A Hybrid Clustering Technique Using Quantitative And Qualitative Data For Wireless Sensor Networks

Clustering is an efficient method to organize sensor nodes in Wireless Sensor Networks (WSNs) for data transmissions and energy saving. To perform clustering, many methods require geographic location data for calculating the distance between sensor nodes. But location data may not always be available due to Global Positioning System (GPS) failures or may not be practical in consideration of all sensor nodes due to the high cost and energy consumption of GPS. Alternatively, Received Signal Strength (RSS) or RSS Indicator (RSSI) has been used to estimate the distance. But many studies have shown that RSS or RSSI is not reliable in practice. In order to mitigate these realistic problems, this paper proposes a hybrid clustering protocol Hybrid Distributed Hierarchical Agglomerative Clustering (H-DHAC) which uses both quantitative location data and binary qualitative connectivity data in clustering for WSNs. Our simulation results reveal that H-DHAC only has a slightly lower percentage of compromise in performance in terms of network life time and total transmitted data compared to similar approaches that use complete location data. However, H-DHAC still outperforms the well known clustering protocols, e.g., LEACH and LEACH-C. On the other hand, the cost of H-DHAC can be significantly lower in comparison to those approaches that use complete quantitative location data, as GPS is not required for all sensor nodes. In addition, H-DHAC still can be operational in the presence of GPS failures. (C) 2014 Elsevier B.V. All rights reserved.