关键词： Mobile edge computing   MCC   energy optimization in mobile edge computing   offloading decision in mobile edge computing  

摘要： With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

关键词： Cost Optimization   Distributed Computing   Game Theory   Mobile Edge Computing   Multi MEC Servers   Nash Equilibrium   Task Offloading  

摘要： With the development of mobile edge computing, how to utilize the computing power of edge computing to effectively and efficiently offload data and to compute offloading is of great research value. This paper studies the computation offloading problem of multi-user and multi-server in mobile edge computing. Firstly, in order to minimize system energy consumption, the problem is modeled by considering the joint optimization of the offloading strategy and the wireless and computing resource allocation in a multi-user and multi-server scenario. Additionally, this paper explores the computation offloading scheme to optimize the overall cost. As the centralized optimization method is an NP problem, the game method is used to achieve effective computation offloading in a distributed manner. The decision problem of distributed computation offloading between the mobile equipment is modeled as a multi-user computation offloading game. There is a Nash equilibrium in this game, and it can be achieved by a limited number of iterations. Then, we propose a distributed computation offloading algorithm, which first calculates offloading weights, and then distributedly iterates by the time slot to update the computation offloading decision. Finally, the algorithm is verified by simulation experiments. Simulation results show that our proposed algorithm can achieve the balance by a limited number of iterations. At the same time, the algorithm outperforms several other advanced computation offloading algorithms in terms of the number of users and overall overheads for beneficial decision-making.

关键词： Mobile Edge Computing   Devices priority   Resources allocation   Penalty function   Local search   Discrete particle swarm optimization  

摘要： Computation offloading within Mobile Edge Computing (MEC) networks is a promising new technique, especially in the 5G era. This technique offers leading-edge services to the users of Smart Mobile Devices (SMDs) to reduce the processing time and battery drain. Thus, SMDs tend to offload their heavy processing tasks to preserve battery power and benefit from an important processing power. However, in the era of the Internet of Things (IoT), several subscribers will compete for the available provided resources. Thus, we consider subscribers with a priority property fixed by their contracts with the service provider. In this work, we study a multi-server MEC network with multiple base stations where each one is equipped with a MEC server and provides offloading services to nearby users. Accordingly, we consider the energy consumption, the critical situations of radio resources' insufficiency as well as a penalty function based on the SMDs' priority. Therefore, we formulated a bi-objective optimization problem that jointly minimizes the overall energy consumption and the penalty function while allocating the local processing frequencies for the SMDs, their transmit powers and the radio resources allocated by the Base Station (BS). Consequently, based on the weighted aggregation approach, we propose and study a heuristic solution called Resources Allocation with Priority Devices (RAPD). Finally, simulation experiments were realized to study the RAPD solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and penalty are very promising. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   collaborative offloading   delivery rate  

摘要： Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.

关键词： Wireless communication   Servers   Optimization   Interference   Convergence   Resource management   Computational modeling   Artificial neural networks   caching   combinatorial optimization   computation offloading   heterogeneous networks   long term evolution   mobile edge computing   resource allocation  

摘要： This paper explores the problem of joint computational offloading and content caching (OCP) in the wireless heterogeneous mobile edge computing (MEC) system, where each small-cell base station (BS) is equipped with the MEC server having the content caching/processing capabilities. The communication and computing resources of the system are allocated to users requesting or offloading their contents via the BSs to minimize the system-wide computational overhead. Due to the non-deterministic polynomial time hardness of the OCP, it is difficult to solve it with an exact integer-programming (IP) method. Instead, the problem is solved by adopting the Hopfield neural network (HNN) based approach. In particular, the HNN model representing the OCP is constructed. The global energy minimum of the model corresponds to a solution of the OCP. However, because of the negativity of diagonal weights, the convergence of this model to a stable state cannot be guaranteed. Subsequently, a range of "synthetic" HNN models with the global convergence property is developed to replace the original HNN. Based on these models, three different search algorithms are formulated and implemented in a long-term evolution advanced network. The algorithms demonstrate an improved performance when compared to other relevant IP methods in simulations.

关键词： UAV trajectory   Mobile edge computing   Energy minimization   Full offloading   Secure communications  

摘要： This paper designs an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system. The main work of the UAV is to communicate with multiple ground users, meanwhile it should satisfy the quality and security via sending jamming signal to against the potential eavesdropper. We aim to maximize the secure computation efficiency, subject to the trajectory of the UAV, the minimum secret rate and the expected bits of tasks. Due to the non-convex of the original problem, we exploit the block coordinate and transform the original problem into a tractable problem. We then propose a two-stage alternative optimization algorithm for secure computation efficiency maximization by jointly optimizing the transmission power and UAV's trajectory. The simulation results show that our proposed algorithms consume much less energy compared to other benchmark schemes. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Processor scheduling   Servers   Autonomous vehicles   Task analysis   Real-time systems   Job shop scheduling   Delays   Autonomous vehicles   computing scheduling   mobile edge computing   restless multi-armed bandit  

摘要： This paper investigates computing resource scheduling for real-time applications in autonomous driving, such as localization and obstacle avoidance. In our considered scenario, autonomous vehicles periodically sense the environment, offload sensor data to an edge server for processing, and receive computing results from the server. Due to mobility and computing latency, a vehicle travels some distance in the duration between the instant of offloading its sensor data and the instant of receiving the computing result. Our objective is finding a scheduling scheme for the edge sever to minimize the above traveled distance of vehicles. The approach is to determine the processing order according to individual vehicle mobility and computing capability of the edge server. We formulate a restless multi-arm bandit (RMAB) problem, design a Whittle index based stochastic scheduling scheme, and determine the index using a deep reinforcement learning (DRL) method. The proposed scheduling scheme avoids the time-consuming policy exploration common in DRL scheduling approaches and makes effectual decisions with low complexity. Extensive simulation results demonstrate that the proposed indexed-based scheme can deliver computing results to the vehicles promptly while adapting to time-variant vehicle mobility.

关键词： Blockchain   Security   Loading   Servers   6G mobile communication   Load modeling   Internet   Block stream loading and execution   blockchain   internet -of- vehicles (IoV)   mobile edge computing  

摘要： 6 G and mobile Internet-of-Vehicles (IoV) technology require a secure, open, and transparent system. Blockchain has the characteristics of decentralization, non-tampering, and traceability, which can improve the robustness, data privacy, and security transparency of the overall system. Therefore, blockchain will be the most promising technology to ensure the security and privacy of 6 G and vehicle networks. In this article, we propose a novel IoV block-streaming service awareness and trusted verification in 6 G. The edge node uploads the microservices and calling diagram of microservices to the blockchain network. The blockchain network serves as an intermediate verification platform for the edge node and the IoV equipment to record the evidence of interaction between the two ends. Moreover, combined with identity-based blind signature technology, we have designed a security scheme in which IoV devices anonymously request services from edge nodes, which can realize the mutual authentication of IoV devices and edge servers while keeping the user's real identity information confidential. In addition, an edge caching mechanism based on user requests and service awareness is designed to pre-compile services at edge nodes, improve the cache hit rate of service requests from vehicle users on the edge server, and achieve efficient processing of resource requests from users.

关键词： Mobile edge computing  

摘要： Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancy between global and local objectives, making the FL model slow to converge. In this paper, we proposed Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected based on the output of Optimal Aggregation. FedPNS can preferentially select nodes that propel faster model convergence. The convergence rate improvement of FedPNS over the commonly adopted Federated Averaging (FedAvg) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node selection. © 2021, CC BY-NC-SA.