Wireless sensor networks (WSNs) are emerging as a key enabling technology for applications domains such as military, homeland security, and environment. However, a major constraint of these sensors is their limited battery. In this dissertation we examine the problem of maximizing the duration of time for which the network meets its coverage objective. Since these networks are very dense, only a subset of sensors need to be in "sense" or "on" mode at any given time to meet the coverage objective, while others can go into a power conserving "sleep" mode. This active set of sensors is known as a cover. The lifetime of the network can be extended by shuffling the cover set over time. In this dissertation, we introduce the concept of a local lifetime dependency graph consisting of the cover sets as nodes with any two nodes connected if the corresponding covers intersect, to capture the interdependencies among the covers. We present heuristics based on some simple properties of this graph and show how they improve over existing algorithms. We also present heuristics based on other properties of this graph, new models for dealing with the solution space and a generalization of our approach to other graph problems.

Wireless sensor networks are made up of a large number of sensors deployed randomly in an ad-hoc manner in the area/target to be monitored. Due to their weight and size limitations, the energy conservation is the most critical issue. Energy saving in a wireless sensor network can be achieved by scheduling a subset of sensor nodes to activate and allowing others to go into low power sleep mode, or adjusting the transmission or sensing range of wireless sensor nodes. In this thesis, we focus on improving the lifetime of wireless sensor networks using both smart scheduling and adjusting sensing ranges. Firstly, we conduct a survey on existing works in literature and then we define the sensor network lifetime problem with range assignment. We then propose two completely localized and distributed scheduling algorithms with adjustable sensing range. These algorithms are the enhancement of distributed algorithms for fixed sensing range proposed in the literature. The simulation results show that there is almost 20 percent improvement of network lifetime when compare with the previous approaches.

Airburst projectiles increase the effectiveness of air defense, by forming clouds of small pellets. In this work, in order to evaluate the effectiveness of airburst projectiles, Single Shot Kill Probability (SSKP) is computed at different burst distances by using three lethality functions defined from different measures of effectiveness. These different measures are target coverage, number of sub-projectile hits on the target and kinetic energy of sub-projectiles after burst. Computations are carried out for two different sub-projectile distribution patterns, namely circular and ring patterns. In this work, for the determination of miss distance, a Monte Carlo simulation is implemented, which uses Modified Point Mass Model (MPMM) trajectory equations. According to the results obtained two different distribution patterns are compared in terms of effectiveness and optimum burst distance of each distribution pattern is determined at different ranges.