Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-7· 0 citations· 16 references
Abstract
Dynamic edge networks suffer from fluctuating bandwidth, latency, and edge-node load, which weaken conventional task offloading strategies under deadline and energy constraints. This study proposes an adaptive task offloading strategy based on dynamic network states. An online broad learning system predicts short-term bandwidth, transmission delay, and load from sliding-window observations. The predictions are embedded into a model predictive control framework for rolling optimization of the local-edge task allocation ratio, while confidence-interval-guided compensation adjusts decisions under prediction uncertainty. ADMM is used for distributed solution. NS-3/Python simulations show that the proposed method achieves a $93\%$ task success rate, 1.22 W average terminal energy consumption, 3.2 switches/s, and 45 ms average completion time, outperforming local execution, reactive offloading, and DQN-based offloading. The proposed strategy improves real-time reliability, energy efficiency, and robustness in highly dynamic edge computing environments.
A Mixed Integer Nonlinear Programming (MINLP) model with the objective of a weighted sum of long-term average task completion rate, total latency and energy consumption is established, which improves the task completion rate by 4% in high load scenarios and achieves a better balance between latency and energy consumpti...
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system pote...
Mamoon M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose...
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations
Traditional resource scheduling strategies fail to fully utilize the computing and storage resources of edge nodes, leading to resource waste and overload of some nodes. This paper collects network topology and edge node resource information data for preprocessing. Then, a state space and action space are defined to re...
