A Wireless Sensor Network (WSN) is a dense network of autonomous devices (or motes) with sensors that cooperatively monitor some physical or environmental conditions. These devices are resource constrained -limited memory, power and computational resources. Thus, any algorithm developed for WSN should be deigned such that the algorithm consumes the resources as minimal as possible. The problem addressed in this thesis is developing a low-complexity algorithm for intrusion detection in the presence of clutter arising from moving vegetation, using Passive Infra-Red (PIR) sensors. The algorithm is based on a combination of Haar Transform (HT) and Support-Vector-Machine (SVM) based training. The spectral signature of the waveforms is used to separate between the intruder and clutter waveforms. The spectral signature is computed using HT and this is fed to SVM which returns an optimal hyperplane that separates the intruder and clutter signatures. This hyperplane obtained by offline training is used online in the mote for surveillance. The algorithm is field-tested in the Indian Institute of Science campus. Based on experimental observations about the PIR sensor and the lens system, an analytical model for the waveform generated by an intruder moving along a straight line with uniform velocity in the vicinity of the sensor is developed. Analysis on how this model can be exploited to track the intruder path by optimally positioning multiple sensor nodes is provided. Algorithm for tracking the intruder path using features of the waveform from three sensors mounted on a single mote is also developed.