Evaluation of Localization Algorithms

This chapter introduces a methodological approach to the evaluation of localization algorithms. The chapter contains a discussion of evaluation criteria and performance metrics followed by statistical/ empirical simulation models and parameters that affect the performance of the algorithms and hence their assessment. Two contrasting localization studies are presented and compared with reference to the evaluation criteria discussed throughout the chapter. The chapter concludes with a localization algorithm development cycle overview: from simulation to real deployment. The authors argue that algorithms should be simulated, emulated (on test beds or with empirical data sets) and subsequently implemented in hardware, in a realistic Wireless Sensor Network (WSN) deployment environment, as a complete test of their performance. It is hypothesised that establishing a common development and evaluation cycle for localization algorithms among researchers will lead to more realistic results and viable comparisons.

Robust Localization Using Identifying Codes

Various real-life environments are exceptionally harsh for signal propagation, rendering well-known trilateration techniques (e.g. GPS) unsuitable for localization. Alternative proximity-based techniques, based on placing sensors near every location of interest, can be fairly complicated to set up, and are often sensitive to sensor failures or corruptions. The authors propose a different paradigm for robust localization based on identifying codes, a concept borrowed from the information theory literature. This chapter describes theoretical and practical considerations in designing and implementing such a localization infrastructure, together with experimental data supporting the potential benefits of the proposed technique.

RF Ranging Methods and Performance Limits for Sensor Localization

Localization or geolocation of wireless sensors usually requires accurate estimates of the distance between nodes in the network. RF ranging techniques can provide these estimates through a variety of methods some of which are well suited to wireless sensor networks. Noise and multipath channels fundamentally limit the accuracy of range estimation, and a number of other implementation related phenomena further impact accuracy. This chapter explores these effects and selected mitigation techniques in the context of low power wireless systems.

Graph Theoretic Techniques in the Analysis of Uniquely Localizable Sensor Networks

In the network localization problem the goal is to determine the location of all nodes by using only partial information on the pairwise distances (and by computing the exact location of some nodes, called anchors). The network is said to be uniquely localizable if there is a unique set of locations consistent with the given data. Recent results from graph theory and combinatorial rigidity made it possible to characterize uniquely localizable networks in two dimensions. Based on these developments, extensions, related optimization problems, algorithms, and constructions also became tractable. This chapter gives a detailed survey of these new results from the graph theorist’s viewpoint.

Theory and Practice of Signal Strength-Based Localization in Indoor Environments

This chapter discusses radio-based positioning. It surveys and compares several received signal strength localization approaches from two broad categories: point-based and area-based. It also explores their performance and means to improve it. It describes GRAIL - a sample positioning system. It finally concludes with a brief discussion of sensor applications that utilize location information.

Calibration and Measurement of Signal Strength for Sensor Localization

A number of practical issues are involved in the use of measured received signal strength (RSS) for purposes of localization. This chapter focuses on device effects and modeling problems which are not well covered in the literature, such as transceiver device manufacturing variations, battery effects on transmit power, nonlinearities in RSSI circuits, and path loss model parameter estimation. The authors discuss both the negative impacts of these effects and inaccuracies, and adaptations used by particular localization algorithms to be robust to them, without discussing any algorithm in detail. The authors present measurement methodologies to characterize these effects for wireless sensor nodes, and report the results from several calibration experiments to quantify each discussed effect and modeling issue.

Introduction to Wireless Sensor Network Localization

Localization is an important aspect in the field of wireless sensor networks that has attracted significant research interest recently. The interest in wireless sensor network localization is expected to grow further with the advances in the wireless communication techniques and the sensing techniques, and the consequent proliferation of wireless sensor network applications. This chapter provides an overview of various aspects involved in the design and implementation of wireless sensor network localization systems. These can be broadly classified into three categories: the measurement techniques in sensor network localization, sensor network localization theory and algorithms, and experimental study and applications of sensor network localization techniques. This chapter also gives a brief introduction to the other chapters in the book with a focus on explaining how these chapters are related to each other and how topics covered in each chapter fit into the architecture of this book and the big picture of wireless sensor network localization.

Theory and Practice of Signal Strength-Based Localization in Indoor Environments

In this chapter, the authors concentrate on signal strength-based localization in indoor wireless networks, with emphasis on 802.11 networks. The authors briefly summarize some architectures and approaches researchers have taken to address this problem. They then present some insight into theoretical limits to location accuracy, and identify that the issues driving research work in this area will not only be location accuracy but other factors like deployment ease, management simplicity, adaptability, and cost of ownership and maintenance. With this insight, they present the LEASE architecture for localization that allows easy adaptability of localization models. The chapter discusses the use of Bayesian networks for localization and presents a zero-configuration Bayesian localization algorithm that simplifies the maintenance of the model. Although presented in the context of signal strength-based localization in indoor environments, the concepts are general enough to be applicable to sensor, ad hoc, mesh, and infrastructure-based deployments. They conclude with some open issues.

MDS-Based Localization

The authors present several network node localization methods that are based on multidimensional scaling (MDS) techniques. Four algorithms are introduced: MDS-MAP(C), MDS-MAP(P), MDS-Hybrid, and RangeQ-MDS. MDS-MAP(C) is a centralized algorithm that simply applies MDS to estimate node positions. In MDS-MAP(P), a local map is built at each node of the immediate vicinity, then these maps are merged together to form a global map. MDS-Hybrid uses MDS-MAP(C) to relatively localize Nr reference nodes. Then, an absolute localization method uses these Nr nodes as anchors to localize the rest of the network. Finally, RangeQ-MDS assumes the absence of an RSSI-distance mapping function. It uses a quantized RSSI-based distance estimation technique (called RangeQ) to achieve more precise hop distances than other range-free approaches do. While MDS-MAP(C), MDS-MAP(P), and MDSHybrid can be range-aware or range-free, RangeQ-MDS is partially range-aware.

Measurements Used in Wireless Sensor Networks Localization

Wireless sensor networks (WSN) localization relies on measurements. Availability of, and the information content in, these measurements depend on the network architecture, connectivity, node time synchronization and the signaling bandwidth between the sensor nodes. This chapter addresses wireless sensor networks measurements in a general framework based on a set of nodes, where each node either emits or receives signals. The emitted signal can for example be a radio, acoustic, seismic, infrared or sonic wave that is propagated in a certain media to the receiver. This general observation model does not make any difference between localization of sensor network nodes or unknown objects, or whether the nodes or objects are stationary or mobile. The information available for localization in wireless cellular networks (WCN) is in literature classified as direction of arrival (DOA), time of arrival (TOA), time difference of arrival (TDOA) and received signal strength (RSS). This chapter generalizes these concepts to the more general wireless sensor networks.