Advanced sensor networks are needed in order to meet the increasing needs of IoT applications, such as automated surveillance, environmental monitoring, smart cities, and so on. Optimal sensor placement, i.e., to select the best subset of sensing locations out of a large set of available locations, keeping in mind the network infrastructure and the inference task, forms an important sensor network design task.

The focus in this project is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account.

System model

A linear measurement model is considered to model observations at the IoT devices.

An information theoretic approach is used to model the transmission rate (between the IoT devices and the fusion center), observation quantization, and encoding.

Numerical experiment and results

Candidate sensing locations are distributed uniformly over a grid to measure the sources as shown in Fig.2a at the fusion center. By forming and solving a numerical optimization problem, sensor locations and, their power rating and spectral bandwidth are optimized subject to a prescribed power and spectral budget as shown in Fig. 2b

An information theoretic approach is used to model the transmission rate (between the IoT devices and the fusion center), observation quantization, and encoding.