Non Uniform Grid Based Cost Minimization and Routing in Wireless Sensor Networks

Introduction

A sensor network field is composed of a large number of sensor nodes that are deployed for different applications. These sensors can observe and respond to phenomena in the physical environment. Such sensor networks are referred to as wireless sensor networks. Wireless sensor networks provide flexibility in deployment and maintenance; exploit the ability of wireless networks to be deployed in several dynamic environments. Connectivity is one of the crusial issues for wireless sensor networks, as information nodes to be sent to be data processing centres (Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002). Different sensor routing mechanisims and sensor placement problems by (Tubaishat & Madria, 2003). Sensor placement also affects the resource management in wireless sensor networks (Dhillon & Chakrabarty, 2003). Asymptotic optimal strip-based pattern, which is optimal to achieve coverage and 2-connectivity was also proposed by (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). If number of sensors within a sensor network field is more, then one grid point may be covered by several numbers of sensors (Chen, Li, Sun, 2007). Virtual force algorithm on node deployment (Coskun, 2008) and Tabu search for maximization of coverage (Aitsaadi, Achir, Boussetta, Pujolle, & Tabu, 2009). Generally all the sensors within the same network field have identical sensing capabilities (Zhang & Hou, 2005). In this process most of the sensors sense the same information influences on lifetime of the sensor network and also increase the total cost of the sensor network field. Minimizing the number of sensors can take the form of an art gallery problem (O’Rourke, 1987). Good routing protocol reduces energy consumption of the network (Sohraby, Minoli, & Znati, 2007). For this reason cost minimization is required without losing information in the sensor field. From the above considerations we develop new algorithm for cost minimization and effective routing (Wang, Hu, & Tseng, 2005). The developed approximate algorithm is on large number of random points, such that solution is obtained in considerable time (Biagioni, & Sasaki, 2003; Sasaki, 2003). Effective routing also reduces the overall system cost (Chong, & Kumar, 2003). Effective routing also reduces energy consumption of the network (Heinzelman, Chandrakasan, & Balakrishnan, 2000)

Cost minimization mechanism process for a given surveillance region (grid points) and sensors of different types (with different ranges and costs) follows from two items. First one determines the placement and type of sensors in the sensor field such that the desired coverage is achieved and cost is minimized. And the second one is how to place the sensors in a field such that every grid point is covered by minimum number of sensors. Node Placement in Sensor Networks reduces routing overheads (Ishizuka, & Aida, 2004). Optimeal sensor placement for effective routng explained (Lin & Chiu, 2005). After the optimal placement is known, we develop an algorithm to see how data is transmitted from one node to another.