This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors’ estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an Indoor Positioning System for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.

This paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL–ETRI Indoor Localization Competition

Localization is one of the main pillars for indoor services. However, it is still very difficult for the mobile sensing community to compare state-of-the-art indoor positioning systems due to the scarcity of publicly available databases. To make fair and meaningful comparisons between indoor positioning systems, they must be evaluated in the same situation, or in the same sets of situations. In this paper, two databases are introduced for studying the performance of magnetic field and Wi-Fi fingerprinting based positioning systems in the same environment (i.e., indoor area). The “magnetic” database contains more than 40,000 discrete captures (270 continuous samples), whereas the “Wi-Fi” one contains 1,140 ones. The environment and both databases are fully detailed in this paper. A set of experiments is also presented where two simple but effective baselines have been developed to test the suitability of the databases. Finally, the pros and cons of both types of positioning techniques are discussed in detail.

The need for constant monitoring of environmental conditions has produced an increase in the development of wireless sensor networks (WSN). The drive towards smart cities has produced the need for smart sensors to be able to monitor what is happening in our cities. This, combined with the decrease in hardware component prices and the increase in the popularity of open hardware, has favored the deployment of sensor networks based on open hardware. The new trends in Internet Protocol (IP) communication between sensor nodes allow sensor access via the Internet, turning them into smart objects (Internet of Things and Web of Things). Currently, WSNs provide data in different formats. There is a lack of communication protocol standardization, which turns into interoperability issues when connecting different sensor networks or even when connecting different sensor nodes within the same network. This work presents a sensorized platform proposal that adheres to the principles of the Internet of Things and theWeb of Things. Wireless sensor nodes were built using open hardware solutions, and communications rely on the HTTP/IP Internet protocols. The Open Geospatial Consortium (OGC) SensorThings API candidate standard was used as a neutral format to avoid interoperability issues. An environmental WSN developed following the proposed architecture was built as a proof of concept. Details on how to build each node and a study regarding energy concerns are presented.