An Energy-Efficient Resource Management System for a Mobile Ad Hoc Cloud

Recently, mobile ad hoc clouds have emerged as a promising technology for mobile cyber-physical system applications, such as mobile intelligent video surveillance and smart homes. Resource management plays a key role in maximizing resource utilization and application performance in mobile ad hoc clouds. Unlike resource management in traditional distributed computing systems, such as clouds, resource management in a mobile ad hoc cloud poses numerous challenges owing to the node mobility, limited battery power, high latency, and the dynamic network environment. The real-time requirements associated with mobile cyber-physical system applications make the problem even more challenging. Currently, existing resource management systems for mobile ad hoc clouds are not designed to support mobile cyber-physical system applications and energy-efficient communication between application tasks. In this paper, we propose a new energy-efficient resource management system for mobile ad hoc clouds. The proposed system consists of two layers: a network layer and a middleware layer. The network layer provides ad hoc network and communication services to the middleware layer and shares the collected information in order to allow efficient and robust resource management decisions. It uses (1) a transmission power control mechanism to improve energy efficiency and network capacity, (2) link lifetimes to reduce communication and energy consumption costs, and (3) link quality to estimate data transfer times. The middleware layer is responsible for the discovery, monitoring, migration, and allocation of resources. It receives application tasks from users and allocates tasks to nodes on the basis of network and node-level information.Comment: 19 Page

A study of research trends and issues in wireless ad hoc networks

Ad hoc network enables network creation on the fly without support of any predefined infrastructure. The spontaneous erection of networks in anytime and anywhere fashion enables development of various novel applications based on ad hoc networks. However, at the same ad hoc network presents several new challenges. Different research proposals have came forward to resolve these challenges. This chapter provides a survey of current issues, solutions and research trends in wireless ad hoc network. Even though various surveys are already available on the topic, rapid developments in recent years call for an updated account on this topic. The chapter has been organized as follows. In the first part of the chapter, various ad hoc network's issues arising at different layers of TCP/IP protocol stack are presented. An overview of research proposals to address each of these issues is also provided. The second part of the chapter investigates various emerging models of ad hoc networks, discusses their distinctive properties and highlights various research issues arising due to these properties. We specifically provide discussion on ad hoc grids, ad hoc clouds, wireless mesh networks and cognitive radio ad hoc networks. The chapter ends with presenting summary of the current research on ad hoc network, ignored research areas and directions for further research

Resource Allocation in Full-Duplex Mobile-Edge Computing Systems with NOMA and Energy Harvesting

This paper considers a full-duplex (FD) mobile-edge computing (MEC) system with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where one group of users simultaneously offload task data to the base station (BS) via NOMA and the BS simultaneously receive data and broadcast energy to other group of users with FD. We aim at minimizing the total energy consumption of the system via power control, time scheduling and computation capacity allocation. To solve this nonconvex problem, we first transform it into an equivalent problem with less variables. The equivalent problem is shown to be convex in each vector with the other two vectors fixed, which allows us to design an iterative algorithm with low complexity. Simulation results show that the proposed algorithm achieves better performance than the conventional methods

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.Comment: 27 pages, 11 figure

Vehicular Edge Computing via Deep Reinforcement Learning

The smart vehicles construct Vehicle of Internet which can execute various intelligent services. Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular services.When offloading the complicated service to the vehicular edge computing node, the decision should consider numerous factors.The offloading decision work mostly formulate the decision to a resource scheduling problem with single or multiple objective function and some constraints, and explore customized heuristics algorithms. However, offloading multiple data dependency tasks in a service is a difficult decision, as an optimal solution must understand the resource requirement, the access network, the user mobility, and importantly the data dependency. Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for Vehicle of Internet, which provides the optimal policy directly from the environment. We formulate the offloading decision of multi-task in a service as a long-term planning problem, and explores the recent deep reinforcement learning to obtain the optimal solution. It considers the future data dependency of the following tasks when making decision for a current task from the learned offloading knowledge. Moreover, the framework supports the pre-training at the powerful edge computing node and continually online learning when the vehicular service is executed, so that it can adapt the environment changes and learns policy that are sensible in hindsight. The simulation results show that KD service offloading decision converges quickly, adapts to different conditions, and outperforms the greedy offloading decision algorithm.Comment: Preliminary report of ongoing wor

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

A Survey on 5G: The Next Generation of Mobile Communication

The rapidly increasing number of mobile devices, voluminous data, and higher data rate are pushing to rethink the current generation of the cellular mobile communication. The next or fifth generation (5G) cellular networks are expected to meet high-end requirements. The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and very high-speed data transfer. The 5G networks would provide novel architectures and technologies beyond state-of-the-art architectures and technologies. In this paper, our intent is to find an answer to the question: "what will be done by 5G and how?" We investigate and discuss serious limitations of the fourth generation (4G) cellular networks and corresponding new features of 5G networks. We identify challenges in 5G networks, new technologies for 5G networks, and present a comparative study of the proposed architectures that can be categorized on the basis of energy-efficiency, network hierarchy, and network types. Interestingly, the implementation issues, e.g., interference, QoS, handoff, security-privacy, channel access, and load balancing, hugely effect the realization of 5G networks. Furthermore, our illustrations highlight the feasibility of these models through an evaluation of existing real-experiments and testbeds.Comment: Accepted in Elsevier Physical Communication, 24 pages, 5 figures, 2 table

Data Management in Industry 4.0: State of the Art and Open Challenges

Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue

Mobile Cloud Computing with a UAV-Mounted Cloudlet: Optimal Bit Allocation for Communication and Computation

Mobile cloud computing relieves the tension between compute-intensive mobile applications and battery-constrained mobile devices by enabling the offloading of computing tasks from mobiles to a remote processors. This paper considers a mobile cloud computing scenario in which the "cloudlet" processor that provides offloading opportunities to mobile devices is mounted on unmanned aerial vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system with frequency division multiplexing between mobile and UAV, the joint optimization of the number of input bits transmitted in the uplink by the mobile to the UAV, the number of input bits processed by the cloudlet at the UAV, and the number of output bits returned by the cloudlet to the mobile in the downlink in each slot is carried out by means of dual decomposition under maximum latency constraints with the aim of minimizing the mobile energy consumption. Numerical results reveal the critical importance of an optimized bit allocation in order to enable significant energy savings as compared to local mobile execution for stringent latency constraints.Comment: 21 pages, 3 figures, 1 Table, accepted in IET Communication

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper