Data collection for distributed surveillance sensor networks in disaster-hit regions
The objective of many applications with the surveillance missions in wireless sensor networks is to provide long-term monitoring of the specific environments, such as disaster-hit regions. These applications usually perform continuous monitoring without any maintenance, even if some sensor nodes fail. A significant challenge when designing the data collection approaches for such systems is that the conventional communication protocols for wireless sensor networks would present low efficiency, since the network topology changes rapidly due to the node failure. Thus the sensor nodes in such systems should use an automatic transmission approach to disseminate their sensed data to the sink in a distributed manner. In this paper, we propose a novel Coding-based Probabilistic Routing (CPR) to address this specific problem of data collection for distributed surveillance sensor networks in disaster-hit regions. CPR dynamically adapts to node failure to collect the maximum data in any given time and chooses an optimal probabilistic routing to decrease the transmission consumption. The extensive simulation results are presented to show that CPR outperforms other strategies.