Abstract
Wireless sensor network (WSN) is a collection of smart sensor nodes cooperated together for achieving the desire of the assigned application. However, these nodes suffer from different limitations, including limited energy sources and limited processing capabilities. Clustering and data aggregation are considered main solutions for prolonging the network lifetime. Clustering is either based on probabilistic models or on artificial intelligence (AI) techniques such as fuzzy logic (FL). Clustering-based probabilistic models in most of the cases lead to inefficient distribution of cluster heads to cover all nodes in the field. At the same time, most of the current fuzzy-based clustering schemes assume that nodes are aware of their geographical location for their operation. On the other hand, most of the current aggregation protocols do not exploit the redundancy and the highly correlated reported values by the nearly deployed nodes. In addition, multimodal nodes, nodes that sense multiple features at the same time, are not considered, up to our knowledge, in any of the current aggregation algorithms. Our contribution in this paper is twofold: (i) introducing fuzzy-based clustering technique that takes node's residual energy, density and number of features sensed in multimodal WSNs and (ii) proposing a novel data aggregation technique-based fuzzy score to identify the uniqueness/importance of the reported data. Our proposed algorithms are compared to some of the current clustering and aggregation algorithms with different WSN settings.