Improved Adaptive Group Testing Algorithms with Applications to Multiple Access Channels and Dead Sensor Diagnosis

TL;DR

This paper introduces improved adaptive group testing algorithms with non-binary test outcomes, enhancing efficiency in conflict resolution on multiple access channels and dead sensor diagnosis in wireless networks.

Contribution

The paper presents novel adaptive group testing algorithms with non-binary results, outperforming previous methods and applicable to MAC conflict resolution and dead sensor detection.

Findings

Algorithms are more efficient than previous group testing methods.

Applicable to conflict resolution in multiple access channels.

Effective for diagnosing dead sensors in wireless networks.

Abstract

We study group-testing algorithms for resolving broadcast conflicts on a multiple access channel (MAC) and for identifying the dead sensors in a mobile ad hoc wireless network. In group-testing algorithms, we are asked to identify all the defective items in a set of items when we can test arbitrary subsets of items. In the standard group-testing problem, the result of a test is binary--the tested subset either contains defective items or not. In the more generalized versions we study in this paper, the result of each test is non-binary. For example, it may indicate whether the number of defective items contained in the tested subset is zero, one, or at least two. We give adaptive algorithms that are provably more efficient than previous group testing algorithms. We also show how our algorithms can be applied to solve conflict resolution on a MAC and dead sensor diagnosis. Dead sensor diagnosis poses an interesting challenge compared to MAC resolution, because dead sensors are not locally detectable, nor are they themselves active participants.