Robust neighbor discovery in multi-hop multi-channel heterogeneous wireless networks

• We present a suite of randomized neighbor discovery algorithms for cognitive radio networks in synchronous as well as asynchronous networks.
• Our algorithms guarantee completion with arbitrary high probability.
• We show that our algorithms are robust to jamming attacks under certain assumptions.

An important first step when deploying a wireless ad hoc network is neighbor discovery in which every node attempts to determine the set of nodes it can communicate within one wireless hop. In the recent years, cognitive radio (CR) technology has gained attention as an attractive approach to alleviate spectrum congestion. A cognitive radio transceiver can operate over a wide range of frequencies possibly spanning multiple frequency bands. A cognitive radio node can opportunistically utilize unused wireless spectrum without interference from other wireless devices in its vicinity. Due to spatial variations in frequency usage and hardware variations in radio transceivers, different nodes in the network may perceive different subsets of frequencies available to them for communication. This heterogeneity in the available channel sets across the network increases the complexity of solving the neighbor discovery problem in a cognitive radio network. In this work, we design and analyze several randomized algorithms for neighbor discovery in such a (heterogeneous) network under a variety of assumptions (e.g., maximum node degree known or unknown) for both synchronous and asynchronous systems under minimal knowledge. We also show that our randomized algorithms are naturally suited to tolerate unreliable channels and adversarial attacks.