RainCloud: Decentralized Coordination and Communication in Heterogeneous IoT Swarms

TL;DR

RainCloud tackles decentralized coordination in heterogeneous IoT swarms by marrying semantic communication with a lightweight ACO-based offloading mechanism. The Rain Cloud System (RCS) supports autonomous, runtime task allocation, node discovery, and adaptive topology through self-actualization to handle failures and join events. Empirical evaluation against Random and Gossip baselines shows ACO achieves lower hops and scalable performance, while Gossip offers robust task allocation at higher messaging costs. The work provides an open-source framework for edge-based decentralized coordination and suggests future work on cycle-free routing, energy-aware pheromones, and hybrid strategies to further improve resilience and efficiency.

Abstract

The increasing volume and complexity of IoT systems demand a transition from the cloud-centric model to a decentralized IoT architecture in the so-called Computing Continuum, with no or minimal reliance on central servers. This paradigm shift, however, raises novel research concerns for decentralized coordination, calling for accurate policies. However, building such strategies is not trivial. Our work aims to relieve the DevOps engineers from this concern and propose a solution for autonomous, decentralized task allocation at runtime for IoT systems. To this end, we present a semantic communication approach and an ad-hoc lightweight coordination strategy based on Ant Colony Optimization (ACO). We compare the ACO strategy with Random Search and Gossip protocol-based algorithms. We conduct accurate experiments with up to a hundred nodes in both a static and a dynamic environment, i.e., with device outages. We show that ACO finds a matching node with the smallest hops and messages sent. While the Gossip strategy can allocate the most tasks successfully, ACO scales better, thus being a promising candidate for decentralized task coordination in IoT clusters.

Figures (7)

• Figure 1: High-level representation of RainCloud's scenario.
• Figure 2: Main components and interaction for the RCS container responsible for a node.
• Figure 3: Concept of forward and backward ants in ACO. The forward ant is depicted on the left-hand side, going from the origin to the target, and the backward ant is on the right.
• Figure 4: LD in static (1st column) and dynamic (2nd column) environments with 100 devices of different queue capacities.
• Figure 5: LD in static (1st column) and dynamic (2nd column) environments with 100 devices of different queue capacities.