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： Internet of things   Artificial intelligence   Mobile edge computing   Remote loading   Service migration  

摘要： In mobile edge computing (MEC) systems, enhancing the learning capabilities of edge nodes through Artificial Intelligence (AI) can improve the efficiency of dynamically allocating resources. For the scenario of edge service migration, various tasks in lightweight IoT devices are offloaded to edge nodes and the services on edge nodes are migrated adaptively to another available node nearer to the users while they move. To speed up network application loading during service migration, this paper proposes an intelligent trace-driven predicting approach (ITPA) that improves the efficiency of I/O scheduling and the hit ratio of caching when migrating services between resource-constrained edge nodes. First, based on the characteristics of sequential access to the binary codes of an application during its startup progress, the request loading list is generated by tracing key I/O requests at that phase. Then, an intelligent algorithm is designed to search and select the key I/O requests in the loading list. Finally, the efficiency of data acquisition is improved by implementing a prefetch strategy for the client side and a three-level caching strategy for the server side. Experimental results show that the ITPA reduces the service startup time during stateless migration.

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： Servers   Task analysis   Autonomous aerial vehicles   Energy consumption   Array signal processing   Time-frequency analysis   Resource management   Energy efficiency   UAV-mounted RIS   mobile edge computing   trajectory design   passive beamforming  

摘要： Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) have been recently applied in the field of mobile edge computing (MEC) to improve the data exchange environment by proactively changing the wireless channels through maneuverable location deployment and intelligent signals reflection, respectively. Nevertheless, they may suffer from inherent limitations, e.g., UAV's low endurance and RIS's finite coverage, which limit their wider applications. UAV-mounted RIS (U-RIS), as a promising integrated approach, can combine the advantages of UAV and RIS to break the limit. Inspired by this, we consider a novel U-RIS assisted MEC system, where a U-RIS is deployed to assist the communication between the ground users and an MEC server. The joint UAV trajectory, RIS passive beamforming and MEC resource allocation design is developed to maximize the energy efficiency (EE) of the system. To tackle the intractable non-convex problem, we divide it into two subproblems and solve them iteratively based on successive convex approximation (SCA) and the Dinkelbach method. Finally we obtain a high-performance suboptimal solution. Simulation results show that the proposed algorithm significantly improves the energy efficiency of the MEC system.

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   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： asymmetric group key agreement   intelligent information system   mobile edge computing   secure communication  

摘要： Security communication and information sharing among mobile devices are important application technologies of the intelligent information system (IIS). Because IIS is vulnerable to attacks, so the security of information sharing among mobile devices is seriously threatened. Thence, it is necessary to establish a secure channel for communication among mobile devices of IIS over an opening network. Group key agreement (GKA) can establish a secure channel among mobile devices of IIS by encryption technology. Due to the resource-constraints of mobile devices, such as weak computing power, small storage capacity, and limited communication range. To address these issues, an asymmetric GKA protocol among terminals of IIS for mobile edge computing (GKA-IIS-MEC) network is proposed in this paper. Adopted asymmetric GKA to achieve the group secure communication mechanisms that message sender unfettered in this protocol;the protocol also uses edge computing environment to migrate the computation and communication loads of mobile devices of IIS to edge nodes, thereby ensuring that mobile devices have lightweight computation and communication loads;and the members participating in the GKA can verify whether the group session keys they calculated are correctness. Under the hardness assumption of bilinear inverse Diffie-Hellman problem, the proposed protocol is proven that it can resist negative attacks. After evaluating the performance of the protocol, GKA-IIS-MEC has higher efficiency than the referred works in terms of time cost, communication consumption, and computation consumption.

关键词： mobile edge computing   blockchain   offloading decision   adaptive data block size   resource allocation  

摘要： With the rapid growth of smart mobile devices, the limited resources and computing performance of mobile devices might increase the delay of processing tasks, resulting in greater energy and cost consumption by terminal devices. This study aimed to focus on the balance of delay and energy consumption in the mobile edge environment, considering the impact of computationally intensive network interference on system performance under blockchain and proposed a joint optimization algorithm for computing offloading and resource allocation of adaptive data block size in the edge environment. First, a computing offloading model and a communication model were established, and the problem model was optimized on that basis. Second, the incentive mechanism of a virtual currency based on blockchain was introduced, and a coordinate descent method was used to formulate the offloading decision of adaptive data block size. Then, a subchannel allocation was based on the improved Hungarian algorithm and the greedy algorithm while satisfying the user delay constraint. Finally, energy minimization problems were converted into power minimization problems, then into a convex optimization problem, and finally into the user's optimal transmit power. Experimental results showed that compared with similar computing offloading algorithms, the proposed offloading decision could reduce the energy consumption of the system while satisfying different time delay requirements of the users and improving the system performance.

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： 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.