H-FCD: Hybrid Fuzzy Centroid and DV-Hop Localization Algorithm in Wireless Sensor Networks

Soft computing-based localizations in wireless sensor networks

“…For the latter approach, the location estimation is computed without this information being available; instead, the number of hops between sensor nodes is explored, e.g., Centroid, Approximate Point in Triangle (APIT), DV-Hop, and Amorphous. Due to the cost and limitations of additional hardware nodes, the range-free localization is promising and is pursued as a cost-effective approach [46][47][48].…”

“…However, since in the research, the focus is on soft computing based approaches, some of the intelligent routing protocols [21,24,25,[28][29][30][31][32][33][34][35][36][37][38]43,48,56] are preferred for probable seamless integration for WSN applications, such as swarm based routing protocols [24], fuzzy multi-objective routing (FMO), genetic algorithm based energy-efficient clustering protocol (GA-EECP), and sensor intelligence routing (SIR-NN based routing) [25]. Note that these further investigations are for future work.…”

Hybrid Fuzzy Centroid with MDV-Hop BAT Localization Algorithms in Wireless Sensor Networks

“…Recently, there have also been proposals designed to estimate the proper weights to improve the proximity error; some of these promising schemes are based on soft computing [20] as the optimization solver for science and engineering problems given the key characteristics of the approach, which are suited to imprecision, uncertainty, and approximation scenarios to achieve practicability and robustness at low cost, for example, Neural Networks (NN), Genetic Algorithms (GA), Support Vector Machines (SVM), Evolutionary Algorithms (EA), and especially Swarm Intelligence (SI), or bioinspired algorithms [9,[21][22][23]. In addition to NN and GA [24], fuzzy logic (FL) systems and metaheuristic-based approaches are commonly explored due to the simplicity, which reduces computational time complexity [25][26][27][28][29][30][31].…”

“…Note that the former two approaches were discussed in our previous work, Katekaew et al [28]. However, to enhance its performance further, with regard to a variation of signal intensities, here, we included an extra step using RSSI normalization during fuzzy Centroid stage.…”

“…These combinations were then integrated as a novel hybrid scheme called Hybrid Fuzzy Centroid and MDV-Hop BAT (E-HFCD) using an additive weight in consideration of node density. In addition to the preliminary investigation in [28], here, we intensively evaluated the impact of node density (both known and unknown nodes) and coverage (signal radius). The investigation of different topologies was also further investigated in both uniform and nonuniform scenarios.…”