Sensor coverage in wireless ad hoc sensor networks

Li, Jason H.; Yu, Miao

doi:10.1504/ijsnet.2007.013202

Cited by 100 publications

(80 citation statements)

References 22 publications

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“…The probabilistic approaches [19][20][21] analyze the node density for ensuring appropriate coverage statistically, but essentially have no guarantee on the result. In contrast, the geometric approaches [22] are able to obtain accurate and reliable results, in which locations are essential.…”

Section: Locationmentioning

confidence: 97%

Location, Localization, and Localizability

Liu

Yang

Wang

et al. 2010

J. Comput. Sci. Technol.

267

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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: Locationmentioning

confidence: 97%

Location, Localization, and Localizability

Liu

Yang

Wang

et al. 2010

J. Comput. Sci. Technol.

267

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“…Coverage problems in sensor networks have received extensive attention with a literature whose tools derive from computational geometry [19,20,29,30], graphtheory [13], dynamical systems [7], and stochastic geometry [17]. Optimal coverage forms a distinct class of art gallery problems, with heavy representation in the computer science and complexity literature.…”

Section: Introductionmentioning

confidence: 99%

Distributed computation of coverage in sensor networks by homological methods

Dłotko

Ghrist

Juda

et al. 2012

AAECC

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Recent work on algebraic-topological methods for verifying coverage in planar sensor networks relied exclusively on centralized computation: a limiting constraint for large networks. This paper presents a distributed algorithm for homology computation over a sensor network, for purposes of verifying coverage. The techniques involve reduction and coreduction of simplicial complexes, and are of independent interest. Verification of the ensuing algorithms is proved, and simulations detail the improved network efficiency and performance.

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“…Distance queries are important in disk graphs as they are widely used to determine coverage in wireless sensor networks, and for routing protocols [5,8,12]. In most potential applications (like military) one would not only desire high accuracy of these estimates but also the actual path producing this estimate.…”

Section: Introductionmentioning

confidence: 99%

Approximate Distance Queries in Disk Graphs

Fürer

Kasiviswanathan

2007

Approximation and Online Algorithms

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We present efficient algorithms for approximately answering distance queries in disk graphs. Let G be a disk graph with n vertices and m edges. For any fixed ǫ > 0, we show that G can be preprocessed in O(m √ nǫ −1 + mǫ −2 log S) time, constructing a data structure of size O(n 3/2 ǫ −1 + nǫ −2 log S), such that any subsequent distance query can be answered approximately in O( √ nǫ −1 + ǫ −2 log S) time. Here S is the ratio between the largest and smallest radius. The estimate produced is within an additive error which is only ǫ times the longest edge on some shortest path.The algorithm uses an efficient subdivision of the plane to construct a sparse graph having many of the same distance properties as the input disk graph. Additionally, the sparse graph has a small separator decomposition, which is then used to answer distance queries. The algorithm extends naturally to the higher dimensional ball graphs.

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Sensor coverage in wireless ad hoc sensor networks

Cited by 100 publications

References 22 publications

Location, Localization, and Localizability

Location, Localization, and Localizability

Distributed computation of coverage in sensor networks by homological methods

Approximate Distance Queries in Disk Graphs

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