关键词： Mobile edge computing   Task scheduling   Dynamic priority   Adaptive resource allocation   Healthcare monitoring systems   IoT  

摘要： The Internet of Things (IoT) has revolutionized healthcare by interconnecting a wide range of devices over the Internet. Cloud computing has traditionally fulfilled the computational demands of these IoT devices, enabling the collection and analysis of data from healthcare environments. However, its suitability for latency-sensitive applications has been limited due to the presence of remote cloud data centers. In response, edge computing has emerged, pushing computational capabilities to the edge of networks to mitigate these limitations. This work presents a novel approach for task scheduling and resource allocation in edge computing environments tailored for healthcare monitoring systems. By implementing dynamic priority-based task scheduling and adaptive resource allocation algorithms, our methodology enhances the handling of resource requests between end devices, edge nodes (ENs), and the cloud. These innovations focus on reducing latency, optimizing resource utilization, and improving overall system performance. Extensive simulations demonstrate the efficacy of our approach, showcasing significant improvements in task execution time, resource utilization, and energy consumption.

关键词： Mobile edge computing   Decode-and-forward relay   Access point selection   Outage probability   Optimal offloading ratio  

摘要： In this paper, we study the outage probability (OP) performance of access point (AP) selection for a relay-assisted mobile edge computing (MEC) system consisting of one single-antenna source, one relay and M multi-antenna (APs). The source has a computational task that needs to be completed with the assistance of APs by offloading. The relay using decode-and-forward (DF) relaying strategy located between them is used to convey the offload task of the source to the APs due to the very far source-APs distance. One AP is selected among M APs to receive the offload task with a maximal ratio combining (MRC) receiver and complete it. We propose two criteria for AP selection including: the AP with the largest computational capability will be selected (LCC) and the AP with the largest MRC gain of the instantaneous relay-AP channels will be selected (LMG). To analyze the performance of these proposed criteria in terms of the OP of the considered system, we first define the system OP of by taking into account three performance metrics in term of communication and computation including the latency, energy consumption and data rate. Then, we figure out an optimal offloading ratio that helps minimize the system OP by analyzing the behavior of these considered metrics. We derive approximate expressions for the system OP of the proposed two criteria under the optimal offloading ratio (OOR) and study the impact of the system parameters on their OP performance. Simulation results are illustrated to validate the accuracy of the proposed two AP selection schemes: the OOR-LCC scheme and the OOR-LMG scheme. In particular, the system OP can be reduced by using multiple antennas at APs as well as multiple APs. The proposed two schemes outperform existing schemes. The OOR-LCC scheme can achieve comparable OP with the OOR-LMG scheme at the transmit power allocated to the source and the relay around an optimal threshold while requiring less feedback information.

关键词： Secured data storage and optimal task offloading   Mobile edge computing   Adaptive multiscale dilated bidirectional long short term memory   Hybrid attribute-based encryption with the elliptical curve cryptography   Improved nuclear reaction optimization  

摘要： Mobile Edge Computing (MEC) integrates edge technologies and provides consumers with low-latency storage and efficient computing services. MEC is an approach that delivers services from the cloud at the network edge and is rapidly evolving. Internet of Things (IoT) devices can run their applications in real-time with minimal latency via MEC. However, task offloading is subject to privacy and security breaches such as data replication, confidential information leaking, and data tampering. Edge technology can provide enhanced security for data transmission and storage, enabling tamper-proof and transparency for IoT, when combined with blockchain. However, the effectiveness of data issue detection in the earlier schemes is relatively low, making the present information storage methods unsuitable for processing the obtained data in blockchain-assisted edge processing. The utilization of resources based on network, storage, processing, and privacy can be further enhanced through the Integration of Blockchain and Edge computing (IBEC). To tackle the privacy issues regarding task offloading, a blockchain-integrated secure data storage scheme is implemented in this paper. The data has to be stored that are collected from various IoT devices. The authentication of the user whoever tries to store that data, is verified with the help of the Adaptive Multiscale Dilated Bidirectional Long Short Term Memory (AMDBi-LSTM) model. This approach leverages the strength of Bi-LSTM to permit authorized people to access the data and enhance the security of the data. Moreover, adding dilation in the developed model can easily identify the authorized user sensitive data without the need for multiple steps and the multiscale helps to increase the security of storing the data. By combining these techniques, the developed AMDBi-LSTM model aids in enhancing the speed of detecting the data issue as much as earlier than the conventional models. The hyper-parameters in the AMDBi-LSTM are optimized

关键词： network protocols   artificial neural networks and deep learning   deep reinforcement learning   task offloading   mobile edge computing   reconfigurable intelligent surfaces   unmanned aerial vehicle   problem-solving in the context of artificial intelligence  

摘要： Multi-access edge computing (MEC) has emerged as a cornerstone technology for deploying 6G network services, offering efficient computation and ultra-low-latency communication. The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) further enhances wireless propagation, capacity, and coverage, presenting a transformative paradigm for next-generation networks. This paper addresses the critical challenge of task offloading and resource allocation in an MEC-based system, where a massive MIMO base station, serving multiple macro-cells, hosts the MEC server with support from a UAV-equipped RIS. We propose an optimization framework to minimize task execution latency for user equipment (UE) by jointly optimizing task offloading and communication resource allocation within this UAV-assisted, RIS-aided network. By modeling this problem as a Markov decision process (MDP) with a discrete-continuous hybrid action space, we develop a deep reinforcement learning (DRL) algorithm leveraging a hybrid space representation to solve it effectively. Extensive simulations validate the superiority of the proposed method, demonstrating significant latency reductions compared to state-of-the-art approaches, thereby advancing the feasibility of MEC in 6G networks.

关键词： Heuristic algorithms   Energy consumption   Delays   Resource management   Interference   Collaboration   Simulation   Optimization methods   Minimization   Computational modeling   Mobile edge computing   multi-agent deep reinforcement learning   resource allocation   small-cell networks   task offloading  

摘要： Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.

关键词： Autonomous aerial vehicles   Trajectory   Delays   Heuristic algorithms   Servers   Multi-access edge computing   Low latency communication   Energy consumption   Computational modeling   Batteries   Unmanned aerial vehicles   mobile edge computing   service caching   task offloading  

摘要： Unmanned aerial vehicles (UAVs) have emerged as a new and flexible paradigm to offer low-latency and diverse mobile edge computing (MEC) services for user equipment (UE). To minimize the service delay, caching is introduced in UAV-assisted MEC networks to bring service contents closer to UEs. However, UAV-assisted MEC is challenged by the heavy communication overhead introduced by service caching and UAV's limited energy capacity. In this article, we propose an online algorithm, OOA, that jointly optimizes caching and offloading decisions for UAV-assisted MEC networks, to minimize the overall service delay. Specifically, to improve the caching effectiveness and reduce the caching overhead, OOA employs a greedy algorithm to dynamically make caching decisions based on UEs' preferences on services and UAVs' historical trajectories, with the goal of maximizing the probability of successful offloading. To realize the rational utilization of energy from a long-term perspective, OOA decomposes the online problem into a series of single-slot problems by scaling the UAV's energy constraint into the objective, and iteratively optimizes UAV trajectory and task offloading at each time slot. Theoretical analysis proves that OOA converges to a suboptimal solution with polynomial time complexity. Extensive simulations based on real world data further show that OOA can reduce the service delay by up to 33% while satisfying the UAV's energy constraint, compared to three state-of-the-art algorithms.

关键词： Optimization   Computational modeling   Reliability   Computer architecture   Servers   Dynamic scheduling   Costs   Industrial Internet of Things   Mobile edge computing   quality of service   resource management   double deep Q network   resource management   double deep Q network  

摘要： In the fast-developing industrial environments, extensive focus on resource management within Mobile Edge Computing (MEC) aims to ensure low-latency QoS, however, some tasks offloaded to the cloud still experience high latency. Additionally, high energy consumption, poor link reliability, and excessive processing delays are intolerable for industrial applications. Compared to general servers, edge computing devices based on Arm architecture exhibit lower latency and higher energy efficiency. This highlights the need for improved heterogeneous Collaborative Edge-Edge Industrial Environments (CEIE) and precise multi-user QoS metrics. Thus, we focus on dynamic resource management within the CEIE architecture to better satisfy diverse industrial applications, formulating a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem to minimize system costs. To reduce the computational complexity of solving the MINLP, we decompose the original problem into multi-user task offloading, Communication Resource Allocation (CmRA), and Computational Resource Allocation (CpRA) problems. These transformed problems are then tackled using DRMQ: an integrated learning optimization approach that combines model-free, priority experience replay-based Double Deep Q-Network (iDDQN) with model-based optimization, accelerating the Q-value function's convergence speed and reducing training time. Extensive simulations show that our proposed optimization scheme can reduce the average weighted system cost by at least 43.168% . Moreover, testbed experiments demonstrate that the proposed algorithm can reduce the average system cost by at least 42.650% in real-world applications, outperforming existing methods.

关键词： Costs   Synchronization   Computational modeling   Internet of Things   Heuristic algorithms   Digital twins   Real-time systems   Delays   Hands   Deep reinforcement learning   Digital twin synchronization   mobility-aware DT migration   cost modeling of DT migration   deep reinforcement learning algorithm   mobile edge computing  

摘要： The past decade witnessed an explosive growth on the number of IoT devices (objects/suppliers), including portable mobile devices, autonomous vehicles, sensors and intelligence appliances. To realize the digital representations of objects, Digital Twins (DTs) are key enablers to provide real-time monitoring, behavior simulations and predictive decisions for objects. On the other hand, Mobile Edge Computing (MEC) has been envisioned as a promising paradigm to provide delay-sensitive services for mobile users (consumers) at the network edge, e.g., real-time healthcare, AR/VR, online gaming, smart cities, and so on. In this paper, we study a novel DT migration problem for high quality service provisioning in an MEC network with the mobility of both suppliers and consumers for a finite time horizon, with the aim to minimize the sum of the accumulative DT synchronization cost of all suppliers and the total service cost of all consumers requesting for different DT services. To this end, we first show that the problem is NP-hard, and formulate an integer linear programming solution to the offline version of the problem. We then develop a Deep Reinforcement Learning (DRL) algorithm for the DT migration problem, by considering the system dynamics and heterogeneity of different resource consumptions, mobility traces of both suppliers and consumers, and workloads of cloudlets. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

关键词： Unmanned aerial vehicles   wirelessly powered transfer   mobile edge computing   task completion ratio  

摘要： Unmanned Aerial Vehicle (UAV)-enabled wirelessly powered Mobile Edge Computing (MEC) is emerging as a powerful technology for boosting computational capability and energy supplementation in Internet of Things (IoT). This work addresses the long-term task completion ratio maximization problem in UAV-enabled wirelessly powered MEC systems. Besides the large number of optimization parameters, the environment can only be partially observed as the UAVs cannot cover the whole network area. Then, it is very challenging to obtain good solutions due to the lack of global information. We introduce a novel distributed Multi-Agent Deep Reinforcement Learning (MADRL) framework for optimizing UAVs' actions and resource allocation, considering the constraints of tasks that vary in size, arrival times, and required computation completion time. To decouple the complicated parameters, we divide the problem into two manageable subproblems-UAVs' action decision and resource allocation under a given UAV's action. We employ a distributed Deep Reinforcement Learning (DRL) scheme for the former subproblem to cope with the partially observable nature. By revealing some important properties of the later subproblem, we design an efficient two-stage optimal algorithm to minimize the total consumed energy of nodes while maximizing the task-completing number. Extensive simulations validate the effectiveness of the proposed framework, achieving over a 50% improvement in task completion ratio compared to baseline schemes in some scenarios.

关键词： Microservice architectures   Adaptation models   Edge computing   Data centers   Servers   Quality of service   Energy consumption   Cloud computing   Energy loss   Delays   Mobile edge computing   service deployment   task offloading   performance evaluation  

摘要： Mobile edge computing (MEC) can improve execution efficiency and reduce overhead for offloading computing tasks to edge servers with more resources. In the microservice system, the current research only considers the cross segment communication cost of computing tasks, does not consider the case of the same end, and ignores the discovery and invocation optimization of associated services. In this paper, we propose CACO, which is a novel content-aware classification offloading framework for MEC based on correlation matrix. CACO first designs an adaptive service discovery model, which can make timely response and adjustment to the changes of the external environment. It then investigates an efficient affinity matrix based service discovery algorithm, which expresses the association relationship between services by constructing a service association matrix. In addition, CACO constructs a relational model by giving different weight coefficients to the delay and energy loss, which improves the delay and energy loss of message processing in a satisfying manner. Simulation results indicate that CACO reduces the total traffic of redundant messages by 46.2% similar to 76.5%, respectively compared with state-of-the-art solutions. Testbed benchmarks show that it can also improve the stability by reducing control overhead by 34.5% $\sim$similar to 81.6% .