关键词： mobile edge computing   task offloading   multi-queue joint optimization   Lyapunov optimization   deep reinforcement learning  

摘要： With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users' long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions.

关键词： mobile edge computing   crowdsourcing   incentive mechanism  

摘要： Crowdsourcing becomes a popular data -collection method to relieve the burden of high cost and latency for data -gathering. Since the involved users in crowdsourcing are volunteers, need incentives to encourage them to provide data. However, the current incentive mechanisms mostly pay attention to the data quantity, while ignoring the data quality. In this paper, we design a Data -quality awaRe IncentiVe mEchanism (DRIVE) for collaborative tasks based on the Stackelberg game to motivate users with high quality, the highlight of which is the dynamic reward allocation scheme based on the proposed data quality evaluation method. In order to guarantee the data quality evaluation response in real-time, we introduce the mobile edge computing framework. Finally, one case study is given and its real -data experiments demonstrate the superior performance of DRIVE.

关键词： Servers   Cloud computing   Task analysis   Computer architecture   Edge computing   Optimization   Costs   Computation offloading   edge computing   mobile edge computing   mobility management   resource allocation  

摘要： Mobile edge computing (MEC) is a promising technology that aims to reduce the total latency of user equipment (UE) by deploying computation resources at the edge of mobile networks. UE mobility is a challenging factor that causes the traditional MEC architecture to suffer from several issues, such as decreased efficiency and frequent service interruptions. One popular method to manage UE mobility is virtual machine (VM) migration, which requires high bandwidth and causes undesirable latency, rendering it impractical for real-time tasks with stringent latency requirements. This paper proposes a hierarchical architecture for MEC networks that facilitates mobility management and mitigates the need for VM migration. In order to utilize this architecture efficiently, a Markov chain-based predictive strategy is introduced to predict UE mobility. Afterward, an optimization problem is formulated to make the optimal long-term offloading decisions for UEs such that their expected cost is minimized subject to latency commitments and resource consumption constraints. Simulation results demonstrate that the proposed scheme reduces the cost of high-mobility UEs by up to 25% compared to traditional schemes. Furthermore, the measures of movement direction predictability and offloading decision popularity are introduced that provide insights into the behavior of the proposed and counterpart schemes.

关键词： Mobile edge computing   backscatter communications   partial offloading   computation energy efficiency  

摘要： In the Internet of Things (IoT) environment, a wide variety of mobile devices (MDs) have become part of it, leading to a dramatic increase in the amount of task data. However, due to the limited battery capacity and computing resources of MDs, a lot of effort is required to be taken on how to process more data with less energy. In this paper, we take into account the low utilization of spectrum resources and the short battery life of the equipment, and a backscatter communication-mobile edge computing (BC-MEC) network system based on Non-orthogonal multiple access (NOMA) communication mode is proposed. In order to maximize the computation energy efficiency (CEE) of the system, we jointly optimize the backscatter coefficient of each MD, the backscatter communication duration, the direct offloading duration, the MEC server processing time, the local processing time, the direct offloading power of each MD, the calculation frequency of the MEC server, and the local calculation frequency of each MD. We then formulate it as a joint fractional optimization problem, which is a non-convex optimization problem that is difficult to solve by heuristic algorithms with high computational complexity. To this end, we transform such a problem into a convex problem and apply the Lagrangian dual method to solve it efficiently. Furthermore, in order to meet different user requirements, two effective iterative Dinkelbach algorithms based on Backscatter Coefficient Updates (DBCU) are proposed to solve this problem. Extensive simulation results demonstrate the superiority of our proposed approach, which improves the system CEE by at least 10% compared to state-of-the-art methods.

关键词： Task analysis   Servers   Mobile handsets   Approximate computing   Real-time systems   5G mobile communication   Performance evaluation   Mobile edge computing   resource allocation   testbed   computer-vision applications   convex optimizations  

摘要： Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique to prolong battery lifetime and enhance the computational capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server that assists users in executing computation-intensive tasks via offloading. Exploiting approximate computing in MEC, we can trade the output accuracy over a subset of offloading data instead of the entire dataset. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, aiming to optimize the trade-off between energy consumption and latency. Due to the mixed-integer nature of this problem, we employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems-namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of the Lagrangian multiplier associated with the primal problem;and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

关键词： Backscatter   Batteries   Switches   Servers   Protocols   Task analysis   Interference   Mobile edge computing   backscatter communication   user reciprocity   resource allocation  

摘要： This article proposes a new cooperative offloading protocol termed "user reciprocity" for backscatter communication (BackCom)-aided mobile edge computing (MEC) systems, whose quintessence is that each user can switch alternately between the active or the BackCom mode in different slots, and one user works in the active mode and the other user works in the BackCom mode in each time slot. Specifically, the user in the BackCom mode can always use the signal transmitted by the user in the active mode for more data transmission in a spectrum-sharing manner. To evaluate the proposed protocol, a computation efficiency (CE) maximization-based optimization problem is formulated by jointly power control, time scheduling, power reflection coefficient (RC) adjustment, and computing frequency allocation, while adhering to constraints on energy budgets, computing frequency thresholds, minimum computed bits, and harvested energy. To solve this non-convex problem, Dinkelbach's method and quadratic transform are first employed to transform the complex fractional forms into linear ones. Then, an iterative algorithm is designed by decomposing the resulting problem to obtain the suboptimal solution. For analytical insights, we not only provide some closed-form solutions, but also derive the analytical performance gain with reciprocal mode. Simulation results demonstrate that the proposed scheme outperforms benchmark schemes regarding the CE.

关键词： Congestion control   AGCM   Mobile edge computing   Fog computing   K-means   5G  

摘要： The congestion management mechanism is essential to manage the explosive evolution of data traffic associated with advanced applications and services in the 5G system. As a result, we suggest a novel methodology to manage congestion for mobile edge computing in the 5G system. Furthermore, the proposed model enhances delay, energy consumption, and throughput. The enhanced random early detection strategy and the K-means approach are used in the suggested model to execute this. Also, a virtual list is realized to maintain packet information and suit more packets. The proposed model is realized in NS2 green cloud simulator. In comparison with the traditional cloud model and the fog computing model, the simulation results confirm that the proposed model reduces delay, boosts throughput, and decreases energy consumption.

关键词： Mobile edge computing   Computation offloading   Multi-agent deep reinforcement learning   Energy harvesting   Resource allocation   Non-orthogonal multiple access  

摘要： Supporting latency -sensitive and computation -intensive applications is hardly possible for mobile devices (MDs) with limited battery capacity and low computing resources. Therefore, mobile edge computing (MEC) and wireless power transfer (WPT) have emerged as promising technologies that allow MDs to offload part or all of their workloads to MEC servers and harvest energy for prolonging their battery lifetime. However, the MEC server's limited computing resources, available communication channel quality, and time -limited energy harvesting (EH) challenge the computation offloading. In this paper, we study the joint problem of decentralized computation offloading and resource allocation (JDCORA) in the environment of non -orthogonal multiple access (NOMA)-assisted MEC with multiple EH -enabled MDs. To learn decentralized offloading policies, we propose a multi -agent deep reinforcement learning (MADRL)-based scheme towards minimizing energy consumption and task completion time, which considers the cooperation between MDs to adjust their strategies. In particular, we improve the multi -agent deep deterministic policy gradient (MADDPG) by applying the features of double actors, double centralized critics, soft value estimation, critic regularization and proportional -based prioritized experience replay (pPER) and propose an algorithm called multi -agent twin actors regularized critics (MATARC). Simulation results demonstrate that the proposed MATARC has a better convergence performance compared to other baseline methods and also reduces the average energy consumption, task completion time and task drop rate.

关键词： UAV   mobile edge computing   video transcoding   utility   DDPG  

摘要： The integration of unmanned aerial vehicles (UAVs) in future communication networks has received great attention, and it plays an essential role in many applications, such as military reconnaissance, fire monitoring, etc. In this paper, we consider a UAV-aided video transmission system based on mobile edge computing (MEC). Considering the short latency requirements, the UAV acts as a MEC server to transcode the videos and as a relay to forward the transcoded videos to the ground base station. Subject to constraints on discrete variables and short latency, we aim to maximize the cumulative utility by jointly optimizing the power allocation, video transcoding policy, computational resources allocation, and UAV flight trajectory. The above non-convex optimization problem is modeled as a Markov decision process (MDP) and solved by a deep deterministic policy gradient (DDPG) algorithm to realize continuous action control by policy iteration. Simulation results show that the DDPG algorithm performs better than deep Q-learning network algorithm (DQN) and actor-critic (AC) algorithm.

关键词： integrated sensing and communication   UAV   mobile edge computing   wireless energy harvesting   genetic algorithm   successive convex approximation algorithm  

摘要： In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)'s task completion latency problem in a communication-sensing-computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs.