Opportunistic Localization Scheme Based on Linear Matrix Inequality

Enabling self-localization of mobile nodes is an important problem that has been widely studied in the literature. The general conclusions is that an accurate localization requires either sophisticated hardware (GPS, UWB, ultrasounds transceiver) or a dedicated infrastructure (GSM, WLAN). In this paper we tackle the problem from a different and rather new perspective: we investigate how localization performance can be improved by means of a cooperative and opportunistic data exchange among the nodes. We consider a target node, completely unaware of its own position, and a number of mobile nodes with some self-localization capabilities. When the opportunity occurs, the target node can exchange data with in-range mobile nodes. This opportunistic data exchange is then used by the target node to refine its position estimate by using a technique based on Linear Matrix Inequalities and barycentric algorithm. To investigate the performance of such an opportunistic localization algorithm, we define a simple mathematical model that describes the opportunistic interactions and, then, we run several computer simulations for analyzing the effect of the nodes duty-cycle and of the native self-localization error modeling considered. The results show that the opportunistic interactions can actually improve the self-localization accuracy of a strayed node in many different scenarios

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an ineffcient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The First two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error

Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters.Comment: shorter version is about to appear in IEEE Transactions of Signal Processing; 22 pages, 15 figure

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled 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

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