Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Power-aware Node Deployment in Wireless Sensor Networks
SUTC '06 Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing -Vol 1 (SUTC'06) - Volume 01
Energy balanced data propagation in wireless sensor networks
Wireless Networks
Analytical modeling and mitigation techniques for the energy hole problem in sensor networks
Pervasive and Mobile Computing
General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies
IEEE Transactions on Mobile Computing
Promoting Heterogeneity, Mobility, and Energy-Aware Voronoi Diagram in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Avoiding Energy Holes in Wireless Sensor Networks with Nonuniform Node Distribution
IEEE Transactions on Parallel and Distributed Systems
Coverage and Lifetime Optimization of Wireless Sensor Networks with Gaussian Distribution
IEEE Transactions on Mobile Computing
A Lifetime Enhancing Node Deployment Strategy in WSN
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
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Energy is one of the scarcest resources in wireless sensor network (WSN). Therefore, the need to conserve energy is of utmost importance in WSN. There are many ways to conserve energy in such a network. One fundamental way of conserving energy is judicious deployment of sensor nodes within the network area so that the energy flow remains balanced throughout the network. This avoids the problem of occurrence of 'energy holes' and ensures prolonged network lifetime. In this paper, we have identified intrinsic features of Archimedes' Spiral and shown its suitability to model the layered WSN area. Next we have transformed the same Spiral in its discrete form and proposed this as a deployment function. A node deployment algorithm is developed based on this deployment function. Further, we have identified necessary constraints involving different network parameters to ensure coverage, connectivity and energy balance of the entire network. Performance of the deployment scheme is evaluated in terms of energy balance and network lifetime. Both qualitative and quantitative analyses are done based on these two performance metrics. Finally the scheme is compared with an existing Gaussian distribution-based deployment scheme and the results confirm the superiority of our scheme in respect to both the metrics over the existing one.