Felgueiras, Portugal
Felgueiras, Portugal

Time filter

Source Type

Mestre P.,CITAB UTAD | Reigoto L.,UTAD | Coutinho L.,UTAD | Correia A.,CM UTAD | And 2 more authors.
Lecture Notes in Electrical Engineering | Year: 2013

Fingerprinting is a location technique, based on the use of wireless networks, where data stored during the offline phase is compared with data collected by the mobile node during the online phase. When this location technique is used in a real-life scenario there is a high probability that the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node being located and the ones previously stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure. © 2013 Springer Science+Business Media Dordrecht.


Mestre P.,CITAB UTAD | Coutinho L.,UTAD | Reigoto L.,UTAD | Matias J.,CM UTAD | And 3 more authors.
Advances in Intelligent and Soft Computing | Year: 2011

Indoor location systems cannot rely on technologies such as GPS (Global Positioning System) to determine the position of a mobile terminal, because its signals are blocked by obstacles such as walls, ceilings, roofs, etc. In such environments the use of alternative techniques, such as the use of wireless networks, should be considered. The location estimation is made by measuring and analysing one of the parameters of the wireless signal, usually the received power. One of the techniques used to estimate the locations using wireless networks is fingerprinting. This technique comprises two phases: in the first phase data is collected from the scenario and stored in a database; the second phase consists in determining the location of the mobile node by comparing the data collected from the wireless transceiver with the data previously stored in the database. In this paper an approach for localisation using fingerprinting based on Fuzzy Logic and pattern searching is presented. The performance of the proposed approach is compared with the performance of classic methods, and it presents an improvement between 10.24% and 49.43%, depending on the mobile node and the Fuzzy Logic parameters. © 2011 Springer-Verlag Berlin Heidelberg.


Matias J.,CM UTAD | Mestre P.,CM UTAD | Correia A.,CM UTAD | Couto P.,CM UTAD | And 2 more authors.
Advances in Intelligent and Soft Computing | Year: 2011

Penalty and Barrier methods are normally used to solve Nonlinear Optimization Constrained Problems. The problems appear in areas such as engineering and are often characterized by the fact that involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. This means that optimization methods based on derivatives cannot be used. A Java based API was implemented, including only derivative-free optimization methods, to solve both constrained and unconstrained problems, which includes Penalty and Barriers methods. In this work a new penalty function, based on Fuzzy Logic, is presented. This function imposes a progressive penalization to solutions that violate the constraints. This means that the function imposes a low penalization when the violation of the constraints is low and a heavy penalization when the violation is high. The value of the penalization is not known in beforehand, it is the outcome of a fuzzy inference engine. Numerical results comparing the proposed function with two of the classic penalty/barrier functions are presented. Regarding the presented results one can conclude that the proposed penalty function besides being very robust also exhibits a very good performance. © 2011 Springer-Verlag Berlin Heidelberg.

Loading CM UTAD collaborators
Loading CM UTAD collaborators