Wireless sensor networks, which consist of many sensor devices communicating with each other in order to sense the environment, is an emerging field in the area of wireless networking. The primary objective in these wireless networks is the efficiency of energy consumption. Since these networks consist of a large number of sensors, allowing some of the nodes to sleep intermittently can greatly increase the network lifetime. Furthermore, some applications do not require 100% coverage of the network field and allowing the coverage to drop below 100%, i.e., partial coverage, can further increase the network lifetime. A sleep scheduling algorithm must be distributed, simple, scalable and energy efficient. In this thesis, the problem of designing such an algorithm which extends network lifetime while maintaining a target level of partial coverage is investigated. An algorithm called Distributed Adaptive Sleep Scheduling Algorithm (DASSA) which does not require location information is proposed. The performance of DASSA is compared with an integer linear programming (ILP) based optimum sleep scheduling algorithm, an oblivious algorithm and with an existing algorithm in the literature. DASSA attains network lifetimes up to 89% of the optimum solution, and it achieves significantly longer lifetimes compared with the other two algorithms. Furthermore, the minimum number of sensors that should be deployed in order to satisfy a given partial coverage target with a certain probability while maintaining connectivity is computed and an ILP formulation is presented for finding the minimum number of sensors that should be activated within the set of deployed sensors.