# Sensor Placement and Resource Allocation for Energy Harvesting IoT Networks

## TL;DR

This paper investigates optimal sensor placement and resource allocation in energy harvesting IoT networks to minimize estimation error, considering both analog and digital transmission schemes, and introduces a randomized rounding algorithm for the complex optimization problem.

## Contribution

It presents a novel joint sensor selection, power, and bandwidth allocation method for energy harvesting IoT networks, including a new randomized rounding algorithm for non-convex optimization.

## Key findings

- Digital transmission can outperform analog with sufficient bandwidth.
- The proposed algorithm effectively handles joint sensor, power, and bandwidth selection.
- Numerical results demonstrate improved estimation accuracy with the proposed approach.

## Abstract

The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus 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. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection. Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.