An Adaptive Swarm Intelligence Algorithm for Resilient Task Execution in Intermittent Edge Environments

Abstract

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The proliferation of Internet of Things (IoT) devices and the rapid growth of mobile edge computing have fostered a complex ecosystem where computational tasks are executed on devices with unpredictable availability. The intermittent connectivity prevalent in edge locations presents a significant challenge to conventional task scheduling and resource allocation models.This paper introduces the Adaptive Swarm for Intermittent Edge Tasks (ASIET), a novel swarm intelligence algorithm engineered to adapt dynamically and maintain high task completion rates within highly dynamic and unreliable environments by handling disconnections, incorporating defined waiting periods, and the maintenance of a knowledge base for intelligently scheduling re-attempts.