An optimal network dimensioning and initial energy assignment minimizing the monetary cost of a heterogeneous WSN

Abstract

In this paper, a novel method is proposed to dimension a randomly deployed heterogeneous Wireless Sensor Network (WSN) of minimum monetary cost satisfying minimum coverage and minimum lifetime requirements. We consider WSNs consisting of two different types of nodes clusterheads and ordinary sensor nodes, randomly deployed over a sensing field. All devices are assumed to be stationary and have identical sensing capabilities. However, the clusterheads are more energetic and powerful in terms of processing and communication capabilities compared to sensor nodes. For such a network, finding minimum cost WSN problem is not a trivial one, since the distribution of the mixture of two different types of devices and the batteries with different initial energies in each type of device primarily determine the monetary cost of a WSN. Therefore, we formulated an optimization problem to minimize the monetary cost of a WSN for given coverage and lifetime requirements. The proposed optimization problem is solved for a certain scenario and the solution is validated by computer simulations. © 2009 IEEE

Dimensioning of Linear and Hierarchical Wireless Sensor Networks for Infrastructure Monitoring with Enhanced Reliability

“…Transmit power efficient sensor placement schemes for linear WSNs mainly focus on minimization of average energy consumption per node and target the objective of maximizing network lifetime [7][8][9] [10]. The limitations to such solutions lie in defining optimization problem with limited network parameters related solely to energy consumption that would in most cases prove inconvenient for real time deployments [11][12] [13].…”

“…Major concerns in the performance of multi-hop linear wireless sensor data acquisition system for structural health monitoring lie in timely detection of events with high fidelity [23][21] utilizing a suitable placement strategy. Traffic aware relay and slave nodes in tree topology can be intelligently placed using methods relying on mapping the solution into Euclidian distance space taking monetary cost as a tuple of coverage parameters [13][16] [17]. Apart from successful event detection in signal processing domain, path losses in terms of incurred packet errors for low channel SNR severely affect the reliability with which data can be communicated over the span of the network [18][19].…”