Edge Computational Intelligence for AI-Enabled IoT Systems
暫譯: 邊緣計算智慧在AI驅動的物聯網系統中的應用
Kulkarni, Shrikaant, Dwivedi, Jaiprakash, Pramanta, Dinda
相關主題
商品描述
Edge computational intelligence is an interface between edge computing and artificial intelligence (AI) technologies. This interfacing represents a paradigm shift in the world of work by enabling a broad application areas and customer-friendly solutions. Edge computational intelligence technologies are just in their infancy. Edge Computational Intelligence for AI-Enabled IoT Systems looks at the trends and advances in edge computing and edge AI, the services rendered by them, related security and privacy issues, training algorithms, architectures, and sustainable AI-enabled IoT systems.
Together, these technologies benefit from ultra-low latency, faster response times, lower bandwidth costs and resilience from network failure, and the book explains the advantages of systems and applications using intelligent IoT devices that are at the edge of a network and close to users. It explains how to make most of edge and cloud computing as complementary technologies or used in isolation for extensive and widespread applications. The advancement in IoT devices, networking facilities, parallel computation and 5G, and robust infrastructure for generalized machine learning have made it possible to employ edge computational intelligence in diverse areas and in diverse ways.
The book begins with chapters that cover Edge AI services on offer as compared to conventional systems. These are followed by chapters that discuss security and privacy issues encountered during the implementation and execution of edge AI and computing services The book concludes with chapters looking at applications spread across different areas of edge AI and edge computing and also at the role of computational intelligence in AI-driven IoT systems.
商品描述(中文翻譯)
邊緣計算智能是邊緣計算與人工智慧（AI）技術之間的介面。這種介面代表了工作世界中的一個範式轉變，使得廣泛的應用領域和以客戶為中心的解決方案成為可能。邊緣計算智能技術仍處於初期階段。《邊緣計算智能在AI驅動的物聯網系統中的應用》探討了邊緣計算和邊緣AI的趨勢與進展、它們所提供的服務、相關的安全與隱私問題、訓練算法、架構以及可持續的AI驅動物聯網系統。
這些技術共同受益於超低延遲、更快的響應時間、更低的帶寬成本以及對網絡故障的韌性，書中解釋了使用位於網絡邊緣且接近用戶的智能物聯網設備的系統和應用的優勢。它說明了如何充分利用邊緣計算和雲計算作為互補技術，或在孤立的情況下用於廣泛的應用。物聯網設備、網絡設施、並行計算和5G的進步，以及針對通用機器學習的穩健基礎設施，使得在多樣化的領域和方式中應用邊緣計算智能成為可能。
本書以比較邊緣AI服務與傳統系統的章節開始。接下來的章節討論了在實施和執行邊緣AI和計算服務過程中遇到的安全和隱私問題。書的最後幾章探討了分佈在不同領域的邊緣AI和邊緣計算的應用，以及計算智能在AI驅動的物聯網系統中的角色。
作者簡介
Shrikaant Kulkarni has 37 years of teaching and research experience at both undergraduate and postgraduate levels. Presently he is a Professor in the Department of Civil Engineering, Padm. Dr. V. B. Kolte College of Engineering, Malkapur, India. He has published over 60 research papers in national and international journals and conferences
Jaiprakash Narain Dwivedi is currently working as an Associate Professor, ECE Department, University Institute of Engineering, Chandigarh University, Mohali, Punjab, India. His interest in research includes machine learning, artificial neural network, pattern recognition, classification, CNN, DNN, deep learning and signal processing.
Dinda Pramanta is an Assistant Professor and a committee member of Mathematical-Data Science-AI Educational Program on Kyushu Institute of Information Sciences from 2021. His research interests include spiking neural networks, hardware, and AI for educational purposes.
Yuichiro Tanaka is an Assistant Professor with Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Japan. His research interests include soft computing, neural networks, hardware, and home service robots. He is a member of IEEE and JNNS.
作者簡介(中文翻譯)
Shrikaant Kulkarni 擁有 37 年的本科及研究生教學與研究經驗。目前他是印度馬爾卡普爾 Padm. Dr. V. B. Kolte 工程學院土木工程系的教授。他在國內外期刊和會議上發表了超過 60 篇研究論文。
Jaiprakash Narain Dwivedi 目前擔任印度旁遮普州莫哈利昌迪加爾大學工程學院電子與通信工程系的副教授。他的研究興趣包括機器學習、人工神經網絡、模式識別、分類、卷積神經網絡 (CNN)、深度神經網絡 (DNN)、深度學習和信號處理。
Dinda Pramanta 是九州資訊科學研究所的助理教授及數學-數據科學-AI 教育計畫的委員，自 2021 年起參與該計畫。他的研究興趣包括脈衝神經網絡、硬體及用於教育目的的人工智慧。
Yuichiro Tanaka 是日本九州科技大學神經形態人工智慧硬體研究中心的助理教授。他的研究興趣包括軟計算、神經網絡、硬體及家庭服務機器人。他是 IEEE 和 JNNS 的成員。
