Low-Latency Oriented Joint Data Compression and Resource Allocation in NOMA-MEC Networks: A Deep Reinforcement Learning Approach

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.

Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs’ restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods.

Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review

For the problems of unreasonable computation offloading and uneven resource allocation in Mobile Edge Computing (MEC), this paper proposes a task offloading and resource allocation strategy based on deep learning for MEC. Firstly, in the multiuser multiserver MEC environment, a new objective function is designed by combining calculation model and communication model in the system, which can shorten the completion time of all computing tasks and minimize the energy consumption of all terminal devices under delay constraints. Then, based on the multiagent reinforcement learning system, system benefits and resource consumption are designed as rewards and losses in deep reinforcement learning. Dueling-DQN algorithm is used to solve the system problem model for obtaining resource allocation method with the highest reward. Finally, the experimental results show that when the learning rate is 0.001 and discount factor is 0.90, the performance of proposed strategy is the best. Furthermore, the proportions of reducing energy consumption and shortening completion time are 52.18% and 34.72%, respectively, which are better than other comparison strategies in terms of calculation amount and energy saving.

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 the current fifth-generation (5G) and Beyond 5G (B5G) era, the Unmanned Aerial Vehicles (UAVs) have been playing a vital role and attracting interest in different application areas in the military, and civil applications such as communications, disaster management, search and rescue, security, control, agriculture, Internet of things (IoT), etc. In these networks, ultra-heterogeneous IoT devices generate time-sensitive traffic. However, those devices have limited resources to compute tasks. Recently, Mobile Edge Computation Offloading (MECO) has been considered as an encouraging model to enable the computation tasks of IoT devices to be performed by MEC servers and support ultra-low latency IoT applications to ensure Quality of services (QoS). However, terrestrial network failure due to natural and human-made disasters has been increasing, and difficult to provide reliable computation offloading and resource allocation services to IoT networks. Nowadays, UAVs have been promising technology to quickly deploy and recover the system to provide efficient services to edge nodes. The offloading and resource allocation problems in current network technology are complex, and offloading task to edge server is vulnerable to security risks. Hence, we utilize a deep reinforcement learning method to handle a complex problem for computation offloading and resource allocations in a dynamic environment. And also, we explore a blockchain-based multi-UAV-assisted MEC architecture in securing and optimizing the offloading problems.

With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.

&lt;p&gt;The vehicular ad hoc network (VANET) has emerged as a heterogeneous network with no fixed infrastructure. In VANET, vehicles are the mobile nodes that communicate with one another and with the infrastructure. One of the major challenges in VANET is to allocate resources efficiently due to environmental characteristics such as highly mobile vehicles, latency-sensitive and short connection times. The conventional resource management mechanisms are not very effective in such an environment. The data-driven Artificial Intelligence (AI) based techniques are a very efficient alternative to traditional statistical techniques. This project proposes an AI-empowered task offloading and computing resource allocation model which can dynamically organize the computing resources in VANET. The model is divided into two layers. First, the task offloading layer, where the Random Forest (RF) algorithm is used to determine whether the vehicle’s computing tasks should be offloaded to the cloud computing (CC) server or mobile edge computing (MEC) server or to be processed locally (in-vehicle computing). Second, the resource allocation layer, where the deep deterministic policy gradient (DDPG) algorithm is used to determine the computing platform again when the task is determined to be offloaded to either MEC servers or the cloud servers. To evaluate the performance of the RF classifier, we applied the model to a real-world driving trajectory dataset then compared the results with a different set of machine learning algorithms namely, K-nearest neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The results show the RF model outperformed other models in classification accuracy score of 99.83% for task offloading decision, where the KNN, MLP and SVM achieved 98%, 94.81% and 90.94%, respectively. Moreover, the DDPG based resource allocation scheme converges within 150 episodes. Thus, the proposed model can find the optimal offload decision and the computational resource allocation simultaneously and achieves a high delay to quality-of-service (QoS) satisfaction ratio.&lt;/p&gt;

This paper considers the computation offloading and resource allocation for system sum cost minimization in a mobile edge computing (MEC)network. The computation offloading decision of the user equipments (UEs)can be made through wireless link to access the MEC server. Our intent is to minimize the weighted sum cost of the system, which takes into account the energy consumption and time delay, for both task processing and data transmissions, subject to the force delay requirement of the devices. We first formulate the problem as a mixed-integer and non-convex optimization scheme. Enlightened by the superior performance of reinforcement learning (RL)on solving resource control problems, we put forward a novel RL-based scheme for computation offloading and resource allocation in MEC network. Specifically, the MEC node is considered as an “agent”, which first makes decision on whether to offloading the computing task to the MEC server, and then utilizes convex optimization algorithms to settle the radio spectrum and computation resource scheduling problem in each decision phase. The simulation results present that the RL-based method can achieve better computation offloading and resource allocation performance compared to those of local computing and remote computing modes. It also achieves very similar or slightly better performance compared to the genetic algorithm based method.

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

In 6G communication systems, the space-air-ground integrated network (SAGIN) integrates satellite and aerial networks, leveraging more communication and computational resources to assist ground networks in handling computationally intensive tasks. However, when multiple tasks compete for limited computational resources simultaneously, it can lead to imbalanced resource distribution and increased task processing delay. Therefore, this paper proposes an evolutionary game resource distribution strategy for 6G SAGIN (EGRDSS), which integrates 6G SAGIN with mobile edge computing, aiming to optimize system delay. First, we proposed a 6G space-air-ground integrated computing offloading network model, which combines air and space-based networks to augment task offloading options for user devices. Then the user devices can offload tasks to base station, unmanned aerial vehicle, or low earth orbit satellite edge servers for processing. Next, with the goal of minimizing task execution delay, we decompose the optimization problem into two subproblems: task offloading decisions and computational resource distribution. We thus propose a particle swarm task offloading optimization algorithm and an evolutionary dynamic game computational resource distribution algorithm to solve the respective subproblems. The particle swarm task offloading optimization algorithm achieves good convergence performance in global search. At the same time, we introduce evolutionary game theory to address the resource distribution problem, characterize the dynamic equilibrium among multiple users competing for limited computational resources, and enable global performance optimization in the complex and heterogeneous SAGIN environment. Simulation experiments validate that EGRDSS has advantages in optimizing system delay. Compared with random offloading, simulated annealing resource allocation algorithm and genetic resource allocation algorithm strategies, task execution delays are reduced by at least 8.32%, 11.6%, and 22%, respectively.