Proximal policy optimization-based committee selection algorithm in blockchain-enabled mobile edge computing systems

In current world, processing the data by the users, researchers, organizations, etc. through online source (ie. cloud) is increasing tremendously, where it offers various services like storing the data, allocating the resources, retrieval of data, security, etc. Since the data is stored in cloud, processing it leads to increase in latency, due to its long-distance communication. As distance increases, data transmission produces a delay between source and destination which is called as latency. To get over this problem, MEC (Mobile Edge Computing) system can be positioned between the cloud infrastructure and the user equipment(UE) in the IoT environment. Instead of sending data to the cloud directly which is collected by IoT sensors, edge computing processes those data within the network. The study of this review focuses on the various techniques such as deep learning algorithms, deep reinforcement learning approaches for offloading decisions and optimization techniques in 5G networks for minimizing the latency in the MEC systems, for producing higher processing capability to the clients. For increasing the MEC system performance, efficient offloading strategies can be used to minimize latency for various tasks.

Unmanned aerial vehicle (UAV) with moderate computing resources has been deployed in current mobile edge computing (MEC) system to enhance the service coverage and computing capabilities of MEC servers. In such a UAV-assisted MEC system, UAV flies to the users with poor communication condition and executes the computation tasks in cooperation with the terrestrial MEC server, then the task latency perceived by users can be greatly reduced. However, frequent flight and computation offloading may drain its limited on-board battery and further deteriorate the service performance. Thus, we investigate the problem of joint online task offloading, flying trajectory and charging scheduling for UAV-assisted MEC system, with the goal of maximizing the system-wide energy efficiency in the long-term. Deriving such complicated but efficient strategies is non-trivial, since the long-term problem is NP-hard and requires future uncertain information (e.g., stochastic arrival of user tasks). To address these dual challenges, we carefully combine an online optimization technique with an approximate optimization method into a joint optimization framework, through (1) decomposing the long-term problem into a series of one-shot problems with Lyapunov optimization technique, and (2) solving the resulted NP-hard and non-linear one-shot problem with block coordinate descent and successive convex approximation methods. The efficacy of proposed online algorithm is verified by both rigorous theoretical analysis and extensive trace-driven evaluations.KeywordsMobile edge computingUnmanned aerial vehicleLyapunov optimizationTask offloadingTrajectory planning

Mobile edge computing (MEC) systems provide mobile devices (MDs) with low-latency cloud services by deploying edge servers (ESs) in the vicinity. In fact, various mobile applications may generate cotasks, each of which is completed only if all its constituent tasks are finished. Existing works have been devoted to the design of MEC offloading and scheduling, but none of them exploits the cotask feature to better utilize the networked computing resources. In this paper, we investigate the problem of joint cotask-aware offloading and scheduling in MEC systems (Cool-Edge), and we formulate it as a mixed integer non-linear program (MINLP), the objective of which is to minimize average cotask completion time (ACCT). To cope with the Cool-Edge problem, we propose two low-complexity algorithms to offload cotasks based on an LP rounding technique and schedule them according to an earliest-cotask-arrival-first rule, respectively, and we further prove the approximation factor jointly achieved by the two algorithms. Finally, we conduct testbed experiments and simulations to demonstrate the effectiveness of our proposed solution, and we also show how ACCT varies with the network environment.

In mobile edge computing (MEC) systems with energy harvesting, the mobile devices are empowered with the energy that harvested from renewable energy sources. On the other hand, mobile devices can offload their computation-intensive tasks to the MEC server to further save energy and reduce the task execution latency. However, the energy harvested is unstable and the mobile devices have to make sure that the energy should not be run out. Moreover, the wireless channel condition between the mobile device and the MEC server is dynamically changing, leading to unstable communication delay. Considering the energy constraints and unstable communication delay, the benefit of computation offloading is limited. In this paper, we investigate D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In our method, the mobile device is allowed to offload its tasks to the MEC server with the help of its neighbor node. More Specifically, the neighbor node acts as a relay to help the mobile device to communicate with the MEC server. Our goal is to minimize the average task execution time by selecting an optimal execution strategy for each task, i.e., whether to execute the task locally, or offload it to the MEC server directly, or offload it to the MEC server with the help of the most suitable neighbor node, or just to drop it. We propose a low-complexity online algorithm, which stem from Lyapunov Optimization-based Dynamic Computation Offloading (LODCO) algorithm, to solve this problem. Extensive simulations verified the effectiveness of the proposed algorithm, where the average task execution time is reduced around 50% as compared to that of the original LODCO algorithm.

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose an SDN (Software Defined Network)-based MEC (Mobile Edge Computing) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDNs programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Secondly, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multi-user stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PER) to minimize energy consumption and processing latency with Multi-Agent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG, Q-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption with dynamic resource allocation. Moreover, SGRA-PER can still keep a higher performance under the increase of SDs, which can be applied in a large-scale MEC system.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

In this paper, we investigate a new mobile blockchain-enabled edge computing (MBEC) network, where mobile users can join the empowered process of public blockchains and meanwhile offload computation-intensive mining tasks to the mobile edge computing (MEC) server. However, the trustiness of the MEC server and the fairness of computation resources allocated by the MEC server for each user become key challenges. To tackle these challenges, we consider an untrusted MEC server and propose a nonce hash computing ordering (HCO) mechanism in MBEC networks. Then we formulate nonce hash computing demands of an individual user as a non-cooperative game that maximizes the personal revenue. Moreover, we also analyze the existence of Nash equilibrium of the non-cooperative game and design an alternating optimization algorithm to achieve the optimal nonce selection strategies for all users. With the proposed HCO mechanism, the MEC server can provide much fairer computation resources for all users, and we can achieve the optimal nonce strategies of hash computing demands by using the proposed alternating optimization algorithm. Numerical results demonstrate that the proposed HCO mechanism can provide fairer computation resource allocation than the traditional weighted round-robin mechanism, and further verify the effectiveness of this alternating optimization algorithm.

In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixed-integer nonlinear programming. The DIRS framework includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. Specifically, we first introduce a novel multiagent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Second, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Levy flight search. Finally, an imitation acceleration scheme is presented to pretrain all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. The simulation results in three typical scenarios demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

To enhance the security in the computational offloading process of Mobile Edge Computing (MEC) systems, this paper proposes a physical layer secure transmission scheme based on Intelligent Reflecting Surface (IRS) assistance. In this scheme, IRS is added to facilitate the task transfer between the user and the MEC server, while suppressing eavesdroppers from eavesdropping on user information, thus achieving a secure computing offload for the system. In order to maximize the safe transmission rate of the system, a flow-form optimization algorithm based on alternating optimization and conjugate gradient is designed to obtain the users transmit power as well as the phase shift of the IRS. Based on this, the computational offload rate of users and the computational resources of MEC servers are optimized by heuristic algorithms to minimize the computational offload delay of MEC systems while ensuring secure transmission. Simulation results show that the proposed scheme can reduce the total computational offload delay while guaranteeing secure transmission of the MEC system compared with other benchmark schemes and traditional artificial noise assisted schemes.

In a mobile edge computing (MEC) system with a large number of low power mobile terminals, proper computation offloading and resource allocation is crucial to achieving desirable system performance. In this paper, we consider the joint computation offloading and resource allocation problem for an uplink MEC system under the min-max fairness criterion. The proposed optimization problem is difficult to solve due mainly to the nonconvex nondifferentialbe objective and the nonlinear coupling of design variables in the constraints. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then develop a novel algorithm based on the concave-convex procedure (CCCP) technique to address the problem. Furthermore, by exploiting the problem structure, an efficient algorithm based on inexact block coordinate descent (IBCD) method is proposed to reduce the computational complexity. Numerical results validate the efficiency of the proposed algorithms.

Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

By providing spatial diversity gain, the incorporation of multiple antennas into mobile edge computing (MEC) systems can improve the transmission performance. Meanwhile, employing energy harvesting (EH) helps enhance the system sustainability. In this paper, we focus on multi-input multi-output (MIMO) MEC systems with EH and studies the computation offloading. The design objective is to minimize the time average of a weighted sum of energy consumption and execution delay, meanwhile stabilizing the battery energy queue. To this end, we formulate the problem as a statistic program and propose a dynamic computation offloading (DCO) algorithm in which the transmitter covariance matrix, CPU-cycle frequencies for local computing, and partial offloading ratio are jointly optimized. Based on Lyapunov optimization, the program is first transformed into a nonconvex per-time slot problem. Then, we solve it by the successive convex approximation (SCA) technique, where a sequence of convex problems are created and solved. Simulation results demonstrate that the proposed algorithm is asymptotically optimal and outperforms several benchmark schemes in terms of both the average system cost and task drop ratio.

We consider the efficient resource allocation task in the blockchain-based mobile edge computing (MEC) system that requires decentralized transaction management to validate transactions between edge servers (ESs) and mobile devices (MDs). In such task allocation process (where MDs' resources are limited and privacy-sensitive), it is a significant challenge to guarantee individual rationality with satisfactory system stability while enabling flexible task offloading under various locality constraints (e.g., communication distance, bandwidth and delay). In this paper, we formulate the target problem as a blockchain-assisted task-resource matching model, and then propose a near optimal locality-aware resource allocation mechanism over smart contract to enable automatic and efficient transactions in MEC system. More specifically, for the service agents selection, we design the preference-based selection strategy to get highest estimated profit. For the flexible task offloading, we develop the minimum delay task graph partitioning algorithm to determine the optimal task offloading solution for MD under different resource bundles. For the task-resource matching, we propose a task-resource matching game (based on potential game) with the second lowest cost strategy to determine the matching of task-resource and decide the price of resource bundle. For the transaction verification and block allocation, we propose a social welfare-driven consensus mechanism to enable verified transaction and fair block allocation in a reward-free way. Strict theoretical analysis and extensive simulations demonstrate that our mechanism guarantees individual rationality, Nash Equilibrium, and stable near optimal solution.

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.