The effects of ranging noise on multihop localization: an empirical study

Whitehouse, Kamin; Karlof, Chris; Woo, Alec; Jiang, Fred; Culler, David

doi:10.1109/ipsn.2005.1440899

Cited by 99 publications

(91 citation statements)

References 29 publications

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“…The results of a multihop localization process are based on a series of single hop multilaterations in an iterative manner [25] . In such a process, errors, coming from each step of multilateration, propagate and accumulate [50][51] .…”

Section: (B) Negative Impact Of Noisy Ranging Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Location, Localization, and Localizability

Liu

Yang

Wang

et al. 2010

J. Comput. Sci. Technol.

264

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Location-aware technology spawns numerous unforeseen pervasive applications in a wide range of living, production, commence, and public services. This article provides an overview of the location, localization, and localizability issues of wireless ad-hoc and sensor networks. Making data geographically meaningful, location information is essential for many applications, and it deeply aids a number of network functions, such as network routing, topology control, coverage, boundary detection, clustering, etc. We investigate a large body of existing localization approaches with focuses on error control and network localizability, the two rising aspects that attract significant research interests in recent years. Error control aims to alleviate the negative impact of noisy ranging measurement and the error accumulation effect during cooperative localization process. Network localizability provides theoretical analysis on the performance of localization approaches, providing guidance on network configuration and adjustment. We emphasize the basic principles of localization to understand the state-of-the-art and to address directions of future research in the new and largely open areas of location-aware technologies.Keywords location-based services (LBS), localization, error control, localizability, wireless ad-hoc and sensor networks LocationThe proliferation of wireless and mobile devices has fostered the demand for context-aware applications, in which location is viewed as one of the most significant contexts. For example, pervasive medical care is designed to accurately record and manage patient movements [1][2] ; smart space enables the interaction between physical space and human activities [3][4] ; modern logistics has major concerns on goods transportation, inventory, and warehousing [5][6] ; environmental monitoring networks sense air, water, and soil quality and detect the source of pollutants in real time [7][8][9][10][11] ; and mobile peer-to-peer computing encourages content sharing and contributing among mobile hosts in the vicinity [12][13] . In brief, location-based service (LBS) is a key enabling technology of these applications and widely exists in nowadays wireless communication networks from the short-range Bluetooth to the long-range telecommunication networks, as illustrated in Fig.1.Recent technological advances have enabled the development of low-cost, low-power, and multifunctional sensor devices. These nodes are autonomous devices with integrated sensing, processing, and communication capabilities. With the rapid development of wireless sensor networks (WSNs), location information becomes critically essential and indispensable. The overwhelming reason is that WSNs are fundamentally intended to provide information on spatial-temporal characteristics of the physical world; hence, it is important to associate sensed data with locations, making data geographically meaningful. For example, a number of applications, such as object tracking and environment monitoring, inherently rely on loc...

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Section: (B) Negative Impact Of Noisy Ranging Resultsmentioning

confidence: 99%

“…Nevertheless, the use of localized unknown nodes as reference nodes inherently introduces substantial cumulative error. Some works characterize the error propagation in multihop localization approaches and make efforts to control error accumulation [50][51] .…”

Section: Distributed Localization Approaches (A) Beacon Based Localizmentioning

confidence: 99%

Location, Localization, and Localizability

Liu

Yang

Wang

et al. 2010

J. Comput. Sci. Technol.

264

View full text Add to dashboard Cite

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“…It is of particular interest to study the impact of range errors on the performance of localization algorithms, because range errors are inherent to WSNs employing simple and low-cost range measurement hardware. According to the empirical study on the impact of range errors on multihop localizations [61], high density and Gaussian noises are the two pre-requisites for the noisy disk model to work. The study also suggests statistical approaches fixing the problem resulted from range errors.…”

Section: Localization Accuracymentioning

confidence: 99%

Algorithmic Aspects of Sensor Localization

Das

Wang

Ghosh

et al. 2010

Monographs in Theoretical Computer Science. An EATCS Series

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Identifying locations of nodes in wireless sensor networks (WSNs) is critical to both network operations and most application level tasks. Sensor nodes equipped with geographical positioning system (GPS) devices are aware of their locations at a precision level of few meters. However, installing GPS devices on a large number of sensor nodes is not only expensive but affects the form factor of these nodes. Moreover, GPS-based localization is not applicable in the indoor environments such as buildings. There exists an extensive body of research literature that aims at obtaining absolute locations as well as relative spatial locations of nodes in a WSN without requiring specialized hardware at large scale. The typical approach consists of employing only a limited number of anchor nodes that are aware of their own locations, and then trying to infer locations of non-anchor nodes using graph theoretic, geometric, statistical, optimization and machine learning techniques. Thus, the literature represents a very rich ensemble of algorithmic techniques applicable to low power, highly distributed nodes with resource-optimal computations. In this chapter we take a close look at the algorithmic aspects of various important localization techniques for WSNs.

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“…The accuracy of the radio-based positioning strongly depends on the proper calibration of the reference devices and the target node [6], [7] as well as a friendly radio environment. Maintaining such a positioning system can be costly and difficult in terms of additional hardware [8], [9], [10], intensive tuning [11], and environmental management.…”

Section: Introductionmentioning

confidence: 99%

LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic Vehicles

Zhan

Shi

2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-It is often important to obtain the real-time location of a small-sized ground robotic vehicle when it performs autonomous tasks either indoors or outdoors. We propose and implement LOBOT, a low-cost, self-contained localization system for small-sized ground robotic vehicles. LOBOT provides accurate real-time, three-dimensional positions in both indoor and outdoor environments. Unlike other localization schemes, LOBOT does not require external reference facilities, expensive hardware, careful tuning or strict calibration, and is capable of operating under various indoor and outdoor environments. LOBOT identifies the local relative movement through a set of integrated inexpensive sensors and well corrects the localization drift by infrequent GPS-augmentation. Our empirical experiments in various temporal and spatial scales show that LOBOT keeps the positioning error well under an accepted threshold.

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The effects of ranging noise on multihop localization: an empirical study

Cited by 99 publications

References 29 publications

Location, Localization, and Localizability

Location, Localization, and Localizability

Algorithmic Aspects of Sensor Localization

LOBOT: Low-Cost, Self-Contained Localization of Small-Sized Ground Robotic Vehicles

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