Locating the nodes: cooperative localization in wireless sensor networks

Abstract

ccurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. Low-power wireless sensors may be many hops away from any other sensors with a priori location information.In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then form a map of the network. Various application requirements (such as scalability, energy efficiency, and accuracy) will influence the design of sensor localization systems. In this article, we describe measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, we show how to calculate a Cramér-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. This is a useful tool to help system designers and researchers select measurement technologies and evaluate localization algorithms. We also briefly survey a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research. INTRODUCTIONDramatic advances in RF and MEMS IC design have made possible the use of large networks of wireless sensors for a variety of new monitoring and control applications [1]- [5]. For example, smart structures will actively respond to earthquakes and make buildings safer; precision agriculture will reduce costs and environmental impact by watering and fertilizing only where necessary and will improve quality by monitoring storage conditions after harvesting; condition-basedA maintenance will direct equipment servicing exactly when and where it is needed based on data from wireless sensors; traffic monitoring systems will better control stoplights and inform motorists of alternate routes in the case of traffic jams; and environmental monitoring networks will sense air, water, and soil quality and identify the source of pollutants in real time.Automatic localization of the sensors in these wireless networks is a key enabling technology. The overwhelming reason is that a sensor's location must be known for its data to be meaningful. As an additional motivation, sensor location information (if it is accurate enough) can be extremely useful for scalable, "geographic" routing algorithms. Note also that location itself is often the data that needs to be sensed; localization can be the driving force for wireless sensor networks in applications such as warehousing and manufacturing logistics.To make these applications viable with possibly vast numbers of sensors, device costs will need to be low (from a few dollars to a few cents depending on the application), sensors will need to last for years or even decades ...

Peer-to-Peer Localization in Urban and Indoor Environments

“…4, the proximate points due to the 4 corner-diffracted paths are close to each other as compared with those involving threebounce building reflected paths. As the noise increases to σ d = 4 m, σ θ = σ φ = 8 • , our proposed scheme has an accuracy of about 39 m as compared to 85 m of Seow and Tan's Line Segment Intersection in [32] for a mobile device located at MD 1 The improvement is about 54%.…”

“…Each set of σ rms is computed based on 10,000 independent simulation runs using MatLab. RD is positioned at (5, 2, 1) and two different MDs are at positioned (13, 4, 1) and (16,8,1) as shown in Figs. 3(a) and 3(b) respectively.…”

Enhancement of localization accuracy in cellular networks via cooperative ad-hoc links

“…The dominant location techniques in ad-hoc networks, e.g., WLANs, are TOA and RSS [5]. Whereas TOA measurements are preferred for sparse networks, for sufficiently high density ones, since the distances are sensibly reduced and hence the likehood to have a fixed reference point close to the located device is substantial, RSS can perform as well as TOA [6].…”

RSS-based sensor localization with unknown transmit power

“…In a WSN localization problem, known-position sensors (anchor sensors) try to estimate the position of unknown-location sensors (source sensors) via noisy measurements [1]. Based on application requirements, accuracy, and efficiency, different types of measurements are employed in localization such as time-of-arrival, time-differenceof-arrival, received-signal-strength (RSS), and angle-of-arrival [1]. RSS is always an interesting method mainly because of its low complexity and cost of devices [1].…”