Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 50 references
Medicine
TL;DR
Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
Abstract
The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
With the rapid development of the Internet of Things (IoT) and mobile computing, edge computing has emerged as a promising paradigm for providing low-latency and energy-efficient services. However, in some extremely computation-intensive scenarios, conventional terrestrial edge computing may fail due to the insufficient computing capability of ground base stations. Fortunately, multi-UAV-assisted edge computing offers a promising solution to this challenge. Nevertheless, existing methods often struggle to provide efficient horizontal cooperative deployment for multiple UAVs with low computational overhead. To address this issue, this paper considers user randomness and inter-UAV collaboration, and proposes a low-complexity yet highly adaptive approach for cooperative deployment and task-scheduling optimization in multi-UAV-assisted edge computing systems. Specifically, we formulate the problem as a stochastic optimization problem that minimizes the energy consumption of ground users while ensuring UAV battery endurance and overall system performance. We then propose a dynamic cooperative deployment and task scheduling (DCDTS) algorithm that integrates K-means clustering with the Lyapunov optimization framework. Through Lyapunov optimization, the original dynamic optimization problem is transformed into a deterministic problem and further decomposed into multiple subproblems that can be solved in parallel. K-means is exploited to enable cooperative UAV deployment and user offloading decisions, while non-convex optimization and nonlinear programming are employed to solve the task-scheduling and resource-allocation subproblem. Extensive parameter analysis and comparative experiments demonstrate that the proposed dynamic cooperative deployment algorithm can effectively reduce user energy consumption while maintaining UAV energy constraints and system performance.
A Lyapunov-based joint optimization framework for UAV-enabled MEC systems achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.
Lei Li, Xue Gao, Quansheng Guan· Electronics· 0 citations
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations
: Mobile edge computing (MEC) is an effective paradigm for supporting latency-sensitive and computation-intensive intelligent applications. However, in dynamic mobile-edge network scenarios, mobile terminals experience time-varying wireless links due to mobility. Tasks may also arrive unpredictably, while multiple terminals compete for limited edge resources. As a result, MEC systems may suffer from service congestion and unbalanced resource utilization, which increases end-to-end latency and energy consumption. This paper investigates cooperative task offloading in dynamic MEC networks. The considered system comprises one macro base station and multiple small base stations equipped with edge-computing resources. In each time slot, each mobile terminal selects a service option, determines the task offloading ratio, and chooses its transmit power for task uploading. This sequential decision process is formulated as a multi-agent problem with continuous action spaces. Under the centralized training and decentralized execution (CTDE) framework, the problem is further modeled as a decentralized partially observable Markov decision process (Dec-POMDP). Standard multi-agent soft actor-critic (MASAC) is not fully suitable for this problem. Its original action model does not handle bounded continuous actions well. Its exploration strength may also be unsuitable at different training stages. Frequent policy updates can further make training unstable when critic estimates are inaccurate. To address these issues, this paper develops an adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC. This method uses a Beta policy to model bounded actions. It adjusts the entropy coefficient during training and delays policy updates to reduce training oscillations. Experimental results show that, under a unified training budget and a consistent evaluation protocol, the proposed method achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings in terms of overall reward, average end-to-end latency, and average energy consumption. In the large-scale scenario, compared with MASAC, it improves the overall reward by 17.8%, reduces the average end-to-end latency by 18.0%, and lowers the average energy consumption by 11.4%.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
An MECN that integrates UAVs as the aerial layer and TESs as the terrestrial layer is introduced, and a quantum-inspired particle swarm optimization-based offloading strategy (QIPSO-TOS) is proposed to facilitate coverage-aware task offloading.
Marlom Bey, P. Kuila, Biswadip Bandyopadhyay et al.· Journal of Supercomputing· 0 citations
A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.
W. Saber, Hanan Algamil, Fifi Farouk et al.· Future Internet· 0 citations