No abstract

Many applications employing wireless sensor networks have been available in real-world scenarios. Their popularity is due to distinctive characteristics, for example, small scale, multisensing capability, and cost-effective deployment. However, there are constraints including routing, reliability, and especially localization, in particular without the aid of global positioning services, the lack of satellite coverage. In addition, if embedded, the overhead will be increased with hardware costs and shortened battery life. Thus, a range-free-based localization scheme is promising and is being pursued as a cost-effective approach. Centroid is one of the pioneer low complexity range-free estimation algorithms, and DV-Hop is another algorithm that has no requirements for distance information. However, their main drawbacks are location estimation precision. Recently, a soft-computing-based approach used to address uncertainty and approximation has been proposed as a low cost solution to gain precision, and, therefore, this research investigates its integration and then proposes a novel hybrid localization algorithm utilizing key characteristics of Centroid and DV-Hop. This hybrid scheme employs an extra weight with signal normalization derived from a fuzzy logic function in Centroid. The research also integrates a BAT algorithm of the modified DV-Hop. These combinations demonstrate the effectiveness in the simulation and location error reduction with time complexity trade-off.