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The fundamental operation in mobile wireless communication is to establish links between neighbor devices. The neighor discovery problem is non-trivial, especially under an urban environment. Various background scenarios, e.g., inside vehicles, at open-air squares, and in the supermarket, lead to complex discovery requirements. Discovery among fast moving devices requires an immediate exchange of emergency messages (minimum latency), while low-speed devices in crowded environments pay more attention to energy efficiency. Typical neighbor discovery protocols give solutions in a relatively stable scenario, which are not suited for the different environments in urban life. In this paper, we first proposed a non-integer framework to include all existing protocols. Then, a decentralized adaptive neighbor discovery protocol, named Anole, was designed under the framework. The protocol leveraged the genetic and similarity algorithms to be aware of and adapt to various scenarios with an appropriate discovery strategy. In the evaluation, we builtbuild different urban scenarios with real taxi and transportation card datasets in Shanghai. Meanwhile, an NS-3 simulator is applied to model device mobility and wireless communication. From the results, our protocol discovered 19 % more links with similar energy consumption than typical protocols.

Anole: An Adaptive Neighbor Discovery Under Urban Environments