08832nam a2200805 i 4500 9780750355933 IOP 20251030124830.0 m eo d cr cn |||m|||a 240715s2024 enka fob 000 0 eng d 9780750355933 ebook 9780750355926 mobi 9780750355919 print 9780750355940 myPrint 10.1088/978-0-7503-5593-3 doi (CaBNVSL)thg00083956 (OCoLC)1446520278 CaBNVSL eng rda CaBNVSL CaBNVSL QA76.583 .B464 2024eb UYQ bicssc COM004000 bisacsh Benedict, Shajulin, author. 97032 Edge intelligence : deep learning-enabled edge computing / Shajulin Benedict. Deep learning-enabled edge computing. Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2024] 1 online resource (various pagings) : illustrations (some color). text rdacontent electronic isbdmedia online resource rdacarrier [IOP release $release] IOP series in next generation computing IOP ebooks. [2024 collection] "Version: 20240601"--Title page verso. Includes bibliographical references. part 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 book 2. 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. Conclusion 3. 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. Conclusion part 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. Conclusion 5. 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. Conclusion 6. 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. Conclusion 7. 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. Conclusion 8. 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. Conclusion part 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. Conclusion 10. 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 platforms part 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. Conclusion 12. 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. Conclusion 13. Challenges and future directions -- 13.1. Edge-AI challenges. Full-text restricted to subscribers or individual document purchasers. Edge 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. Academic and commercial researchers. Also available in print. Mode of access: World Wide Web. System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader. Shajulin 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. Title from PDF title page (viewed on July 15, 2024). Edge computing. 97033 Artificial intelligence. 58441 Artificial intelligence. bicssc 58441 COMPUTERS / Artificial Intelligence / General. bisacsh Institute of Physics (Great Britain), publisher. 17702 Print version: 9780750355919 9780750355940 IOP (Series). Release 24. IOP series in next generation computing. 96544 IOP ebooks. 2024 collection. https://iopscience.iop.org/book/mono/978-0-7503-5593-3 E-Books 68491 68491