DICOMIST: An methodology for Performing Distributed Computing in Heterogeneous ad hoc Networks

Abstract

The Internet of Things (IoT) has emerged as a cornerstone technology, transforming how we interact with our surroundings. Despite their widespread adoption, IoT devices encounter challenges related to processing capabilities and connectivity, frequently necessitating the delegation of tasks to remote cloud servers. This offloading, essential for enhancing user experience, poses challenges, particularly for latency-sensitive applications. Edge-centric paradigms like fog and mist computing have emerged to address these challenges, bringing computational resources closer to end-users. However, efficiently managing task offloading in dynamic IoT environments remains a complex issue. This paper introduces DIstributed COmputing for MIST (dicomist), a methodology designed to facilitate task offloading in IoT settings. Dicomist utilizes wireless mesh networks to organize mobile nodes, employing clustering and classification techniques. Tasks are treated as consensus problems, enabling distributed computation among selected nodes. Real-world experiments demonstrate dicomist’s effectiveness, underscoring its potential to enhance task offloading in IoT environments.