A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Abstract

Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

High uncertainty aware localization and error optimization of mobile nodes for wireless sensor networks

“…Recently, in [19], a fault-filtering method is adopted to identify fault-reference nodes so that localization error can be minimized. This technique mainly focuses on distance estimation for the implementation of the fault-filtering method based on the communication range of the nodes.…”

A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm

“…Hence, to estimate the locations of sensor nodes in random deployment, localization algorithms are used [18]. But, the locations obtained from localization algorithms are only estimated locations with errors [19]. These errors impact the efficiency of the deployment algorithms.…”