AbstractsComputer Science

Highly accurate localization in wireless sensor networks (WSNs) has been considered as one of the most significant challenges in wireless sensor networks. Significant efforts have been made in order to uplift the solutions to this challenging problem as, localization of a signal source in a wireless sensor network is now appealing for a range of real life applications, including emergency services, navigational systems, and civil/military surveillance. For instance, a couple of seconds of delay in identifying a location of an injured victim could create life threatening situations. During the last few years, several techniques have been proposed to provide an accurate estimation of the location of an unknown sensor node. Received-signal-strength (RSS), angle-of-arrival (AOA), time-difference-of-arrival (TDOA) and time-of-arrival (TOA) to name a few. While these techniques are quick to produce fairly accurate location estimation, they suffer effects from non-line-of-site (NLOS) conditions, unavailability of one or more sensors, or the requirement of expensive receivers, all of which would lead to poor or no location estimation at all. Motivated by the above observations, this thesis aims to develop two novel localization algorithms for localization in WSNs. Furthermore, it suggests to use Dempster-Shafter theory as an efficient tool for localization purposes in WSNs.In this thesis two new localization schemes are proposed. One proposed algorithm for localization in WSNs simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurements. The accuracy and convergence reliability of the proposed hybrid scheme is also enhanced by incorporating RSS measurements from Wi-Fi networks via cooperative communications between Wi-Fi and sensor networks. Simulation results show that the proposed hybrid positioning approach significantly outperforms each individual method. The advantages of the proposed scheme, which include providing high location accuracy, fast convergence, low complexity implementation, and low power consumption, make it an attractive localization solution via WSNs. A low cost data fusion technique for node positioning that fuses different parameters obtainable from signal measurements, such as received-signal-strength (RSS), angle, and time observations is proposed next. Such a tool enables additional network-based parameters (e.g. hop-counts, delays, etc.) to be easily incorporated to enhance the accuracy of the classification process. The proposed classifier records an improved accuracy of 83.7% from its initial 38.3% accuracy in locating the cell associated with a sensor node at low computational complexity.