On multiple access for distributed dependent sources: a content-based group testing approach

In this paper we consider the multiple access problem with distributed dependent sources. We derive the optimal designs for the case of N correlated binary sources whose data is modelled as a two-state Markov chain. The solution can be classified as a group testing technique where data values at the sensors are determined through the successive refinements of the tests over smaller groups. The tests form, progressively, an accurate map of the sensor data at the central receiver. We derive the conditions on the parameters of the data model for which the group testing approach is superior to time sharing. In contrast to standard multiple access techniques, this is the first method proposed for data retrieval from distributed dependent sources which is content-based rather than user-based.

I. Introduction

The goal of our work is to design multiple access communication strategies to transmit the information from a set of distributed dependent sources to a central receiver through a multiple access channel. In sensor networks, sensor nodes are often deployed in large scale to observe physical events or measurements from the environment. The detected events or quantized measurements at the sensors naturally classify them into groups of the same state. For example, in the binary detection problem, the sensors that have detected a certain event will be grouped into one class, while the other nodes will be grouped into another class. In the case of quantized measurements, all sensors observing data within the same quantization level also constitute a certain class. By reliably identifying the class for which each sensor resides, the central node is able to reconstruct the entire sensor field or to accurately locate the occurrence of an event.