关键词： Trajectory   Task analysis   Energy consumption   Resource management   Trajectory planning   Genetic algorithms   Processor scheduling   Mobile edge computing   unmanned aerial vehicle   evolutionary algorithm   multi-chrome genetic algorithm  

摘要： This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs (variable number of UAVs) are deployed to serve Internet of Things devices (IoTDs). We aim to minimize the sum of hovering and flying energies of UAVs by optimizing the trajectories of UAVs. The problem is very complicated as we have to consider the deployment of stop points (SPs), the association between UAVs and SPs, and the order of SPs for UAVs. To solve the problem, this paper proposed a novel genetic trajectory planning algorithm with variable population size (GTPA-VP), which consists of two phases. In the first phase, a genetic algorithm (GA) with a variable population size is used to update the deployment of SPs. Accordingly, a multi-chrome GA is adopted to find the association between UAVs and SPs, an optimal number of UAVs, and the optimal order of SPs for UAVs. The effectiveness of the proposed GTPA-VP is demonstrated through several experiments on a set of ten instances with up to 200 IoTDs. It is evident from the experimental results that the proposed GTPA-VP outperforms the benchmark algorithms in terms of the energy consumption of the system.

关键词： Mobile edge computing   multi-task multi-server   non-orthogonal multiple access   processing capability   resource allocation   task offloading  

摘要： By offloading computationally intensive tasks of smart end devices to edge servers deployed at the edge of the network, mobile edge computing (MEC) has become a promising technology to provide computing services for Internet of Things (IoT) devices. In order to further improve the access capability of MEC and increase the spectrum utilization efficiency, in this article, Non-Orthogonal Multiple Access (NOMA) technology is introduced into MEC systems and we study the computing offloading problem of multi-user, multi-task and multi-server through joint optimization of task offloading and resource allocation, we intend to maximize the system's processing capability as an optimization goal. To solve the proposed mixed integer nonlinear programming (MINLP) problem, the objective optimization problem is firstly decoupled into two sub-problems of resource allocation and task allocation. Secondly the resource allocation problem is further decomposed into computation resource optimization and communication resource allocation. For the communication resource allocation, it first fixed power allocation, then the sub-channel allocation problem is regarded as a many-to-one matching problem between sub-channels and users. In addition, we propose a low-complexity sub-optimal matching algorithm for sub-channel allocation to maximize the offloading efficiency. Based on our proposed sub-channel allocation scheme, the transmission power allocation is regarded as a convex optimization problem, which is tackled by Lagrangian multiplier method. Finally, under the condition of resource allocation, the tasks of all end devices (EDs) are allocated. Experimental numerical results show that the proposed scheme can effectively decrease latency and energy consumption of networks, improve system processing capability, and further improve MEC system performance.

关键词： Monocular ranging   Kalman filter   SIFT   KCF   Mobile edge computing  

摘要： Distance perception is the basis and necessary prerequisite of environment perception, attitude perception and obstacle avoidance for both intelligent vehicle and unmanned vehicle. Passive ranging is a critical part of machine vision measurement. Most passive ranging methods based on machine vision apply binocular technology that needs strict hardware conditions and lacks universality. Therefore, the monocular vision ranging method is one of the mainstream distance sensing methods at present. In order to improve the accuracy of monocular vision ranging, a monocular vision ranging method based on pixel area and aspect ratio is proposed. Subsequently, this method improves the stability of real-time target detection by introducing Kalman filter processing. Experimental results display that that ranging used by this method has higher accuracy. The mean relative error of the depth measurement is 5% when it is 3-10 m. After introducing Kalman filter, the stability of real-time ranging processing is improved by 25.21%. In this paper, the ROS smart car with real-time target tracking is also realized by the method based on the combination of SIFT-KCF target detection and tracking and monocular ranging.

关键词： Mobile edge computing   server placement   heuristic algorithm   performance optimization  

摘要： The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through minimizing both communication delay and load difference as the optimization goal. Then, an Edge Server placement based on meta-Heuristic alGorithM (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom's real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches.

关键词： Blockchain technology   blockchain applications   blockchain and 5g   5g blockchain   wireless blockchain   wireless consensus   byzantine generals   byzantine fault tolerance   blockchain history   bitcoin   Ethereum   pow   proof-of-work   smart contracts   mobile edge computing   blockchain ai   hyperledger  

ISBN: (数字)9781119790815 ISBN: (纸本)9781119790808

摘要： Explore foundational concepts in blockchain theory with an emphasis on recent advances in theory and practice In Wireless Blockchain: Principles, Technologies and Applications, accomplished researchers and editors Bin Cao, Lei Zhang, Mugen Peng, and Muhammad Ali Imran deliver a robust and accessible exploration of recent developments in the theory and practice of blockchain technology, systems, and potential application in a variety of industrial sectors, including manufacturing, entertainment, public safety, telecommunications, public transport, healthcare, financial services, automotive, and energy utilities. The book presents the concept of wireless blockchain networks with different network topologies and communication protocols for various commonly used blockchain applications. You’ll discover how these variations and how communication networks affect blockchain consensus performance, including scalability, throughput, latency, and security levels. You’ll learn the state-of-the-art in blockchain technology and find insights on how blockchain runs and co-works with existing systems, including 5G, and how blockchain runs as a service to support all vertical sectors efficiently and effectively. Readers will also benefit from the inclusion of: A thorough introduction to the Byzantine Generals problem, the fundamental theory of distributed system security and the foundation of blockchain technology An overview of advances in blockchain systems, their history, and likely future trends Practical discussions of Proof-of-Work systems as well as various Proof-of-“X” alternatives, including Proof-of-Stake, Proof-of-Importance, and Proof-of-Authority A concise examination of smart contracts, including trusted transactions, smart contract functions, design processes, and related applications in 5G/B5G A treatment of the theoretical relationship between communication networks and blockchain Perfect for electrical engineers, industry professionals, and students and research

关键词： Tactical edge network   federated learning   mobile edge computing   weighted average solution   constant elasticity substitution solution  

摘要： In this study, we focus on the federated learning (FL) based tactical edge network platform to cooperatively operate computation-hungry tasks as efficiently as possible. Based on the incentive of FL model training, each individual device makes offloading decisions for their tactical tasks in resource-constrained network environments. According to the ideas of two different bargaining solutions - weighted average solution and constant elasticity substitution solution - edge server and IoT devices work together in a coordinated manner to approximate a well-balanced system performance. Therefore, we can reach an agreement while exploring the mutual benefits to leverage a reciprocal consensus between different viewpoints. The main novelty of our approach is to investigate the dual-interactive bargaining process based on the interdependent relationship between IoT devices and the tactical edge server. To the best of our knowledge, this is the first work that jointly considers different bargaining solutions to handle tactical edge-assisted task offloading services. Based on the numerical simulation, it is demonstrated that the proposed approach can increase the system throughput, device payoff and device fairness up to 10%, 15% and 20%, respectively, in comparison with existing protocols.

关键词： Audit trail   Hyperledger   Mobile Edge Computing   Optimized Blockchain   Software Defined Network  

摘要： The Internet of Things (IoT) refers to a network of physical objects that are embedded with sensors are able to collect and transfer information over a wireless network with human conciliation. In confined Home Patient Observation system light-weight wearable/embedded sensing element devices connected to record the patient health conditions. Adopting looming technologies like 5G and Mobile Edge Computing (MEC) a multi-staged journey builds ultra-low latency. The health care sector is drowning in information. An overflow of clinical records, patient history, complicated billing information, medical analysis and lot builds it very troublesome to manage such information at intervals the health care sector in a systematized approach. Escorted by IoT, since data mobility occurs, hackers gain access to clinical IoT device, acquire management and revamp data which oblige health partitioners take actions which will injury the health of their client. i) To overthrow security issues introduces Optimized blockchain with hyperledger fabric in edge-cloud computing is proposed. Since data stored in blockchain are immutable, the permission granted patients can only access their records using hash value. Thus, blockchains will provide an immutable audit trail of health facts. ii) Software Defined Network (SDN) deliver Quality of Service (QoS) steer further from network congestion. © 2021 The authors and IOS Press.

关键词： Telecommunications   5G mobile communication   Edge computing   Cloud computing   Ecosystems   Computational modeling   Cellular networks   Mobile edge computing   multi-access edge computing   telecom operator   cloud ownercess edge computing   5G and beyond   heterogeneous cellular networks   ecosystem   telecom operator   cloud owner   revenue   CAPEX   OPEX  

摘要： The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators' decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players' revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.

关键词： Bandwidth   Cloud computing   Throughput   Mobile handsets   Heuristic algorithms   Computational modeling   Partitioning algorithms   Mobile edge computing   computation partitioning   resource allocation  

摘要： Mobile edge computation partitioning is an effective technique to improve the applications performance on the mobile devices by selectively offloading some computations from the devices to the nearby edge cloud. Most previous works focus on the computation partitioning for a single user. Recent works begin to study the computation partitioning in a multiple user environment in which a number of users compete for the constrained computation resources on the edge cloud. However, these works neglect the fact that the users normally also share the network resources to access the edge cloud, and thus the allocation of bandwidth to the users significantly affects the overall performance of the users. In this paper, we study network aware mobile edge computation partitioning in multi-user environments, i.e., to decide for each user which parts of the application should be offloaded onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is new in that we consider the competition among users for the network bandwidth as well as the computation resources in a multi-user environment. With a set of novel models and formulations, we transform the problem into the classic Multi-class Multi-dimensional Knapsack Problem, and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. We further consider the user mobility and design effective online algorithms that could be easily deployed in practical systems. Comprehensive trace driven simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.

关键词： Performance evaluation   Network topology   Image edge detection   Urban areas   Cognition   Graph neural networks   Topology   Hotspot analysis   mobile edge computing   human-centric IoT   graph attention networks  

摘要： Mobile edge computing works well with the rapid growth of human-centric IoT applications by providing timely and in-situ data caching and processing. Edge oriented urban hotspot prediction is to predict the high traffic regions in case to provide basis for further deployment and replenishment of edge servers. Most of the existing hotspot analysis work is not well suited for human-centric IoT application scenarios especially for large-scale urban city areas. Instead, in this paper we propose edge-oriented hotspot prediction scheme based on information Interactive Graph Attention Networks (I-GAN) for urban city within the human-centric IoT and MEC application scenarios. I-GAN makes reasoning on the user service needs by learning the interactive relations of multifaceted human-centric factors: including online human behavior and offline crowd flow through graph attention networks. On the other hand, I-GAN makes representation of urban-scale edge service capability by graph embeddings through inductive learning with distributed multi-layer graph convolutions. Based on it, I-GAN makes matching between them and then predicts the edge-oriented hotspots with time variance. The performance evaluations are based on realistic dataset of a southern city of China provided by China Unicom. I-GAN is compared with the other related hotspot analysis schemes. The results show that I-GAN is with much better prediction accuracy with multiple time dimensions.