Localization method for low-power wireless sensor networks

Context awareness is an important issue in ambient intelligence to anticipate the desire of the user and, in consequence, to adapt the system. In context awareness, localization is very important to enable a responsive environment for the users. Focusing on this issue, this paper presents a localization system based on the use of Wireless Sensor Networks devices. In contrast to a traditional RFID, these devices offer the possibility of a collaborative sensing and processing of environmental information. The proposed system is a range-free localization algorithm that uses fuzzy inference to process the RSSI measurement and to estimate the position of mobile devices. The main goal of the algorithm is to reduce the power consumption and the cost of the devices, especially for the mobiles ones, maintaining the accuracy of the inferred position

Collaborative Localization in Wireless Sensor Networks via Pattern Recognition in Radio Irregularity Using Omnidirectional Antennas

In recent years, various received signal strength (RSS)-based localization estimation approaches for wireless sensor networks (WSNs) have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments

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

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

PASSIVE TIME SYNCHRONIZATION IN SENSOR NETWORKS USING OPPORTUNISTIC FM RADIO SIGNALS

ABSTRACT Time synchronization is a critical piece of infrastructure for any wireless sensor network. It is necessary for applications such as audio localization, beam-forming, velocity calculation, and duplicate event detection. All of which require the coordination of multiple nodes. Recent advances in low-cost, low-power wireless sensors have led to an increased interest in large-scale networks of small, wireless, low-power sensor nodes. Because of the more stringent power and cost requirements that this technology is driving, current time synchronization techniques must be updated to capitalize on these advances. One time synchronization method developed specifically for wireless sensor networks is Reference Broadcast Synchronization. In RBS, a reference broadcast is transmitted to sensor nodes that require synchronization. Be recording the time of arrival, nodes can then use those time stamps to synchronize with each other. This project aimed to make the RBS system even more robust, energy efficient, and cost effective by replacing the reference broadcast with an ambient RF signal (FM, TV, AM, or satellite signals) already prevalent in the environment. The purpose of this project was to demonstrate the viability of using Opportunistic RF synchronization by 1.) quantifying error, 2.) applying this synchronization method in a real world application, and 3.), implementing a wireless sensor network using Android smart phones as sensor nodes. Many of the objectives for the project were successfully completed. For convenience and economic reasons, an FM signal was chosen as the reference broadcast. FM Radio Synchronization error was then quantified using local FM Radio stations. The results of this experiment were very favorable. Using 5 second segments for correlation, total error was found to be 0.208±4.499μs. Using 3 second segments, average error was 2.33 ± 6.784μs. Using 400ms segments, synchronization error was calculated to be 4.76 ± 8.835μs. These results were comparable to sync errors of methods currently in widespread use. It was also shown that Opportunistic RF Synchronization could be used in real world applications as well. Again FM was the RF signal of choice. FM Radio Synchronization was tested in an Audio Localization experiment with favorable results. Implementation of an Android Wireless Sensor Network according to our specifications, however, could not be achieved. HTC EVO 4G’s were programmed to communicate through TCP / IP network connections, record audio with a microphone, and to record FM Radio streams from the EVO’s internal FM radio. Although recording these two sources separately as different data tracks was successful, simultaneous recording of these streams could not be accomplished (simultaneous recording is essential for Opportunistic RF Synchronization). Although the Android smart phone implementation was not a total success, this project still provided data that supported the practical use of Opportunistic RF Synchronization.AFRLNo embarg

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