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Nowadays, the rapidly developed Internet of Things requires the ability handling information efficiently to deal with the intelligent applications. Wireless sensor networks (WSNs), which act as an important interface between physical environment and Internet of Things, have been applied in numerous applications. As a kind of important application of WSNs, the continuous objects boundary detection is popular in industry. However, the long-term maintenance for the traditional WSNs, which are used to monitor the leakage of continuous objects, is expensive. Thus, we use sparse WSNs to address this issue. But, the inaccuracy of the sparse network is a big problem while the information of continuous objects is used to arrange retreat path for people. To access this problem, we propose our mechanism, which used hybrid network to compromise the accuracy and cost of maintenance. The sensing holes will be detected by using Voronoi diagram, before the network starts to work. After the static sensor nodes get the value of the toxic air, the mechanism can calculate the high variation location, which give weights to the sensing holes, in the static sensor networks. Thus, the sensing holes, which selected by both spatial and data variation factors will be list in a target nodes list for the mobile sensor node. Finally, the optimal path considering both distance and priority for the mobile sensor will be plan out. Experimental evaluation shows that there is an optimal amount of the static nodes decided by the sensing radius and the size of area. And it reduces the energy consumption by the static networks.
Xiang et al. (Sun,) studied this question.