Data similarity aware dynamic node clustering in wireless sensor networks

Abstract

Wireless Sensor Networks (WSNs) have been used by several kinds of urban and nature monitoring applications as an important interface between physical and computational environments. Node clustering is a common technique to organize data traffic, reduce communication overhead and enable better network traffic management, improving scalability and energy efficiency. Although current clustering protocols treat various kinds of dynamicity in the network, such as mobility or cluster-head rotations, few solutions consider the readings similarity, which could provide benefits in terms of better use of compression techniques and reactive detection of anomalous events. For maintaining similarity aware clusters, the synchronization of the cluster’s average reading would allow a distributed and adaptive operation. In this article, we propose an architecture for dynamic and distributed data-aware clustering, and the Dynamic Data-aware Firefly-based Clustering (DDFC) protocol to handle spatial similarity between node readings. The DDFC operation takes into account the biological principles of fireflies to ensure distributed synchronization of the clusters’ similar readings aggregations. DDFC was compared to other protocols and the results demonstrated its capability of maintaining synchronized cluster readings aggregations, thereby enabling nodes to be dynamically clustered according to their readings.