08664nam a2200757 i 45000010014000000030004000140050017000180060019000350070015000540080041000690200025001100200024001350200025001590200027001840240035002110350025002460350022002710400040002930500027003330720016003600720023003761000033003992450084004322460042005162640098005583000071006563360021007273370026007483380032007744900027008064900044008334900034008775000043009115040041009545050319009955050234013145050275015485050230018235050354020535050372024075050585027795050285033645050312036495050412039615050277043735050253046505050070049035060075049735200896050485210041059445300029059855380036060145380078060505451303061285880057074316500020074886500029075086500037075376500060075747100054076347760049076888300031077378300045077688300034078138560059078479780750355933IOP20251030124830.0m    eo  d        cr cn |||m|||a240715s2024    enka   fob    000 0 eng d  a9780750355933qebook  a9780750355926qmobi  z9780750355919qprint  z9780750355940qmyPrint7 a10.1088/978-0-7503-5593-32doi  a(CaBNVSL)thg00083956  a(OCoLC)1446520278  aCaBNVSLbengerdacCaBNVSLdCaBNVSL 4aQA76.583b.B464 2024eb 7aUYQ2bicssc 7aCOM0040002bisacsh1 aBenedict, Shajulin,eauthor.1 aEdge intelligence :bdeep learning-enabled edge computing /cShajulin Benedict.3 aDeep learning-enabled edge computing. 1aBristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) :bIOP Publishing,c[2024]  a1 online resource (various pagings) :billustrations (some color).  atext2rdacontent  aelectronic2isbdmedia  aonline resource2rdacarrier1 a[IOP release $release]1 aIOP series in next generation computing1 aIOP ebooks. [2024 collection]  a"Version: 20240601"--Title page verso.  aIncludes bibliographical references.  apart I. Introduction. 1. Edge intelligence -- 1.1. Edge computing -- 1.2. History of edge computing -- 1.3. Edge intelligence -- 1.4. Advantages : edge intelligence -- 1.5. Challenges : edge intelligence -- 1.6. Applications -- 1.7. The need for this book -- 1.8. Potential readers -- 1.9. Organization of the book8 a2. Edge computing architectures -- 2.1. Internet of Things -- 2.2. IoT-enabled components -- 2.3. Learning techniques : machine learning -- 2.4. Learning techniques : deep learning -- 2.5. Edge-AI architectures -- 2.6. Conclusion8 a3. Edge OS and programming models -- 3.1. Operating systems -- 3.2. Objectives of OS in edge devices (Edge OS) -- 3.3. Taxonomy of OS : toward edge -- 3.4. Edge OS : examples -- 3.5. Process states in edge-OS -- 3.6. FreeRTOS : OS for embedded devices -- 3.7. Conclusion8 apart II. Learning techniques. 4. Edge intelligence : learning techniques -- 4.1. Edge intelligence : a need -- 4.2. Federated learning -- 4.3. DNN splitting -- 4.4. Transfer learning -- 4.5. Gossip learning -- 4.6. Conclusion8 a5. Inference/prediction techniques -- 5.1. Learning stages -- 5.2. Inference knowledge levels -- 5.3. Distributed inferences -- 5.4. Distributed inferences : implementation strategies -- 5.5. Interactive versus batched inferences -- 5.6. Partitioning distributed inferences -- 5.7. Accelerating distributed inferences : strategies -- 5.8. Conclusion8 a6. Edge resources and accelerators -- 6.1. Edge resources : basics -- 6.2. Micro-controller-level devices : an edge? -- 6.3. Micro-processor-level devices : general purpose -- 6.4. GPUs, TPUs, and FPGAs : special purpose -- 6.5. SoC, SoM, system-on-board -- 6.6. Edge accelerators -- 6.7. Commercial edge accelerators -- 6.8. Examples and use-cases -- 6.9. Conclusion8 a7. Performance analysis of edge-enabled applications -- 7.1. Performance concerns -- 7.2. Model-specific performance concerns -- 7.3. Architecture-specific performance concerns -- 7.4. Algorithm-specific performance concerns -- 7.5. Data-specific performance concerns -- 7.6. Performance monitoring : a need -- 7.7. Performance monitoring : metrics -- 7.8. Energy-efficiency methods -- 7.9. Carbon efficiency methods -- 7.10. Workload scheduling and performance impacts -- 7.11. Performance monitoring tools -- 7.12. Cloud/fog/edge-level performance monitoring -- 7.13. Conclusion8 a8. Security in edge-AI systems -- 8.1. Existing security challenges -- 8.2. Security attacks in edge-AI -- 8.3. Data-specific vulnerabilities -- 8.4. Security architectures in edge-AI -- 8.5. Preventing security breaches : strategies -- 8.6. Tools and solutions -- 8.7. Conclusion8 apart III. Tools and solutions. 9. Frameworks : edge-AI platforms -- 9.1. Essential characteristics -- 9.2. Types of framework -- 9.3. Resource-allocation frameworks -- 9.4. Cloud-specific frameworks -- 9.5. Application-specific frameworks -- 9.6. Distributed federated learning frameworks -- 9.7. Conclusion8 a10. Orchestration platforms : computing continuum -- 10.1. Orchestration and integration -- 10.2. Algorithmic/application orchestration -- 10.3. Workload orchestration -- 10.4. Hierarchical versus non-hierarchical orchestration -- 10.5. Adaptiveness in orchestration -- 10.6. Automation in orchestration -- 10.7. Metric-oriented orchestration -- 10.8. Orchestration frameworks -- 10.9. Integration platforms8 apart IV. Applications. 11. Edge-AI applications -- 11.1. Applications -- 11.2. Edge-AI for healthcare -- 11.3. Industrial applications using edge-AI -- 11.4. Edge-AI for agriculture -- 11.5. Edge-AI for forensics -- 11.6. Edge-AI for mobility/logistics -- 11.7. Conclusion8 a12. Business opportunities using edge-AI -- 12.1. Digital business -- 12.2. Economic impacting factors -- 12.3. Business opportunities : edge-AI platforms -- 12.4. Cost models -- 12.5. Economic simulators for FaaS implementation -- 12.6. Conclusion8 a13. Challenges and future directions -- 13.1. Edge-AI challenges.  aFull-text restricted to subscribers or individual document purchasers.3 aEdge Intelligence: deep learning-enabled edge computing is a book that targets researchers and practitioners who are interested in applying intelligence without compromising data privacy. The book reveals the existing edge-AI techniques and forecasts future edge-AI integration methods. The book delves into edge computing architectures after describing relevant basic technologies such as IoT, cloud computing, and other security-related architectures. The book starts with an explanation of all relevant basic technologies. It offers a smooth transition from the basics to insightful practical sessions for practitioners. The ideas of providing innovative ideas and applications in the later part of the book can enthuse researchers and developers to engage themselves in innovating newer products with the application of edge intelligence. Part of IOP Series in Next Generation Computing.  aAcademic and commercial researchers.  aAlso available in print.  aMode of access: World Wide Web.  aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.  aShajulin Benedict graduated in 2001 from Manonmaniam Sunderanar University, India, with Distinction. In 2004, he received an M.E Degree in Digital Communication and Computer Networking from A.K.C.E, Anna University, Chennai. He is the University second rank holder for his masters. He did his PhD degree in the area of Grid scheduling under Anna University, Chennai (Supervisor--Dr. V Vasudevan, Director, Software Technologies Group of TIFAC Core in Network Engineering). He was affiliated to the same group and published more papers in international journals. He served as Professor at SXCCE Research Centre of Anna University-Chennai. Later, he visited TUM Germany for teaching Cloud Computing as Guest Professor of TUM Germany. Now, he works at the Indian Institute of Information Technology Kottayam, Kerala, India, an institute of national importance of India. He serves as Director/PI/Representative Officer of AIC-IIIT Kottayam (Sec.8 Company) for nourishing young entrepreneurs of India. He is also working as a Guest Professor of TUM Germany. His research interests include HPC/Cloud/Grid scheduling, performance analysis of parallel applications (including exa-scale), cloud computing, IIoT, blockchain, parallel compilers, and so forth. His webpage can be found at: sbenedictglobal.com.  aTitle from PDF title page (viewed on July 15, 2024).  aEdge computing.  aArtificial intelligence. 7aArtificial intelligence.2bicssc 7aCOMPUTERS / Artificial Intelligence / General.2bisacsh2 aInstitute of Physics (Great Britain),epublisher. 8iPrint version:z9780750355919z9780750355940 0aIOP (Series).pRelease 24.  aIOP series in next generation computing.  aIOP ebooks.p2024 collection.4 uhttps://iopscience.iop.org/book/mono/978-0-7503-5593-3