A multi-cast algorithm for robust average consensus over internet of things environments

The initial cloud-managed-network conceptualization of the IoT has mutated into a sparsely coupled, distributed system of interacting smart objects, or things. In this context, distributed control techniques have emerged as a tool for performing tasks locally without the support of a back-end infrastructure. Consensus algorithms are the flagship of distributed control and find application in solving problems as varied as distributed task assignment, distributed estimation, and distributed optimization. Unfortunately, typical consensus algorithms deteriorate their performance when faced with realistic communication phenomena as asynchronous communications, packet losses and channel delays. In this work, an algorithm for achieving average consensus over an IoT environment is introduced and evaluated extensively in a large-scale IoT testbed. Evaluation results show that the proposed algorithm achieves average consensus in all evaluated scenarios despite heterogeneity in the network in terms of processing and networking capabilities, packet losses and delays.