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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
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
MDM : Fundamentals, Security, and the Modern Desktop : Using Intune, Autopilot, and Azure to Manage, Deploy, and Secure Windows 10
\"The first major book on MDM written by Group Policy and Enterprise Mobility MVP and renowned expert, Jeremy Moskowitz! With Windows 10, organizations can create a consistent set of configurations across the modern enterprise desktop--for PCs, tablets, and phones--through the common Mobile Device Management (MDM) layer. MDM gives organizations a way to configure settings that achieve their administrative intent without exposing every possible setting. One benefit of MDM is that it enables organizations to apply broader privacy, security, and application management settings through lighter and more efficient tools. MDM also allows organizations to target Internet-connected devices to manage policies without using Group Policy (GP) that requires on-premises domain-joined devices. This makes MDM the best choice for devices that are constantly on the go. With Microsoft making this shift to using Mobile Device Management (MDM), a cloud-based policy-management system, IT professionals need to know how to do similar tasks they do with Group Policy, but now using MDM, with its differences and pitfalls...Renowned expert and Microsoft Group Policy and Enterprise Mobility MVP Jeremy Moskowitz teaches you MDM fundamentals, essential troubleshooting techniques, and how to manage your enterprise desktops.\"--provided by publisher.
Book
Enhancing patient healthcare with mobile edge computing and 5G: challenges and solutions for secure online health tools
Patient-focused healthcare applications are important to patients because they offer a range of advantages that add value and improve the overall healthcare experience. The 5G networks, along with Mobile Edge Computing (MEC), can greatly transform healthcare applications, which in turn improves patient care. MEC plays an important role in the healthcare of patients by bringing computing resources to the edge of the network. It becomes part of an IoT system within healthcare that brings data closer to the core, speeds up decision-making, lowers latency, and improves the overall quality of care. While the usage of MEC and 5G networks is beneficial for healthcare purposes, there are some issues and difficulties that should be solved for the efficient introduction of this technological pair into healthcare. One of the critical issues that blockchain technology can help to overcome is the challenge faced by MEC in realizing the most potential applications involving IoT medical devices. This article presents a comprehensive literature review on IoT-based healthcare devices, which provide real-time solutions to patients, and discusses some major contributions made by MEC and 5G in the healthcare industry. The paper also discusses some of the limitations that 5G and MEC networks have in the IoT medical devices area, especially in the field of decentralized computing solutions. For this reason, the readership intended for this article is not only researchers but also graduate students.
Journal Article
Machine learning-based computation offloading in edge and fog: a systematic review
Today, Mobile Cloud Computing (MCC) alone can no longer respond to the increasing volume of data and satisfy the necessary delays in real-time applications. In addition, challenges such as security, energy consumption, storage space, bandwidth, lack of mobility support, and lack of location awareness have made this problem more challenging. Expanding applications such as online gaming, Augmented Reality (AR), Virtual Reality (VR), metaverse, e-health, and the Internet of Things (IoT) have brought up new paradigms for processing big data. Some of the paradigms that have emerged in the last decade are trying to alleviate cloud computing problems jointly. Mobile Edge Computing (MEC) and Fog Computing (FC) are the most critical techniques that serve the IoT. One of the common points of the above paradigms is the offloading of IoT tasks. This paper reviews machine learning-based computation offloading mechanisms in the edge and fog environment. This review covers three significant areas of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We discuss various performance metrics, tools, and case studies and analyze their advantages and disadvantages. We systematically elaborate on open issues and research challenges that are crucial for the next decade.
Journal Article
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