Reliable prediction and monitoring of dynamically changing environments are essential for a safer and healthier society. Sensor networks play a significant role in fulfilling this task. The two fundamental aspects of environmental sensor networks (ESNs) include the need for accuracy as well as low-complexity and energy efficient sensing modalities. One of the wonted challenges of ESNs is high resolution environment monitoring in the presence of sensing overheads (such as number of sensors, battery life, maintenance). Limiting the number of sensing resources yet still guarantee a desired resolution of the unknown environmental field necessitates resource-efficient sensing framework. On the other hand, the physical behavior of many environmental fields can be predicted using statistical models. Cognizance of the physical properties of environmental fields motivates opportunistic sensor placement to dynamically monitor the environment. In this thesis, we present signal processing methods for resource-efficient environment monitoring exploiting the physical properties of environmental fields. We mainly focus on a general class of environmental fields that obey standard physical properties (such as diffusion, advection) responsible for the spatio-temporal evolution of the field. We first discuss different mathematical representations to link the sensor measurements with the unknown field intensities. Statistical characterizations of different physical properties of environmental fields such as space-time correlation and the dynamics of field propagation are also discussed. A comprehensive environment monitoring framework is presented that encompasses sensor management, measurement accumulation, and field estimation. We propose a spatio-temporal sensor management method which can be applied for stationary as well as non-stationary environmental fields. We formulate a unified optimization framework that provides the number and the most informative sensing locations to deploy sensors guaranteeing a desired estimation accuracy in terms of the mean square error (MSE). The main objective is to implement “sparse-sensing” in an environment monitoring perspective while also achieving a prescribed accuracy. We also propose different strategies to solve the proposed optimization problem for both online and offline applications. We present a practical example of environment monitoring, i.e., dynamic rainfall monitoring using rain-induced attenuation measurements from commercial microwave links. We describe different methods to incorporate some physical properties of rainfall (such as the physics behind the rainfall propagation, spatial effects such as sparsity, correlation etc.) in the dynamic monitoring setup. We also compare the estimation performance of the developed technique with standard estimators such as an extended Kalman filter (EKF). We extend the proposed sparsity-enforcing spatio-temporal sensor management method for a broader class of environmental fields consisting of a combination of both stationary and non-stationary components. We develop an algorithm for sensor placement followed by field estimation using a kriged Kalman filter (KKF), which is used for the estimation of the aforementioned type of field. We also consider the scenario, where the prior physical knowledge regarding the environmental field is either unavailable or inaccurate. In these circumstances, we discuss some methods to estimate the underlying dynamics of the field, i.e., the state/process model using the observed measurements. While estimating the process model, we consider both the scenario, where the true value/ground truth of the field is known as well as the scenario where it is unknown.
|Award date||15 Oct 2018|
|Publication status||Published - 2018|