DeCoRIC: Decentralized Connected Resilient IoT Clustering

Maintaining peer-to-peer connectivity with low energy overhead is a key requirement for several emerging Internet of Things (IoT) applications. It is also desirable to develop such connectivity solutions for non-static network topologies, so that resilience to device failures can be fully realized. Decentralized clustering has emerged as a promising technique to address this critical challenge. Clustering of nodes around cluster heads (CHs) provides an energy-efficient two-tier framework for peer-to-peer communication. At the same time, decentralization ensures that the framework can quickly adapt to a dynamically changing network topology. Although some decentralized clustering solutions have been proposed in the literature, they either lack guarantees on connectivity or incur significant energy overhead to maintain the clusters. In this paper, we present Decentralized Connected Resilient IoT Clustering (DeCoRIC), an energy-efficient clustering scheme that is self-organizing and resilient to network changes while guaranteeing connectivity. Using experiments implemented on the Contiki simulator, we show that our clustering scheme adapts itself to node faults in a time-bound manner. Our experiments show that DeCoRIC achieves 100% connectivity among all nodes while improving the power efficiency of nodes in the system compared to the state-of-the-art techniques BEEM and LEACH by up to 110% and 70%, respectively. The improved power efficiency also translates to longer lifetime before first node death with a best-case of 109% longer than BEEM and 42% longer than LEACH.Comment: 10 pages, 8 figures, 3 tables, accepted in ICCCN 202

Adaptive Algorithm for Finding Connected Dominating Sets in Uncertain Graphs

The problem of finding a minimum-weight connected dominating set (CDS) of a given undirected graph has been studied actively, motivated by operations of wireless ad hoc networks. In this paper, we formulate a new stochastic variant of the problem. In this problem, each node in the graph has a hidden random state, which represents whether the node is active or inactive, and we seek a CDS of the graph that consists of the active nodes. We consider an adaptive algorithm for this problem, which repeat choosing nodes and observing the states of the nodes around the chosen nodes until a CDS is found. Our algorithms have a theoretical performance guarantee that the sum of the weights of the nodes chosen by the algorithm is at most $O(\alpha \log (1/\delta))$ times that of any adaptive algorithm in expectation, where $\alpha$ is an approximation factor for the node-weighted polymatroid Steiner tree problem and $\delta$ is the minimum probability of possible scenarios on the node states.Comment: This is the accepted version of a paper to be published by IEEE/ACM Transactions on Networkin