Abstract
Background: Wireless Sensor Networks (WSNs) consist of sensor nodes which are able to sense a variety of physical phenomenon. It is a well-known fact that in most of the real world applications related to WSNs, the deployment is random as manual deployment is not possible due to harsh environments and other complexities. Therefore, there is no prior knowledge about the position of the sensor nodes that are deployed to form a wireless sensor network. In order to overcome this aspect related to position estimate of sensor nodes vari-ous localization schemes and algorithms have been proposed. The schemes and algorithms proposed so far also offer advantages like optimal routing, energy conservation, security, topology control besides providing the accurate position estimation of deployed sensor nodes. This paper explicitly presents a survey on the existing learning and non-learning based algorithms that have special role in localization of sensor nodes for WSNs.
Methods: Comparative analysis of prominent learning and non-learning based algorithms. The analysis is based in terms of physical measurement, accuracy, computation and hardware cost and energy efficiency.
Result: Detailed reporting of learning and non-learning based algorithms with their sub-categories. The study and survey of various algorithms throw light on their applicability and use for WSNs.
Conclusion: Characteristics exhibited by the variants of the discussed algorithms are featured in order to make them application centric and cost effective.
Keywords: WSNs, ToA, Centroid, HiRLoc, RSSI, Kalman Filter, PSO.
Graphical Abstract
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