Mobile Edge Computing Server Research Articles

DTN-Assisted Dynamic Cooperative Slicing for Delay-Sensitive Service in MEC-Enabled IoT via Deep Deterministic Policy Gradient With Variable Action

Network Slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with Multi-access Edge Computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this paper, we propose a paradigm of dynamic cooperative slicing based on Digital Twin Network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models including prediction and decision-making in DTN. Then we realize dynamic cooperative slicing through the built basic models and function models. Secondly, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Thirdly, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a Deep Deterministic Policy Gradient algorithm with Variable Action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks

Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.

Delay Minimization Using Hybrid RSMA-TDMA for Mobile Edge Computing

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.

Research on Offloading Strategy for Mobile Edge Computing Based on Improved Grey Wolf Optimization Algorithm

With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints; secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem; finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption.

Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning

The joint problem of task offloading, collaborative computing and resource allocation for Multi-access Edge Computing (MEC) is a challenging issue. In this paper, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a non-convex mixed-integer optimization problem. In order to solve the problem, we propose a Deep Reinforcement Learning (DRL) based bi-level optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper-level, while the computing resource allocation subproblem is solved at the lower-level. We combine Dueling-Deep-Q-Network (DQN), Double-DQN and add adaptive parameter space Noise (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> N-DQN) to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in Energy Efficiency (EE) and task completion rate compared to other DRL-based approaches and other benchmark schemes under various network parameter settings.

Achieving Cooperative Mobile-Edge Computing Using Helper Scheduling

This paper investigates computing task offloading from an Internet-of-Thing (IoT) device with limited transmit power to a mobile-edge computing (MEC) server located beyond the communication range of the IoT device. We propose an opportunistic cooperative offloading (OCO) strategy that recruits the IoT device’s nearby spatially random idle-state users as helpers and opportunistically schedules one of them to partially execute the latency-critical task, and forwards the rest portion of the task to the MEC server. For the OCO strategy, we investigate the helper scheduling under three cases of system information availability, i.e., the global, partial, and distance information cases, and develop an offloading-outage optimal scheduling scheme for each case. For each scheduling scheme, an approximate expression is derived for the offloading-outage probability, with which the achieved diversity order is also theoretically evaluated. Simulation results verify our performance analysis for the OCO strategy using helper scheduling and show its achieved offloading-outage/energy consumption reduction over multi-helper cooperative offloading that fully uses all helpers for cooperation. In addition, the advantages of the proposed helper scheduling schemes over existing scheduling schemes are also demonstrated by simulations.

Energy-Efficient Task Transfer in Wireless Computing Power Networks

The Sixth Generation (6G) wireless communication aims to enable ubiquitous intelligent connectivity in future Space-Air-Ground-Ocean integrated networks, with extremely low latency and enhanced global coverage. However, the explosive growth in Internet of Things devices poses new challenges for smart devices to process the generated tremendous data with limited resources. In 6G networks, conventional mobile edge computing systems encounter serious problems to satisfy the requirements of ubiquitous computing and intelligence, with extremely high mobility, resource-limitation, and time-variability. In this paper, we propose the model of Wireless Computing Power Networks (WCPN), by jointly unifying the computing resources from both end devices and MEC servers. Furthermore, we formulate the new problem of task transfer, to optimize the allocation of computation and communication resources in WCPN. The main objective of task transfer is to minimize the execution latency and energy consumption with respect to resource limitations and task requirements. To solve the formulated problem, we propose a multi-agent Deep Reinforcement Learning (DRL) algorithm to find the optimal task transfer and resource allocation strategies. The DRL agents collaborate with others to train a global strategy model through the proposed asynchronous federated aggregation scheme. Numerical results show that the proposed scheme can improve computation efficiency, speed up convergence rate, and enhance utility performance.

Computation Efficiency Maximization for UAV-Assisted Relaying and MEC Networks in Urban Environment

Unmanned aerial vehicle (UAV)-assisted relaying and mobile edge computing (MEC) networks is a popular and promising technology to provide high-quality computation service to ground users (GUs). However, prior works on UAV-assisted relaying and MEC networks do not consider the complicated channel conditions in urban environment. Furthermore, the optimization of computation efficiency, defined as the ratio of computation resources (in terms of CPU frequency) and energy consumption, is not studied in prior works. In this work, we consider an UAV-assisted relaying and MEC networks composed of GUs, two MEC servers carried by one rotary-wing UAV and one base station (BS), respectively. Meanwhile, we consider the probabilistic Line-of-Sight (LoS) channel model and the Rician fading model in urban environments to make the channel model more practical and accurate to urban environments. Further, we aim to solve the maximization problem of computation efficiency by jointly optimizing computation resources, computation offloading, bandwidth and UAV trajectory. For the nonconvex formulated problem, we proposed an algorithm based on the Dinkelbachs method, the block coordinate descent (BCD) method and successive convex approximate (SCA) technique. Numerical results demonstrate that the proposed scheme can efficiently improve the computation efficiency compared to other traditional schemes in the practical simulation environment.

Large-Scale Computation Offloading Using a Multi-Agent Reinforcement Learning in Heterogeneous Multi-Access Edge Computing

Recently, existing computation offloading methods have provided extremely low service latency for mobile users (MUs) in multi-access edge computing (MEC). However, this remains a challenge in large-scale mixed cooperative-competitive MUs heterogeneous MEC environments. Moreover, existing methods focus more on all offloaded tasks handled by static resource allocation MEC servers (ESs) within a time interval, ignoring on-demand requirements of heterogeneous tasks, resulting in many tasks being dropped or wasting resources, especially for latency-sensitive tasks. To address these issues, we present a decentralized computation offloading solution based on the Attention-weighted Recurrent Multi-Agent Actor-Critic (ARMAAC). First, we design a recurrent actor-critic framework to assist MU agents in remembering historical resource allocation information of ESs to better understand the future state of ESs, especially in dynamic resource allocation. Second, an attention mechanism is introduced to compress the joint observation space dimension of all MUs agent to adapt to large-scale MUs. Finally, the actor-critic framework with double centralized critics and Dueling network is redesigned considering the instability and convergence difficulties caused by the sensitive relationship between the actor and critic networks. The experiments show that ARMAAC improves task completion rates and reduces average system cost by 11.01%~14.03% and 10.45%~15.56% compared with baselines.

Optimal Computation Offloading in Collaborative LEO-IoT Enabled MEC: A Multiagent Deep Reinforcement Learning Approach

Recently, Low Earth Orbit (LEO) satellite-based Internet of Things (LEO-IoT) becomes attractive for computation offloading in mobile-edge computing (MEC) since it can overcome terrain obstacles, such as in depopulated villages and disaster sites. However, it is extremely hard to allocate bandwidth and power resources jointly with multiple users and satellites. In this paper, we study offloading in collaborative LEO-IoT where satellites forward data from users to the MEC server, with the goal of making offloading fast and energy efficient. To achieve this goal, we first define the data offloading in collaborative LEO-IoT as an optimization problem with resource constraints. Then we formulate the optimization problem as a Partially Observable Markov Decision Processes (POMDP), which differs from the existing Markov Decision Processes (MDP) work for the offloading scenario. We further propose a novel Multi-Agent Information Broadcasting and Judging (MAIBJ) algorithm to allocate resources in a collaborative manner. Finally, extensive experiments are conducted with various configurations and the results show that MAIBJ can shorten at least 33% of transmission latency and save at least 42% of energy consumption compared with several baseline algorithms.

Offloading Decision and Resource Allocation for Satellite Edge Computing

Plenty of research show that edging computing can be applied in satellite network and it has been a hotpot. By deploying the MEC server, satellites can provide enough computing resources for tasks and reduce the delay required for task processing. Compared with traditional edge computing architectures, satellite edge computing (SEC) has a greater coverage area. In this paper, offloading decisions and resource allocation in low-orbit satellites are considered and an optimization algorithm MMTORA is proposed. The algorithm decomposes the problem to be optimized into two sub-problems, and they can be solved by the Nash equilibrium solution and Lagrange multiplier algorithm, respectively. We demonstrate that MMTORA can converge quickly and prove its superiority through simulation results.

Resource Allocation for 5G Network Considering Privacy Protection in Edge Computing Environment

In the field of Internet of Things, mobile communication will be deeply integrated with industrial construction, biomedicine, agricultural production, transportation and other industries to fully realize the “Internet of Everything” and realize smart city and sustainable development. This paper intends to solve problems in increasing latency and consumption of energy and security after deploying Mobile Edge Computing (MEC) servers in single cell multi-user network model, and has advocated a strategy of allocating the resources of 5G communication in regards to the protection mechanism for privacy in the environment of edge computing. First of all, solution model for the problem is characterized as a model with time delay and energy consumption. Second, privacy is used to measure the uncertainty of data in order to quantify privacy data while computing the allocation of tasks and facilitate the design of objective function. Finally, the task migration problem of minimizing energy consumption under the limitation of completion time is constructed as a nonlinear 0–1 programming problem. Besides, we design the optimal resource allocation strategy of discrete algorithm of binary particle swarm optimization (BPSO) for problem solution. Results indicate that while the quantity of users is 100, the total user expenditure of proposed method is 18.3J, which is lower than 20.0J and 30.7J of the compared methods. Moreover, the proposed method has lower latency and energy consumption, which can well balance the load of MEC servers.

System design and Optimization of Mobile Edge Computing in the NOMA Wireless Tactile Internet of Things Network

Mobile edge computing (MEC) is an essential technique in next-generation networks to serve ultra-low latency and computation-intensity applications. At the same time, nonorthogonal multiple access (NOMA) is a technique to help multi-user service, saving energy and increasing spectrum efficiency. In this study, we investigate the NOMA MEC-based wireless Tactile Internet of Things (IoT) network and propose the optimization algorithms for system and users performance: We propose a network model consisting of a MEC server at the access point that supports computation for two sensor clusters in the Tactile IoT environment. We analyze the performance of the system and cluster heads (CHs) using the successful computation probability (SCP). Asymptotic SCP at high SNRs was analyzed and compared by us to give a better view of the system’s behavior. Then, we maximize the SCP of the proposed system and simultaneously maximize the SCP of the CHs to clarify the performance trade-off problem in the NOMA MEC network by proposing low-complexity meta-heuristic algorithms. Monte-Carlo simulation results show that our proposed approach can significantly improve system performance by up to 30% compared to OMA traditional methods.

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.

Deep reinforcement learning based adaptive threshold multi-tasks offloading approach in MEC

Offloading tasks and data is an important use case for Mobile Edge Computing (MEC) that benefits a wide variety of mobile applications on different platforms, including autonomous vehicles and smartphones. A moving vehicle may pass through multiple Roadside Units (RSUs) and the MEC servers located within them as it travels. On-board applications generate structured tasks and can offload them to observable servers. However, since the mobile node cannot determine the information of the global server, choosing the best server becomes a key issue. In this paper, we address the problem of the offloading threshold in traditional Optimal Stopping Theory (OST) based decision models that cannot adapt to the environment. We use Deep Q-Network (DQN) to provide the model with the optimal offloading threshold for adaptive environments and propose a DQN-based Adaptive Threshold Multi-task Offloading approach (DQN-ATMOO). This method uses the principle of OST to solve the offloading order decision problem, divides structured tasks into sub-tasks, and executes offloading in sequence, with the goal of maximizing the probability of offloading to the best server and minimizing the total offloading delay. We experimentally compare and evaluate the proposed model using simulated and real datasets. The results show that the proposed model can be effectively implemented on mobile nodes and significantly reduces the total expected processing time.

A renewable-energy-driven energy-harvesting-based task scheduling and energy management framework

Aiming to provide low-cost MEC services for mobile devices in areas without/lacking infrastructure, we propose a renewable-energy-driven energy-harvesting-based task scheduling and energy management framework. First, for mobile devices, the constructed energy consumption minimization problem is solved by an alternating-optimization-based algorithm. Then, we design an energy management algorithm based on sampling average approximation to derive the optimal charging/discharging strategies, number of energy storage units, and renewable energy utilization. The simulation results show that the proposed framework can significantly reduce the cost of MEC servers and prolong the working hours of mobile devices.

Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning

Driven by the construction of smart cities, networks and communication technologies are gradually infiltrating into the Internet of Things (IoT) applications in urban infrastructure, such as automatic driving. In the Internet of Vehicles (IoV) environment, intelligent vehicles will generate a lot of data. However, the limited computing power of in-vehicle terminals cannot meet the demand. To solve this problem, we first simulate the task offloading model of vehicle terminal in Mobile Edge Computing (MEC) environment. Secondly, according to the model, we design and implement a MEC server collaboration scheme considering both delay and energy consumption. Thirdly, based on the optimization theory, the system optimization solution is formulated with the goal of minimizing system cost. Because the problem to be resolved is a mixed binary nonlinear programming problem, we model the problem as a Markov Decision Process (MDP). The original resource allocation decision is turned into a Reinforcement Learning (RL) problem. In order to achieve the optimal solution, the Deep Reinforcement Learning (DRL) method is used. Finally, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm to deal with task offloading and scheduling optimization in high-dimensional continuous action space, and the experience replay mechanism is used to accelerate the convergence and enhance the stability of the network. The simulation results show that our scheme has good performance optimization in terms of convergence, system delay, average task energy consumption and system cost. For example, compared with the comparison algorithm, the system cost performance has improved by 9.12% under different task sizes, which indicates that our scheme is more suitable for highly dynamic Internet of Vehicles environment.

A pragmatic approach of Parkinson disease detection using hybrid case-based reasoning neuro-fuzzy classification system over Mobile edge computing

Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier.

Dynamic Computation Offloading and Server Deployment for UAV-Enabled Multi-Access Edge Computing

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.