We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox’s integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20–30 m distances between neighboring nodes. Categories and Subject Descriptors C.3 [Computer Systems Organization]: Special-Purpose and Application-Based Systems—Signal processing

Location knowledge of nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks. This paper introduces a novel iterative method ILS for node localization starting with a relatively small number of anchor nodes in a large network. At each iteration, nodes are localized using a least-squares based algorithm. The computation is lightweight, fast, and any-time. To prevent error from propagating and accumulating during the iteration, the error control mechanism of the algorithm uses an error registry to select nodes that participate in the localization, based on their relative contribution to the localization accuracy. Simulation results have shown that the active selection strategy significantly mitigates the effect of error propagation. The algorithm has been tested on a network of Berkeley Mica2 motes with ultrasound TOA ranging devices. We have compared the algorithm with more global methods such as MDS-MAP and SDP-based algorithm both in simulation and on real hardware. The iterative localization achieves comparable location accuracy in both cases, compared to the more global methods, and has the advantage of being fully decentralized.

0.1 Introduction Many wireless sensor network applications require information about the geographiclocation of each sensor node. Besides the typical application of correlating sensor readings with physical locations, approximate geographical localization is also neededfor applications such as location-aided routing [2], geographic routing [3], geographic routing with imprecise geographic coordinates [4, 5], geographic hash tables [6], andfor many data aggregation applications.

Localization is an important and extensively studied problem in ad-hoc wireless sensor networks. Given the connectivity graph of the sensor nodes, along with additional local information (e.g. distances, angles, orientations etc.), the goal is to reconstruct the global geometry of the network. In this paper, we study the problem of localization with noisy distance and angle information. With no noise at all, the localization problem with both angle (with orientation) and distance information is trivial. However, in the presence of even a small amount of noise, we prove that the localization problem is NP-hard. Localization with accurate distance information and relative angle information is also hard. These hardness results motivate our study of approximation schemes. We relax the non-convex constraints to approximating convex constraints and propose linear programs (LP) for two formulations of the resulting localization problem, which we call the weak deployment and strong deployment problems. These two formulations give upper and lower bounds on the location uncertainty respectively: No sensor is located outside its weak deployment region, and each sensor can be anywhere in its strong deployment region without violating the approximate distance and angle constraints. Though LP-based algorithms are usually solved by centralized methods, we propose distributed, iterative methods, which are provably convergent to the centralized algorithm solutions. We give simulation results for the distributed algorithms, evaluating the convergence rate, dependence on measurement noises, and robustness to link dynamics.

We consider the problem of localizing wireless nodes in an outdoor, open-space environment, using ad-hoc radio ranging measurements, e.g., 802.11. We cast these ranging measurements as a set of distance constraints, thus forming an over-determined system of equations suitable for non-linear least squares optimization. However, ranging measurements are often subject to errors, induced by multipath signals and variations in path loss, unreliable hardware or antenna connectors, or imperfection in measurement models. Such potentially large, non-Gaussian errors in the measurement data ultimately produce inaccurate localization solutions. We propose a new error-tolerant localization method, called snapinducing shaped residuals (SISR), to identify automatically “bad nodes ” and “bad links ” arising from these errors, so that they receive less weight in the localization process. In particular, SISR snaps “good nodes ” to their accurate locations and gives less emphasis to other nodes. While the mathematical techniques used by SISR are similar to robust statistics, SISR’s exploitation of the snap-in effect in localization appears to be novel. We provide analysis on the principle of SISR, illustrate errors in real-world measurements, and demonstrate a working SISR implementation in field experiments on a testbed of 37 wireless nodes, as well as show the superior performance of SISR in simulation with a larger number of nodes.

Sensor localization from only connectivity information is a highly challenging problem. To this end, our result for the first time establishes an analytic bound on the performance of the popular MDS-MAP algorithm based on multidimensional scaling. For a network consisting of n sensors positioned randomly on a unit square and a given radio range r = o(1), we show that resulting error is bounded, decreasing at a rate that is inversely proportional to r, when only connectivity information is given. The same bound holds for the range-based model, when we have an approximate measurements for the distances, and the same algorithm can be applied without any modification. 1

Abstract — Numerous wireless sensor network algorithms assume that individual sensors possess location information. However, state of the art localization algorithms often achieve acceptable performance only under restrictive assumptions. For instance, some algorithms necessitate regular sensor deployment or centralized computations. Other algorithms require a high proportion of position aware nodes or the ability to accurately infer emission distance or emission direction of received radio signals. We propose the Elastic Localization Algorithm (ELA), a distributed, scalable, robust and efficient localization algorithm. ELA only presumes that a few percent of the sensors know their location and that an estimation of the maximum communication range is available. We provide extensive simulation data describing the precision, the convergence speed, and the communication load of ELA, using networks composed of thousands of sensors. In addition, we submit ELA to testing considering the influence of maximum range and beacon position misestimation, irregular radio patterns, asynchronous nodes, packet losses, particular topologies, and sensor mobility. Our results indicate that ELA performs better than four state of the art, distributed, range-free localization algorithms, in terms of accuracy. Its low communication and execution costs make its implementation possible on resource constrained sensor nodes. The key advantage of ELA is its robustness to phenomena occurring in real wireless sensor networks, such as asymmetry, packet loss, and irregular radio ranges. I.

Abstract — In sensor network localization, multihop based approaches were proposed to approximate the shortest paths to Euclidean distances between pairwise sensors. A good approximation can be achieved when sensors are densely deployed in a convex area, where the shortest paths are close to straight lines connecting pairwise sensors. However, in a concave network, the shortest paths may deviate far away from straight lines, which leads to erroneous distance estimation and inaccurate localization results. In this paper, we propose an improved multihop algorithm which can recognize and filter out the erroneous distance estimation, and therefore achieve accurate

Abstract — Data dissemination is an indispensible protocol component for the emerging large-scale sensor networks. In this paper, we propose a secure data dissemination protocol that enhances directed diffusion to operate in the presence of compromised sensors. Our proposed solution, Secure Diffusion, utilizes a novel security primitive called location-binding keys, and exploits the available end-to-end feedback loop in Directed Diffusion. In Secure Diffusion, sensor nodes use pairwise neighbor keys to establish secure gradients, and the sink uses location-binding keys to authenticate the received sensing data. By differentiating authentic data from fabricated ones, the sink can selectively reinforce data paths and assist intermediate nodes in local reinforcement decisions to combat compromised nodes. Our security analysis shows that, in the presence of compromised nodes, Secure Diffusion can ensure both high-quality delivery of authentic data and local containment of malicious traffic. I.

In this paper, we introduce a distributed strategy for localization in a connected wireless sensor network composed of limited range sensors. Our distributed algorithm is computed through the network and provides sensor position estimation from local connectivity measurements. This work takes advantage of a conditionally and locally convex criterion that is easier to compute than the non-convex Kruskal’s Stress. In addition, no initialization is required such as estimating distances between sensors and absolute reference positions. Our iterative technique is distributed among sensors and guarantees the minimization of a global cost function. It might be used in a Mobile Ad-hoc Network framework (MANet) thanks to its fast convergence, its low computational cost and its much higher accuracy compared to state-of-the-art methods. Index Terms: localization, distributed signal processing, sensor network