关键词： Containers   Edge computing   Resource management   Costs   Transformers   Long short term memory   Computational modeling   Processor scheduling   Base stations   Artificial intelligence   Parked vehicles   vehicles edge computing   mobile edge computing   container   reinforcement learning   LSTM   transformer  

摘要： Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm.

关键词： Bilinear pairing   Extension field   Authentication   Key agreement   Mobile edge computing  

摘要： We show the Rakeei-Moazami key agreement scheme [Wireless Networks, 2022, 28, 2983-2999] is flawed. It neglects the basic structure of bilinear pairing over elliptic curve, and presents some false equalities. Some point multiplications over the underlying elliptic curve are falsely specified, which result in the failure of later integrity authentication for the user and server. These false computations can be revised by introducing another hash function which maps a value of bilinear pairing to an element in the base finite field.

关键词： Mobile edge computing   Edge server placement   User mobility   Deep reinforcement learning   Access delay  

摘要： As an emerging computing paradigm, Mobile Edge Computing (MEC) significantly enhances user experience and alleviates network congestion by strategically deploying edge servers in close proximity to mobile users. However, the effectiveness of MEC hinges on the precise placement of these edge servers, a critical factor in determining the Quality of Experience (QoE) for mobile users. While existing studies predominantly focus on optimizing edge server placement in static scenarios, they often fall short when faced with user mobility, resulting in a degradation of QoE. To address this challenge, we propose an adaptive edge server placement approach that leverages Deep Reinforcement Learning (DRL) to select the base stations for placing edge servers in a dynamic MEC environment. Our objective is to minimize access delay by optimizing edge server placement for adapting to dynamic environment. To tackle the vast action space associated with edge server placement, we introduce a novel activation function in the actor neural network for efficient exploration. Furthermore, to enhance the adaptability of the derived edge server placement strategy, we meticulously design a new reward function, which takes into account the minimization of total access delay within dynamic MEC scenarios. Finally, to validate the effectiveness of our proposed method, extensive experiments were conducted using the Shanghai Telecom dataset. The results demonstrate that our approach outperforms baseline methods in minimizing access delay for users in dynamic MEC scenarios.

关键词： Mobile edge computing   Wireless power transfer   Green energy   Resource management   Deep reinforcement learning  

摘要： Recently, the integration of mobile edge computing (MEC) and wireless power transfer (WPT) technologies presents a transformative approach to overcoming the energy limitations of wireless devices (WDs), thereby enhancing both the sustainability and operational efficiency of mobile networks. This paper introduces a novel green-prioritized hybrid energy supply system that harnesses both renewable and grid energy, which aims at optimizing energy use and computational power in mobile networks under dynamic conditions. Specifically, we formulate a long-term average grid energy minimization problem (LAGEMP) to reduce grid energy consumption while maintaining robust and efficient network operations. To solve the complex and dynamic LAGEMP, we propose an action space reduction scheme and an enhanced deep deterministic policy gradient (EDDPG) algorithm, which incorporates the cross-entropy method (CEM). These introduced enhanced approaches not only reduce the computational load but also expedite the convergence of network training, thereby optimizing both energy usage and task offloading strategies. Simulation results reveal that the EDDPG algorithm significantly outperforms existing strategies and algorithms, and achieves near-optimal task offloading efficiency with reduced grid energy.

关键词： Autonomous aerial vehicles   Resource management   Edge computing   Energy consumption   Delays   Radio access networks   Internet of Things   Heuristic algorithms   Satellites   Servers   Aerial computing   mobile edge computing   uncrewedaerial vehicle   high altitude platform   soft actor-critic algorithm   offloading decision   user association   resource allocation  

摘要： In recent years, applications are becoming increasingly computation-intensive and delay-sensitive owing to the rapid growth of Internet of Things (IoT) devices among ground users (GUs). Mobile edge computing (MEC) presents crucial computational support, but conventional MEC services often fail in remote areas and in disaster scenarios. This study presents a hierarchical aerial computing platform leveraging uncrewed aerial vehicles (UAVs) and high-altitude platform (HAP) to meet the computation demands and latency requirements of various IoT applications for GUs. We propose a joint offloading decision, user association, and resource allocation (JOUR) scheme, utilizing binary offloading from GUs to UAVs and partial offloading from UAVs to HAP. The proposed scheme minimizes the energy consumption and latency while maximizing the load balancing. A matching game-based algorithm addresses the GUs offloading decision and GUs-UAVs association, followed by an enhanced soft actor-critic (ESAC) algorithm for UAV partial offloading decision, UAV computation resource allocation, and HAP computation resource allocation. Our simulation results demonstrate the effectiveness of the JOUR scheme in reducing the energy consumption and latency, while improving the load balancing and task completion rates. This demonstrates its potential for optimizing the hierarchical aerial computing platforms in dynamic IoT environments.

关键词： Mobile Edge Computing   Task offloading   Task size prediction   Deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) has been identified as an innovative paradigm to improve the performance and efficiency of mobile applications by offloading computation-intensive tasks to nearby edge servers. However, the effective implementation of task offloading in MEC systems faces challenges due to uncertainty, heterogeneity, and dynamicity. Deep Reinforcement Learning (DRL) provides a powerful approach for devising optimal task offloading policies in complex and uncertain environments. This paper presents a DRL-based task offloading approach using Deep Deterministic Policy Gradient (DDPG) and Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithms. The proposed solution establishes a distributed system, where multiple mobile devices act as Reinforcement Learning (RL) agents to optimize their individual performance. To reduce the computational complexity of the neural networks, Gated Recurrent Units (GRU) are used instead of Long Short-Term Memory (LSTM) units to predict the load of edge nodes within the observed state. In addition, a GRU-based sequencing model is introduced to estimate task sizes in specific scenarios where these sizes are unknown. Finally, a novel scheduling algorithm is proposed that outperforms commonly used approaches by leveraging the estimated task sizes to achieve superior performance. Comprehensive simulations were conducted to evaluate the efficacy of the proposed approach, benchmarking it against multiple baseline and state-of-the-art algorithms. Results show significant improvements in terms of average processing delay and task drop rates, thereby confirming the success of the proposed approach.

关键词： decentralisation   electronic medical records   Ethereum   InterPlanetary File System   machine learning   mobile edge computing  

摘要： The healthcare industry is undergoing a transformative shift with the integration of blockchain technology and Artificial Intelligence (AI). Traditional healthcare systems struggle with data security, lack of transparency, and inefficiencies in resource allocation, leading to increased risks and operational challenges. AI-driven models provide intelligent solutions by enabling predictive diagnostics, fraud detection, and personalised treatment plans, while blockchain ensures data integrity, security, and decentralised access control. The synergy between AI and blockchain enhances decision-making, optimises resource utilisation and fosters trust in healthcare systems by automating processes with greater transparency and security. AI-powered analytics can extract meaningful insights from vast healthcare datasets, improving patient outcomes and streamlining supply chains. Meanwhile, blockchain's immutable ledger safeguards medical data, preventing breaches and ensuring regulatory compliance. This paper presents a comprehensive review of blockchain solutions in healthcare, exploring their impact on enhancing security, promoting transparency and improving energy efficiency. Additionally, this paper also presents insights on the integration of AI and blockchain in healthcare. It categorises existing blockchain-based frameworks and highlights emerging trends, challenges, and future research directions. This review aims to serve as a foundational reference for researchers and practitioners developing secure, transparent, and intelligent healthcare systems.

关键词： Mobile edge computing   Resource pricing   Stackelberg game   Task offloading  

摘要： With the rapid advancement of fifth-generation communication technologies, the data produced by the Internet of Everything is growing exponentially. As mobile cloud computing struggles to keep up with the demands for massive data processing and low latency, mobile edge computing (MEC) has emerged as a solution. By shifting services from centralized cloud platforms to edge servers located closer to data sources, MEC achieves reduced latency, enhanced computing efficiency, and an improved user experience. This paper introduces a task offloading algorithm designed for a multi-base station cooperative mobile edge environment, addressing the challenges of task offloading and resource pricing. The system architecture includes a macro base station and several micro base stations, strategically deployed in a densely populated mobile device area. Each mobile device serves as an autonomous decision-making unit, offloading tasks to an optimal base station. We model the interactions between base stations and end-users using a Stackelberg game approach, with strategy optimization achieved through a multi-agent deep deterministic policy gradient algorithm. The proposed TO-SG-MADDPG algorithm intelligently coordinates the policies of multiple base stations and end-users by centralized training and distributed execution, resulting in globally optimal task offloading and resource pricing. The results demonstrate that the proposed algorithm not only reduces the task loss rate but also safeguards the interests of all stakeholders.

关键词： Mobile edge computing   Load balancing   Particle swarm optimization   Task offloading  

摘要： . With the rapid advancement of technology, the proliferation of portable smart devices, such as smartphones and smart bracelets, has led to increasingly demanding task-processing requirements from users. Mobile Edge Computing (MEC) addresses the latency issues inherent in traditional cloud computing by offloading tasks to proximate edge servers for execution. However, the dynamic nature of user locations and the stochasticity of task generation introduce task distribution imbalances, with some edge servers becoming overloaded while others still need to be utilized. Therefore, load balancing among edge servers has emerged as a critical challenge in MEC. To address this challenge, we propose an Edge Server Load Balancing method based on Particle Swarm Optimization called ESLB-PSO, which achieves load balancing of each edge server by minimizing the maximum response time of tasks. This method models the MEC environment as a topology, abstracting the corresponding problem according to the operational principles of real-world edge servers. Each particle represents a solution, and the task offloading plan is optimized by adjusting the particle's speed, direction, and approach toward the global optimum. Experimental results demonstrate that ESLB-PSO outperforms baseline methods, effectively achieving load balancing across edge servers, and obtains a better maximum response time of the tasks.

关键词： Deep reinforcement learning   Unmanned aerial vehicle (UAV)   Task offloading   Mobile edge computing   Multi-UAV   DTLCM  

摘要： Mobile Edge Computing (MEC) addresses the demands of computation-intensive network services by providing processing capabilities at the network's edge, reducing service latency. Unmanned Aerial Vehicles (UAVs) enhance MEC due to their flexibility, broad coverage, and reliable wireless communication. This paper investigates task offloading in a UAV-assisted MEC system with multiple collaborating UAVs, optimizing task allocation in binary offloading mode to enhance system efficiency. System performance is evaluated in terms of energy consumption and task delay, while the proposed framework jointly optimizes UAV trajectory design, binary offloading, computation resource allocation, and communication resource management to achieve better resource utilization. To overcome the limitations of traditional heuristic-based approaches, we propose an innovative Deep Reinforcement Learning (DRL)-based task offloading framework and conduct a comparative analysis of Deep Deterministic Policy Gradient with Distance to Task Location and Capability Matching (DDPG with DTLCM), Deep Q-Network (DQN), and Q-Learning. Our approach leverages DTLCM to enhance decision-making, allowing UAVs to assign tasks more intelligently by factoring in both their proximity to the task and their computational capabilities. Experimental results show that DDPG with DTLCM consistently outperforms both DQN and Q-Learning, delivering more stable task allocation and better adaptability in dynamic scenarios. While DQN is more stable than Q-Learning in high-dimensional state spaces, it still suffers from discretization constraints. In contrast, DDPG with DTLCM benefits from a continuous action space, enabling more flexible and fine-tuned decision-making. These insights underscore the strength of policy-gradient DRL methods, particularly when augmented with context-aware mechanisms like DTLCM, in advancing UAV-assisted MEC task scheduling and resource optimization.