Wildlife Assessment Using Wireless Sensor Networks

Abstract

The endangered species always drew the attention of the scientific community since their disappearance would cause irreplaceable loss. To help these species to survive, their habitat is protected by the laws of environmental protection. Sometimes this protection is not enough, because their natural evolution is the main cause of their disappearance. However, to save them, it is sometimes possible to transfer them elsewhere that should be similar to their previous habitat to avoid disturbing the balance of wildlife. To model a habitat, several parameters must be of interest and are generally defined by experts. This is the case for the number of singing birds which will be studied in this paper. Today, advances in sensor technology enable the monitoring of species and their habitat at a very low cost. Indeed, the increasing sophistication of wireless sensors bids opportunities that enable new challenges in a lot of areas, including the surveillance one. Progress in their miniaturization leads to micro-sensors of size of cubic millimeters which, used in large quantity, produce huge amounts of data. This paper promotes the use of sensors for monitoring bird endangered in their habitat. Actual methods for counting endangered birds use mainly human labor and because they are not really comprehensive leads to poor estimation. The use of sensors deployed in critical environments can help the census of these species and even generate new data on their customs. Among the challenges that the use of the sensor technology enable, energy efficiency is the most critical for these wireless networks since battery depletion totally disables a sensor. In addition, designing algorithms for wireless networks stems from the distributed computer science domain with limited devices. Memory space and computational power are often of a magnitude less thanmiles than their desktop counterparts. This paper investigate the problem and proposes to approximate the number of birds by geometric means derived in a graph problem. Our paper is organized as follows. First, Section 2 provides an overview of techniques generally used to estimate the locations of multiple sources with a unknown sensor network. Section 4 details our heuristics used to count birds. Section 5 introduces a distributed algorithm for counting birds. Experimentation confirms the effectiveness of our counting systems in Section 6. Then we conclude in Section 7 and gives an overview of our future work. 1