Wireless Sensor Networks (WSNs) are commonly used to monitor physical or environmental parameters such as temperature, sound, velocity, etc. Such networks find application in different areas including military, environmental, medical and industrial ones. For applications that require long term monitoring, data collection with limited resources (power, bandwidth) is a challenging problem. In addressing these challenges, we study a network architecture that relies on integrating sensing and random channel access to achieve energy efficiency. Specifically, this thesis focuses on the use of WSNs for target localization and tracking. In a random access framework, distributed sensor nodes transmit data packets to the fusion center at will, maintaining a given average transmission rate. The fusion center discards erroneous packets and those packets that have collided, and uses the remaining ones to recover the target information. Target localization is formulated as a sparse recovery problem, whose solution is sought through norm-1 regularized minimization techniques. This solution feeds the subsequent tracking phase, where the knowledge of target signatures is exploited to design an adaptive algorithm of low complexity. An adaptive framework is also developed, in which loss of tracking triggers a new localization phase. System performance is illustrated through computer simulation, showing that target localization and tracking can be achieved using only a fraction of sensors' measurements, conveyed in a random access fashion.
展开▼
