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Computation offloading can efficiently expand edge resources and is widely used to perform computing-intensive and delay-sensitive tasks. The inability of existing offloading strategies to pay attention to both packet loss problem and performance problems caused by channel noise usually lead to serious encoding costs and retransmission costs in offloading by traditional communication protocols. To address these issues, we propose a dynamic analog-digital coding QUIC (DQUIC) protocol to ensure the efficiency and reliability of edge computing data transmission. The DQUIC protocol uses a dynamic encoding method based on continuous slot communication state to handle sudden errors with a small encoding cost. Moreover, we design a dynamic multi-access edge computing (MEC) model using the DQUIC protocol for communication, which considers the impact of channel noise on communication rate and channel packet loss rate. In the dynamic MEC environment, the double deep Q-learning (DDQN) algorithm is used to solve the offloading decision problem and find the optimal offloading strategy. The experimental results demonstrate that our computation strategy, which leverages DQUIC, surpasses those strategies grounded in the DQUIC protocol and Coco protocol within a dynamic MEC environment. [ABSTRACT FROM AUTHOR]

Efficient offloading and scientific task scheduling are crucial for managing computational tasks in research environments. This involves determining the optimal location for executing a workflow task and allocating the task to computing resources to optimize performance. The challenge is to minimize completion time, energy consumption, and cost. This study proposes three methods: latency-centric offloading (LCO) for delay-sensitive applications; energy-based offloading (EBO) for energy-saving; and efficient offloading (EO) for balanced task distribution across tiers. Scheduling in this paper uses a genetic algorithm (GA) with a weighted sum objective function considering makespan, cost, and energy for internet of things-cloud-fog (IoT-fog-cloud). Comparative studies involving montage, Cybershake, and epigenomics workflows indicate that LCO excels in terms of makespan and cost but ranks the lowest in energy. EBO excels in energy efficiency, aligning closely with the base method. EO competes effectively with the base method in terms of makespan and cost but consumes more energy. This research enables the selection of the most suitable method based on the type of application and its prioritization of makespan, energy, or cost. [ABSTRACT FROM AUTHOR]

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption. [ABSTRACT FROM AUTHOR]

Advancing in communication systems requires nearby devices to act as networks when devices are not in use. Such technology is mobile edge computing, which provides enormous communication services in the network. In this research, we explore a multiuser smart Internet of Vehicles (IoV) network with mobile edge computing (MEC) assistance, where the first edge server can assist in completing the intense computing jobs from the vehicular users. Many currently available works for MEC networks primarily concentrate on minimising system latency to ensure the quality of service (QoS) for users by designing some offloading strategies. Still, they need to account for the retail prices from the server and, as a result, the budgetary constraints of the users. To solve this problem, we present a Dueling Double Deep Q Network (D3QN) with an Optimal Stopping Theory (OST) strategy that helps to solve the multi-task joint edge problems and minimises the offloading problems in MEC-based IoV networks. The multi-task-offloading model aims to increase the likelihood of offloading to the ideal servers by utilising the OST characteristics. Lastly, simulators show how the proposed methods perform better than the traditional ones. The findings demonstrate that the suggested offloading techniques may be successfully applied in mobile nodes and significantly cut the anticipated time required to process the workloads. [ABSTRACT FROM AUTHOR]

The current task offloading technique needs to be performed more effectively. Onboard terminals cannot execute efficient computation due to the explosive expansion of data flow, the quick increase in vehicle population, and the growing scarcity of spectrum resources. As a result, this study suggests a task-offloading technique based on reinforcement learning computing for the Internet of Vehicles edge computing architecture. The system framework for the Internet of Vehicles has been initially developed. Although the control centre gathers all vehicle information, the roadside unit collects vehicle data from the neighborhood and sends it to a mobile edge computing server for processing. Then, to guarantee that job dispatching in the Internet of Vehicles is logical, the computation model, communications approach, interfering approach, and concerns about confidentiality are established. This research examines the best way to analyze and design a computation offloading approach for a multiuser smart Internet of Vehicles (IoV) based on mobile edge computing (MEC). We present an analytical offloading strategy for various MEC networks, covering one-to-one, one-to-two, and two-to-one situations, as it is challenging to determine an analytical offloading proportion for a generic MEC-based IoV network. The suggested analytic offload strategy may match the brute force (BF) approach with the best performance of the Deep Deterministic Policy Gradient (DDPG). For the analytical offloading design for a general MEC-based IoV, the analytical results in this study can be a valuable source of information. [ABSTRACT FROM AUTHOR]

With the large-scale establishment of cross-camera networks, edge computing plays an important role in real-time tasks with its abundant edge resources and flexible task offloading strategy. Conventional studies usually utilize cross-camera network topology and real-time task status to generate subtask offloading strategies. However, most existed approaches focus on utilizing static environment information to generate a fixed offloading strategy for single-target optimization, while dynamic environment information and joint optimization objectives are often ignored. In this paper, we model the computing process of cross-camera tasks as a Markov Decision Process (MDP) integrating spatiotemporal correlation, to make full use of the dynamic environment information in the edge computing network. In addition, to achieve multi-objective optimization of cross-camera tasks, this paper develops a joint Q learning equation that integrates multiple utility indicators and proposes a Deep Spatio-Temporal Q Learning (Deep-STQL) algorithm to solve the equation. Based on the camera frame rate and cross-camera task frame rate, a large number of experimental data show that our proposed Deep-STQL algorithm has significantly improved the convergence, hit rate, average processing delay, drop rate of subtask and computing load of real-time cross-camera tasks compared with the baselines. [ABSTRACT FROM AUTHOR]

Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and mobile edge computing (MEC) technologies advance, more workloads are being transferred to edge servers for processing. The offloading strategy is a crucial aspect of this development, considering all relevant factors and requiring a rapid development approach. This research aims to develop and propose an innovative solution for fine-grained task offloading scheduling in the context of the evolving technologies of the Internet of Things (IoT) and mobile edge computing (MEC). The research addresses the challenges posed by task dependencies within user-generated applications processed across distributed edge servers. This study presents a solution for fine-grained task offloading scheduling by representing user-generated applications as a directed acyclic graph (DAG). We propose a multi-population cooperative elite algorithm (MCE-PSO) based on particle swarm optimization to address the challenge of offloading tasks with dependencies in MEC, reducing application execution time and energy consumption. The proposed method uses a Penalty-based algorithm to minimize energy usage. The results of our experiments show that MCE-PSO outperforms benchmark algorithms, reducing overhead by up to 82.6%, 36.6%, and 16.3%. This demonstrates the efficiency and reliability of our system. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a key solution to realizing a cost-efficient and intelligent Industrial Internet of Things (IIoT). To improve training efficiency and mitigate the straggler effect of FL, this paper investigates an edge-assisted FL framework over an IIoT system by combining it with a mobile edge computing (MEC) technique. In the proposed edge-assisted FL framework, each IIoT device with weak computation capacity can offload partial local data to an edge server with strong computing power for edge training. In order to obtain the optimal offloading strategy, we formulate an FL loss function minimization problem under the latency constraint in the proposed edge-assisted FL framework by optimizing the offloading data size of each device. An optimal offloading strategy is first derived in a perfect channel state information (CSI) scenario. Then, we extend the strategy into an imperfect CSI scenario and accordingly propose a Q-learning-aided offloading strategy. Finally, our simulation results show that our proposed Q-learning-based offloading strategy can improve FL test accuracy by about 4.7% compared to the conventional FL scheme. Furthermore, the proposed Q-learning-based offloading strategy can achieve similar performance to the optimal offloading strategy and always outperforms the conventional FL scheme in different system parameters, which validates the effectiveness of the proposed edge-assisted framework and Q-learning-based offloading strategy. [ABSTRACT FROM AUTHOR]

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

The integrated satellite-terrestrial relay network (ISTRN) is a necessary part of the next-generation wireless communication system, and has important practical significance for accelerating the construction of my country's air-space-terrestrial integrated network system.In the traditional ISTRN architecture, a large amount of signaling needs to be forwarded to the ground control center for processing, which increases the delay of network control and management.A new cloud fog computing architecture was proposed, which constructs a sub-regional edge fog computing layer between the ground access and the central cloud to improve the flexibility of business flow management and control.Under the cloud network framework, a Q-learning based edge computing offloading strategy was designed, and the offloading performance was evaluated by time delay and energy consumption.Simulation results show that, compared with Min-min algorithm and backtracking algorithm, Q-learning based computational offload algorithm has better performance in terms of time delay and energy consumption, and can achieve a balance between the joint optimization of time delay and energy consumption.

Multi-access edge computing (MEC) is a novel computing paradigm that leverages nearby MEC servers to augment the computational capabilities of users with limited computational resources. In this paper, we investigate the computational offloading problem in multi-user multi-server MEC systems with energy harvesting, aiming to minimize both system latency and energy consumption by optimizing task offload location selection and task offload ratio.We propose a hierarchical computational offloading strategy based on multi-agent reinforcement learning (MARL). The proposed strategy decomposes the computational offloading problem into two sub-problems: a high-level task offloading location selection problem and a low-level task offloading ratio problem. The complexity of the problem is reduced by decoupling. To address these sub-problems, we propose a computational offloading framework based on multi-agent proximal policy optimization (MAPPO), where each agent generates actions based on its observed private state to avoid the problem of action space explosion due to the increasing number of user devices. Simulation results show that the proposed HDMAPPO strategy outperforms other baseline algorithms in terms of average task latency, energy consumption, and discard rate. [ABSTRACT FROM AUTHOR]

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least. [ABSTRACT FROM AUTHOR]

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate how to analytically design an analytical offloading strategy for a multiuser mobile edge computing (MEC)-based smart internet of vehicle (IoV), where there are multiple computational access points (CAPs) which can help compute tasks from the vehicular users. As it is difficult to derive an analytical offloading ratio for a general MEC-based IoV network, we turn to provide an analytical offloading scheme for some special MEC networks including one-to-one, one-to-two and two-to-one cases. For each case, we study the system performance by using the linear combination of latency and energy consumption, and derive the analytical offloading ratio through minimizing the system cost. Simulation results are finally presented to verify the proposed studies. In particular, the proposed analytical offloading scheme can achieve the optimal performance of the brute force (BF) scheme. The analytical results in this paper can serve as an important reference for the analytical offloading design for a general MEC-based IoV.

Abstract In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help accomplish the intensive calculating tasks from the vehicular users. For the MEC networks, most existing works mainly focus on minimizing the system latency to guarantee the user’s quality of service (QoS) through designing some offloading strategies, which, however, fail to consider the pricing from the server and hence fail to take into account the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC-based IoV networks and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then, the Lagrange multiplier method is employed to allocate the calculating capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperform the conventional ones. In particular, the proposed scheme can effectively reduce the system latency by up to 56% compared to the conventional schemes.

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions. [ABSTRACT FROM AUTHOR]

Introducing partial task offloading into vehicle edge computing networks (VECNs) can ease the burden placed on the Internet of Vehicles (IoV) by emerging vehicle applications and services. In this circumstance, the task offloading ratio and the resource allocation of edge servers (ES) need to be addressed urgently. Based on this, we propose a best response-based centralized multi-TaV computation resource allocation algorithm (BR-CMCRA) by jointly considering service vehicle (SeV) selection, offloading strategy making, and computing resource allocation in a multiple task vehicle (TaV) system, and the utility function is related to the processing delay of all tasks, which ensures the TaVs’s quality of services (QoS). In the scheme, SeV is first selected from three candidate SeVs (CSVs) near the corresponding TaV based on the channel gain. Then, an exact potential game (EPG) is conducted to allocate computation resources, where the computing resources can be allocated step by step to achieve the maximum benefit. After the resource allocation, the task offloading ratio can be acquired accordingly. Simulation results show that the proposed algorithm has better performance than other basic algorithms.

Due to the limitation of the computing power and energy resources, an unmanned aerial vehicle (UAV) team usually offloads the inspection task to the cloud for processing when performing emergency fault inspection, which will lead to low efficiency of transmission line inspection. In order to solve the above problems, this paper proposes a task offloading strategy based on deep reinforcement learning (DRL), aiming for the application of a multi-UAV and single-edge server. First, a "device-edge-cloud" collaborative offloading architecture is constructed in the UAV edge environment. Secondly, the problem of offloading power line inspection tasks is classified as an optimization problem to obtain the minimum delay under the constraints of edge server computing and communication resources. Finally, the problem is constructed as a Markov decision, and a deep Q-network (DQN) is used to obtain the minimum delay of the system. In addition, an experience replay mechanism and a greedy algorithm are introduced in the learning process to improve the offloading accuracy. The experimental results show that the proposed offloading strategy in this paper saves 54%, 37% and 26% of the task completion time, respectively, compared with local offloading, cloud offloading and random offloading. It effectively reduces the UAV inspection delay and improves the transmission line inspection efficiency. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VMs). Firstly, the system model of the offloading and scheduling of workflow tasks in a multi-user and multi-VMs MEC network is built. Then, the problem of how to determine the optimal offloading and scheduling scheme of workflow to minimize the total energy consumption of the system while meeting the deadline constraint is formulated. To solve this problem, the improved genetic algorithm is adopted to obtain the optimal offloading strategy and scheduling. Finally, the simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes. [ABSTRACT FROM AUTHOR]

Computation offloading is an effective method in mobile edge computing (MEC) to relieve user equipment (UE) from the limited computation resource and battery capacity. Meanwhile, simultaneous wireless information and power transmission (SWIPT) can be applied to MEC to extend the operating time of the equipment. However, in multi-user network environment, diverse computation task requirements and changeable network channel states make it challenging to obtain offloading strategy timely and accurately. To address the issue, we propose an intelligent computation offloading scheme (iCOS) based on enhanced priority deep deterministic policy gradient (EPDDPG) algorithm to minimize the energy consumption of all the UEs by jointly optimizing the offloading decision, the central processing unit (CPU) frequency and the power split ratio in a dynamic SWIPT-MEC network. In particular, we improve the traditional fully-connected network structure to obtain both discrete and continuous action outputs, and accelerate neural network parameter updates by using prioritized experience tuples. Furthermore, we use dynamic voltage and frequency scaling (DVFS) technology to dynamically adjust the CPU frequency of local computing, and employ SWIPT technology to balance the charging and communication according to the obtained strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the energy cost of UEs, and complete more computation tasks within the delay limit. [ABSTRACT FROM AUTHOR]

Multi-access edge computing(MEC) technology sinks computing and storage resources to the edge of the network, which can greatly improve the computing power and real-time performance of the Internet of things (IoT) system. However, MEC is often constrained by the growth of computing demand and energy constraints. Thus, efficient computing offloading and energy consumption optimization mechanism is an important research direction in MEC technology. To ensure the computing efficiency while maximizing the energy efficiency in the computing process, a two-level edge nodes (ENs) relay network model is proposed, and an optimal energy consumption algorithm (OECA) for joint optimization of computing resources and channel resources is designed. Firstly, the energy efficiency in MEC is modeled as a 0-1 knapsack problem. Secondly, the system adaptively selects the computing mode and allocates wireless channel resources, aiming at minimizing the overall energy consumption of the system. Finally, the OECA algorithm is simulated and verified in Python environment. The simulation results show that OECA can increase the network capacity by 18.3% and reduce the energy consumption by 13.1% compared with the offloading strategy algorithm based on directed acyclic graph algorithm (DAGA). [ABSTRACT FROM AUTHOR]

Abstract With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment. When the number of user uploaded data is 10K bits and the number of terminals is 15, it still shows the excellent network performance of low-overhead and low-latency.

In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help accomplish the intensive calculating tasks from the vehicular users. For the MEC networks, most existing works mainly focus on minimizing the system latency to guarantee the user's quality of service (QoS) through designing some offloading strategies, which, however, fail to consider the pricing from the server and hence fail to take into account the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC-based IoV networks and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then, the Lagrange multiplier method is employed to allocate the calculating capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperform the conventional ones. In particular, the proposed scheme can effectively reduce the system latency by up to 56% compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how to analytically design an analytical offloading strategy for a multiuser mobile edge computing (MEC)-based smart internet of vehicle (IoV), where there are multiple computational access points (CAPs) which can help compute tasks from the vehicular users. As it is difficult to derive an analytical offloading ratio for a general MEC-based IoV network, we turn to provide an analytical offloading scheme for some special MEC networks including one-to-one, one-to-two and two-to-one cases. For each case, we study the system performance by using the linear combination of latency and energy consumption, and derive the analytical offloading ratio through minimizing the system cost. Simulation results are finally presented to verify the proposed studies. In particular, the proposed analytical offloading scheme can achieve the optimal performance of the brute force (BF) scheme. The analytical results in this paper can serve as an important reference for the analytical offloading design for a general MEC-based IoV. [ABSTRACT FROM AUTHOR]

Fog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on one or two targets to improve system performance, however, the actual system needs to meet a comprehensive demand. Therefore, the joint optimization of multi-objective in multiple scenarios is a very meaningful problem. Inspired by this, the paper highlights the joint optimization research for fog computing, which proposes a Joint Computation offloading, Data compression, Energy harvesting, and Application scenarios (JCDEA) algorithm. The related mathematical model is constructed and the cost expressions of local computing, fog computing, and cloud computing are derived. Through the proposed algorithm, solving the computation offloading strategy is transformed into solving the minimum cost and is simplified by controlling strategy factors. Moreover, five simulation experiments are conducted and the meaningful conclusions are drawn, which contain that (1) the cost of fog computing is lower than that of local and cloud computing in most time slots and cloud computing can compensate for fog computing in complex environments; (2) the cost increases approximately linear with the amount of offloaded data; (3) the number of user devices and the compression ratio affect the fog-to-cloud ratio (FCR), while the FCR affects the cost; and (4) the related offloading strategy distribution and the cost are obtained for different scenarios. The JCDEA algorithm always outperforms than that of the random selection algorithm in all scenarios.

Wireless Sensor Nodes (WSNs) cover almost everywhere, and they carry a tremendous amount of computation. As their resources are still limited, they are still struggling to meet the demands of computeintensive tasks. Offloading sensor nodes' tasks using wireless networks to the cloud (edge cloud/far cloud) becomes an essential means to improve sensor nodes' performance. By analyzing the sensor nodes' offloading process, we found numerous three granular phenomena. A collection of sensor tasks on each device can perform its tasks locally, at the edge node, and in the far cloud, respectively. There is mutual gaming between the devices due to shared bandwidth and other factors. This paper proposes an offloading model for sensor tasks, drawing on three-way game theory ideas. First, we define the cost of offloading mobile devices. Second, through a three-way game, the model divides the mobile senor tasks into different locations rationally and efficiently. The game determines the places where tasks are offloaded, i.e., the set of tasks is divided into three parts by a pair of thresholds, which are offloaded to three locations, i.e., local, edge nodes, and cloud servers. Different devices will have different thresholds for partitioning, and we repeat iterations of adjusting both thresholds to find the optimal range of threshold partitioning based on the cost function. The simulation experiments results show that the strategy and the corresponding algorithm are beneficial and can optimize the energy consumption well under the premise of meeting the quality of service. [ABSTRACT FROM AUTHOR]

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least.

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm.

The cloud radio access network (C-RAN) with mobile edge computing (MEC) structure which consists of a baseband unit (BBU) pool integrating with an MEC server and several remote radio heads (RRHs) beside the mobile terminals can help users with computational resource-intensive tasks and bring extra profits to network operator at the same time. This paper presents a novel task-aware C-RAN with MEC structure and formulates a profit maximization problem by jointly optimizing offloading strategy, radio and computational resources allocation under the constraints of offloading latency, fronthaul capacity along with limited bandwidth and computational resource. To solve this NP-hard optimization problem in a distributed and efficient way, we propose a spectrum efficiency (SE)-based joint optimization for offloading and resource allocation (SJOORA) scheme which decomposes the original problem into two sub-problems. A SE-based offloading strategy is proposed with confirmed resource allocation, and on the other hand, bandwidth and computational resource allocation problem is solved by using a Lagrangian multiplier method with predetermined offloading strategy. Finally, by solving these two sub-problems iteratively, a suboptimal solution is obtained for the original problem. The simulation results show that the proposed SJOORA scheme can effectively increase the profit of network operator with relative lower complexity.

Mobile edge computing (MEC) sinks the functions and services of cloud computing to the edge of the network to provide users with storage and computing resources. For workflow tasks, the interdependency and the sequence constraint being among the tasks make the offloading strategy more complicated. To obtain the optimal offloading and scheduling scheme for workflow tasks to minimize the total energy consumption of the system, a workflow task offloading and scheduling scheme based on an improved genetic algorithm is proposed in an MEC network with multiple users and multiple virtual machines (VMs). Firstly, the system model of the offloading and scheduling of workflow tasks in a multi-user and multi-VMs MEC network is built. Then, the problem of how to determine the optimal offloading and scheduling scheme of workflow to minimize the total energy consumption of the system while meeting the deadline constraint is formulated. To solve this problem, the improved genetic algorithm is adopted to obtain the optimal offloading strategy and scheduling. Finally, the simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.

With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment. When the number of user uploaded data is 10K bits and the number of terminals is 15, it still shows the excellent network performance of low-overhead and low-latency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed joint computation offloading and resource allocation optimization (JCORAO) scheme in heterogeneous networks with mobile edge computing. An optimization problem is formulated to provide the optimal computation offloading strategy policy, uplink subchannel allocation, uplink transmission power allocation, and computation resource scheduling. The optimization problem is decomposed into two sub-problems due to the NP-hard property. In order to analyze the offloading strategy, a sub-algorithm named distributed potential game is built. The existence of Nash equilibrium is proved. To jointly allocate uplink subchannel, uplink transmission power, and computation resource for the offloading mobile terminals, a sub-algorithm named cloud and wireless resource allocation algorithm is designed. The solutions for subchannel allocation consist of uniform zero frequency reuse method without interference and fractional frequency reuse method based on Hungarian and graph coloring with interference. A distributed JCORAO scheme is proposed to solve the optimization problem by the mutual iteration of the two sub-algorithms. Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.

As a new network architecture, the air-ground cooperative network is a support for future 6G network to achieve ubiquitous connectivities. To effectively relieve the computational pressure of massive data in 6G wireless networks, Unmanned Aerial Vehicles (UAVs) equipped with Mobile Edge Computing (MEC) servers have become an emerging technology that provides computing resources for Mobile Devices (MDs). Due to limited on-board energy and computational capabilities, this paper investigates a multi-UAV collaborative assisted MEC architecture. The optimization problem of minimizing the total computational cost is constructed by jointly optimizing the UAVs trajectories and MDs offloading strategies scheduling. The coupling between the optimization variables and the non-convexity of the problem can make it difficult to solve directly. To address the above concerns, the non-convex optimization problem is converted into a Markov decision process. The UAVs-assisted Offloading Strategy based on Reinforcement Learning (UOS-RL) algorithm is proposed to address the convergence difficulties caused by the high-dimensional continuous action space. Furthermore, the experience data generated by agents interacting with the environment is highly differentiated due to the highly dynamic variation of the environment. Hence, a Priority Experience Replay (PER) mechanism is proposed to improve the training efficiency of the UOS-RL algorithm based on the priority of the experience data. Simulation results show that the proposed PER-UOS-RL algorithm outperforms the existing works in terms of the computational cost. [ABSTRACT FROM AUTHOR]

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Internet of Things (IoT) is swiftly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. However, how different service architecture and offloading strategies quantitatively impact the end-to-end performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices. This paper exploits potential network performance that manifests within the edge-cloud environment, then investigates and compares the impacts of two types of architectures: Loosely-Coupled (LC) and Orchestrator-Enabled (OE). Further, it introduces three customized offloading strategies in order to handle various requirements for IoT latency-sensitive applications. Through comparative experiments, we observed that the computational requirements exerts more influence on the IoT application’s performance compared to the communication requirement. However, when the system scales up to accommodate more IoT devices, communication bandwidth will turn to be the dominant resource and becomes the essential factor that will directly impact the overall performance. Thus, orchestration is a necessary procedure to encompass optimized solutions under different constraints for optimal offloading placement.

With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.