Collaborative positioning applied to network optimization in IoT environments (TELCO4IOT)

Description

The latest forecasts on the increase in connected devices (IoT paradigm) and the traffic exchanged between them, stress the need for the efficient management of the resources available in wireless networks. Location of mobile devices is an essential information to achieve that goal. Most of the devices are able to obtain their position, but this process tend to strangle the devices in the terms of the resources (battery consumption, generated traffic, etc.). Moreover this information requires to be constantly updated, which applies to all the devices connected to the network.

Nowadays, diversifying and densifying access networks to absorb the expected growth in traffic, boost the possibilities of geo-positioning solutions: improving accuracy, latency, robustness of the system, etc. However, this redundancy in terms of networks generates two clear challenges: 1) the management of an overwhelming amount of data in devices and network equipment  and 2) the management of the network infrastructure in an efficient and flexible way, in order to minimize the impact on energy waste and CO₂ emissions.

This project addresses for some of these aspects, a fact that represents an important challenge and that frames it in an area that has not yet been exploited and that, therefore, has enormous potential in the field of research. Firstly, this project proposes the design of collaborative positioning algorithms for mobile devices, suitable for use in scenarios where GPS can not be used (e.g., indoors, urban canyons, devices powered by batteries, etc. .), with a triple objective: 1) to maximize the quality of service, 2) to minimize energy consumption and 3) to maximize the scalability of the location system. The coordinated optimization in this scenario is important, since the three objectives cited tend to be detrimental to each other: maximizing efficiency, for example, means increasing device consumption and, often, limiting the scalability of the system.

Secondly, the concept of location middleware is used to deal with the massive and constant positioning of IoT devices, the management of data and its accessibility from the Internet (Internet of Data). Machine learning algorithms are applied to estimate the present and future positions of the network nodes, as well as the data mining algorithms to generate social and/or economic knowledge, which can be turned into new services or applied to the optimization of the network itself. Third, the location of IoT nodes can be used to study a geocasting solution that combines minimum latency in the delivery of data, robustness to changes in the network topology, low error rate and minimum power consumption. In addition, the data generated after applying the data mining processes will be used to identify the individual and group mobility patterns of the network devices to optimize the provision of services. Lastly, this project proposes the creation of a positioning platform, in which to implement and evaluate the solutions presented in the project. 

Period

Start date: 01/01/2019 
End date: 30/09/2022
Status: Ongoing

Funding

Funded by the ERDF and the Spanish Government

Budget

Project: 55.660,00 €
Spanish Government contribution: 55.660,00 €

Participants

Enrica Valeria Zola (GRXCA), Israel Martin Escalona (GRXCA), Francisco Barceló Arroyo (GRXCA), Francisco Javier Ozon Gorriz (GRXCA), Sergio Machado Sanchez (GRXCA).

Further information

Further information can be found here.