Robust Location Detection With Sensor Networks

Abstract

Abstract-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 NPcomplete 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.

Localization and identification in networks using robust identifying codes

“…Identifying codes have since been extended and applied to location detection in hostile environments [6,7], to energy balancing of such systems [8], and to dynamic location detection agents [9]. In the first example, a coverage area is quantized into a finite number of clusters.…”

“…Applications such as fault detection and diagnosis in network and specifically in processor arrays [5], location detection in sensor networks [6,7], environmental monitoring in utility networks [10], and others, have been studied extensively, using different approaches that have resulted in a large variety of practical algorithms as well as theoretical results. In this paper we focus on a combinatorial approach, which is closely related to error-correcting codes, and specifically to identifying codes.…”

Efficient clustering algorithms for self-organizing wireless sensor networks

“…Examples include environmental monitoring, smart homes and offices, surveillance, and intelligent transportation systems [1,17,21]. As societal reliance on wireless sensor network technology increases, we can expect the size and complexity of individual networks as well as the number of networks to increase dramatically.…”

“…Wireless sensor networks can be exposed to highly dynamic, and hostile environments [21,26], and therefore, they must be tolerant to the failure and loss of connectivity of individual nodes. Clearly, algorithms for wireless sensor networks must be distributed or decentralized in order to prevent single points of failure.…”

Enhanced Fingerprint-Based Location Estimation System in Wireless LAN Environment

“…However, we can only receive GPS services in outdoor environment, as the GPS devices rely on signals from the satellite system surrounding the globe. Therefore many indoor positioning solutions [1] [2] [3] [4] based on radio frequency (RF) has been proposed in recent years. Among these previous works, Wi-Fi (IEEE 802.11) positioning become a hot topic for indoor positioning, as Wireless LAN (WLAN) has became a basic infrastructure of many buildings.…”