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International audienceIndoor localization has been the subject of many studies and still remains a very difficult issue due to the presence of multiple paths, fading, shadowing and timely variations in the environment. This paper studies the use of the Received Signal Strength Indicator (RSSI) metric for range estimation in a wireless sensor network. We perform experimental studies which show that, despite a moderate antenna directivity (confirmed by experience in an anechoic chamber), the signal attenuation depends on both of the anchor node and the target node antennas orientation. We also show that the log normal shadow model, often used to model indoor propagation, is not accurate enough to capture irregularities in an office environment. In a 4mx3m office with five anchor nodes and one target node, a confidence interval of about 1m is reached by a maximum likelihood-based localization, only slightly better than random localization

Research efforts at network design in the area of Autonomic Networking and Self-Managing Networks have reached a maturity level that forms a strong foundation toward standardization of architectural principles of the Self-Managing Future Internet. Therefore, an Industry Specification Group (ISG) on Autonomic network engineering for the self-managing Future Internet (AFI) has been established under the auspices of the European Telecommunications Standards Institute (ETSI). Upon its creation, the main stakeholders agreed to harmonize the previous developments and the most recent trends in the very vital field of autonomic and self-managing networks. Particularly, the life cycle of AFI is structured by Work Items providing the foundation for ETSI Group Specifications. So far AFI has been focusing on scenarios, use cases, and requirements for the autonomic/selfmanaging Future Internet, as well as on architectural reference model for autonomic networking and self-management. Most recently, AFI has continued with a new Work Item on requirements analysis and specification of implementation-oriented solutions for autonomics and self-management. At the same time, as a part of the global ecosystem, AFI is establishing strategic liaisons with the standards developing organizations and research community

This paper presents and evaluates a method to localize devices that communicate using a wireless network. The distances that separate a blind node, willing to determine its position, and a set of anchor nodes, that know their locations, are evaluated using the signal attenuation (RSSI) measured on data packets. However, multipath effects, frequent in an indoor scenario, introduce randomness in signal propagation, reducing localization accuracy. We propose to use a maximum likelihood estimator on a two-modes Gaussian Mixture model approach to detect and exclude outlier measurements. We evaluate and compare this method using experimental measurements.