Fuzzy Centroid localization schemes for unbalanced deployments of wireless sensor networks

This paper proposes a novel methodology to mitigate the effect of unbalanced known nodes' positions for location ap-proximation in wireless sensor networks. in a practical deploy-ment,some nodes may not properly be in uniform places,and per-haps,due to unequal power consumption of large-scale networks while performing sensing,computing,and transmitting tasks. k-means clustering is applied to select a representative of the known nodes where their positions are close together,and each of which will be then fed into fuzzy logic systems to determine a proper weight to finally use in the actual location determination process with weighted centroid. the effectiveness of our methodology is evaluated via a large scale simulation with regard to node density,coverage,and topology,against a traditional centroid,its fuzzy systems,and dv-hop.