Proofs and Performance Evaluation of Greedy Multi-Channel Neighbor Discovery Approaches
Niels Karowski, Konstantin Miller

TL;DR
This paper introduces low-complexity, optimized algorithms for passive neighbor discovery in multi-channel wireless networks, significantly improving discovery times and efficiency over standard methods like IEEE 802.15.4.
Contribution
It presents a family of algorithms that generate efficient listening schedules for neighbor discovery, optimizing various performance metrics and compatible with common wireless standards.
Findings
Schedules outperform Passive Scan by up to 4x in mean discovery time
Schedules discover up to 300% more neighbors over time
Analytical and simulation results validate the effectiveness of proposed algorithms
Abstract
The accelerating penetration of physical environments by objects with information processing and wireless communication capabilities requires approaches to find potential communication partners and discover services. In the present work, we focus on passive discovery approaches in multi-channel wireless networks based on overhearing periodic beacon transmissions of neighboring devices which are otherwise agnostic to the discovery process. We propose a family of low-complexity algorithms that generate listening schedules guaranteed to discover all neighbors. The presented approaches simultaneously depending on the beacon periods optimize the worst case discovery time, the mean discovery time, and the mean number of neighbors discovered until any arbitrary in time. The presented algorithms are fully compatible with technologies such as IEEE 802.11 and IEEE 802.15.4. Complementing the…
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Taxonomy
TopicsNeural Networks and Applications · Algorithms and Data Compression · Advanced Data Compression Techniques
