Wireless sensor networkingis a research area which is growing very fast and has attracted too much attention in the past few years. This has been caused by advances in the development of low-cost sensor devices equipped with wireless network interfaces. Since a large number of sensor nodes are required to be linked together in these networks, there will be new challenges alongside with new applications coming up for such networks. There are a lot of applications in many fields defined for wireless sensor networks including medical, industrial, and home networks. One of the most important applications of wireless sensor networks is target tracking. In this application the sensor nodes in the network are used to sense and detect a target and track it until it goes out of the monitored field. These nodes are wireless and usually run on batteries and this is the most important power source they have. In some cases wireless nodes are deployed in harsh places with minimum accessibility so minimizing the power consumption is a very important issue which has attracted a lot of attention through years. Hence, in this research, we tried to reduce power consumption in wireless sensor networks with the application of target tracking by introducing two new algorithms. At first we introduced different mobility models which are used in simulation of wireless ad-hoc networks to iect the performance of the proposed algorithms. These models were iected according to the average speed of mobile node and its direction. The goal of this analysis is iection of the relation between the average speed of mobile node and its tendency to change its direction in various mobility models. We concluded that the time a mobile node would spend on a constant direction cannot be predicted according to its speed. Other movement parameters of mobility models were also studied. Average traveled distance in constant direction in different speeds was calculated. The other calculated parameter was the pattern of locations where the mobile node changed its direction. The proposed algorithm is a cluster based target tracking algorithm which is based on dynamic clustering. It is a semi localized cluster head selection algorithm which consumes much less power compared to its distributed version. First a basic clustering algorithm was introduced and then we modified this algorithm to create our proposed algorithm. In basic algorithm there is a distributed process which selects cluster head and cluster members cooperatively. In the proposed algorithm cluster head and cluster members of next cluster are chosen by current cluster head. The simulations show a noticeable reduction in power consumption in terms of reduction in sent and received messages and reduction in Keywords Target Tracking, Mobility Models, Dynamic Clustering, Target Movement History