Minimizing energy consumption in surveillance sensor networks using clusterization.

One of the most challenging problems in autonomous wireless sensor networks for surveillance multiple objects is to find an optimal balance between energy efficiency and measurement accuracy. Each moving object can be discovered at the same time by dozens sensors. Such a number of sensors very often is not necessary for tracking of the moving objects. The goal is to develop an algorithm which can turn on and off sensors depends on a number of objects moving through the area and their (objects) distances from sensors in proximity. This approach will increase sensor networks' energy efficiency and a number of objects, which can be monitored by the network. For each moving object the suggested algorithm creates an optimal object domain, a cluster of sensors, which are sensing this object and, at the same time, which are sensing a minimal number of other moving objects. Initial information for building of each object domain is based on a matrix of relationships A = {a (i,j) = 1 if sensor (i) and sensor (j) are sensing the same object and a (i,j) = 0 otherwise}. Two approaches can be considered for optimization energy efficiency of surveillance sensor networks: dynamic and static sensors' clusterizations. The dynamic clusterization requires a real time recalculation of the object domain, when the object is moving to another location [1-3]. Network management protocols, which are based on the dynamic clusterization, require additional energy for their functioning. The authors introduce an approach which allows optimizing a structure of wireless sensor networks in the off-line mode. The goal of this approach is to find an optimal number of sensors and their allocations from viewpoints of energy efficiency and measurement accuracy during a designing stage. A possible location of the moving object is considered as a Chebyshev point of convex polygon, which includes the optimal object domain. Because prices of sensors are falling, a number of sensors and their allocations now can serve as network optimization factors. The suggested approach is illustrated by an example of wireless sensor networks for monitoring of moving objects through a protected area.