Cost Minimization of Sensor Placement and Routing in Wireless Sensor Networks: Placement and Routing Issues in a Random Plane

Cost Minimization of Sensor Placement and Routing in Wireless Sensor Networks: Placement and Routing Issues in a Random Plane

Tata Jagannadha Swamy (Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), India) and Garimella Rama Murthy (International Institute of Information Technology Hyderabad (IIITH), India)
DOI: 10.4018/978-1-4666-9941-0.ch006
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Wireless Sensor Nodes (WSNs) are small in size and have limited energy resources. Recent technological advances have facilitated widespread use of wireless sensor networks in many real world applications. In real life situations WSN has to cover an area or monitor a number of nodes on a plane. Sensor node's coverage range is proportional to their cost, as high cost sensor nodes have higher coverage ranges. The main goal of this paper is to minimize the node placement cost with the help of uniform and non-uniform 2D grid planes. Authors propose a new algorithm for data transformation between strongly connected sensor nodes, based on graph theory.
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Literature Review

The concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided(Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002). Intuitively, a denser infrastructure would lead to a more effective sensor network. It can provide higher accuracy and has a larger aggregate amount of energy available. However, if not properly managed, a denser network can (Tubaishat & Madria, 2003). The proposed approach is aimed at optimizing the number of sensors and determining their placement to support distributed sensor networks. The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections. The proposed algorithms address coverage optimization under the constraints of imprecise detections and terrain properties. These algorithms are targeted at average coverage as well as at maximizing the coverage of the most vulnerable grid points. (Dhillon & Chakrabarty, 2003). Asymptotic optimal strip-based pattern, which is optimal to achieve node coverage and connectivity proposed (Bai, Santosh, Dong, Yun, & Ten, 2006). Optimal sensor placement patterns to achieve coverage and k-connectivity (k < 6). Note that k- connectivity can provide fault tolerance (Bai, Xuan, Yun, Lai, & Jia, 2008).

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