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- On the Use of Perfect Sequences and Genetic Algorithms for Estimating the Indoor Location of Wireless SensorsPublication . Ferreira, Marco; Bagarić, J.; Lanza-Gutierrez, Jose M.; Mendes, Silvio; Pereira, João; Gomez-Pulido, Juan A.Determining the indoor location is usually performed by using several sensors. Some of these sensors are fixed to a known location and either transmit or receive information that allows other sensors to estimate their own locations. The estimation of the location can use information such as the time-of-arrival of the transmitted signals, or the received signal strength, among others. Major problems of indoor location include the interferences caused by the many obstacles in such cases, causing among others the signal multipath problem and the variation of the signal strength due to the many transmission media in the path from the emitter to the receiver. In this paper, the creation and usage of perfect sequences that eliminate the signal multipath problem are presented. It also shows the influence of the positioning of the fixed sensors to the precision of the location estimation. Finally, genetic algorithms were used for searching the optimal location of these fixed sensors, therefore minimizing the location estimation error.
- Planning the Deployment of Indoor Wireless Sensor Networks Through Multiobjective Evolutionary TechniquesPublication . Lanza-Gutierrez, Jose M.; Gomez-Pulido, Juan A.; Mendes, Silvio; M. Ferreira; Pereira, J. S.This work deals with how to efficiently deploy an indoor wireless sensor network, assuming a novel approach in which we try to leverage existing infrastructure. Thus, given a set of low-cost sensors, which can be plugged into the grid or powered by batteries, a collector node, and a building plan, including walls and plugs, the purpose is to deploy the sensors optimising three conflicting objectives: average coverage, average energy cost, and average reliability. Two MultiObjective (MO) genetic algorithms are assumed to solve this issue, NSGA-II and SPEA2. These metaheuristics are applied to solve the problem using a freely available data set. The results obtained are analysed considering two MO quality metrics: hypervolume and set coverage. After applying a statistical methodology widely accepted, we conclude that SPEA2 provides the best performance on average considering such data set.