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Neighbor discovery is a fundamental task for wireless networks deployment. Itis essential for setup and maintenance of networks and is typically aprecondition for further communication. In this work we focus on passivediscovery of networks operating in multi-channel environments, performed bylistening for periodically transmitted beaconing messages. It is well-knownthat performance of such discovery approaches strongly depends on the structureof the adopted Beacon Interval (BI) set, that is, set of intervals betweenindividual beaconing messages. However, although imposing constraints on thisset has the potential to make the discovery process more efficient, there isdemand for high-performance discovery strategies for BI sets that are asgeneral as possible. They would allow to cover a broad range of wirelesstechnologies and deployment scenarios, and enable network operators to selectBI's that are best suited for the targeted application and/or devicecharacteristics. In the present work, we introduce a family of novellow-complexity discovery algorithms that minimize both the Expected MeanDiscovery Time (EMDT) and the makespan, for a quite general family of BI sets.Notably, this family of BI sets completely includes BI's supported by IEEE802.15.4 and a large part of BI's supported by IEEE 802.11. Furthermore, wepresent another novel discovery algorithm, based on an Integer Linear Program(ILP), that minimizes EMDT for arbitrary BI sets. In addition to analyticallyproving optimality, we numerically evaluate the proposed algorithms usingdifferent families of BI sets and compare their performance with the passivescan of IEEE 802.15.4 w.r.t. various performance metrics, such as makespan,EMDT, energy usage, etc.

Optimized Asynchronous Passive Multi-Channel Discovery of Beacon-Enabled Networks