Abstract
Improving the quality of monitoring and guaranteeing target coverage and connectivity in energy harvesting wireless sensor networks (EH-WSNs) are important issues in near-perpetual environmental monitoring. Existing solutions only focus on the utility of coverage or energy efficient coverage by considering target connectivity for battery-powered WSNs. This paper focuses on optimizing the maximum monitoring frequency with guaranteed target coverage and connectivity in EH-WSNs. We first analyzed the factors affecting monitoring quality and the energy harvesting model. Thereafter, we presented the problem formulation and proposed the algorithm for maximizing monitoring frequency and guaranteeing target coverage and connectivity (MFTCC) that is based on graph theory. Furthermore, we presented the corresponding distributed implementation approach. On the basis of the existing energy harvesting prediction model, expensive simulations show that the proposed MFTCC algorithm achieves high average maximum monitoring frequency and energy usage ratio. Moreover, it obtains a higher throughput than existing target monitoring methods.
Highlights
Wireless sensor network (WSN) is a multihop wireless network consisting of many sensor devices and is widely applied in environmental monitoring, precision agriculture, and natural disaster relief [1, 2]
To further verify the validation of the network performance of the MFTCC algorithm, we evaluate the performance of these different algorithms by ns-2 simulations
The routing is established by the transmission path, and the transmission between the sensor nodes and the sink node utilizes a UDP connection
Summary
Wireless sensor network (WSN) is a multihop wireless network consisting of many sensor devices and is widely applied in environmental monitoring, precision agriculture, and natural disaster relief [1, 2]. E coverage problem involves the effective monitoring of targets by the optimal deployment or activation scheduling of sensor nodes, that is, the coverage problem is a performance optimization problem of ensuring that the monitoring target is covered by one or multiple sensor nodes. In this context, numerous coverage optimization algorithms have been proposed [7,8,9,10,11,12,13,14]. For the barrier coverage problem, Nguyen and So-In and Silvestri and Goss [13, 14] proposed a distributed deployment algorithm and an autonomous deployment algorithm, respectively
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