210 Int. J. Sensor Networks, Vol. 12, No. 4, 2012 Copyright © 2012 Inderscience Enterprises Ltd. Dynamic topology construction of wireless sensor network using computational geometric approach Sarbani Roy* and Nandini Mukherjee Department of Computer Science and Engineering, Jadavpur University, Kolkata 700 032, India Email: sarbani.roy@ieee.org Email: nmukherjee@cse.jdvu.ac.in *Corresponding author Abstract: Monitoring a large area with wireless sensor networks (WSNs) requires a very large number of sensor nodes which entails more energy consumption. One of the main design challenges in the WSNs is energy efficiency to prolong the network operable lifetime. Generally, most of the energy is spent for radio communication between sensor nodes. Another important requirement of WSN is that it should be self-organising, i.e. sensing ranges and transmission ranges are dynamically restructured with changing topology. Moreover, sensor nodes with variable sensing and transmission ranges facilitate less energy consumption and enhance the capacity of WSN significantly. An effective approach for energy conservation is turning off extraneous nodes, while the remaining nodes stay active to provide continuous monitoring service. An efficient planning of WSN using this approach can control the energy consumption of the whole network. In this paper, by using computational geometry theoretic, we propose a general algorithmic framework for dynamic topology construction of WSN for a given environmental monitoring application. Keywords: WSN; wireless sensor network; Voronoi diagram; Delaunay triangulation; coverage; node scheduling. Reference to this paper should be made as follows: Roy, S. and Mukherjee, N. (2012) ‘Dynamic topology construction of wireless sensor network using computational geometric approach’, Int. J. Sensor Networks, Vol. 12, No. 4, pp.210–222. Biographical notes: Sarbani Roy joined as a faculty member in Department of Computer Science and Engineering, Jadavpur University, India in 2006. She received the PhD degree in Engineering from Jadavpur University, Kolkata, India in July, 2008, and the MTech degree in Computer Science & Engineering, the MSc degree in Computer and Information Science and the BSc Honours degree in Computer Science from University of Calcutta, Kolkata, India in 2002, 2000 and 1998 respectively. Her research interests are in the area of grid and cloud computing, distributed computing and wireless sensor networks. She is a member of the IEEE. Nandini Mukherjee joined as a faculty member in Department of Computer Science and Engineering, Jadavpur University, India in 1992. In 1996 she received Commonwealth Scholarship tenable at UK. She completed her PhD in Computer Science from University of Manchester, UK in 1999. She has become a Professor in Jadavpur University in 2006. Currently she is the director of School of Mobile Computing and Communication, Jadavpur University. Her research interests are in the area of high performance parallel computing, grid computing and wireless sensor networks. She is a senior member of IEEE. 1 Introduction In recent time, wireless sensor networks (WSNs) have attracted lot of research interests for their applicability in national security, disaster management, surveillance, military, healthcare, and environmental monitoring applications. WSN consists of a number of low-cost sensors scattered in a geographical area of interest and connected by a radio interface. Sensors gather information from the environment and send it to a gateway or sink nodes. Usually, sensor nodes are equipped with limited energy and computational resources which keep their cost low. The set of nodes that are deployed in a sensor network can be either a homogeneous or heterogeneous group of nodes. A homogeneous group is a group in which all of the nodes have the same capabilities. A heterogeneous group is one in which some nodes are more powerful than the other nodes. Deployment of sensor nodes is one of the major issues in WSN environment. Generally, deployment of sensor nodes in WSN can be categorised as either a dense deployment or a sparse deployment (Mulligan et al., 2010). In case of dense deployment, sensor nodes are placed near