With fast advances in camera sensor devices and wide applications of wireless camera sensor networks (WCSNs), optimizing the tradeoff between power consumption and coverage rate of WCSNs attracts a great deal of research attention. In contrast to 2D WCSNs, 3D WCSNs capture more accurate and comprehensive information for surveillant applications. The percolation theory has been proved to be powerful and effective in characterizing the exposure path prevention using 2D WCSNs. While percolation theory can be potentially extended into 3D WCSNs to improve the power and coverage performances, there are still many new challenges remaining unsolved. On the other hand, the clustering algorithm is widely cited as an efficient power saving and interference mitigation technique for WCSNs. However, how to integrate the clustering technique with 3D percolation theory in WCSNs is still an open problem. To overcome the aforementioned challenges, in this paper we propose the 3D percolation theory-based exposure-path prevention scheme for optimizing the tradeoff between power consumption and coverage rate over clustered WCSNs. First, we apply and extend the bond-percolation theory to derive the optimal density of camera sensors deployed in 3D WCSNs subject to the minimum exposure-path prevention probability constraint. Then, we apply the mutual entropy to analyze the dependency among 3D neighboring camera sensors, justifying the bond-percolation theory in 3D WCSNs. Finally, we apply the new low energy adaptive clustering hierarchy (LEACH) architecture into our 3D WCSNs for power saving and interference mitigation. The conducted extensive simulations show that our proposed schemes outperform the other existing schemes in optimizing the tradeoff between power consumption and coverage rate over 3D WCSNs.
