Download or read book A Smart Edge Computing Resource Formed by On the go Networking of Cooperative Nearby Devices Using an AI offloading Engine to Solve Computationally Intensive Sub tasks for Mobile Cloud Services written by Ali Al-ameri and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Energy Efficient Computation Offloading in Mobile Edge Computing written by Ying Chen and published by Springer Nature. This book was released on 2022-10-30 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce an end-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions. Researchers working in mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.
Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size
Download or read book Machine Learning for Edge Computing written by Amitoj Singh and published by CRC Press. This book was released on 2022-07-29 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it discusses how to build AI models, i.e., model training and inference, on edge. This book provides insights into this new inter-disciplinary field of edge computing from a broader vision and perspective. The authors discuss machine learning algorithms for edge computing as well as the future needs and potential of the technology. The authors also explain the core concepts, frameworks, patterns, and research roadmap, which offer the necessary background for potential future research programs in edge intelligence. The target audience of this book includes academics, research scholars, industrial experts, scientists, and postgraduate students who are working in the field of Internet of Things (IoT) or edge computing and would like to add machine learning to enhance the capabilities of their work. This book explores the following topics: Edge computing, hardware for edge computing AI, and edge virtualization techniques Edge intelligence and deep learning applications, training, and optimization Machine learning algorithms used for edge computing Reviews AI on IoT Discusses future edge computing needs Amitoj Singh is an Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India. Vinay Kukreja is a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India. Taghi Javdani Gandomani is an Assistant Professor at Shahrekord University, Shahrekord, Iran.
Download or read book Joint Resource Management and Task Scheduling for Mobile Edge Computing written by Xinliang Wei and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, edge computing has become an increasingly popular computing paradigm to enable real-time data processing and mobile intelligence. Edge computing allows computing at the edge of the network, where data is generated and distributed at the nearby edge servers to reduce the data access latency and improve data processing efficiency. In addition, with the advance of Artificial Intelligence of Things (AIoT), not only millions of data are generated from daily smart devices, such as smart light bulbs, smart cameras, and various sensors, but also a large number of parameters of complex machine learning models have to be trained and exchanged by these AIoT devices. Classical cloud-based platforms have difficulty communicating and processing these data/models effectively with sufficient privacy and security protection. Due to the heterogeneity of edge elements including edge servers, mobile users, data resources, and computing tasks, the key challenge is how to effectively manage resources (e.g. data, services) and schedule tasks (e.g. ML/FL tasks) in the edge clouds to meet the QoS of mobile users or maximize the platform's utility. To that end, this dissertation studies joint resource management and task scheduling for mobile edge computing. The key contributions of the dissertation are two-fold. Firstly, we study the data placement problem in edge computing and propose a popularity-based method as well as several load-balancing strategies to effectively place data in the edge network. We further investigate a joint resource placement and task dispatching problem and formulate it as an optimization problem. We propose a two-stage optimization method and a reinforcement learning (RL) method to maximize the total utilities of all tasks. Secondly, we focus on a specific computing task, i.e., federated learning (FL), and study the joint participant selection and learning scheduling problem for multi-model federated edge learning. We formulate a joint optimization problem and propose several multi-stage optimization algorithms to solve the problem. To further improve the FL performance, we leverage the power of the quantum computing (QC) technique and propose a hybrid quantum-classical Benders' decomposition (HQCBD) algorithm as well as a multiple-cuts version to accelerate the convergence speed of the HQCBD algorithm. We show that the proposed algorithms can achieve the consistent optimal value compared with the classical Benders' decomposition running in the classical CPU computer, but with fewer convergence iterations.
Download or read book Latency aware Resource Management at the Edge written by Klervie Toczé and published by Linköping University Electronic Press. This book was released on 2020-02-19 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing diversity of connected devices leads to new application domains being envisioned. Some of these need ultra low latency or have privacy requirements that cannot be satisfied by the current cloud. By bringing resources closer to the end user, the recent edge computing paradigm aims to enable such applications. One critical aspect to ensure the successful deployment of the edge computing paradigm is efficient resource management. Indeed, obtaining the needed resources is crucial for the applications using the edge, but the resource picture of this paradigm is complex. First, as opposed to the nearly infinite resources provided by the cloud, the edge devices have finite resources. Moreover, different resource types are required depending on the applications and the devices supplying those resources are very heterogeneous. This thesis studies several challenges towards enabling efficient resource management for edge computing. The thesis begins by a review of the state-of-the-art research focusing on resource management in the edge computing context. A taxonomy is proposed for providing an overview of the current research and identify areas in need of further work. One of the identified challenges is studying the resource supply organization in the case where a mix of mobile and stationary devices is used to provide the edge resources. The ORCH framework is proposed as a means to orchestrate this edge device mix. The evaluation performed in a simulator shows that this combination of devices enables higher quality of service for latency-critical tasks. Another area is understanding the resource demand side. The thesis presents a study of the workload of a killer application for edge computing: mixed reality. The MR-Leo prototype is designed and used as a vehicle to understand the end-to-end latency, the throughput, and the characteristics of the workload for this type of application. A method for modeling the workload of an application is devised and applied to MR-Leo in order to obtain a synthetic workload exhibiting the same characteristics, which can be used in further studies.
Download or read book Edge Computing and Data Extraction written by Meghana N. Satpute and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Combinatorial Optimization field consists of selecting best or optimal object or optimal set of objects from finite set of possibilities. Many computer science problems can be formulated as combinatorial optimization problems. This dissertation mainly focused on two of such problems: finding optimal offloading scheme for mobile edge computing and multi-document summarization. Due to high processing power and internet connectivity, smartphones are used widely for many applications like email, face recognition, natural language processing, interactive games etc. People prefer mobile devices to use for these applications due to their ease of use. However, because of hardware limitations, mobile devices have limited battery life, power and capacity. Researchers are constantly looking for ways to maximize the usage of these resources. To reduce the load of applications on mobile devices and use the resources efficiently, it is necessary to move some load of applications to remote servers in such a way that the applications will run seamlessly. Computation offloading for mobile-edge computing (MEC) is a mechanism to utilize resources well by moving resource-intensive units (functions, components, etc.) to edge servers at network edge. Computational offloading is formulated as a graph cut problem and a solution based on spectral graph theory is proposed. It is observed that this computation offloading problem can also be transformed as n-fold integer programming by mapping the remaining computing resources to a virtual component. To ensure reliability, a reliable shadow component scheme between multilevel servers is designed. Various computer science tasks can be modelled as data subset selection problem which deal with finding representative subset from the data based on some criteria such as importance. Some examples of data subset selection in Natural Language Processing (NLP) are extractive summarization and sub-corpus selection. In both the cases, some part of original data is filtered out. In extractive summarization, important sentences from the original document set which convey the meaning of the document set are selected. In sub-corpus selection, part of a corpus needed for a particular task at hand is selected. Combinatorial optimization deals with finding best subset after comparing different subsets using objective function. In this work, some important problems such as computation offloading and multi-document summarization are formulated as non-linear combinatorial optimization algorithms. Since these problems are NP-hard, research work is done to find some intrinsic features to propose efficient solutions.
Download or read book Fog Radio Access Networks F RAN written by Mugen Peng and published by Springer Nature. This book was released on 2020-08-12 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction of Fog Radio Access Networks (F-RANs), from both academic and industry perspectives. The authors first introduce the network architecture and the frameworks of network management and resource allocation for F-RANs. They then discuss the recent academic research achievements of F-RANs, such as the analytical results of theoretical performance limits and optimization theory-based resource allocation techniques. Meanwhile, they discuss the application and implementations of F-RANs, including the latest standardization procedure, and the prototype and test bed design. The book is concluded by summarizing the existing open issues and future trends of F-RANs. Includes the latest theoretical and technological research achievements of F-RANs, also discussing existing open issues and future trends of F-RANs toward 6G from an interdisciplinary perspective; Provides commonly-used tools for research and development of F-RANs such as open resource projects for implementing prototypes and test beds; Includes examples of prototype and test bed design and gives tools to evaluate the performance of F-RANs in simulations and experimental circumstances.
Download or read book Cognitive Hyperconnected Digital Transformation written by Ovidiu Vermesan and published by CRC Press. This book was released on 2022-09-01 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business.
Download or read book Cloud Radio Access Networks written by Tony Q. S. Quek and published by Cambridge University Press. This book was released on 2017-02-02 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book on Cloud Radio Access Networks (C-RANs), covering fundamental theory, current techniques, and potential applications.
Download or read book A Reinforcement Learning based Framework for Resource Allocation and Task Assignment in Mobile Edge Computing Networks written by Li-Tse Hsieh and published by . This book was released on 2021 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks near mobile users with low latency. This dissertation first investigates one of the MEC applications, adaptive wireless video streaming, to understand the insights. An edge-controlled adaptive streaming (ECAS) scheme is designed, and a prototype is implemented, which provides a unified transmission and in-network processing of multiple video streams and orchestrates the dynamic resource allocation and bitrate adaptation among them based on the MEC concept. Next, the dissertation proposes and investigates a stochastic framework that enables cooperation among the various entities of a MEC system. Specifically, the task assignment problem is investigated for cooperative MEC networks in which a set of geographically distributed heterogeneous edge nodes not only cooperate with remote cloud data centers but also help each other to jointly process user tasks. The challenges in optimizing task assignment are addressed under dynamic network environments when the task arrivals, node computing capabilities, and network states are non-stationary and not known a priori. A novel stochastic framework is developed to model the interactions of the involved entities, including the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. We cast the cooperative task assignment as a dynamic online optimization problem and formulate it as a Markov Decision Process (MDP). Several centralized and distributed online Reinforcement Learning-based algorithms are designed and evaluated to obtain the optimal task assignment policy by capturing various dynamics and heterogeneity of the available computing resources and network communication conditions with no assumption on prior knowledge of them. Further, by leveraging the structure of the underlying problem, a function decomposition technique is proposed, and a post-decision state is introduced, which are incorporated with Reinforcement Learning to reduce the search space and computation complexity. The evaluation results validate the convergence of the algorithms and demonstrate the proposed online learning-based schemes outperform the state-of-the-art benchmark algorithms.
Download or read book Machine Learning in Mobile Edge Computing written by Heting Liu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The demand for supporting AI-based applications on mobile devices has been rapidly increasing. To meet this demand, mobile edge computing (MEC) has emerged as a new computing paradigm that enables AI inference at the network edge. Although edge servers offer lower latency, their resources are limited compared to cloud servers. Therefore, effectively managing edge server resources to support edge inference becomes a challenging issue. Additionally, AI-based applications on edge devices generate massive amounts of data that can be utilized for model training by uploading it to the server. However, sharing data poses challenges due to the increasing privacy concerns. Federated learning has emerged as a solution to train models with geographically dispersed edge devices without sharing their local datasets. Due to the limited bandwidth of wireless networks and the heterogeneity of edge devices, enhancing the efficiency of federated learning becomes another challenge in edge computing. The goal of this dissertation is to address these challenges in edge based model training and inference by developing the following techniques. First, we propose a deep reinforcement learning based server selection algorithm to reduce overall system costs when supporting edge inference. We identify the research challenges of server selection in a time-varying MEC system, where the server selection considers system dynamics such as user mobility and server workload. Then we model the server selection decision as a Markov Decision Process, and propose a deep reinforcement learning based algorithm to solve it. Second, we propose techniques to improve the utility of video analytics applications through edge computing. We study the server resource-aware offloading problem for video analytics, and formulate it as an optimization problem, where the goal is to maximize the utility which is a weighted function of accuracy and frame processing rate. We propose an online learning algorithm based on the Bayesian Optimization framework to select server and resolution using local observations, and make it adaptable for time-varying environments. The regret bound of the proposed algorithm is derived, and extensive evaluations are conducted to demonstrate its superior performance. Third, we propose a communication-efficient federated learning framework for heterogeneous edge devices. We identify that gradient quantization should be adaptive to the training process and the clients' communication capability to reduce the training time for heterogeneous clients. We then design an algorithm to minimize the wall-clock training time by exploiting the change of gradient norm to adjust the quantization resolution in each training round to reduce the communication cost while maintaining accuracy. Finally, data heterogeneity at edge devices brings challenges to federated learning. To address this, we propose a dynamic clustering based algorithm for personalized federated learning. Through experiments, we identify that clustering clients with similar data distributions helps address the data heterogeneity problem, and the grouping structure should be adaptive to the training process to improve the model accuracy. We further enhance our algorithm with layer-wise aggregation to both improve model accuracy and reduce communication overhead.
Download or read book Edge AI written by Xiaofei Wang and published by Springer Nature. This book was released on 2020-08-31 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
Download or read book Cloud Computing written by Nikos Antonopoulos and published by Springer Science & Business Media. This book was released on 2010-07-16 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cloud computing continues to emerge as a subject of substantial industrial and academic interest. Although the meaning and scope of “cloud computing” continues to be debated, the current notion of clouds blurs the distinctions between grid services, web services, and data centers, among other areas. Clouds also bring considerations of lowering the cost for relatively bursty applications to the fore. Cloud Computing: Principles, Systems and Applications is an essential reference/guide that provides thorough and timely examination of the services, interfaces and types of applications that can be executed on cloud-based systems. The book identifies and highlights state-of-the-art techniques and methods for designing cloud systems, presents mechanisms and schemes for linking clouds to economic activities, and offers balanced coverage of all related technologies that collectively contribute towards the realization of cloud computing. With an emphasis on the conceptual and systemic links between cloud computing and other distributed computing approaches, this text also addresses the practical importance of efficiency, scalability, robustness and security as the four cornerstones of quality of service. Topics and features: explores the relationship of cloud computing to other distributed computing paradigms, namely peer-to-peer, grids, high performance computing and web services; presents the principles, techniques, protocols and algorithms that can be adapted from other distributed computing paradigms to the development of successful clouds; includes a Foreword by Professor Mark Baker of the University of Reading, UK; examines current cloud-practical applications and highlights early deployment experiences; elaborates the economic schemes needed for clouds to become viable business models. This book will serve as a comprehensive reference for researchers and students engaged in cloud computing. Professional system architects, technical managers, and IT consultants will also find this unique text a practical guide to the application and delivery of commercial cloud services. Prof. Nick Antonopoulos is Head of the School of Computing, University of Derby, UK. Dr. Lee Gillam is a Lecturer in the Department of Computing at the University of Surrey, UK.
Download or read book Handbook of Cloud Computing written by Nayyar Dr. Anand and published by BPB Publications. This book was released on 2019-09-20 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Great POSSIBILITIES and high future prospects to become ten times folds in the near FUTUREKey features Comprehensively gives clear picture of current state-of-the-art aspect of cloud computing by elaborating terminologies, models and other related terms. Enlightens all major players in Cloud Computing industry providing services in terms of SaaS, PaaS and IaaS. Highlights Cloud Computing Simulators, Security Aspect and Resource Allocation. In-depth presentation with well-illustrated diagrams and simple to understand technical concepts of cloud. Description The book "e;Handbook of Cloud Computing"e; provides the latest and in-depth information of this relatively new and another platform for scientific computing which has great possibilities and high future prospects to become ten folds in near future. The book covers in comprehensive manner all aspects and terminologies associated with cloud computing like SaaS, PaaS and IaaS and also elaborates almost every cloud computing service model.The book highlights several other aspects of cloud computing like Security, Resource allocation, Simulation Platforms and futuristic trend i.e. Mobile cloud computing. The book will benefit all the readers with all in-depth technical information which is required to understand current and futuristic concepts of cloud computing. No prior knowledge of cloud computing or any of its related technology is required in reading this book. What will you learn Cloud Computing, Virtualisation Software as a Service, Platform as a Service, Infrastructure as a Service Data in Cloud and its Security Cloud Computing - Simulation, Mobile Cloud Computing Specific Cloud Service Models Resource Allocation in Cloud Computing Who this book is for Students of Polytechnic Diploma Classes- Computer Science/ Information Technology Graduate Students- Computer Science/ CSE / IT/ Computer Applications Master Class Students-Msc (CS/IT)/ MCA/ M.Phil, M.Tech, M.S. Researcher's-Ph.D Research Scholars doing work in Virtualization, Cloud Computing and Cloud Security Industry Professionals- Preparing for Certifications, Implementing Cloud Computing and even working on Cloud Security Table of contents1. Introduction to Cloud Computing2. Virtualisation3. Software as a Service4. Platform as a Service5. Infrastructure as a Service6. Data in Cloud7. Cloud Security 8. Cloud Computing - Simulation9. Specific Cloud Service Models10. Resource Allocation in Cloud Computing11. Mobile Cloud Computing About the authorDr. Anand Nayyar received Ph.D (Computer Science) in Wireless Sensor Networks and Swarm Intelligence. Presently he is working in Graduate School, Duy Tan University, Da Nang, Vietnam. He has total of fourteen Years of Teaching, Research and Consultancy experience with more than 250 Research Papers in various International Conferences and highly reputed journals. He is certified Professional with more than 75 certificates and member of 50 Professional Organizations. He is acting as "e;ACM DISTINGUISHED SPEAKER"e;
Download or read book Fundamentals of Internet of Things written by Sudhir Kumar and published by CRC Press. This book was released on 2021-11-25 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Internet of Things (IoT) networks have revolutionized the world and have innumerable real-time applications on automation. A few examples include driverless cars, remote monitoring of the elderly, remote order of tea or coffee of your choice from a vending machine, and home/industrial automation amongst others. Fundamentals of Internet of Things build the foundations of IoT networks by leveraging the relevant concepts from signal processing, communications, net-works, and machine learning. The book covers two fundamental components of IoT networks, namely, the Internet and Things. In particular, the book focuses on networking concepts, protocols, clustering, data fusion, localization, energy harvesting, control optimization, data analytics, fog computing, privacy, and security including elliptic curve cryptography and blockchain technology. Most of the existing books are theoretical and without many mathematical details and examples. In addition, some essential topics of the IoT networks are also missing in the existing books. Features: • The book covers cutting-edge research topics • Provides mathematical understanding of the topics in addition to relevant theory and insights • Includes illustrations with hand-solved numerical examples for visualization of the theory and testing of understanding • Lucid and crisp explanation to lessen the study time of the reader The book is a complete package of the fundamentals of IoT networks and is suitable for graduate-level students and researchers who want to dive into the world of IoT networks.
Download or read book Fog Computing in the Internet of Things written by Amir M. Rahmani and published by Springer. This book was released on 2017-06-07 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes state-of-the-art approaches to Fog Computing, including the background of innovations achieved in recent years. Coverage includes various aspects of fog computing architectures for Internet of Things, driving reasons, variations and case studies. The authors discuss in detail key topics, such as meeting low latency and real-time requirements of applications, interoperability, federation and heterogeneous computing, energy efficiency and mobility, fog and cloud interplay, geo-distribution and location awareness, and case studies in healthcare and smart space applications.
