Éditeur(s) : HAL CCSD ACM
Résumé : International audience
Applications over wireless sensor networks are growing quickly. Traditional software development tools are not well adapted to this technology. In particular, adequate testing methodologies are required. Many issues have not been formally addressed as energy-conservation, congestion control, reliability data dissemination, security. In this paper, we present a pragmatic approach to detect some kind of anomalies based on defined scenarios on wireless sensor networks. A scenario is considered as a set of events which should happen on the network in an ordered way. We discuss a formal architecture able to perform these scenarios over the wireless sensor networks and to raise alarms if necessary. This methodology has been implemented on a prototype and has been experimented with various examples. This contribution is a first attempt for a formal testing methodology for wireless sensor networks.
Proceedings of the 6th ACM workshop on QoS and security for wireless and mobile networks

Droits : info:eu-repo/semantics/OpenAccess
hal-00596118
https://hal.archives-ouvertes.fr/hal-00596118
https://hal.archives-ouvertes.fr/hal-00596118/document
https://hal.archives-ouvertes.fr/hal-00596118/file/Q2sWinet_10.pdf
DOI : 10.1145/1868630.1868641

Éditeur(s) : HAL CCSD ACM
Résumé : International audience
In this study, we use an adapted version of Pitsburgh-like learning classifier system to perform over classification tasks. The Adapted Pittsburgh Classifier System, enhanced with a new mechanism, allows us to consider the classification problems and their treatment by a given LCS in a different manner. Our aim is to exhibit elements in order to prove that, using the action covering mechanism, this system is able to build an inner map of a given classification learning sample. In the context of this paper, this map is built using an intrisic property of Pittsburgh-like CS: the use of various collections of classifiers amongst a unique population.
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
Portland,Oregon, United States

hal-00520609
https://hal.archives-ouvertes.fr/hal-00520609
DOI : 10.1145/1830761.1830823

Éditeur(s) : HAL CCSD ACM
Résumé : International audience
In 2008 a scientific paper written by Butz and al. investigated Matching in Learning Classifier Systems. Matching represents at least 65% of the computational time when executing a classifier system as reported in Llora and al. We propose to modify that encoding using a fast and accurate matching replacing standard matching algorithm for character-based classifier systems.
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation
Portland, Oregon, United States

hal-00520610
https://hal.archives-ouvertes.fr/hal-00520610
DOI : 10.1145/1830761.1830820

Éditeur(s) : HAL CCSD ACM New York, NY, USA
Résumé : International audience
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning litterature to assess the performances of learning methods when facing problems containing non markov situations. Through this study, we discuss on the performances of the APCS on maze type environments and also of the efficiency of an improvement of the APCS learning method inspired from the XCS : the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular sub-optimal solutions to these maze problem. This statement is shown through a comparison of the results obtained by the XCS, XCSM and XCSMH on distinct maze problems with these obtained by the APCS.
Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation
Atlanta, GA, United States

hal-00520607
https://hal.archives-ouvertes.fr/hal-00520607
DOI : 10.1145/1388969.1389013

Éditeur(s) : HAL CCSD ACM
Résumé : International audience
Environmental resources (e.g., air quality, water quantity) are needed to understand fundamental questions such as global change. Such resources are often collected from sensors, including humans acting as sensors. Tools have emerged to manage such data in the form of time series and, in particular, the Sensor Observation Service (SOS) which offers a framework based on predefined relational database schema. Environmental observatories can be built using such frameworks, allowing to address specific key scientific questions by collecting and sharing large-scale environmental data. However, the strict schema of SOS database makes it difficult to integrate some data that cannot be directly mapped to the schema. Guidelines and best practices are offered in the literature in order to reuse standards from the Semantic Web but they do not cover all needs. In particular, they do not help to reflect the fact that a single environmental database can lead to several SOS models. Since being aware of these multiple possibilities is crucial for a better use of the observatories, we argue that some extensions of the existing works are required. In this paper, we thus propose an extension of existing vocabularies to achieve this goal. Our contribution is illustrated on the real case of the Lebanese-French O-LiFE environmental observatory.
6th ACM International Conference on Web Intelligence, Mining and Semantics
Nimes, France

lirmm-01381082
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381082
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381082/document
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01381082/file/presentationwims_s1_2.pdf
DOI : 10.1145/2912845.2912856

Éditeur(s) : HAL CCSD ACM
Résumé : International audience
This study presents two algorithms that count birds with wireless sensors equipped with microphones. Audio inputs are parametrized to get some kind of fingerprints which are used to recognize the song of the birds in a classification process. Afterward, counting algorithms derive an estimate of the number of singing birds in the habitat. Unlike traditional approaches which leverage the audio waveform to get the location of the targets, we use a trilateration technique on graphs derived from the detection of the birds. These graphs exhibit nice properties which enable the finding of the estimate in polynomial time. Further, experiments are conducted and confirmm the efficiency of our counting algorithms even in the presence of noise.
Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
New York, United States

hal-00602263
https://hal.archives-ouvertes.fr/hal-00602263
DOI : 10.1145/1641876.1641891