# Backscatter Data Collection with Unmanned Ground Vehicle: Mobility Management and Power Allocation

## TL;DR

This paper proposes an energy-efficient data collection scheme using unmanned ground vehicles with backscatter communication, balancing movement and communication energy to optimize data collection from IoT devices.

## Contribution

It introduces a joint mobility and power allocation model for UGV-based backscatter data collection, along with an algorithm achieving minimum energy consumption.

## Key findings

- Lower noise power favors minimal UGV movement.
- Higher noise power encourages UGV to move closer to IoT devices.
- The proposed algorithm balances movement and communication energy effectively.

## Abstract

Collecting data from massive Internet of Things (IoT) devices is a challenging task, since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this work proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem unfortunately involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. Simulation results show that if the noise power is small (e.g., -100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., -60 dBm), the UGV should spend more motion energy to get closer to IoT users.