Boundary recognition in sensor networks by topological methods

Wang, Yue; Gao, Jie; Mitchell, Joseph S. B.

doi:10.1145/1161089.1161104

Cited by 294 publications

(265 citation statements)

References 16 publications

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“…Second, even if we assume that the initial sensor network is isotropic, unbalanced power consumption among nodes will easily create holes in the network. Recently, a distributed method [40] has been proposed to detect hole boundary by using only the connectivity information. Based on that work, REP [41] is proposed to deal with the "distance mismatch" problem in anisotropic networks.…”

Section: Hop Count Measurementsmentioning

confidence: 99%

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: Hop Count Measurementsmentioning

confidence: 99%

Location, Localization, and Localizability

Liu

Yang

Wang

et al. 2010

J. Comput. Sci. Technol.

264

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“…One important application of network planarization is topology discovery [18] [8] [9]. In topology discovery, we use the large faces in a planarized network to find holes and the outer boundary of the sensor field.…”

Section: B Topology Discoverymentioning

confidence: 99%

“…Our results compare favorably with the known results on topology discovery. In [8], [9], [18], the authors presented very nice algorithms for detecting holes and outer boundaries. Their algorithms work for large holes (radius are 5 times the communication range of the sensors or more) or very dense networks (e.g., average degree larger than 15).…”

Section: B Topology Discoverymentioning

confidence: 99%

“…Such protocols use the faces in the planar subgraph to perform perimeter routing, which guarantees packet delivery. Since then, network planarization has also become a fundamental tool in numerous other applications, including data-centric storage [17], network localization [15] and topology discovery [18] [8] [9]. Here the data-centric storage schemes use the planar graph to help determine on which set of nodes to store each datum, as well as for routing; the network localization schemes can use the properties of planar graphs to facilitate localization; and the topology discovery schemes can use the faces of the planar graph to recognize and locate boundaries and holes in the sensor field.…”

Section: Introductionmentioning

confidence: 99%

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Robust Planarization of Unlocalized Wireless Sensor Networks

Zhang

Jiang

Chen

2008

IEEE INFOCOM 2008 - The 27th Conference on Computer Communications

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Abstract-Wireless sensor networks need very efficient network protocols due to the sensors' limited communication and computation capabilities. Network planarization -finding a planar subgraph of the network that contains all the nodes -has been a very important technique for many network protocols. It first became the foundation of various well known routing protocols, including GPSR, GOAFR and several other protocols. Since then, it has also been used in numerous other applications, including data-centric storage, network localization, topology discovery, etc. However, an important problem remains: network planarization itself is very difficult. So far, efficient planarization algorithms exist only for very restrictive models: the network must be a unit-disk graph, and accurate measurements related to the node locations (e.g., node positions or angles between adjacent links) need to be known. For more practical network models, where the transmission ranges are usually not uniform and sensors cannot obtain their accurate location information via expensive localization devices, no efficient planarization algorithm is available.In this paper, we present a novel method that robustly planarizes sensor networks of a realistic model: networks with non-uniform transmission ranges and unlocalized sensors (that is, static sensors whose locations are unknown). Our method starts with a simple shortest path between two nodes, and progressively planarizes the whole network. It achieves both efficiency and a good planarization result. We present two planarization algorithms for different settings. Our results not only solve the planarization problem, but also outperform some known results in the graph drawing research field. We demonstrate the practical performance of our method -as well as its application in topology discovery, -through extensive simulations.

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“…Examples are intrusion detection, data gathering [19], mundane services like efficient routing within the network [6,15], or event detection [5]. In many situations, holes can also be considered as indicators for insufficient coverage or connectivity.…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks

Schieferdecker

Völker

Wagner

2011

Experimental Algorithms

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Abstract. We propose two novel algorithms for distributed and location-free boundary recognition in wireless sensor networks. Both approaches enable a node to decide autonomously whether it is a boundary node, based solely on connectivity information of a small neighborhood. This makes our algorithms highly applicable for dynamic networks where nodes can move or become inoperative. We compare our algorithms qualitatively and quantitatively with several previous approaches. In extensive simulations, we consider various models and scenarios. Although our algorithms use less information than most other approaches, they produce significantly better results. They are very robust against variations in node degree and do not rely on simplified assumptions of the communication model. Moreover, they are much easier to implement on real sensor nodes than most existing approaches.

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Boundary recognition in sensor networks by topological methods

Cited by 294 publications

References 16 publications

Location, Localization, and Localizability

Location, Localization, and Localizability

Robust Planarization of Unlocalized Wireless Sensor Networks

Efficient Algorithms for Distributed Detection of Holes and Boundaries in Wireless Networks

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