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

Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data

A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance

Energy Analysis of Received Signal Strength Localization in Wireless Sensor Networks

This paper presents the investigation of energy demands during localization of wireless nodes in ad-hoc networks. We focus on the method based on the received signal strength (RSS) to estimate the distances between the nodes. To deal with the uncertainty of this technique, statistical methods are used. It implies more measurement samples to be taken and consequently more energy to be spent. Therefore, we investigate the accuracy of localization and the consumed energy in the relation to the number of measurement samples. The experimental measurements were conducted with IRIS sensor motes and their results related to the proposed energy model. The results show that the expended energy is not related linearly to the localization error. First, improvement of the accuracy rises fast with more measurement samples. Then, adding more samples, the accuracy increase is moderate, which means that the marginal energy cost of the additional improvement is higher

Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

The paper aims at localization of the anchor node(fixed node) by pursuit nodes (movable node) in outdoor location.Two methods are studied for node localization. The first methodis based on LNSM (Log Normal Shadowing Model) technique tolocalize the anchor node and the second method is based on Hy-brid TLBO (Teacher Learning Based Optimization Algorithm)-Unilateral technique. In the first approach the ZigBee protocolhas been used to localize the node, which uses RSSI (ReceivedSignal Strength Indicator) values in dBm. LNSM technique isimplemented in the self-designed hardware node and localizationis studied for Outdoor location. The statistical analysis usingRMSE (root mean square error) for outdoor location is done anddistance error found to be 35 mtrs. The same outdoor locationhas been used and statistical analysis is done for localizationof nodes using Hybrid TLBO-Unilateral technique. The Hybrid-TLBO Unilateral technique significantly localizes anchor nodewith distance error of 0.7 mtrs. The RSSI values obtained arenormally distributed and standard deviation in RSSI value isobserved as 1.01 for outdoor location. The node becomes 100%discoverable after using hybrid TLBO- Unilateral technique

Locating sensors with fuzzy logic algorithms

In a system formed by hundreds of sensors deployed in a huge area it is important to know the position where every sensor is. This information can be obtained using several methods. However, if the number of sensors is high and the deployment is based on ad-hoc manner, some auto-locating techniques must be implemented. In this paper we describe a novel algorithm based on fuzzy logic with the objective of estimating the location of sensors according to the knowledge of the position of some reference nodes. This algorithm, called LIS (Localization based on Intelligent Sensors) is executed distributively along a wireless sensor network formed by hundreds of nodes, covering a huge area. The evaluation of LIS is led by simulation tests. The result obtained shows that LIS is a promising method that can easily solve the problem of knowing where the sensors are located.Junta de Andalucía P07-TIC-0247

A Bayesian strategy to enhance the performance of indoor localization systems

This work describes the probabilistic modelling af a Bayesian-based mechanism to improve location estimates of an already deployed location system by fusing its outputs with low-cost binary sensors. This mechanism takes advantege of the localization captabilities of different technologies usually present in smart environments deployments. The performance of the proposed algorithm over a real sensor deployment is evaluated using simulated and real experimental data

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial