Robust position-based routing in wireless Ad Hoc networks with unstable transmission ranges

Barrière, Lali; Fraigniaud, Pierre; Narayanan, Lata

doi:10.1145/381448.381451

Cited by 111 publications

(103 citation statements)

References 12 publications

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“…We prove properties of the output network of RTC and ε-RTC assuming that the input network G is a UDG. However, our results hold even when G is a quasi UDG [1].…”

Section: Introductionmentioning

confidence: 46%

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Topology Control with Limited Geometric Information

Lillis

Pemmaraju

2006

Lecture Notes in Computer Science

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Abstract. Topology control is the problem of selecting neighbors for each node in a wireless network, so that the resulting network has a number of useful properties. More precisely, a topology control protocol P takes as input a network G and aims to construct a spanning subgraph GP , that is sparse, "energy minimizing" and has sufficient connectivity so as to guarantee multiple short paths between pairs of nodes in G. Currently, topology control protocols assume that nodes in G reside in some Euclidean (usually, 2-dimensional) space and rely on geometric information such as node locations and pairwise distances between nodes to produce GP with appropriate properties. However, these protocols are extremely sensitive to errors in location information and this feature makes them impractical because errors in location and distance information are pervasive in practical systems. This paper presents and analyzes two randomized topology control protocols that are tolerant to errors in pairwise distance estimates. The first protocol, called RTC (short for randomized topology control) uses no geometric information, relying only on connectivity information and is therefore completely immune to errors in location or distance information. The second protocol, called ε-RTC, generalizes the first protocol. Allowing for errors in distance estimates, but assuming that relative errors are bounded above by ε, the second protocol produces an output network that is symmetric, connected, sparse, and has good spanner properties. As ε → 0, ε-RTC behaves like the XTC protocol (R. Wattenhofer and A. Zollinger, "XTC: A practical topology control algorithm for ad-hoc networks", WMAN 2004) and for large values of ε, it behaves like RTC. Our results hold whenever the input network is a unit disk graph or even a quasi unit disk graph.

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“…We prove properties of the output network of RTC and ε-RTC assuming that the input network G is a UDG. However, our results hold even when G is a quasi UDG [1].…”

Section: Introductionmentioning

confidence: 46%

“…Let the lengths of the edges be |ab| = |dc| = (1 − ε)/2 and |ac| = |bd| = 1/2. Then the neighborhood orderings ≺ according to (1) are:…”

Section: G Xt C May Be Disconnectedmentioning

confidence: 99%

Topology Control with Limited Geometric Information

Lillis

Pemmaraju

2006

Lecture Notes in Computer Science

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“…For example, the quasi unit disk graph model (QUDG) [3,4] is a generalization of the UDG that takes imperfections into account as they may arise from non-omnidirectional antennas or small obstacles. These QUDGs are related to so-called civilized graphs.…”

Section: Model 422 (General Graph (Gg))mentioning

confidence: 99%

“…Also, in the case of an error (such as a lost message), only the k-neighborhood of a node is affected. 3 Having defined the most common types of algorithm, we now look at some algorithmic aspects in more detail. As mentioned, the message complexity-the total number of messages sent by an algorithm-is a main evaluation criterion of distributed algorithms.…”

Section: Example 449mentioning

confidence: 99%

Modeling Sensor Networks

Schmid

Wattenhofer

2008

Algorithms and Protocols for Wireless Sensor Networks

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“…성능 분석 본 장에서는 제안하는 알고리즘의 성능을 시뮬레이 션을 통해 DV-Hop [12] , REP [16] , 그리고 Pattern-driven [17] 사용한다. DOI는 QUDG 통신 모델 [26] 을 사용한다. …”

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Multihop Range-Free Localization with Virtual Hole Construction in Anisotropic Sensor Networks

Lee

Kim

2013

The Journal of Korea Information and Communications Society

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Key Words : range-free localization, anisotropic networks, range estimation, network localization, connectivity ABSTRACT This paper presents a multihop range-free localization algorithm to estimate the physical location of a normal node with local connectivity information in anisotropic sensor networks. In the proposed algorithm, a normal node captures the detour degree of the shortest path connecting an anchor pair and itself by comparing the measured hop count and the expected hop count, and the node estimates the distances to the anchors based on the detour degree. The normal node repeats this procedure with all anchor combinations and pinpoints its location using the obtained distance estimates. The proposed algorithm requires fewer anchors and less communication overhead compared to existing range-free algorithms. We showed the superiority of the proposed algorithm over existing range-free algorithms through MATLA simulations.

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Robust position-based routing in wireless Ad Hoc networks with unstable transmission ranges

Cited by 111 publications

References 12 publications

Topology Control with Limited Geometric Information

Topology Control with Limited Geometric Information

Modeling Sensor Networks

Multihop Range-Free Localization with Virtual Hole Construction in Anisotropic Sensor Networks

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