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Abstract-We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Molé uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable "cloud"-based fingerprint distribution system. Molé's localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.

Abstract-Existing cellular technologies are rapidly coming to their performance limits. This is due not only to the growth in data traffic and in the number of connected terminals, but also because we are on the verge of new era, where everyone and everything will be connected, with more demanding and varied requirements that cannot be satisfied by current networks. On account of this, efforts are being made all over the world to design new wireless technologies that will support the expected demands for the next decade. These technologies, embraced under the commercial name of 5 th generation, are currently being studied, and in this tutorial paper we will give an overview of the main trends that are likely to make their way in the next-generation standards.