Towards Enhanced Location and Sensing Services for the Internet of Things

Abstract

Location discovery (i.e., Localization) and sensing coverage services in Wireless Sensor Networks (WSNs) have received significant attention from the Internet of Things (IoT) research community. The usage of WSNs within IoT mandates taking into account IoT characteristics when considering sensing coverage. These characteristics include heterogeneity, large scale, dynamicity, and multiple ownership. Anchors are typically used to enable localization in IoT settings. Anchor misplacement or errors in anchor location readings can cause significant disruption to location-based services in IoT. This thesis investigates the anchor misplacement problem, provides an analytical study of both localization, and sensing coverage under the presence of anchor misplacement. We utilize two tools from computational geometry Voronoi Diagram (VD) and Delaunay Triangulation (DT) to partition the target region in order to make the problem solvable and easy to follow. We also borrow a graph-theoretic tool called Graph History to more closely understand the impact of anchor misplacement on sensing coverage. These tools allow us to locally study, analyze, and detect the impact of anchor misplacement in its vicinity. We analyze the problem of anchor misplacement, its impact on localization and sensing coverage, and we also identify new types of sensing coverage holes. We also present heuristics to mitigate the impact of anchor misplacement and improve the reliability and accuracy of WSN services. Our research approach and solution for the anchor misplacement problem can be utilized in a multiplicity of localization and sensing coverage applications regardless of the sensors or deployment types including IoT. Results show that our proposed algorithms are far more conducive to IoT context. They provide higher detection rates of misplaced anchors and sensing coverage holes, and more effective mitigation which result in higher enhancement of IoT services.