关键词： Backscatter communications   computation bits   computation energy efficiency   energy harvesting   mobile edge computing  

摘要： In this paper, we consider a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple Internet-of-Things (IoT) nodes harvest energy from the energy signals transmitted by a power beacon (PB) and utilize the harvested energy for local computing and task offloading via hybrid backscatter communication (BackCom) and active transmission (AT). Considering the limited computation capacity of the MEC server and the quality-of-service (QoS) and energy-causality constraints per IoT node, we propose two resource allocation schemes to maximize the total computation bits of all the IoT nodes and the system computation energy efficiency (EE), respectively, by jointly optimizing the computation frequency and time of the MEC server and each IoT node, the transmit power of the PB and each IoT node, and the BackCom power reflection coefficient and the time for energy harvesting (EH), BackCom, and AT of each IoT node. The non-convex computation bits maximization problem is transformed to a convex one by introducing a series of auxiliary variables and proof by contradiction, and then solved by the existing convex tools. The system computation EE maximization is a non-convex nonlinear programming problem. We propose a two-layer iterative algorithm to solve it optimally and devise a reduced-complexity iterative algorithm to solve it sub-optimally by leveraging the block coordinate decent technique. Computer simulations validate the convergence of the proposed iterative algorithms and their superior performance over the benchmark schemes in terms of the computation bits or EE.

关键词： space-air-ground integrated network   mobile edge computing   resource scheduling   service framework  

摘要： Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work.

关键词： Mobile edge computing   coded computing   task offloading  

摘要： The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by $(k,r)$ linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable $V$ -structure with respect to the coding parameter $r$ , under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

关键词： computing offloading   genetic algorithm   Internet of vehicles   mobile edge computing  

摘要： The emergence of delay-sensitive and computation-intensive vehicle applications has imposed great challenges on the Internet of vehicles with limited computing resources. As a supplement to cloud computing, mobile edge computing (MEC) can effectively solve problems like limited computing resources of vehicles. But the high mobility of vehicles and the long distance between the central cloud and the vehicles decrease the offloading efficiency. Most existing schemes neither consider the high mobility of vehicles nor fully use the idle resources around the vehicles. In order to solve the above challenges, an offloading scheme of vehicle edge computing based on the genetic algorithm (GAVECOS) is proposed. In this scheme, we consider the computing resources of local vehicles, edge servers and idle vehicles around the task vehicles to offload the task. Then an optimization problem is developed to minimize the total cost of the system. And we use the genetic algorithm to solve this optimization problem. Finally, a simulation platform is built to verify the algorithm. The simulation results show that the total system cost of this scheme is significantly lower than that of the other four schemes when the delay consumption and energy consumption are considered. © 2022 Journal of Nanjing Institute of Posts and Telecommunications. All rights reserved.

关键词： Mobile edge computing   Internet of vehicles   Connected autonomous vehicles   SPEA2  

摘要： Internet of vehicles (IoV) is gradually combined with connected autonomous vehicles (CAV), which accelerates the development of CAV. In order to meet the service requirements of CAV, mobile edge computing (MEC) provides IoV with a novel paradigm which provides services by fast processing vehicle tasks at the road side units distributed near target vehicles. In this way, vehicle tasks can be offloaded to edge servers deployed in road side units (RSU). A vehicle tasks offloading problem requires load balance of edge servers to be maintained with minimum total time cost. Thus, we proposed a vehicle tasks offloading method (VTO) in which the vehicle tasks offloading problem is formulated as a multi-objective optimization problem. Hence, we design a multi-objective optimization evolutionary algorithm basing on improving the strength pare to evolutionary algorithm (SPEA2) and technique for order preference by similarity to ideal solution (TOPSIS) and multiple criteria decision making (MCDM). Through theoretical analysis and experimental evaluation, the results shows that the performance of VTO is effective and efficient.

关键词： Task analysis   Blockchains   Internet of Things   Peer-to-peer computing   Security   Hafnium   Privacy   Blockchain   mobile edge computing   task offloading   security   privacy   deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.

关键词： Local delay   mobile edge computing   MEC   stochastic geometry   VANET   urban area  

摘要： This article studies the local delay modeling and analysis in a mobile edge computing (MEC) based vehicular ad hoc network (VANET) for an urban scenario where vehicle nodes request for computing services from an edge node. Based on stochastic geometry, theoretical local delay models are derived for uplink transmission of a service request and downlink transmission of a computing result, respectively. In deriving the theoretical models, the road distribution of an urban area is modeled as a Poisson line process (PLP), the spatial distribution of vehicle nodes on each street are modeled as an independent one-dimensional homogeneous Poisson point process (PPP), and edge nodes are deployed along each street with a certain probability. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access, and independent Nakagami-m fading is considered over all wireless links. Based on the derived models, the impacts of major parameters on the local delay are investigated through numerical results. The derived local delay models can be used to help parameter settings in the deployment of edge nodes in an urban area.

关键词： Mobile edge computing   Service Offloading   Uncertainty   IoT  

摘要： To solve the problem of limited computing ability in mobile devices, edge computing is adopted as a feasible solution which provides services for IoT devices in different geographical locations. However, due to the service uncertainties, including the network congestion and the performance degradation of edge nodes, novel offloading strategies must be developed to accommodate the uncertain situations. In view of this challenge, software-defined network (SDN) is integrated with edge computing to make service offloading more flexible. Technically, an optimal IoT service offloading (OSO) method with uncertainty is proposed. In OSO, the completion time and load balance variance are two optimization goals for developing offloading strategies, and then the non-dominated sorting genetic algorithm-II (NSGA-II) is fully investigated to improve the performance in completion time and load balance variance. Moreover, the optimal strategy is selected by using Simple Additive Weighting (SAW) and Multiple Criteria Decision Making (MCDW). Finally, the experimental evaluation is conducted by comparing OSO with other methods to verify the superiority of it.

关键词： Mobile edge computing   Energy consumption   Game model  

摘要： The active field of research is to achieve fast answers with minimal energy usage in mobile edge computing. The latest development of mobile edge computing (MEC) is a new model for allowing access to advanced computing resources at the edge of the network, near-end devices, enabling an abundance of latency-sensitive services needed for Internet of Things (IoT) devices. IoT devices associatedwith the Internet has enlarged intense and the data created by these devices. This would need offloading IoT tasks to dischargehigh computation and storage to resource-rich nodeslikeCloud Computing and Mobile Edge Computing. One of the considerations of cloud-based IoT environments is resource management, which generally revolves around workload balance, resource allocation, resource provisioning, task scheduling, and quality of service to attain performance enhancements. Effective resource management is a challenging problem in mobile edge computing and can deliver high-quality services to mobile devices. When a user demands (tasks) run on a local mobile device, energy consumption rises instead of in the cloud. In the server environment, latency becomes a problem where the task is done rather than on the handheld device. Hence in this paper, the mobile Edge computing-based resource management and task scheduling (MEC-RMTS) framework has been proposed for efficient task-offloading strategies, low latency, and scalable IoT network computing for smartphone devices. Thus, the suggestedmethodhelps the resource manager in the mobile edge computing system concerning scheduling the offloading tasks to reduce the total service time and enhance the effectiveness of mobile edge computing resources. First, the proposed system uses Power Usage (PU) to decrease energy consumption, a Transmission problem for smartphone phones. Using the almost convex technique, designers answer PU and present a gradient-dependent Game Model (GM). Then, as a mixed-intelligent, non-linear program model, the issu

关键词： Wireless communication technology   Mobile edge computing   Law classification   Linear graph   Text classification model   KAOS  

摘要： Because the similarity between texts is not fully considered in traditional methods, it takes a long time to classify text, and the accuracy of text classification is not high and the classification results are not comprehensive. A new classification algorithm of legal text law based on wireless communication technology and mobile edge computing is proposed. The linear graph is established to obtain the sample subspace, and the similarity of the sample data in the subspace is calculated, and the samples with high similarity are preliminarily divided. According to the preprocessing results, use wireless communication technology to design the legal text conversion unit, and further convert the highly relevant data into a space. Then establish a text classification model based on mobile edge computing technology, select Kaos as the modeling language to realize the model construction, and use the model to complete the fast and accurate classification of legal texts. The highest classification accuracy of the proposed method is 95%. The analysis of specific comparative experiments shows that the proposed algorithm can solve the problems of the existing methods, such as low classification accuracy, long classification time and incomplete classification results of legal texts, and has high use value.