Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.

Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking

We propose a novel framework for location detection with sensor networks, based on the theory of identifying codes. The key idea of this approach is to allow sensor coverage areas to overlap so that each resolvable position is covered by a unique set of sensors. In this setting, determining a sensor-placement with a minimum number of sensors is equivalent to constructing an optimal identifying code, an NP-complete problem in general. We, thus, propose and analyze new polynomial-time algorithms for generating irreducible (but not necessarily optimal) codes for arbitrary topologies. Our algorithms incorporate robustness properties that are critically needed in harsh environments. We further introduce distributed versions of these algorithms, allowing sensors to self-organize and determine a (robust) identifying code without any central coordination. Through analysis and simulation, we show that our algorithms produce nearly optimal solutions for a wide range of parameters. In addition, we demonstrate a tradeoff between system robustness and the number of active sensors (which is related to the expected lifetime of the system). Finally, we present experimental results, obtained on a small testbed, that demonstrate the feasibility of our approach.

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Robust location detection with sensor networks

Consider a connected undirected graph G = (V, E), a subset of vertices C ⊆ V, and an integer r ≥ 1; for any vertex v ∈ V, let B r (v) denote the ball of radius r centered at v, i.e., the set of all vertices within distance r from v. If for all vertices v ∈ V (respectively, v ∈ V\C), the sets Br(v) ∩ C are all nonempty and different, then we call C an r-identifying code (respectively, an r-locating-dominating code). We study the smallest cardinalities or densities of these codes in chains (finite or infinite) and cycles.

Identifying and locating-dominating codes on chains and cycles

Let G =( V;E) be a connected undirected graph and S a subset of vertices. If for all vertices v 2 V , the sets Br(v) \ S are all nonempty and dierent, where Br(v) denotes the set of all points within distance r from v ,t hen we callS an r-identifying code. We give constructive upper bounds on the best possible density of r-identifying codes in four innite regular graphs, for small values of r.

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Identifying Codes with Small Radius in Some Infinite Regular Graphs

Extremal cardinalities for identifying and locating-dominating codes in graphs

Minimizing the size of an identifying or locating-dominating code in a graph is NP-hard

The minimum density of an identifying code in the king lattice

http://biblio.telecom-paristech.fr/cgi-bin/download.cgi?id=8697

Irène Charon

Papers

Minimizing the size of an identifying or locating-dominating code in a graph is NP-hard

Identifying and locating-dominating codes on chains and cycles

Extremal cardinalities for identifying and locating-dominating codes in graphs

The minimum density of an identifying code in the king lattice

Complexity results for identifying codes in planar graphs