Localization in Wireless Sensor Networks by Hidden Markov Model

The Sensor Network Localization problem deals with estimating the geographical location of all nodes in Wireless Sensor Network focusing on those node sensors to be equipped with Global Positioning System (GPS), but it is often too expensive to include GPS receiver in all sensor nodes. In the contributed localization method, sensor networks with non-GPS nodes derive their location from limited number of GPS nodes. The nodes are capable of measuring received signal strength that could benefit from the interactions of nodes with mixed types of sensors for WSN. In this paper, localization is achieved by Hidden Markov Model (HMM) and compared with particle filter to infer that Received Signal Strength Indication (RSSI) sensors are better suited for localization when location data need to propagate through multiple hop by using Semi-Markov Smooth (SMS) mobility model to estimate error, energy, control overhead with respect to node density, time and transmission range.