Centroid based 3D localization technique using RSSI with a mobile robot

Knowledge of sensor node 3D location in a sensor network is more important, because many practical applications needs to know the location of sensor data source. This paper presents a new technique for finding indoor 3D location of a sensor node by using Received Signal Strength Indication (RSSI). Proposed localization algorithm is derived from centroid algorithm with composition of empirical path loss model. It gives less error in estimating 3D location of sensor node in a sensor network when compared to its actual location. Algorithm has been implemented and analysed by using IITH motes and a Mobile Robot

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications. In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution. Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices. In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed. The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions. Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms. With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.Junta de Andalucía P07-TIC-0247

Relative Span weighted localization of uncooperative nodes in wireless networks

Increasingly ubiquitous wireless technologies require novel localization techniques to pinpoint the position of an uncooperative node, whether the target be a malicious device engaging in a security exploit or a low-battery handset in the middle of a critical emergency. Such scenarios necessitate that a radio signal source be localized by other network nodes efficiently, using minimal information. We propose two new algorithms for estimating the position of an uncooperative transmitter, based on the received signal strength (RSS) of a single target message at a set of receivers whose coordinates are known. As an extension to the concept of centroid localization, our mechanisms weigh each receiver's coordinates based on the message's relative RSS at that receiver, with respect to the span of RSS values over all receivers. The weights may decrease from the highest RSS receiver either linearly or exponentially. Our simulation results demonstrate that for all but the most sparsely populated wireless networks, our exponentially weighted mechanism localizes a target node within the regulations stipulated for emergency services location accuracy

3D Localization Techniques using RSSI with a Mobile Robot

Knowledge of sensor node 3D location in a sensor network is more important, because many practical applications needs to know the location of sensor data source. This report presents three new technique for finding indoor 3D location of a sensor node by using Received Signal Strength Indication (RSSI). First, Centroid based 3D localization technique is derived from centroid algorithm with composition of empirical path loss model. This algorithm has been implemented and analyzed by using IITH motes and a Mobile Robot

Human Sensing and Indoor Location: From coarse to fine detection algorithms based on consumer electronics RF mapping

Depois de definida toda a arquitectura para captura dos dados de Wi-Fi, foi desenvolvido um software para a criação de uma base de dados contendo informações de posição e de força de sinal. Inicialmente, foi desenvolvido um algoritmo preliminar de localização. Posteriormente, implementou-se um algoritmo de classificação para mapear os dados recebidos dos sensores com os guardados anteriormente.Foram feitos testes usando estes algoritmos, e comparados os resultados.Em fase de escrita do documento