关键词： Increased Responsiveness   Internet of Things   Mobile Edge Computing   Task Computation Latency   Time Critical Responses  

摘要： INTRODUCTION: The Internet of Things (IoT) has transformed daily life by interconnecting digital devices via integrated sensors, software, and connectivity. Although IoT devices excel at real-time data collection and decision-making, their performance on complex tasks is hindered by limited power, resources, and time. To address this, IoT is often combined with cloud computing (CC) to meet time-sensitive demands. However, the distance between IoT devices and cloud servers can result in latency issues. OBJECTIVES: To mitigate latency challenges, Mobile Edge Computing (MEC) is integrated with IoT. MEC offers cloud-like services through servers located near network edges and IoT devices, enhancing device responsiveness by reducing transmission and processing latency. This study aims to develop a solution to optimize task offloading in IoT-MEC environments, addressing challenges like latency, uneven workloads, and network congestion. METHODS: This research introduces the Game Theory-Based Task Latency (GTBTL-IoT) algorithm, a two-way task offloading approach employing Game Matching Theory and Data Partitioning Theory. Initially, the algorithm matches IoT devices with the nearest MEC server using game-matching theory. Subsequently, it splits the entire task into two halves and allocates them to both local and MEC servers for parallel computation, optimizing resource usage and workload balance. RESULTS: GTBTL-IoT outperforms existing algorithms, such as the Delay-Aware Online Workload Allocation (DAOWA) Algorithm, Fuzzy Algorithm (FA), and Dynamic Task Scheduling (DTS), by an average of 143.75 ms with a 5.5 s system deadline. Additionally, it significantly reduces task transmission, computation latency, and overall job offloading time by 59%. Evaluated in an ENIGMA-based simulation environment, GTBTL-IoT demonstrates its ability to compute requests in real-time with optimal resource usage, ensuring efficient and balanced task execution in the IoT-MEC paradigm. CONCLUSION:

关键词： mobile crowdsensing   mobile edge computing   lyapunov optimization   bipartite graph matching  

摘要： Mobile CrowdSensing (MCS) has become a powerful sensing paradigm for information collection recently. As sensing becomes more complicated, it is beneficial to deploy edge servers between users and the cloud center with a so-called mobile edge computing. Instead of directly offloading the sensing data to the cloud center, mobile users offload the sensing data to the edge servers. Then, the edge server processes and transmits the data to the cloud center in a distributed and parallel manner. It's however critically important to balance cost, such as energy consumption, and the stability of the queues on both mobile users and edge servers. Therefore, to minimize the data offloading cost while maintaining system stability, we should carefully design the sensing data offloading strategy for edge-based crowdsensing. To this end, we formulate a double-queue Lyapunov optimization problem and propose a sensing data offloading strategy. We analyze the upper bounds of the total offloading cost and queue backlog. We further formulate the heterogeneous sensing data problem as the minimum weight bipartite graph matching problem and develop an approach that is based on Kuhn-Munkres algorithm. Finally, we conduct simulations based on three mobility sets. Simulation results show that the proposed techniques outperform several state-of-art algorithms in overall cost, system stability, and other performance metrics.

关键词： Mobile edge computing   Partial computation offloading   Load-balancing   Multi-agent reinforcement learning  

摘要： Mobile edge computing (MEC) can enhance the computation performance of end-devices by providing computation offloading service at the network edge. However, given that both end-devices and edge servers have finite computation resources, inefficient offloading policies may lead to overload, thereby increasing the computation delays of tasks. In this paper, we investigate a multi-task partial computation offloading problem combined with a queue model. Based on achieving load-balancing across the MEC system, our objective is to minimize the long-standing average task-processing cost of the end-devices while ensuring the delay thresholds of tasks. For this purpose, a distributed offloading algorithm utilizing the multi-agent deep reinforcement learning (MADRL) method is proposed. Specifically, through interacting with the MEC environment and accumulating experience data, the device agents can collaborate to optimize their local offloading decisions over continuous time-slots, which includes adjusting the transmission power and determining the tasks' offloading ratios under the dynamic wireless channel conditions. Exhaustive experimental results demonstrate that in contrast with the baseline algorithms, the proposed offloading algorithm can not only better balance the computation loads between the end-devices and the MEC server, but also more effectively reduce the task-processing cost of the end-devices, as well as the percentage of timeout tasks.

关键词： Internet of Things   Mobile edge computing   Nonorthogonal multiple access   Unmanned aerial vehicles   Physical layer security  

摘要： This paper studies security task offloading for dual unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) in Internet of Things (IoT) systems, in which one UAV is used to aid ground edge devices (EDs) in computing the offloaded tasks, another UAV as a jammer to discourage an eavesdropper. We employ uplink nonorthogonal multiple access (NOMA) to EDs to carry out the offloading procedure and so offer a four-phase protocol to ensure secure task offloading. To evaluate the system's security offload performance, we derive a closed-form formula for the secrecy successful computation probability (SSCP) using the fading Nakagami-m channel model. In addition, to increase the system's security offload performance, we create an SSCP maximization problem by optimizing parameters such as the location and altitude of dual UAVs, and the offloading ratio of ED. To solve this problem, a firefly algorithm (FFA)-based technique was used. Finally, Monte Carlo simulation results are presented to validate the accuracy of our analysis by simulating system performance under various system parameter values, such as dual UAV's location and altitude;average transmit signal-to-noise ratio (SNR), offloading ratio and comparison between NOMA and orthogonal multiple access (OMA) schemes for the considered system.

关键词： Mobile edge computing   Spectrum resource allocation   Internet of vehicles   Data prioritization   Multi-agent deep reinforcement learning  

摘要： Intelligent transportation systems (ITS) are facing the limitation of spectral resources and stringent real time communication requirements. How to effectively allocate system resources for maximizing the performance in Internet of Vehicles (IoV) is still a substantial challenge, particularly the priority and urgency of different types of data need to be focused. To improve the allocation spectrum resources and optimize transmission power while taking into the dynamic characteristics of vehicles and data priorities account, we design a time-series-based multi-agent deep reinforcement learning framework (NL-MAPPO for short), in this paper. First, we formulate the joint optimization problem as a multi-agent Markov decision process to ensure the minimization of transmission delays and energy consumption when the total vehicle-to-vehicle (V2V) link capacity is maximized. Here, V2V link capacity refers to the maximum achievable data rate for direct communication between vehicles, which depends on factors such as signal strength, interference, and available bandwidth. Then, we design a multi-agent resource allocation algorithm based on a shared-critic mechanism to realize the global sharing of channel information and solve the optimization problem. Finally, to improve efficiency, we also introduce a time series-based channel information extraction mechanism to capture the temporal characteristics of channel information. The simulation experiments were conducted and the results demonstrated that our proposed NL-MAPPO can demonstrate superiority in multiple metrics.

关键词： Mobile edge computing   Unmanned aerial vehicle (UAV)   Task offloading   Trajectory optimization   Resource allocation  

摘要： Integrating Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) offers enhanced coverage and computational support for mobile IoT devices (MIDs). Due to the inherent computational capacity and energy constraints of UAV, existing UAV-assisted MEC systems struggle to satisfy computation-intensive network services for numerous MIDs. To address this issue, this paper proposes a UAV and ground-based Edge Cloud (EC) collaboration MEC system. Specifically, the EC is equipped with an energy transmitter to provide wireless power transfer (WPT) to the UAV, thereby collaboratively managing backlog tasks in scenarios where task and energy arrive stochastically. To achieve optimal service delivery, we formulate a long-term stochastic optimization problem aiming to jointly optimize UAV energy consumption and system throughput while ensuring task queue stability. However, this NP-hard problem posed by the stochastic nature of task arrivals and energy constraints, we develop an online offloading and trajectory control (OOTC) algorithm. This algorithm uses Lyapunov optimization theory to transform the long-term stochastic optimization problem into a deterministic per-slot optimization problem. The OOTC algorithm decouples control decisions across consecutive time slots, reducing computational complexity and ensuring queue stability without relying on statistical knowledge. We further decompose it into three subproblems, and derive suboptimal solutions by the Successive Convex Approximation (SCA) and the Lagrangian duality. Simulations show OOTC algorithm outperforms benchmarks and maintains stability.

关键词： Intelligent wireless control networks   Mobile edge computing   Multi-agent deep reinforcement learning   Dynamic multi-channel access   Traffic prediction  

摘要： Effective service distribution management is essential in Intelligent Wireless Communication Networks to meet the increasing Quality of Service (QoS) demands across various applications. Traditional transmission strategies often prioritize high-QoS data, which can lead to access starvation for lower-priority data in resource-constrained environments. To address this, we propose a QoS-aware adaptive service distribution strategy that balances the needs of high-and low-priority data without compromising the performance of either. Leveraging enhanced Multi-Agent Deep Deterministic Policy Gradient (e-MADDPG), our solution dynamically optimizes service distribution in mobile edge networks. By employing a gated recurrent unit-enhanced reinforcement learning framework, we enable intelligent agents to collaboratively decide channel access based on real-time traffic conditions. The proposed multi-criteria Decision-based Multi-channel Access algorithm allows high-priority data to defer access if necessary, improving the completion rates of lower-priority data. Furthermore, our method integrates network slicing and computation offloading to enhance service adaptability, ensuring efficient use of edge resources. Simulation results confirm that our framework significantly outperforms existing approaches in terms of channel utilization, QoS adherence, and overall network efficiency.

关键词： Mobile edge computing   internet of things   deep reinforcement learning   communication security   task offloading optimization  

摘要： As the network society progresses, Mobile Edge Computing (MEC) has emerged as a transformative technology, enabling low-latency and high-efficiency data processing, which is reshaping the evolution of the Internet of Things (IoT). By offloading computational tasks to the network edge and delivering services closer to the data source, MEC effectively mitigates data transmission latency and enhances system responsiveness, positioning itself as a pivotal enabler of future intelligent societies. This study addresses the critical issue of communication security in urban environments, where eavesdropping poses significant threats, by proposing a novel joint optimization framework that integrates UAV flight trajectory planning, user scheduling and computational resource allocation. To navigate the complexities of high-dimensional state and continuous action spaces, we leverage the Deep Deterministic Policy Gradient (DDPG) algorithm for iterative policy optimization. The objective is to minimize task processing delays while ensuring secure communication. Experimental results substantiate the efficacy of the proposed algorithm, demonstrating its ability to achieve a balanced trade-off between communication security and task processing efficiency, across varying weight coefficients, thereby enhancing the overall performance of MEC systems in IoT contexts.

关键词： UAV communication   Aerial inspection   Trajectory design   Mobile edge computing   Deep reinforcement learning  

摘要： This paper investigates an Unmanned Aerial Vehicle (UAV)-based detection system deployed in an urban environment. Cellular network-connected UAVs collect data from multiple inspection points scattered in urban buildings and upload the data to a ground base station (GBS). Our goal is to minimize the energy consumption of the UAVs while accomplishing the data uploading task by designing the UAV inspection sequence, UAV path planning, and UAV correlation rate. To solve this intractable non-convex problem, we propose the EEGA-TD3 algorithm. First, an adaptive genetic algorithm is proposed to obtain the optimal inspection sequence by considering the energy consumption and throughput of the UAV and the transmission task. Subsequently, we utilize the dual-delay deep deterministic policy gradient (TD3) algorithm to optimize the UAV flight trajectory. The scheme is able to realize continuous control and guide the UAV to dynamically adjust its flight strategy according to the amount of data. Simulation results show that the proposed algorithm is able to flexibly select the trajectory scheme that accomplishes the data transmission task and saves energy compared to the traditional algorithm.

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.