Max-Lifetime Sleep Scheduling for Surveillance Applications of Wireless Sensor Networks

The rapidly increasing capabilities and low costs of sensor devices have made wireless sensor networks possible for a wide range of applications such as environmental monitoring, industrial automation, healthcare, and object tracking and localization. The sensors are typically powered by batteries with limited capacity. Thus, energy is the most critical resource in wireless sensor networks and maximizing the network lifetime under severe energy constraints is one of the prime goals in wireless sensor networks. An effective technique for prolonging the network lifetime is to deploy sensor nodes redundantly and keep only a small number of sensors active at a time to fulfill the duty while switching the rest to the sleep mode. The challenging issue is how to schedule the sensors to sleep or work, such that the network lifetime can be maximized. In this project, the researchers take comprehensive algorithmic studies of the max-lifetime sleep scheduling problem for various surveillance duties such as connected coverage and k-coverage. By exploiting the geometric nature of wireless sensor networks, they propose to develop polynomial-time constant-approximation algorithms. Furthermore, based on the approximation algorithms, they will develop efficient distributed protocols for sensor nodes to collaborate with each other to perform the required duty.