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Mobile Edge Computing (MEC) represents a novel paradigm dedicated to addressing the challenge of facilitating rapid access to an immense volume of content over mobile networks. However, improper cache placement and usage, coupled with fluctuating requests for cached data at diverse timeframes, exhibits considerable variability. Despite the abundance of optimization techniques, a majority of them lack the adaptive capacities needed to navigate dynamic caching environments efficiently. Furthermore, many studies employ online deep learning methodologies, but a slow convergence speed during the training process can potentially compromise caching performance and hinder dynamic goal adjustment in alignment with realistic provider requirements. We propose an integrative utility function encapsulating the worth of cached content and the cost associated with transmission links. By dynamically modifying weight values, this function can concurrently meet the performance and link cost demands of edge computing caching systems. To enhance the real-time response of the caching policy and the efficiency of deep learning, we introduce a Collaborative two-stage Deep Reinforcement Learning (CDRL) framework for devising the caching policy model. CDRL utilizes Double Deep Reinforcement Learning (DDQN) for pre-training in the caching environment to make pre-caching decisions and employs a Deep State-Action-Reward-State-Action (SARSA) algorithm for online training and caching decision-making. Experimental results convincingly demonstrate the proposed method's efficacy in improving the cache hit rate, service latency, and link cost. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) swarms have emerged as a promising technology for various applications, such as delivery, surveillance, and infrastructure inspection. An additional feature of deploying large UAV swarms is their use in mobile offloading networking. At the same time, this implies a key challenge in the efficient management of the computational and networking requirements for these offloading processes. This paper aims to fill this gap through a systematic literature review (SLR) analysing the research on distributed task offloading in UAV swarms. We conducted a systematic search of major scientific databases to identify relevant literature published between 2019 and 2023. A total of 63 papers were selected through a multistage screening process and their key contributions. This SLR aims to provide the current state of research on UAV swarm task offloading, including considerations for computation offloading, the role of UAV swarms, different aspects of UAV swarms, the number of UAVs in swarms impacting performance, and open issues. Our review also highlights UAV swarm offloading in various domains and discusses the challenges and limitations that must be addressed to ensure the widespread adoption of this technology. We outline the future research directions and potential applications of UAV swarm offloading, including its integration with other technologies. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor–critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C. [ABSTRACT FROM AUTHOR]

The increasing computational demand for real-time mobile applications has led to the development of mobile edge computing (MEC), with support from unmanned aerial vehicles (UAVs), as a promising paradigm for constructing high-throughput line-of-sight links for ground users and pushing computational resources to network edges. Users can reduce processing latency and the load on their local computers by delegating tasks to the UAV in its role as an edge server. The coverage capacity of a single UAV is, however, very limited. Moreover, it will be easy to intercept the data that is transferred to the unmanned aerial vehicle. Thus, for UAV-assisted mobile edge computing, we proposed a transmission technique based on multi-agent deep reinforcement learning in this study. The recommended approach to maximize UAV deployment first applies the particle swarm optimization algorithm. Then, deep reinforcement learning is utilized to optimize the secure offloading to maximize the system utility and minimize the quantity of information eavesdropping, taking into consideration different user task types with diverse preferences for processing time and residual energy of computing equipment. The results of the simulation demonstrate that, in comparison to the single-agent strategy and the benchmark, the multi-agent approach can optimize offloading more successfully and produce higher system utility. [ABSTRACT FROM AUTHOR]

This review paper explores the significance of machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) techniques in improving traffic management based on cloud and mobile edge computing (MEC) architectures. The key findings and contributions of this review highlight the potential of these techniques for transforming traffic management systems through data-driven decision-making, adaptive control, and optimization. The challenges identified in this field include data availability and quality, scalability and computational requirements, privacy and security concerns, and ethical considerations. In conclusion, ML, DL, RL, and DRL techniques, in conjunction with cloud and MEC architectures, have significant implications for improving traffic management. Their ability to process and analyse large-scale and real-time traffic data enables improved traffic flow, reduced congestion, enhanced energy efficiency, and enhanced overall transportation system performance. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network's sustainable operation and growth. In this paper, we consider a WPT-MEC network with user cooperation to migrate the double near–far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability. Rigorous mathematical analysis and experimental results show that our algorithm achieves  O (1 / V) , O (V)  trade-off between computation rate and queue stability. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). However, maximizing the long-term energy efficiency (EE) of a user-cooperative WPT-MEC system presents significant challenges due to uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. In this paper, we propose an online control algorithm to maximize the long-term EE of a WPT-MEC system by making decisions on time allocations and transmission powers of mobile devices (MDs) for a three-node network. We formulate a stochastic programming problem considering the stability of network queues and time-coupled battery levels. By leveraging Dinkelbach's method, we transform the fractional optimal problem into a more manageable form and then use the Lyapunov optimization technique to decouple the problem into a deterministic optimization problem for each time slot. For the sub-problem in each time slot, we use the variable substitution technique and convex optimization theory to convert the non-convex problem into a convex problem, which can be solved efficiently. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline algorithms, achieving a 20% improvement in energy efficiency. Moreover, our algorithm achieves an [ O (1 / V) , O (V) ] trade-off between EE and network queue stability. [ABSTRACT FROM AUTHOR]

High altitude platforms (HAPs) have been considered a promising paradigm to assist mobile edge computing (MEC) in wide-area internet of things (IoT) scenarios due to their wide coverage, large payload capacity, and excellent channel quality. In this paper, a HAP-assisted MEC system is investigated, where the HAP is deployed as an edge server to provide computation offloading services for IoT terminal devices (TDs) with limited local computing capability. Offloading is enabled by uplink and downlink communications between TDs and HAP in a clustered non-orthogonal multiple access (C-NOMA) manner. Considering potential economic benefits and expected environmental impact, we aim to minimize system energy consumption, subject to the constraints on maximum tolerable delay, transmit power budget, and uplink and downlink decoding power. To this end, an iterative algorithm based on relaxation and successive convex approximation methods is proposed, which efficiently solves this challenging non-convex problem by alternately optimizing power allocation and HAP deployment. Numerical results show that the proposed scheme significantly reduces the system energy consumption compared to the other benchmark scheme. It is also shown that the system energy consumption is indirectly effected by the decoding order in the uplink and downlink C-NOMA. [ABSTRACT FROM AUTHOR]

The growing need for applications with low latency and high bandwidth in 5G networks has shifted the emphasis towards integrating edge computing into mobile network architectures. The optimal deployment of edge servers (ESs) is crucial for balancing performance and cost. Current research primarily focuses on the optimal deployment of ESs. However, there is limited attention on forecasting the workload of ESs, particularly in scenarios where they are placed in their optimal positions, which is crucial for accommodating anticipated variations in user densities and workload fluctuations expected in the near future. This research forecasting of workload in 5G networks by leveraging the Temporal Hierarchical Attention Mechanism Network (THAMNET) model for accurate Internet traffic forecasting. Addressing this gap, the Max–Min Fairness Allocation Scheme (MMF-AS) algorithm is implemented to ensure a balanced workload distribution. This approach facilitates fair allocation of Base Stations (BSs) to ESs, taking into account factors such as workload, distance, and connectivity. A novel approach is introduced by integrating the THAMNET forecasting model with the Particle Swarm Optimization (PSO) and MMF-AS algorithms. The proposed method considers predicted future traffic demands, and connectivity between ESs and BSs, and aims to achieve balanced workload distribution, higher utilisation rates, minimised latency, and reduced energy consumption. Experimental results demonstrate equitable workload distribution 25.95%, utilisation rate 17.57%, and notable reductions in latency 47.61% and energy consumption 32.28% compared to the non-forecasting comparison. [ABSTRACT FROM AUTHOR]

To deal with the coming Multi-access Edge Computing (MEC)-based 5G and the future 6G wireless mobile network environment, a Multi-access Edge Computing (MEC)-based video streaming method using the proposed quality-aware video bitrate adaption and MEC server handoff control mechanisms for Dynamic Adaptive Streaming over HTTP (MPEG-DASH) video streaming was proposed in this work. Since the user is moving, the attached Base Station (BS) of the cellular network can be changed, i.e., the BS handoff can happen, which results in the corresponding MEC server handoff. Thus, this work proposed the MEC server handoff control mechanism to make the playing quality be smooth when the MEC server handoff happens. To have the MEC server to be able to derive the video bit rate for each video segment of the MPEG-DASH video streaming and to have the smooth video streaming when the MEC server handoff happens, the proposed method (i) derives the estimated bandwidth using the adaptive filter mechanism, (ii) generates some candidate video bit rates by considering the estimated bandwidth and the buffer occupancy situation in the client side and then (iii) selects a video bit rate from the candidate ones considering video quality’s stability. For the video quality’s stability concern, the proposed method considered not only (i) both bandwidth and buffer issues but also (ii) the long-term quality variation and the short-term quality variation to have the adaptive video streaming. The results of the performance evaluation, which was executed in a lab-wide experimental LTE network eNB system, shown that the proposed method has the more stable video quality for the MPEG-DASH video streaming over the wireless mobile network. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy efficiency maximization for mobile edge computing (MEC) in intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) communications. In particular, UAV can collect the computing tasks of the terrestrial users and transmit the results back to them after computing. We jointly optimize the users' transmitted beamforming and uploading ratios, the phase shift matrix of IRS, and the UAV trajectory to improve the energy efficiency. The formulated optimization problem is highly non-convex and difficult to be solved directly. Therefore, we decompose the original problem into three sub-problems. We first propose the successive convex approximation (SCA) based method to design the beamforming of the users and the phase shift matrix of IRS, and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios. For the trajectory optimization, we propose a block coordinate descent (BCD) based method to obtain a local optimal solution. Finally, we propose the alternating optimization (AO) based overall algorithm and analyzed its complexity to be equivalent or lower than existing algorithms. Simulation results show the superiority of the proposed method compared with existing schemes in energy efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Unmanned Aerial Vehicle (UAV) swarms have emerged as a promising technology for various applications, such as delivery, surveillance, and infrastructure inspection. An additional feature of deploying large UAV swarms is their use in mobile offloading networking. At the same time, this implies a key challenge in the efficient management of the computational and networking requirements for these offloading processes. This paper aims to fill this gap through a systematic literature review (SLR) analysing the research on distributed task offloading in UAV swarms. We conducted a systematic search of major scientific databases to identify relevant literature published between 2019 and 2023. A total of 63 papers were selected through a multistage screening process and their key contributions. This SLR aims to provide the current state of research on UAV swarm task offloading, including considerations for computation offloading, the role of UAV swarms, different aspects of UAV swarms, the number of UAVs in swarms impacting performance, and open issues. Our review also highlights UAV swarm offloading in various domains and discusses the challenges and limitations that must be addressed to ensure the widespread adoption of this technology. We outline the future research directions and potential applications of UAV swarm offloading, including its integration with other technologies.

In the development of industrial internet of things (IIoT), the application of mobile edge computing (MEC) has significantly enhanced the efficiency of task computation offloading. However, the risk of data privacy leakage persists during the computation offloading process. Considering the diversity of task data sensitivity and the variability in server security protection capabilities, this paper proposes a method for assessing privacy satisfaction. To ensure offloading efficiency while effectively enhancing privacy security, we have proposed an IIoT cloud-edge-device computation offloading algorithm based on dual-Q network deep reinforcement learning, named D2PCO, to optimize the computation offloading process in IIoT tasks. The incorporation of the dual-Q network notably enhances the algorithm's learning ability and efficiency in dealing with complex decision-making problems. Experimental results show that the proposed D2PCO algorithm significantly improves user privacy satisfaction while ensuring low delay. Compared with MA3MCO, DDPG, shortest distance priority, and random scheduling algorithms, it reduces the average offloading delay by 4.15%, 9.98%, 13.2%, and 26.47% and increases privacy satisfaction by 0.8%, 4.26%, 10.15%, and 30.30%, respectively. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are critical for modern intelligent processing but cause significant latency and energy consumption issues on mobile devices due to their high computational demands. Moreover, different tasks have different accuracy demands for DNN inference. To balance latency and accuracy across various tasks, we introduce PreDNNOff, a method that offloads DNNs at a layer granularity within the Mobile Edge Computing (MEC) environment. PreDNNOff utilizes a binary stochastic programming model and Genetic Algorithms (GAs) to optimize the expected latency for multiple exit points based on the distribution of task inference accuracy and layer latency regression models. Compared to the existing method Edgent, PreDNNOff has achieved a reduction of about 10% in the expected total latency, and due to the consideration of different tasks' varying requirements for accuracy, it has a broader applicability. [ABSTRACT FROM AUTHOR]

Abstract Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.

Abstract Patient-focused healthcare applications are important to patients because they offer a range of advantages that add value and improve the overall healthcare experience. The 5G networks, along with Mobile Edge Computing (MEC), can greatly transform healthcare applications, which in turn improves patient care. MEC plays an important role in the healthcare of patients by bringing computing resources to the edge of the network. It becomes part of an IoT system within healthcare that brings data closer to the core, speeds up decision-making, lowers latency, and improves the overall quality of care. While the usage of MEC and 5G networks is beneficial for healthcare purposes, there are some issues and difficulties that should be solved for the efficient introduction of this technological pair into healthcare. One of the critical issues that blockchain technology can help to overcome is the challenge faced by MEC in realizing the most potential applications involving IoT medical devices. This article presents a comprehensive literature review on IoT-based healthcare devices, which provide real-time solutions to patients, and discusses some major contributions made by MEC and 5G in the healthcare industry. The paper also discusses some of the limitations that 5G and MEC networks have in the IoT medical devices area, especially in the field of decentralized computing solutions. For this reason, the readership intended for this article is not only researchers but also graduate students.

Summary: Network architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC. [ABSTRACT FROM AUTHOR]

Computational offloading is an essential technology in Mobile Edge Computing (MEC). To address the shortage of computational offloading strategies in multi-user and multi-MEC server scenarios,this paper proposes a hybrid artificial bee colony approach (Artificial Reverse Sine-Cosine, ARSC). First, the opposition-based learning strategy is used to initialize the population and optimize the initial solution of the population. Then the global optimal bootstrap information of the sine-cosine algorithm is exploited to improve the local search capability in the employed bee stage. Finally, to balance the global and local search capability of the approach, the step size factor is adapted by introducing dynamic perception. Simulation results show that the proposed ARSC approach outperforms offloading strategies based on particle swarm algorithm and artificial bee colony algorithm in convergence, latency, and energy consumption. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has emerged as a promising technology that can revolutionize the future of mobile networks. MEC brings compute and storage capabilities to the edge of the network closer to end-users. This enables faster data processing and improved user experience by reducing latency. MEC has the potential to decrease the burden on the core network by transferring computational and storage responsibilities to the edge, thereby reducing overall network congestion. Load balancing is critical for effectively utilizing the resources of the MEC. This ensures that the workload is distributed uniformly across all of the available resources. Load balancing is a complex task and there are various algorithms that can be used to achieve it, such as round-robin, least connection, and IP hash. To differentiate between heavily loaded and lightly loaded servers, current load balancing methods use an average response time to gauge the load on the edge server. Nevertheless, this approach has lower precision and may result in an unequal distribution of the workload. Our study introduces a dynamic threshold calculation technique that relies on a response-time threshold of the edge servers using K-means clustering. K-means based proposed algorithm classifies the servers in two sets (here K = 2), i.e., overloaded and lightly loaded edge servers. Consequently, workload is migrated from overloaded to lightly loaded servers to evenly distribute the workload. Experimental results show that the proposed technique reduces latency and improves resource utilization. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios. [ABSTRACT FROM AUTHOR]

In order to address the problem of rapid increase of data throughput in fifth-generation wireless communication networks, mobile edge caching has become a useful solution. It can reduce the burden on the backhaul link and core network, cut down service latency by storing network content on edge devices. So far, most edge caching solutions have mainly focused on optimizing cooperative content caching, and ignored the efficiency of content transmission. This paper studied cooperative edge caching and wireless bandwidth allocation problems in ultra-dense networks, calculated the overall similarity between the base stations by using cosine similarity and Gaussian similarity, and grouped the small base stations according to total similarity in the network. The caching and radio bandwidth allocation problems were modeled as a long-term mixed-integer non-linear programming(LT-MINLP). Then, the cooperative edge caching and wireless bandwidth allocation problem were transformed into a constrained Markov decision process. Finally, it proposed cooperative edge caching and radio resource allocation scheme by using the DDPG model. And it proposed deep reinforcement learning based-edge content cooperative caching and bandwidth allocation algorithm CBDDPG. The proposed base station group strategy increased the file sharing opportunity among base stations, the cache scheme of the proposed CBDDPG algorithm used DDPG dual-network mechanism, which could better capture the regularity of user requests and optimize cache deployment. The proposed CBDDPG algorithm was compared to three baseline algorithms, sach as RBDDPG, LCCS and CB-TS in experiments. Experimental results show that the proposed strategy can effectively enhance the content cache hit ratio, reduce the delay of content delivery and improve the user experience. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks. [ABSTRACT FROM AUTHOR]

Emerging network architectures and technical service requirements for B5G and 6G will enable MEC with CF-mMIMO, helping to handle compute-intensive and latency-sensitive tasks in distributed IoT. For CF-mMIMO-assisted MEC systems, this paper aimed to minimize the delay in completing computational tasks of different task types under energy constraint. In order to solve the above goals, this paper designed a task offloading strategy based on UEs, multiple APs and CPU (central processing unit) for cloud-edge-end collaboration. Specifically, according to the different data types of each UE and AP service, this paper firstly used the convex optimization and graph matching methods to alternately iterate to optimize the offload association and task ratio. Then, under the limitation of the backhaul link, this paper used an improved binary whale optimization algorithm to further offload the tasks of unallocated terminals and associated access points to the cloud with efficient processing. Compared with other meta-heuristics such as ant colony optimization algorithm and hybrid gray wolf optimization algorithm, the proposed algorithm has better performance on discrete offload optimization problems, which can provide a good offload optimization strategy for distributed systems and greatly reduce the average delay of the whole network. [ABSTRACT FROM AUTHOR]

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependencyaware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience. [ABSTRACT FROM AUTHOR]

Patient-focused healthcare applications are important to patients because they offer a range of advantages that add value and improve the overall healthcare experience. The 5G networks, along with Mobile Edge Computing (MEC), can greatly transform healthcare applications, which in turn improves patient care. MEC plays an important role in the healthcare of patients by bringing computing resources to the edge of the network. It becomes part of an IoT system within healthcare that brings data closer to the core, speeds up decision-making, lowers latency, and improves the overall quality of care. While the usage of MEC and 5G networks is beneficial for healthcare purposes, there are some issues and difficulties that should be solved for the efficient introduction of this technological pair into healthcare. One of the critical issues that blockchain technology can help to overcome is the challenge faced by MEC in realizing the most potential applications involving IoT medical devices. This article presents a comprehensive literature review on IoT-based healthcare devices, which provide real-time solutions to patients, and discusses some major contributions made by MEC and 5G in the healthcare industry. The paper also discusses some of the limitations that 5G and MEC networks have in the IoT medical devices area, especially in the field of decentralized computing solutions. For this reason, the readership intended for this article is not only researchers but also graduate students. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy efficiency maximization for mobile edge computing (MEC) in intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) communications. In particular, UAV can collect the computing tasks of the terrestrial users and transmit the results back to them after computing. We jointly optimize the users’ transmitted beamforming and uploading ratios, the phase shift matrix of IRS, and the UAV trajectory to improve the energy efficiency. The formulated optimization problem is highly non-convex and difficult to be solved directly. Therefore, we decompose the original problem into three sub-problems. We first propose the successive convex approximation (SCA) based method to design the beamforming of the users and the phase shift matrix of IRS, and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios. For the trajectory optimization, we propose a block coordinate descent (BCD) based method to obtain a local optimal solution. Finally, we propose the alternating optimization (AO) based overall algorithm and analyzed its complexity to be equivalent or lower than existing algorithms. Simulation results show the superiority of the proposed method compared with existing schemes in energy efficiency. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) supported by unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) can provide ubiquitous communication and computing services for internet of things (IoT) devices, particularly in remote areas and emergency situations caused by natural disasters. Additionally, integrating IRS and UAV can enhance the network’s secure service performance to address the significant challenges posed by colluding eavesdroppers in the system. To address the contradiction between high-security communication rate and low energy consumption, this paper introduces security energy efficiency as a performance metric for the system. To improve the security energy efficiency of the system, we propose an IRS-assisted UAV-MEC system for the joint optimization of computing resources, IRS phase shift, and UAV trajectory to maximize system’s performance. The optimization problem is a non-convex fraction problem, and complex coupling relations exist among the physical variables. We propose an iterative algorithm with a double-loop structure to solve the problem, in which the outer loop is based on the Dinkelbach method, and the inner loop is based on the block coordinate descent (BCD) algorithm to decompose the total problem. Finally, numerical simulations show that our proposed algorithm improves the system’s security energy efficiency compared with benchmark schemes, obtains a good trade-off between the security communication rate and energy consumption of the system, and improves the security communication performance of the system in the presence of colluding eavesdroppers.

Utilizing Unmanned Aerial Vehicles (UAVs) as flying edge nodes to support task offloading from terminal devices has recently attracted significant research attention. However, the literature lacks a systematic perspective on this emerging topic. The goals are to understand the volume and trends of research, identify use case scenarios and proposed architectures, classify the core topics addressed, explore group techniques explored, recognize task types considered, and summarize open issues needing further work. Publications are mapped by type and source from 2019 to 2023 to assess the maturity and activity level in this field over time. Various use case scenarios for UAV-enabled Mobile Edge Computing (MEC) task offloading are identified and categorized, and different proposed architectures for offloading between UAV-MEC platforms are summarized. Techniques for offloading decision-making and performance enhancement are grouped to identify popular and less explored methods. The literature is also mapped based on the types of tasks considered for offloading to UAV-enabled MEC platforms to recognize the focus areas. Open issues that are briefly discussed across papers but require additional research are summarized on basis of the gaps identified. This systematic perspective consolidates existing research in an organized manner to guide future works and establish a coherent taxonomy to organize future studies and reviews. Overall, mapping trends helps characterize research maturity, guiding its continued development.

The sixth-generation (6G) network is envisioned to shift its focus from the service requirements of human beings to those of Internet-of-Things (IoT) devices. Satellite communications are indispensable in 6G to support IoT devices operating in rural or disaster areas. However, satellite networks face the inherent challenges of low data rate and large latency, which may not support computationintensive and delay-sensitive IoT applications. Mobile Edge Computing (MEC) is a burgeoning paradigm by extending cloud computing capabilities to the network edge. Using MEC technologies, the resourcelimited IoT devices can access abundant computation resources with low latency, which enables the highly demanding applications while meeting strict delay requirements. Therefore, an integration of satellite communications and MEC technologies is necessary to better enable 6G IoT. In this survey, we provide a holistic overview of satellite-MEC integration. We first categorize the related studies based on three minimal structures and summarize current advances. For each minimal structure, we discuss the lessons learned and possible future directions. We also summarize studies considering the combination of minimal structures. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated satellite-MEC network.

The domain of Mobile Edge Computing (MEC) has seen rapid growth, making consumer behavior research an essential element in many applications. Nevertheless, in MEC systems that are decentralized and have limited resources, challenges arise in ensuring both data integrity and access control. This paper introduces a new technique called Quantum Hash-Based Attribute-Based Encryption (QH-ABE) to address these issues. Previously, there were several methods available to guarantee data integrity and access control in MEC. However, these systems had limits in managing large and intricate datasets, lacked a standardized protocol for revocation, and incurred significant computational costs. We were prompted by these restrictions to suggest a more efficient technique, which included using the QH-ABE method in our study. The suggested solution integrates hash functions with quantum computing concepts to enhance security and control access in MEC-enabled consumer behavior research. The suggested technique provides several benefits compared to traditional hash algorithms by using hash functions. In this technique, we introduce a Recursive Non-Linear Polynomial graph-centered Integrity Algorithm (RNLPIA). RNLPIA enhances security by thwarting covert alterations to data and guaranteeing tamper-evident measures via the generation of unique hash values derived from the content of the data. The suggested method’s efficacy and efficiency are proved by a comprehensive experimental assessment, highlighting its capability to fulfill the data integrity and access control needs of MEC situations. The performance of our technology showcases its potential and paves the way for using quantum computing technologies for accessing control and data security. This work contributes to the progress of privacy-preserving, secure consumer behavior analysis in the dynamic MEC environment.

Mobile Edge Computing (MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements. In this paper, we propose a flexible MEC-based requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption. To address the dimensional differences between time and energy, we introduce two normalized parameters and then derive the computational overhead of processing tasks. Different from existing works, this paper considers practical variations in the user request patterns, and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay, task workload and power constraints. Since the resulting problem is non-convex, we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution. Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor–critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C.

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network with user cooperation to migrate the double near–far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability. Rigorous mathematical analysis and experimental results show that our algorithm achieves O(1/V),O(V) trade-off between computation rate and queue stability.

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS. [ABSTRACT FROM AUTHOR]

In this paper, we consider mobile edge computing (MEC) networks against proactive eavesdropping. To maximize the transmission rate, IRS assisted UAV communications are applied. We take the joint design of the trajectory of UAV, the transmitting beamforming of users, and the phase shift matrix of IRS. The original problem is strong non-convex and difficult to solve. We first propose two basic modes of the proactive eavesdropper, and obtain the closed-form solution for the boundary conditions of the two modes. Then we transform the original problem into an equivalent one and propose an alternating optimization (AO) based method to obtain a local optimal solution. The convergence of the algorithm is illustrated by numerical results. Further, we propose a zero forcing (ZF) based method as sub-optimal solution, and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes. [ABSTRACT FROM AUTHOR]

The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network's edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system's energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system's potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system's carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system's carbon emissions and tasks' delay and improve the total size of completed tasks. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a transformative paradigm that enhances mobile services performance and operational efficiency. Given the frequent mobility of users, it is essential to migrate services to continuously align with user movements. However, making service migration decisions requires careful consideration of factors like latency, resource availability, and user demand to ensure optimal performance and user satisfaction. Therefore, in this paper, we first formulate the service migration in MEC systems as an optimization problem, with the objective of minimizing the system's energy consumption and migration cost while considering delay as the model's constraint. Next, we propose an energy-efficient, cost-effective and QoS-aware heuristic-based method, called ECQ, to effectively solve the model. To reflect a comprehensive understanding of real-world MEC environments, we also consider the incorporation of heterogeneous server configurations and the non-universal presence of servers across all base stations. By accommodating these aspects, ECQ emerges as a promising solution for the dynamic service migration optimization challenges in time-sensitive and realistic MEC contexts. Finally, through various simulation scenarios, the performance of the proposed ECQ is evaluated in terms of response time, migration costs, energy consumption, and the number of migrations. The results demonstrate that the proposed algorithm outperforms other methods in terms of overall performance; specifically, it ensures high QoS for users while sustainably managing the system's energy consumption and keeping migration costs within acceptable levels. In particular, when compared to the FullMig strategy, the proposed method achieves a noteworthy reduction in migration costs and energy consumption by approximately 25% and 50%, respectively. Meanwhile, the average response time in FullMig is only about 12% less than that of our method. Additionally, in comparison to the E-ware method, our ECQ exhibits an impressive response time reduction of approximately 42%, all while maintaining migration costs and energy consumption at levels comparable to the E-ware approach. [ABSTRACT FROM AUTHOR]

With the advantages of real-time data processing and flexible deployment, unmanned aerial vehicle (UAV)-assisted mobile edge computing systems are widely used in both civil and military fields. However, due to limited energy, it is usually difficult for UAVs to stay in the air for long periods and to perform computational tasks. In this paper, we propose a full-duplex air-to-air communication system (A2ACS) model combining mobile edge computing and wireless power transfer technologies, aiming to effectively reduce the computational latency and energy consumption of UAVs, while ensuring that the UAVs do not interrupt the mission or leave the work area due to insufficient energy. In this system, UAVs collect energy from external air-edge energy servers (AEESs) to power onboard batteries and offload computational tasks to AEESs to reduce latency. To optimize the system's performance and balance the four objectives, including the system throughput, the number of low-power alarms of UAVs, the total energy received by UAVs and the energy consumption of AEESs, we develop a multi-objective optimization framework. Considering that AEESs require rapid decision-making in a dynamic environment, an algorithm based on multi-agent deep deterministic policy gradient (MADDPG) is proposed, to optimize the AEESs' service location and to control the power of energy transfer. While training, the agents learn the optimal policy given the optimization weight conditions. Furthermore, we adopt the K-means algorithm to determine the association between AEESs and UAVs to ensure fairness. Simulated experiment results show that the proposed MODDPG (multi-objective DDPG) algorithm has better performance than the baseline algorithms, such as the genetic algorithm and other deep reinforcement learning algorithms. [ABSTRACT FROM AUTHOR]

The adoption of User Equipment (UE) is on the rise, driven by advancements in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), the Internet of Things (IoT), and Artificial Intelligence (AI). Among these, MEC stands out as a pivotal aspect of the 5G network. A critical challenge within the realm of MEC is task offloading. This involves optimizing conflicting factors like execution time, energy usage, and computation duration. Additionally, addressing the offloading of interdependent tasks poses another significant hurdle that requires attention. The developed models are single objective, task dependency, and computationally expensive. As a result, the Immune whale differential evolution optimization algorithm is proposed to offload the dependent tasks to the MEC with three objectives: minimizing the execution delay and reducing the energy and cost of MEC resources. The standard Whale optimization is incorporated with DE with customized mutation operations and immune system to enhance the searching strategy of Whale optimization. The proposed HIWDEO secured reduced energy and overhead of UE to execute its tasks. The comparison between the developed model and other optimization approaches shows the superiority of HIWDEO. [ABSTRACT FROM AUTHOR]

Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios. [ABSTRACT FROM AUTHOR]

In many IIoT architectures, various devices connect to the edge cloud via gateway systems. For data processing, numerous data are delivered to the edge cloud. Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency. There are two types of costs for this kind of IoT network: a communication cost and a computing cost. For service efficiency, the communication cost of data transmission should be minimized, and the computing cost in the edge cloud should be alsominimized. Therefore, in this paper, the communication cost for data transmission is defined as the delay factor, and the computing cost in the edge cloud is defined as the waiting time of the computing intensity. The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs. That is, a device chooses a routing path to the selected edge cloud based on the costs. The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load. The performance of the proposed method is validated through extensive computer simulation. When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states, the proposed method reduced both the average delay and the service pause counts to about 25% of the existing method. [ABSTRACT FROM AUTHOR]

Abstract The unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system leverages the high maneuverability of UAVs to provide efficient computing services to terminals. A dynamic deployment algorithm based on double deep Q-networks (DDQN) is suggested to address issues with energy limitation and obstacle avoidance when providing edge services to terminals by UAV. First, the energy consumption of the UAV and the fairness of the terminal’s geographic location are jointly optimized in the case of multiple obstacles and multiple terminals on the ground. And the UAV can avoid obstacles. Furthermore, a double deep Q-network was introduced to address the slow convergence and risk of falling into local optima during the optimization problem training process. Also included in the learning process was a pseudo count exploration strategy. Finally, the improved DDQN algorithm achieves faster convergence and a higher average system reward, according to experimental results. Regarding the fairness of geographic locations of terminals, the improved DDQN algorithm outperforms Q-learning, DQN, and DDQN algorithms by 50%, 20%, and 15.38%, respectively, and the stability of the improved algorithm is also validated.