On the efficient and fast response for sensor deployment in sparse wireless sensor networks

Wireless sensor networks have recently become new techniques and popular research issues. A wireless sensor network consists of a large number of sensor nodes that have the capabilities of sensing, computing and wireless transmission. Wireless sensor networks (namely WSNs) assist people in working under dangerous environments, provide long-term target observations and track on moving objects. Consequently, WSNs decrease risk and increase efficiency. Although WSNs have been studied extensively, several problems should be addressed, such as sensor-deployment policy, data aggregation/fusion issue, and data transmission issue. An efficient sensor-deployment approach could decrease cost, minimize transmission delay and reduce time complexity. Most studies have proposed the probability-based sensor-deployment policies to monitor an overall area. However, not the entire network is interested to be sensed/monitored. Monitoring of an entire area brings several disadvantages: (1) high cost of placing large number of sensors, (2) long delay of data transmission, (3) slow response and (4) unnecessary data aggregation. Furthermore, previous works were lack of considering the difference between the sensing and the transmission radii, and then yield inaccurate analysis. This work thus proposes an efficient sensor placement approach (namely ESP) for a sparse interested area with considering of obstructers that block the data transmission and sensing signal. Additionally, the issue of different radii of sensing and transmission is analyzed in detail. Numerical results demonstrate that the proposed ESP approach requires the least number of sensor nodes under various network sizes and different number of obstacles. Simulation results indicate that the number of sensor nodes decreases when the sensing or transmission radius increases. The running time of ESP, O(K^2), is also analyzed, which is better than that of the probability-based approaches, O(N^2), where K is the number of interested grids and N is the number of grids.

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