Energy-Efficient Offloading Task in the 5G Edge-Cloud Continuum Using Probabilistic Spiking Networks With Tactical Unit Optimization

Abstract

In order to maximize resource usage, minimize latency, and enhance energy efficiency in Mobile Edge Computing (MEC), task offloading is crucial in the 5G Edge-Cloud Continuum. Traditional methods suffer from high computational complexity and static decision-making, leading to inefficiencies. This work proposes a Probabilistic Spiking Neural Network with Tactical Unit Algorithm (PSNN-TUA) for adaptive, low-power task offloading. The system operates across three tiers: Device Layer, MEC Layer, and Cloud Layer, categorizing tasks as delay-sensitive, energy-sensitive, or latency-insensitive. Execution is modeled using Alpha-Beta-Gamma (ABG) path loss and OFDMA-based transmission. The PSNN-based decision model represents UEs, MEC servers, and cloud servers as spiking neurons, using spike probabilities to allocate offloading tasks according to network conditions and resource availability. The decision-making process based on membrane potentials facilitates appropriate work allocation and enhances computing efficiency. The TUA executes three operational stages namely Searchers Action followed by Executors Action then finishes with Assessors Combat Assessment to find optimal PSNN hyperparameters. The experimental data shows PSNN-TUA delivers superior performance compared to other methods by reaching 98.5% MEC availability with 12.3 ms latency and 0.75 J/task energy efficiency alongside 97.2% task completion and 0.9% packet loss and 1.3% failure rate which demonstrates its effectiveness for 5G MEC environments.