Wireless Sensor Networks are emerging as a key sensing technology, with diverse military and civilian applications. In these networks, a large number of sensors perform distributed sensing of a target field. Each sensor is a small battery-operated device that can sense events of interest in its sensing range and can communicate with neighboring sensors. A sensor cover is a subset of the set of all sensors such that every point in the target field is in the interior of the sensing ranges of at least k different sensors in the subset, where k is a given positive integer. The lifetime of the network is the time from the point the network starts operation until the set of all sensors with non-zero remaining energy does not constitute a sensor cover. An important goal in sensor networks is to design a schedule, that is, a sequence

Abstract – A simple system for measuring the peer to peer radio frequency time of flight between two identical sensor motes for distance measurement is presented. This scheme uses a 2.4 GHz radio, simple real time processing, and offline range extraction. Methods for reducing error from clock offset and multipath propagation are presented and implemented on prototype hardware. Measurement results are presented including measurements taken in a coal mine. Typical ranging accuracies are between 1 mRMS and 3 mRMS. 1

Abstract—In this paper we characterize the bounds on localization accuracy in signal strength based localization. In particular, we provide a novel and rigorous analysis of the relative receiver-transmitter geometry and the effect of this geometry on the potential localization performance. We show that uniformly spacing sensors around the target is not optimal if the sensortarget ranges are not identical and is not necessary in any case. Indeed, we show that in general the optimal sensor-target geometry for signal strength based localization is not unique. I.

Sensor location information is a prerequisite to the utility of most sensor networks. In this paper we present a robust and low-complexity algorithm to self-localize and orient sensors in a network based on angle-of-arrival (AOA) information. The proposed non-iterative subspace-based method is robust to missing and noisy measurements and works for cases when sensor orientations are either known or unknown. We show that the computational complexity of the algorithm is O(mn 2), where m is the number of measurements and n is the total number of sensors. Simulation results demonstrate that the error of the proposed subspace algorithm is only marginally greater than an iterative maximum-likelihood estimator (MLE), while the computational complexity is two orders of magnitude less. Additionally, the iterative MLE is prone to converge to local maxima in the likelihood function without accurate initialization. We illustrate that the proposed subspace method can be used to initialize the MLE and obtain near-Cramér-Rao performance for sensor localization. Finally, the scalability of the subspace algorithm is illustrated by demonstrating how clusters within a large network may be individually localized and then merged. Categories and Subject Descriptors C.2.4 [Computer-communication networks]: Distributed systems; C.3 [Special-purpose and application-based systems]: Signal processing systems

Abstract — The LANdroids project requires robots to autonomously localize, track, and follow (a task also known as tethering) other robots or humans in an unknown environment with limited sensing abilities. In this paper, we present a localization and tethering approach that relies solely on wireless signal strength and robot odometry without requiring any known reference points in the domain. We introduce a datadriven, probabilistic model that maps received signal strength (RSS) values to real-world distance distributions and embed this model in a grid-based localization algorithm that successfully performs the LANdroids tethering task. We furthermore show, that it is possible to improve localization through the addition of a compass sensor and inter-robot information sharing. I.

Abstract—Many applications in wireless sensor networks require sensor nodes to obtain their absolute or relative geographical positions. Although various localization algorithms have been recently proposed, most of them require nodes to be equipped with range-determining hardware to obtain distance information. In this paper, we propose a concentric anchor beacon (CAB) localization algorithm for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes can determine in which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. We also propose two heuristics, namely CAB with Equal Area and CAB with Equal Width, to determine the transmitting power levels of the beacons. Simulation results show that the estimation error is reduced by half when anchors transmit beacons at two different power levels instead of at a single power level. CAB also gives a lower estimation error than some other range-free localization schemes (e.g., Centroid and Approximated Point-In-Triangulation) when the anchor-to-node range ratio is less than 4. Index Terms—Localization, position estimation, wireless sensor networks. I.

Abstract—This paper addresses the sensor selection problem which is a very important issue where many sensors are available to track a target. In this problem, we need to select an appropriate group of sensors at each time to perform tracking in a wireless sensor network (WSN). As the theoretical tracking performance is bounded by posterior Cramer-Rao lower bound (PCRLB), it is used as a criterion to select sensors. Based on the PCRLB, sensor selection algorithms with and without sensing range constraint are developed. Without sensing range limit, exhaustive enumeration is first adopted to search all possible combinations for sensor selection. To reduce complexity of enumeration, second, we restrict the selected sensors to be within a fixed area in the WSN. With sensing range constraint, a circle will be drawn with the help of communication range for sensor selection. In a similar manner, two approaches, namely, selecting all sensors inside the circle or using enumeration to select sensors within the circle are presented. The effectiveness of the proposed methods is validated by computer simulation results in target tracking for WSNs. 1.

We propose a lightweight localisation approach for supporting distance and range queries in ad hoc wireless sensor networks. In contrast to most previous localisation approaches we use a distance graph as spatial representation where edges between nodes are labelled with distance constraints. This approach has been carefully designed to satisfy the requirements of a concrete application scenario with respect to the spatial queries that need to be supported, the required accuracy of location information, and the capabilities of the target hardware. We show that this approach satisfies the accuracy requirements of the example application using simulations. We describe the implementation of the algorithms on wireless sensor nodes.

Abstract — We investigate the fundamental performance limits of target localization, where a network of sensors observe and cooperatively estimate the 2D location of a target. Taking a general view of a sensor as any device whose observations depend statistically on target position, we consider the binary hypothesis testing problem of choosing between the correct target location and an incorrect location at a distance r from it, given the outputs of all sensors in the network. By considering a random placement of sensors in an infinitely large sensing area, we obtain upper and lower bounds on the error probability of this hypothesis testing problem. The error bounds depend only on the type of sensor and are independent of the detailed geometry of the sensor network deployment. This provides a compact comparison of the localization performance of sensors whose characteristics might differ widely (e.g., received signal strength, proximity and time of arrival sensors). Also the bounds decrease exponentially with the density of sensors, and the rate of decrease is shown to have a simple geometric interpretation. I.

Estimating the positions of sensor nodes is a fundamental and crucial problem in ad hoc wireless sensor networks (WSNs). In this paper, an accurate node localization method for WSNs is devised based on the weighted least squares technique with the use of time-of-arrival measurements. Computer simulations are included to evaluate the performance of the proposed approach by comparing with the classical multidimensional scaling method and Cramér-Rao lower bound. 1.