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Mobile crowd computing: potential, architecture, requirements, challenges, and applications
Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd computing (MCC) is the computing paradigm that uses public-owned SMDs to garner affordable high-performance computing (HPC). Though several empirical works have established the feasibility of mobile-based computing for various applications, there is a lack of comprehensive coverage of MCC. This paper aims to explore the fundamentals and other nitty–gritty of the idea of MCC in a comprehensive manner. Starting with an explicit definition of MCC, the enabling backdrops and the detailed architectural layouts of different models of MCC are presented, along with categorising different types of MCC based on infrastructure and application demands. MCC is compared extensively with other HPC systems (e.g. desktop grid, cloud, clusters and supercomputers) and similar mobile computing systems (e.g. mobile grid, mobile cloud, ad hoc mobile cloud, and mobile crowdsourcing). MCC being a complex system, various design requirements and considerations are extensively analysed. The potential benefits of MCC are meticulously mentioned, with special discussions on the ubiquity and sustainability of MCC. The issues and challenges of MCC are critically presented in light of further research scopes. Several real-world applications of MCC are identified and propositioned. Finally, to carry forward the accomplishment of the MCC vision, the future prospects are briefly elucidated.
Journal Article
Edge computing : fundamentals, advances and applications
\"This reference text presents the state-of-the-art in edge computing, its primitives, devices and simulators, applications, and healthcare-based case studies. The text provides integration of blockchain with edge computing systems and integration of edge with Internet of Things (IoT) and cloud computing. It will facilitate the readers to setup edge-based environment and work with edge analytics. It covers important topics including cluster computing, fog computing, networking architecture, edge computing simulators, edge analytics, privacy-preserving schemes, edge computing with blockchain, autonomous vehicles, and cross-domain authentication. Aimed at senior undergraduate, graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and information technology, this text: Discusses edge data storage security with case studies and blockchain integration with edge computing system. Covers theoretical methods with the help of applications, use cases, case studies, and examples. Provides healthcare real-time case studies are elaborated in detailed by utilizing the virtues of homomorphic encryption. Discusses real-time interfaces, devices, and simulators in detail\"-- Provided by publisher.
BOOK
Edge computing: current trends, research challenges and future directions
The edge computing (EC) paradigm brings computation and storage to the edge of the network where data is both consumed and produced. This variation is necessary to cope with the increasing amount of network-connected devices and data transmitted, that the launch of the new 5G networks will expand. The aim is to avoid the high latency and traffic bottlenecks associated with the use of Cloud Computing in networks where several devices both access and generate high volumes of data. EC also improves network support for mobility, security, and privacy. This paper provides a discussion around EC and summarized the definition and fundamental properties of the EC architectures proposed in the literature (Multi-access Edge Computing, Fog Computing, Cloudlet Computing, and Mobile Cloud Computing). Subsequently, this paper examines significant use cases for each EC architecture and debates some promising future research directions.
Journal Article
Privacy in mobile and pervasive computing
It is easy to imagine that a future populated with an ever-increasing number of mobile and pervasive devices that record our minute goings and doings will significantly expand the amount of information that will be collected, stored, processed, and shared about us by both corporations and governments. The vast majority of this data is likely to benefit us greatly--making our lives more convenient, efficient, and safer through custom-tailored and context-aware services that anticipate what we need, where we need it, and when we need it. But beneath all this convenience, efficiency, and safety lurks the risk of losing control and awareness of what is known about us in the many different contexts of our lives. Eventually, we may find ourselves in a situation where something we said or did will be misinterpreted and held against us, even if the activities were perfectly innocuous at the time. Even more concerning, privacy implications rarely manifest as an explicit, tangible harm. Instead, most privacy harms manifest as an absence of opportunity, which may go unnoticed even though it may substantially impact our lives. In this Synthesis Lecture, we dissect and discuss the privacy implications of mobile and pervasive computing technology. For this purpose, we not only look at how mobile and pervasive computing technology affects our expectations of--and ability to enjoy--privacy, but also look at what constitutes \"privacy\" in the first place, and why we should care about maintaining it. We describe key characteristics of mobile and pervasive computing technology and how those characteristics lead to privacy implications. We discuss seven approaches that can help support end-user privacy in the design of mobile and pervasive computing technologies, and set forward six challenges that will need to be addressed by future research. The prime target audience of this lecture is researchers and practitioners working in mobile and pervasive computing who want to better understand and account for the nuanced privacy implications of the technologies they are creating. Those new to either mobile and pervasive computing or privacy may also benefit from reading this book to gain an overview and deeper understanding of this highly interdisciplinary and dynamic field.
Book
Vehicular Edge Computing and Networking: A Survey
As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.
Journal Article
Cloud computing
An \"overview of the implications of the cloud phenomenon and the opportunities and risks associated with it\"-- Provided by publisher.
Book
MATLAB for neuroscientists : an introduction to scientific computing in MATLAB
This is the first comprehensive teaching resource and textbook for the teaching of Matlab in the Neurosciences and in Psychology. Matlab is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as \"black boxes\".
eBook
A Survey on the Computation Offloading Approaches in Mobile Edge/Cloud Computing Environment: A Stochastic-based Perspective
Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive code or at least a part of it will be transferred to the nearby resource-rich servers. Different approaches have been proposed to help make better decisions in respect of whether, where, when, and how much to offload and to improve the efficiency of the offloading process in the literature. On the other hand, the dynamic behavior of mobile devices running on-demand applications faces the offloading to the new challenges, which could be described as stochastic behaviors. Therefore, various stochastic offloading models have been proposed in the literature. However, to the best of the author’s knowledge, despite the existence of plenty of related offloading studies in the literature, there is not any systematic, comprehensive, and detailed survey paper focusing on stochastic-based offloading mechanisms. In this paper, we propose a survey paper concerning the stochastic-based offloading approaches in various computation environments such as Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Fog Computing (FC) in which to identify new mechanisms, a classical taxonomy is presented. The proposed taxonomy is classified into three main fields: Markov chain, Markov process, and Hidden Markov Models. Then, open issues and future unexplored or inadequately explored research challenges are discussed, and the survey is finally concluded.
Journal Article