Data-aided Sensing where Communication and Sensing Meet: An Introduction

TL;DR

This paper introduces data-aided sensing (DAS), a method that optimizes data collection in IoT by intelligently selecting devices for upload, reducing communication overhead and enabling efficient tasks like federated learning.

Contribution

It presents a novel framework for integrating sensing and communication in IoT through centralized and distributed DAS approaches, improving data collection efficiency.

Findings

Centralized DAS optimizes device selection for data upload.

Distributed DAS employs random access based on local measurements.

DAS enables efficient data collection for IoT applications like federated learning.

Abstract

Since there are a number of Internet-of-Things (IoT) applications that need to collect data sets from a large number of sensors or devices in real-time, sensing and communication need to be integrated for efficient uploading from devices. In this paper, we introduce the notion of data-aided sensing (DAS) where a base station (BS) utilizes a subset of data that is already uploaded and available to select the next device for efficient data collection or sensing. Thus, using DAS, certain tasks in IoT applications, including federated learning, can be completed by uploading from a small number of selected devices. Two different types of DAS are considered: one is centralized DAS and the other is distributed DAS. In centralized DAS, the BS decides the uploading order, while each device can decide when to upload its own local data set among multiple uploading rounds in distributed DAS. In distributed DAS, random access is employed where the access probability of each device is decided according to its local measurement for efficient uploading.