关键词： cooperative robots   mobile edge computing   energy consumption   resource management  

摘要： Mobile edge computing(MEC) deployment in a multi-robot cooperation(MRC) system is an effective way to accomplish the tasks in terms of energy consumption and implementation latency. However, the computation and communication resources need to be considered jointly to fully exploit the advantages brought by the MEC technology. In this paper, the scenario where multi robots cooperate to accomplish the time-critical tasks is studied, where an intelligent master robot(MR) acts as an edge server to provide services to multiple slave robots(SRs) and the SRs are responsible for the environment sensing and data collection. To save energy and prolong the function time of the system, two schemes are proposed to optimize the computation and communication resources, respectively. In the first scheme, the energy consumption of SRs is minimized and balanced while guaranteeing that the tasks are accomplished under a time constraint. In the second scheme, not only the energy consumption, but also the remaining energies of the SRs are considered to enhance the robustness of the system. Through the analysis and numerical simulations, we demonstrate that even though the first policy may guarantee the minimization on the total SRs’ energy consumption, the function time of MRC system by the second scheme is longer than that by the first one.

关键词： proximity detection   mobile edge computing   road networks   constrained multiobjective optimization  

摘要： In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： Mobile edge computing   Stackelberg game   Computing offloading   Utility maximization  

摘要： We propose a new approach for the organic integration of edge cloud offloading decision and Stackelberg game pricing to address the problem that the current Stackelberg games all allocate edge cloud computing resources equally and ignore the difference of different users' demand for computing resources. Firstly, the Stackelberg game theory is used to establish a model of the optimal amount of data to be offloaded by users and the optimal number of computing resource blocks to be purchased, which converts the multivariate offloading decision problem of users into a univariate optimization problem, simplifies the offloading decision problem of users, and proves the existence of Nash equilibrium. Secondly, the KKT condition is applied to realize the offloading decision of users to purchase the optimal computing resource blocks. The upper and lower bounds of edge cloud pricing are established. Finally, a dynamic programming-based offloading (DPPO) algorithm for edge cloud pricing is proposed to achieve the optimal pricing of edge cloud utility and maximize each user's own utility. The simulation results show that the proposed method not only achieves the equilibrium of edge cloud utility and user utility, but also has good convergence and scalability. The DPPO algorithm yields better results than with different pricing and offloading strategies.

关键词： NOMA   Delays   Task analysis   Energy consumption   Silicon carbide   Decoding   Optimization   Mobile edge computing   task offloading   energy consumption   delay   multiple access  

摘要： The wireless networks beyond the fifth generation (5G) are envisioned to be the platform that will support a vast amount of diversified data-driven applications with stringent requirements in terms of computational accuracy, delay, and energy efficiency. The fulfillment of this objective can be achieved by the convergence of communication and computing networks, enabling the exploitation of edge computing resources and the joint orchestration of the corresponding resources. Mobile edge computing (MEC), which refers to the use of edge serves for offloading tasks from mobile devices, is a particularly promising approach to provide the required computational performance for emerging internet-of-things applications, such as the smart grids, smart industry, healthcare, and smart farming. In this work, we propose the use of an advanced multiple access technique and its joint design with adaptive task offloading, in order to reduce delay and energy consumption. More specifically, the use of generalized hybrid orthogonal/non-orthogonal multiple access (OMA/NOMA) for MEC is introduced, which is theoretically superior to other alternatives from the existing literature. In more detail, the proposed scheme is based on the joint utilization of dynamic user scheduling among OMA/NOMA phases and variable decoding order during the successive interference cancellation in NOMA phase. Also, the system's orchestration is optimized for both full and partial task offloading. Specifically, in full offloading scenario, the user scheduling, time allocation, and power control are jointly optimized. Regarding partial offloading, the computational resources, i.e., the clock speed of the local processors and the number of offloaded bits, are jointly optimized with the communication resources, taking into account the constraint of the energy that is consumed for both local processing and task offloading, which is particularly challenging due to the non-convex nature of the corresponding optimizat

关键词： Book reviews   Mobile computing   Edge computing   Cloud computing   Servers   Multi-access edge computing   Computer architecture   6G mobile communication  

摘要： This book offers comprehensive, self-contained knowledge on mobile edge computing (MEC), not only covering the basics, essential topics, and future directions, but also detailing the design and implementation of communications, computing, and caching in MEC. The first chapter provides an overview of MEC by outlining its technological characteristics and distinctions from cloud computing. Mobile cloud computing (MCC) integrates cloud computing with mobile devices to enhance mobile device capabilities such as computing and storage. Compared to mobile cloud computing (MCC), MEC provides an IT service environment and cloud computing capabilities at the edge of the network, within the radio access network, and close to mobile subscribers. Thus, the user experience can be further improved by the execution of computation- and storage-sensitive applications through computing in the edge cloud and the delivery of related computing services. This book provides basic concepts of MEC, explores the promising application scenarios of MEC integrated with emerging technologies, and gives insights into possible future directions of MEC. Due to the broad coverage on both the principle and applications of MEC, the book allows for easy cross-referencing and is suitable for researchers seeking a comprehensive but quick view of the current and future research focus about MEC and the related areas.

关键词： Delays   Servers   Energy consumption   Costs   Optimization   Array signal processing   Task analysis   Hybrid-dependency   mobile edge computing   parallel offloading and computation  

摘要： This letter proposes an effective dependency-aware subtask offloading and computation scheme in mobile edge computing (MEC) enabled networks, where we construct sequential execution windows of the MEC server by incorporating the hybrid dependencies among subtasks of an application, and the subtasks within the same subtask execution window could be offloaded and computed in parallel. By doing so, the completion delay and energy consumption of the application can be reduced. To make the proposed scheme achieve the minimum completion delay and energy consumption, we further jointly optimize the transmission rate and start execution time of subtasks within each execution window. The formulated problem is non-convex and difficult to solve. To make it tractable, we design a bisection search and successive convex approximation based iterative algorithm. Simulation results validate that compared with other existing schemes, our proposed scheme could reduce the summation of the completion delay and energy consumption by 10.36%.

关键词： Enterprise cloud   Mobile edge computing   Delay tolerance   Data congestion avoidance   Ant colony algorithm   Algorithm design  

摘要： Traditional large-scale enterprise cloud system has the problem of large delay. Therefore, a delay tolerant data congestion avoidance algorithm based on modular computing is designed. The resource allocation rules of rbfr strategy in traditional enterprise cloud are improved to change the traditional interactive overall mode and make it *** modular thinking to construct the data congestion control algorithm, mainly in the control layer to achieve the model, through the introduction of ant colony algorithm in modularization, the design of delay tolerant data congestion avoidance algorithm is realized. In order to verify the algorithm's congestion avoidance effect, comparative experiments are designed. Experimental results show that the packet loss rate of large enterprise cloud system under this algorithm is lower than that of traditional delay tolerant data congestion avoidance algorithm.

关键词： Low delay   Mobile edge computing   Data processing   IC design   Global routing   Wirelength optimization   Internet of Things  

摘要： With the development of Internet of Things (IoT), real-time decision making has gradually become one of the important characteristics in mobile edge computing (MEC) environment of IoT. Therefore, in the Integrated Circuit (IC) design of MEC environment for IoT, low delay is one of the important optimization objectives. In the many routing competitions, wirelength, overflow, and runtime are the main evaluation standards, so how to reduce wirelength, overflow, and runtime has become a major challenge. However, the existing work lacks the excellent optimization ability in wirelength, overflow, and runtime, or only considers some of the optimization objectives. Therefore, we consider the wirelength, overflow, and runtime as the optimization objectives and propose a high-quality global routing algorithm of IC design in MEC environment, including the following effective strategies: (1) A hybrid topology optimization strategy combining Prim algorithm and divide-and-conquer method, (2) a heuristic search algorithm considering the congestion and the wirelength of nets. Due to the use of the Fast Lookup Table (FLUTE) algorithm to construct the topology of each net, there are too many Steiner points. For this reason, we used Prim algorithm and divide-and-conquer method to construct the topology, and thus it can avoid the problem of redundant Steiner points. In addition, we propose a congestion area identification method based on interval division to determine the area and order of nets to rip-up and reroute (R&R). Furthermore, a heuristic search algorithm that considers both the congestion and the wirelength of nets is used to optimize the total wirelength in the R&R stage. In terms of the total overflow, the total wirelength and the runtime, the experimental results show that the proposed strategies have achieved effective optimization, which can better satisfy the demand of low delay of IC design for data processing in MEC environments of IoT.

关键词： mobile edge computing   UAV communication   resource allocation   3D deployment   air-ground cooperation  

摘要： Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air-ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV-TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.

关键词： Computation offloading   Mobile edge computing   Resource allocation   Ultra-dense network(UDN)  

摘要： With the proliferation of computing intensive and data intensive applications, mobile edge computing (MEC) has been proposed to provide powerful computing power for users at the edge of wireless networks. However, when a large number of users offload computing tasks to edge servers, the network load and the transmission delays will increase. Targeting the joint optimization of task unloading and resource allocation, an algorithm is proposed to minimize the total cost of ultra-dense network (UDN). In this paper, the joint optimization problem is divided into several sub-problems. First, the minimum local cost is calculated. Second, in order to calculate the minimum edge overhead, the task unloading and resource allocation problem is divided into the channel allocation sub-problem and the power allocation and unloading decision sub-problem. Finally, the corresponding Lagrange function is introduced, and the interior penalty function method and the frank Wolfe method are used to solve the sub-problem recursively. Simulation results demonstrate that compared with the traditional basic algorithms, the proposed algorithm can effectively reduce the total system overhead, energy overhead and transmission delay. © 2022, Periodical Office of Nanjing University of Posts and Telecommunications. All right reserved.