaccurate,counts were tedious,and 2%of listings were oversold or incorrectly shipped.When 20%of items dont have the potential for restocking,comp

222、anies like FRIOCONNECT cover the sunk costs associated with the mistake.To manage inventory,the process of counting 100,000+items and 5,000 SKUs took five employees 10 days to complete.Without the counts,overselling or sending wrong items became more frequent.Gaining real-time,high-precision invento

223、ry visibility,once a luxury,was now a strategic imperative.FrioCONNECT recognized that automation of cycle counts was necessary to ensure correct inventory was represented on sales channels and to support the customer experience.Partnering with system integrator AVALTON and Sonys AITRIOS team,the PH

224、OTOBAR solution was created,leveraging Sonys AITRIOS platform and IMX500 intelligent image sensor.In 2022,FRIOCONNECT dispatched over 75,000 items and realized a net profit of nearly 5-8%per product.They determined,based on their 7,800 square foot Doylestown,PA warehouse,that 348 IMX500-equipped dev

225、ices would be needed to cover 58 racks and 696 shelves.The solution automates cycle counting in real time,using edge devices to capture shelf images,process on-chip,and send only inventory-relevant data to an API for analysis and counting.Now,the 10-day-long process can be completed twice a day or a

226、s often as necessary.Empowered by vision-based inventory management,FrioCONNECT increased order picking by 20%,optimized existing space,and is poised to gain back a projected$31,200 in error-based lost revenue from 2022,breaking even on their deployment of vision AI within a year,now with plans to e

227、xpand the use of the technology across remaining warehouses.Sonys vision-based AI for inventory management and automation(Credit:Sony)4243Customer Behavior Analysis:Personali-zation at ScaleIn todays retail landscape,personalization is increasingly vital,with customers seeking experiences that cater

228、 specifically to their preferences and behaviors.Edge AI is instrumental in this shift,enabling the collection and analysis of customer data in real time.Brands leverage AI engines to parse through data gathered from online browsing,past purchases,and in-store interactions,which allows for tailored

229、product recommendations.This use of data creates personalized shopping experiences and targeted marketing strategies,enabling retailers to meet the growing consumer demand for personalization with greater precision and effectiveness.Beyond online personalization,Edge AI is transforming the in-store

230、experience.Through the integration of interactive displays and the analysis of data from in-store customer interactions,Edge AI aids retailers in comprehensively understanding and catering to consumer preferences.Retailers can employ smart cameras and sensors within the store to track and analyze cu

231、stomer movements and their interactions with products in real time.This rich stream of data informs decisions on product placement,store layout,and marketing strategies.By leveraging these insights,shops are empowered to optimize the environment,making it more engaging,tailored,and responsive to the

232、 unique behaviors and preferences of their customers.Implementing Edge AI in customer behavior analysis presents mutual benefits for retailers and consumers.For retailers,the enhanced understanding of customer preferences leads to increased sales and better inventory management.Consumers,on the othe

233、r hand,enjoy a more personalized shopping experience with recommendations and offers that are relevant to their interests and needs.This synergy is often supported by industry data and case studies,showcasing significant improvements in customer satisfaction and loyalty.Smart carts can transform the

234、 shopping experience by providing customers with product recommendations,navigating them through the store,and facilitating seamless checkout processes(Credit:Caper).Qualcomm Technologies:The Power to Transform Retail with On-Device Generative AIOn-device AI fuels a more capable,cost-efficient,relia

235、ble,private,secure,and promising path forward for retail.Capable of working in harmony with cloud AI,edge devices deliver a faster,more efficient,and highly optimized AI with computing power you can rely on.Qualcomm technologies are helping retailers stay competitive in the digital age by transformi

236、ng retail with on-device,generative AI.Last year,the breakthroughs in generative AI were monumentally exciting.Aware of its vast potential for several years,Qualcomm Technologies,Inc.has made it so that AI is holistically engineered into our processors.This year,in-store and online shopping are expe

237、cted to remain equally popular.As physical retail regains momentum,we see an opportunity to fulfill consumers expectations by enabling seamless and integrated in-store experiences.From on-device Generative AI applications for front-line workers and retail analytics to biometric Point-of-Sales(POS)pa

238、yments and frictionless self-checkout and loss prevention,Qualcomm processors power many commercially available retail solutions and help deliver real results through:Generating insights for retailers decision-making Boosting loss prevention to address the industrys$112B“retail shrink”problem Automa

239、ting manual tasks to augment the workforce and minimize error rates Enhancing overall customer experience as consumers increasingly return to in-person shoppingOur ecosystem of top-notch customers and collaborators is making these transformations a reality.Their products address many of the primary

240、challenges retailers face in areas such as frictionless checkout,loss prevention,inventory management,and operational analytics while also enhancing the all-around shoppers journey.Learn more about how Qualcomm technologies are transforming retail in 2024 and explore further in this infographic.For

241、more information,check out Qualcomm Technologies retail solutions.4445Checkout Automa-tion for Reduced Waiting TimesOne of the perennial pain points in the retail experience is the checkout process.Traditional checkout methods are often plagued with long queues and transactional delays,detracting fr

242、om customer satisfaction and efficiency.These inefficiencies not only frustrate customers but also impact the overall throughput of retail operations Edge AI steps in as a game-changer,offering innovative solutions to streamline the checkout process.Examples of such innovations include automated che

243、ckout systems and smart carts.Automated checkout systems,which use computer vision and sensor fusion,are capable of identifying items in a cart and processing transactions without the need for manual scanning.This approach significantly speeds up the checkout process,enhancing customer satisfaction

244、by reducing wait times.Smart carts,on the other hand,are equipped with barcode scanners and payment systems that allow customers to scan and pay for items as they shop,further reducing queue times.Another application of Edge AI in retail is through self-checkout kiosks.These kiosks utilize machine l

245、earning algorithms to quickly identify items,offer various payment options,and even provide personalized offers based on the customers shopping history.This not only enhances the checkout experience but also reduces wait times,thereby improving overall customer satisfaction and efficiency in retail

246、operations.By minimizing the time spent in queues and making transactions more seamless,retailers can ensure a more positive end-to-end shopping experience for their customers.Moreover,these technologies often lead to better resource allocation within stores,as employees freed from traditional check

247、out roles can focus on customer service and other value-added activities.The integration of specific Edge AI devices and technologies in retail is not just a futuristic concept but a present reality.From smart shelves to checkout automation,these innovations are reshaping the retail landscape,offeri

248、ng high levels of efficiency,personalization,and customer satisfaction like never before.As we look ahead,the continued advancement and integration of these technologies promise to revolutionize the retail experience further,making it more adaptive,responsive,and customer-centric.In addition to secu

249、rity and surveillance,cameras can play a crucial role in gathering valuable insights into customer behavior,enabling personalized shopping experiences.4647Chapter VI:Energy Efficiency and SustainabilityInnovating with Edge AI The global demand for energy is at an all-time high,driven by population g

250、rowth and economic development.This relentless consumption exerts immense strain on our natural resources and contributes significantly to climate change.In response,improving energy efficiency and sustainability has become an absolute necessity.Initiatives such as COP28 and the global stocktake tha

251、t took place in November 2023 highlight this urgency,establishing ambitious benchmarks for international efforts.The search for solutions must extend beyond traditional methodologies,necessitating innovative approaches.A notable example of such innovation is highlighted in a McKinsey report,which su

252、ggests that adopting AI-driven methods in grids could enhance energy efficiency by 2-5%at the very point of generation,with up to 10%improvement in production and 30%in cost savings.This shift towards Edge AI in areas such as energy consumption monitoring,renewable energy integration,and smart grid

253、management marks the next logical step in harmonizing our energy needs with the planets health.Smart Energy Moni-toring for Consumer Awareness and Cost SavingsSmart metering,bolstered by Edge AI,signifies a significant advancement in the way energy consumption is monitored.This combination not only

254、facilitates real-time monitoring but also propels consumer engagement to new heights.By providing detailed insights into energy usage patterns,consumers are empowered to make informed decisions,leading to more sustainable consumption habits.Edge AI plays a crucial role in this ecosystem by processin

255、g data on the spot,which helps quickly identify areas of excessive use or inefficiency.Building on enhanced consumer awareness,Edge AI extends its impact to the operational level,driving efficiency and sustainability in various sectors.Implementing Edge AI in energy management systems has proven to

256、be a game-changer for cost savings.In sectors such as commercial real estate,Edge AI has been instrumental in optimizing(HVAC systems,resulting in significant energy and cost savings.Manufacturing is another area where Edge AI has made a substantial impact,streamlining energy consumption in line wit

257、h production needs and thus lowering operational costs.These instances exemplify how Edge AI can foster consumer engagement and effectively reduce energy expenditure,aligning economic benefits with sustainability objectives.“Edge AI is a crucial technology in this world of finite resources.For examp

258、le,it allows us to monitor and optimize electricity and water consumption in real time.Manufacturing,agriculture and logistics can thus minimize their impact,leading to huge cost savings and lowering the carbon footprint.”Fabio Violante,CEO of ArduinoEdge-AI-powered smart energy meters revolutionize

259、 energy management by providing precise,real-time data on consumption.4849Innovations in Renewable Energy Integration Powered by Edge AIRenewable energy sources,such as solar and wind,are at the core of our efforts for a sustainable future.Edge AI enhances the integration of these renewable sources

260、by optimizing their operation and efficiency.For solar energy systems,Edge AI algorithms adjust panel angles in real time to capture maximum sunlight.At the same time,such algorithms can optimize the blade rotation of wind turbines to harness wind energy more efficiently.The wind power generation ma

261、intenance market has also expanded,reaching approximately 35.7 billion RMB in China in 2022.Utilizing AI visual inspection,manufacturers have developed systems that ensure over 95%accuracy in detecting critical issues like ice accretion and cracks,significantly reducing maintenance costs and enhanci

262、ng turbine efficiency.By ensuring more reliable and efficient wind power generation,these Edge AI systems support the integration of wind energy into microgrids,which are essential for decentralized energy systems.This not only contributes to the resilience and sustainability of energy supply but al

263、so accelerates the transition towards a more diversified and renewable energy mix,aligning with global environmental and energy security goals.This real-time data processing and decision-making capability of Edge AI ensures that renewable energy sources operate at their peak efficiency,significantly

264、 increasing their output and reliability.As Edge AI continues to refine the efficiency and output of renewable sources,it sets the stage for their increased adoption and integration into global energy systems.In countries leading in renewable energy,such as Germany and Denmark,Edge AI has played a p

265、ivotal role in managing the variability of renewable sources,ensuring a stable energy supply to the grid.Similarly,in remote and emerging regions,Edge AI has facilitated the deployment of microgrids powered by renewable sources,providing reliable energy access to communities.The collaboration betwee

266、n Engie Energy Access and Atlas AI in Kenya serves as a compelling example of how AI can facilitate the deployment of microgrids powered by renewable sources,thus providing reliable energy access to remote and Integrating renewable energy poses challenges due to its variability and the need for adva

267、nced grid management to ensure reliability and stability.emerging regions.By leveraging Atlas AIs predictive analytics and high-resolution geospatial data,Engie was able to identify high-density areas with significant demand potential for off-grid solar solutions.This data-driven approach enabled En

268、gie to target and expand their renewable energy solutions specifically to communities most in need,resulting in a 48%increase in monthly sales in a pilot program.The use of AI in this context not only optimized Engies operational focus and product offerings but also significantly contributed to enha

269、ncing energy access in underserved areas.These examples underline the global applicability of Edge AI in enhancing the scalability and effectiveness of renewable energy,marking a step forward in the global pursuit of sustainability.Proactive Smart Grid Management through Edge AI SolutionsSmart grid

270、management represents a key area where Edge AI is making a significant impact,particularly in balancing supply and demand with greater precision.By leveraging real-time data analysis,Edge AI enables the grid to respond dynamically to changes in energy demand and supply conditions.This not only impro

271、ves the reliability of the energy supply but also enhances the overall efficiency of the grid.For instance,during times of low demand,Edge AI can facilitate the storage of excess energy or manage its redistribution,optimizing grid operations.Another area where Edge AI demonstrates its potential is i

272、n predictive maintenance,where its capabilities let it identify potential system vulnerabilities before they escalate.This foresight allows for preemptive adjustments to the grid,improving its resilience against disruptions and ensuring a stable energy supply.Predictive maintenance,enabled by Edge A

273、I,can significantly reduce downtime and operational costs,further enhancing grid reliability and efficiency.The discussion on smart grids just cant be completed without addressing the crucial integration and enhancement of electric vehicle(EV)charging infrastructure.The widespread adoption of EVs hi

274、nges heavily on a robust and intelligent charging network.In 2023,the European Commission passed a law Optimizing EV charging infrastructure with Edge AI is crucial for balancing grid demands and facilitating the transition to electric mobility(Credit:Ather Energy on Unsplash).50mandating the expans

275、ion of the EV charging infrastructure to match the growth in EV adoption.For every battery-electric car registered in a member state,public charging stations must offer a power output of 1.3 kW.Furthermore,starting in 2025,there will be a requirement to install fast recharging stations with a minimu

276、m power of 150 kW every 60 km along the trans-European transport network(TEN-T),ensuring widespread and efficient charging options for EV users.Taking a look at another initiative,the government of India is focusing on improving the EV charging infrastructure in its capital.The plan involves setting

277、 up at least one charging station every 3 square kilometers,supporting the wider goal of electric vehicles making up 25 percent of all new vehicle registrations by the end of this year.In this regard,Edge AI presents a game-changing opportunity to revolutionize this very infrastructure.Imagine charg

278、ing stations that predict peak usage,dynamically allocate power based on vehicle needs,and even integrate with renewable energy sources for a truly sustainable experience.Through AI algorithms crunching real-time data on station availability,battery health,and even weather patterns,waiting times can

279、 plummet while charging efficiency skyrockets.Predictive maintenance becomes possible,preventing downtime and ensuring smooth operation.Furthermore,edge AI empowers drivers with features like personalized charging plans and real-time updates on station status,fostering a seamless and stress-free jou

280、rney.This revolution extends beyond individual stations,optimizing grid stability by intelligently managing power demands across entire networks.By harnessing the power of edge AI,we can unlock a future where EV charging is not just convenient but efficient,sustainable,and personalized,truly paving

281、the way for a cleaner and more electrified tomorrow.Transforming the Conventional Energy Sector with Edge AIEdge AI is redefining safety and efficiency in the conventional energy sector,as well.On drilling floors,Edge AI-equipped cameras enhance safety by monitoring hazardous conditions in real time

282、,significantly reducing the risk of accidents.Leak detection,traditionally reliant on extensive sensor networks,is revolutionized through Edge AI,where a single camera can identify leaks and other anomalies,streamlining monitoring processes and reducing equipment needs.Furthermore,in transportation

283、and refinery operations,Edge AI optimizes routes and processes,improving efficiency and reducing environmental impact.Illustrating the impact of Edge AI,Aramco,a leading hydrocarbon producer,has markedly improved its efficiency by adopting the technology.In its Khurais oil field,thousands of IoT sen

284、sors have enhanced oil well monitoring,cut power consumption by 18%,and reduced maintenance costs by 30%.The deployment of drones and wearable tech has also slashed inspection times by up to 90%.These applications of Edge AI not only promote operational safety and efficiency but also support the sec

285、tors shift towards more sustainable practices,highlighting the technologys potential to transform conventional energy production and management.Edge AIs transformative impact on the energy sector is undeniable.Beyond smart metering and cost reduction,it optimizes renewable integration and enhances g

286、rid efficiency.While challenges persist,Edge AI represents a pivotal shift towards a sustainable future.Through responsible development and collaborative efforts,we can harness its power to unlock a cleaner,greener,and more resilient energy ecosystem,where sustainability is not a dream but a tangibl

287、e reality.5253Chapter VII:Agriculture and Food ProductionEdge AI Enabling Smart Agriculture As the global population,projected to surge to 8.5 billion by 2030 and 9.9 billion by 2050,demands more food,the agricultural sector faces the dual challenges of increasing production sustainably and ensuring

288、 food security.With the dire need to significantly ramp up production to meet this escalating demand,the adoption of Edge AI becomes not just advantageous but essential.Edge AI is central to transforming food production,enabling advancements in precise management of crop/livestock,efficient resource

289、 utilization,enhanced quality assurance and beyond,addressing a spectrum of challenges with innovative solutions.Improved Crop Monitoring and Analytics for Maximizing YieldEdge AI enhances agricultural productivity by enabling precise monitoring of crop health,growth patterns,and soil conditions.By

290、processing data from an array of sensorssuch as moisture,pH,and nutrient sensors and high-resolution imaging technologies,Edge AI algorithms can identify early indicators of stress,disease,or nutrient imbalances in crops.This timely intervention allows for tailored management practices,such as targe

291、ted application of fertilizers or pesticides,leading to healthier crops and maximized yields.Edge-AI-based systems can go a step further by harnessing real-time data on soil moisture and nutrient levels to optimize irrigation schedules and nutrient application,ensuring crops receive precisely what t

292、hey need for optimal growth.This targeted approach not only conserves valuable resources but is also crucial in drought-prone areas,potentially turning the tide between crop failure and a successful harvest.Furthermore,Edge AI empowers precision farming to become more controlled and accurate.Utilizi

293、ng data-driven strategies,such as targeted irrigation and fertilization tailored to the unique needs of each plant,Edge AI significantly improves crop performance while reducing environmental footprints.Variable rate technology(VRT),for instance,applies water,fertilizers,and pesticides at the right

294、moment and location,maximizing efficiency and minimizing waste.Together,these Edge AI-driven practices represent a leap forward in sustainable agriculture,combining resource conservation with enhanced crop yields.Edge AI also plays a pivotal role in promoting sustainable agriculture by enabling prac

295、tices that conserve resources and reduce chemical usage.By providing detailed insights into crop and soil health,it helps in implementing conservation tillage,cover cropping,and integrated pest management strategies more effectively.This not only supports the health of the ecosystem but also ensures

296、 long-term agricultural productivity and food security.Edge AIs applications extend beyond immediate farm-level benefits,laying Edge-AI-powered drones are transforming agriculture by enabling precise aerial monitoring and data collection,significantly improving crop health analysis and the efficienc

297、y of resource application(Credit:CropWatch).“Integrating Edge AI into agriculture is about leveraging technology to optimize resources,maximize yields,and ensure food security for a growing population.”Samir Jaber,Editor-in-Chief5455a foundation for broader impacts on climate adaptation,economic sus

298、tainability,and informed policy-making.It is instrumental in adapting agricultural practices to changing climatic conditions and managing risks effectively.It also plays a crucial role in biodiversity conservation,monitoring ecosystem health,and promoting practices that sustain biodiversity within a

299、gricultural landscapes.The insights garnered from Edge AI applications can empower farmers with improved economic sustainability and influence agricultural policy-making,ensuring practices that are both productive and sustainable.Streamlining Livestock Manage-ment with Edge AIEdge AI introduces a ne

300、w era in livestock management,focusing on enhancing animal welfare while optimizing productivity.By employing real-time monitoring technologies,Edge AI systems track the health,behavior,and nutritional status of livestock,enabling early detection and treatment of illnesses,stress,or dietary deficien

301、cies.This proactive approach not only improves the welfare of animals but also contributes to more efficient farming operations.From facial recognition for individual animal identification to automated systems for tailored nutrition and health management,Edge AI is transforming livestock care with p

302、recision and personalization.These technologies enable monitoring and management on a per-animal basis,improving the efficiency of breeding programs,and enhancing meat and milk quality.By leveraging Edge AI,farmers can make informed decisions that boost productivity while adhering to high welfare st

303、andards,showcasing a future where technology and traditional farming converge for superior outcomes.Edge AI and IoT can come together and enhance herd management,enabling real-time tracking of health and behavior.(Credit:Nvidia)Food Quality AssuranceEdge AI plays a critical role in minimizing food w

304、aste and mitigating contamination risks throughout the supply chain.By employing advanced imaging and sensor technologies,Edge AI systems can detect early signs of spoilage or contamination in food products,allowing for immediate corrective actions.This capability is instrumental in ensuring that fo

305、od storage conditions are optimized,significantly extending shelf life and reducing waste.Additionally,Edge AI aids in the identification of potential contaminants before products reach consumers,enhancing food safety and quality.The application of Edge AI in food production can go beyond waste redu

306、ction,significantly contributing to higher food safety standards.Through real-time monitoring and analysis,Edge AI can enable the detection of pathogens,toxins,and other harmful substances at various stages of the food supply chain.This proactive approach to food safety can not only help in preventi

307、ng health risks but also boost consumer confidence in food products.Edge AI-driven systems can facilitate compliance with stringent food safety regulations,ensuring that products meet all necessary standards before distribution.Edge AI also streamlines supply chain operations,from demand forecasting

308、 to inventory management,ensuring the efficient delivery of fresh products.This operational efficiency is instrumental in reducing food waste and operational expenses.Furthermore,Edge AI fosters a transparent food system,where consumers gain insights into the food production journey,bolstering confi

309、dence and engagement.Edge AI is at the forefront of todays agricultural revolution,driving advancements in crop monitoring,livestock management,and food quality assurance.Its ability to process and analyze data in real time is transforming traditional farming practices,making agriculture more effici

310、ent,sustainable,and productive.As we look to the future,the continuous evolution of Edge AI holds the promise of further innovations,ensuring food security and environmental conservation for generations to come.5657Chapter VIII:Automotive and TransportationAn Edge-AI-Powered Automotive Sector In rec

311、ent years,Edge AI has proven its value as a game-changer in the automotive industry by enabling real-time performance and various optimizations across different use cases of the transport and mobility ecosystem.Such use cases are found not only within popular AI systems like autonomous vehicles but

312、also in use cases that foster more efficient traffic management.Autonomous Vehicles:Enhancing Safety and Efficiency on the RoadAutonomous vehicles are at the forefront of disruptive innovation in the transportation sector.Edge AI is vital in enabling these vehicles to navigate and make critical deci

313、sions in real time based on the fast processing of a large volume of sensor data.Edge AI technologies are deployed in all five levels of autonomous vehicles,ranging from Level 0(no automation)to Level 5(full automation).Specifically,Edge AI systems improve the real-time functionalities of vehicles o

314、f lower automation levels while boosting the autonomy of vehicles that fall into higher levels of automation.For instance,Edge AI enhances the autonomy of partial-automation vehicles(i.e.,Level 3)by boosting their advanced real-time perception,decision-making,and control functionalities.These functi

315、onalities help autonomous vehicles drive more safely and improve their environmental performance.Nowadays,relevant Edge AI functionalities can be implemented and deployed on different types of embedded devices of modern vehicles,such as On-Board Units(OBUs),enabling fast and real-time inference.Furt

316、hermore,Edge AIs integration with other technological trends,such as Autonomous,Connected,Electric,and Shared(ACES)mobility,unlocks synergistic benefits for the transport ecosystem.Based on Edge AI,ACES technologies can enable collaborative intelligence functionalities close to the field.Such functi

317、onalities include,for example,route optimization,congestion reduction,and enhanced energy efficiency.The Holon Mover,an autonomous mini-bus(Credit:Benteler)“Innovating with Edge AI in automotive isnt just about driving smarter cars;its about redefining the road ahead,enhancing safety,and empowering

318、vehicles to make split-second decisions,making every journey safer and more efficient.Samir Jaber,Editor-in-Chief5859Real-Time Traffic Management and Smart ParkingReal-time optimizations are at the heart of efficient traffic management systems,enabling dynamic adaptations to rapidly changing conditi

319、ons.Here,Edge AI empowers traffic management systems to process data from various sources(e.g.,sensors,cameras,transport infrastructure)in real time.Analyzing such data at the networks edge enables the development of intelligent traffic management solutions that optimize signal timings,prioritize em

320、ergency vehicles,and dynamically adjust traffic flows.This way,Edge AI solutions help reduce congestion and improve transport efficiency.The real-time data processing functionalities of Edge AI are essential in use cases that involve busy intersections where split-second decision-making is required.

321、For such cases,Edge AI enables the development of intersection control systems that identify and prioritize vehicles while being able to detect anomalies or potential hazards.Such functionalities are key to ensuring smooth and safe traffic flows.Furthermore,finding parking spaces in urban environmen

322、ts is one of the most pressing challenges,especially in highly populated megacities.With Edge AI,smart parking systems can provide real-time feedback about parking positions based on on-device AI.These systems leverage edge devices,such as parking sensors and cameras,to monitor parking occupancy and

323、 guide drivers to available spaces efficiently and in a timely fashion.Hence,Edge AIs processing power on edge devices enables smart parking systems characterized by low latency and enhanced real-time decision-making capabilities,improving the overall parking experience for drivers.The Canada Infras

324、tructure Bank(CIB)and Quebec-based charging network operator FLO have announced a plan to install over 2,000 public DC fast charger ports across the country by 2027(Credit:FLO).Electric Vehicles:Enhancing Battery Management and Charging Infrastruc-ture OptimizationDuring the last few years,the world

325、 has increasingly embraced electric vehicles(EVs).EVs require battery management optimizations and the availability of EV charging infrastructures.To this end,Edge AI can facilitate real-time monitoring and analysis of EV batteries in ways that enhance their performance,lifespan,and overall reliabil

326、ity.Moreover,thanks to Edge AI algorithms,EVs can dynamically adjust charging patterns based on factors like battery condition,power availability,and user preferences.At the same time,Edge AIs integration with EV charging stations enables intelligent load management,balances energy demand,and optimi

327、zes charging schedules.Also,the proper distribution of the computational load at the edge can improve the grids stability and efficiency by reducing strain during peak demand.Fleet Management and Traffic Sign RecognitionEdge AI empowers fleet management systems to collect and process a wealth of veh

328、icle data,including location,fuel consumption,driver behavior,and vehicle health.The processing of these data at the edge provides fleet managers with real-time insights into fleet performance for implementing routing optimizations,reducing fuel consumption,and enhancing the operational efficiency o

329、f their fleet.Thus,Edge-AI-driven fleet management solutions can effectively help with cost reduction,improved logistics,and enhanced customer experience.Similarly,driver safety and compliance rely primarily on traffic sign recognition.Edge AI systems enable vehicles to detect and interpret traffic

330、signs in real time.As such,they can provide drivers with real-time information about speed limits,traffic rules,and potential hazards.In this direction,advanced image processing algorithms can be deployed at the edge based on Edge AI approaches like TinyML for on-device image analysis.Therefore,Edge

331、 AI systems can enhance driver awareness,reduce the risk of accidents,and foster compliance with traffic regulations.The above-listed use cases indicate how Edge AI transforms the transport sector to enable real-time functionalities across various use cases.With its ability to process vast amounts o

332、f data at the edge,Edge AI empowers transport stakeholders to enhance safety,optimize operations,and improve the overall efficiency of modern transportation systems.Moreover,it helps reduce the attack surface of transport applications and limit the amount of potentially sensitive data(e.g.,driving p

333、atterns information)from being shared within a cloud infrastructure.Edge AI can also boost privacy and data protection for consumer-facing applications in the transport sector.In the future,Edge AI systems will continue to play a significant role in shaping the evolution of the automotive and transportation industry.6061Chapter IX:Generative AI at the EdgeWhere Gen AI Meets Edge Computing The conv