Lightweight neighborhood cardinality estimation in dynamic wireless networks

Cattani, Marco; Zúñiga, Marco; Loukas, Andreas; Langendoen, Koen

doi:10.1109/ipsn.2014.6846751

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…In macronetworks several solutions exists but are not well suited for nanonetworks. For a presentation of related work less suitable to our context, such as mobile phones and sensor networks (low density networks), one can refer to the related work given in [7].…”

Section: Related Workmentioning

confidence: 99%

“…Estreme [7] is a statistic method to estimate the number of neighbors. It works in networks where all nodes perform periodic but random events within a given period, such as sensor networks.…”

Section: Estremementioning

confidence: 99%

“…The key idea of the method is that the time difference between two consecutive events captures the density of the neighborhood: the shorter the time difference, the higher the density. It targets networks where nodes have "one hundred neighbors" [7]. All nodes estimate concurrently the density.…”

Section: Estremementioning

confidence: 99%

See 2 more Smart Citations

Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

Arrabal

Dhoutaut

Dedu

2018

2018 27th International Conference on Computer Communication and Networks (ICCCN)

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Extremely dense wireless network topologies gradually become a reality, especially through wireless sensors networks and more recently nanonetworks. Electromagnetic nanonetworks are expected to allow a very large amount of extremely small and capability-limited devices to communicate with each others. In nanonetworks, even in a communication range limited to tens of centimeters, thousands of neighbors can be found. Information diffusion and routing protocols would greatly benefit from having an accurate estimation of the density of nodes. However, in this context, most traditional wireless communication protocols are not suited. We propose Density Estimator for Dense Networks (DEDeN), a distributed algorithm able to provide the required density estimation. It allows confidence tuning and can cope with an extreme range of local densities. A formal analysis of DEDeN is provided and corroborated by extensive simulations. DEDeN interest is then demonstrated through application to two information diffusion protocols tailored for very dense networks, and also to a routing protocol specific to nanonetworks.

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Section: Related Workmentioning

confidence: 99%

Section: Estremementioning

confidence: 99%

See 1 more Smart Citation

Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

Arrabal

Dhoutaut

Dedu

2018

2018 27th International Conference on Computer Communication and Networks (ICCCN)

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“…Cases also exist where maintaining neighborhood information is the application itself. Examples are mechanisms for quickly computing the neighborhoods' cardinality [Cattani et al 2014], and neighbor discovery protocols for mobile WSNs; for example, used in wildlife monitoring scenarios [Pásztor et al 2010]. In the latter, several solutions exist [Dutta and Culler 2008;Kandhalu et al 2010;Zhang et al 2012] that allow designers to use radios as proximity sensors.…”

Section: Motivation and State Of The Artmentioning

confidence: 99%

Neighborhood View Consistency in Wireless Sensor Networks

Jhumka

Mottola

2016

ACM Trans. Sen. Netw.

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Wireless sensor networks (WSNs) are characterized by localized interactions, that is, protocols are often based on message exchanges within a node's direct radio range. We recognize that for these protocols to work effectively, nodes must have consistent information about their shared neighborhoods. Different types of faults, however, can affect this information, severely impacting a protocol's performance. We factor this problem out of existing WSN protocols and argue that a notion of neighborhood view consistency (NVC) can be embedded within existing designs to improve their performance. To this end, we study the problem from both a theoretical and a system perspective. We prove that the problem cannot be solved in an asynchronous system using any of Chandra and Toueg's failure detectors. Because of this, we introduce a new software device called pseudocrash failure detector (PCD), study its properties, and identify necessary and sufficient conditions for solving NVC with PCDs. We prove that, in the presence of transient faults, NVC is impossible to solve with any PCDs, and thus define two weaker specifications of the problem. We develop a global algorithm that satisfies both specifications in the presence of unidirectional links, and a localized algorithm that solves the weakest specification in networks of bidirectional links. We implement the latter atop two different WSN operating systems, integrate our implementations with four different WSN protocols, and run extensive micro-benchmarks and full-stack experiments on a real 90-node WSN testbed. Our results show that the performance significantly improves for NVC-equipped protocols; for example, the Collection Tree Protocol (CTP) halves energy consumption with higher data delivery.

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“…In other words, the aim is to determine the number of nodes distributed randomly and uniformly in a given field. There are several benefits for cardinality estimation in graphs such as energy efficiency [1], mobile communication and coding schemes design [2], and distributed storage [3], [4]. Furthermore, the paper proposes that each node discovers its neighbours which help network designers enhancing coverage and connectivity [5].…”

Section: Introductionmentioning

confidence: 99%

Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

Douik

Aly

Al-Naffouri

et al. 2017

Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017

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This paper introduces a novel algorithm for cardinality, i.e., the number of nodes, estimation in large scale anonymous graphs using statistical inference methods. Applications of this work include estimating the number of sensor devices, online social users, active protein cells, etc. In anonymous graphs, each node possesses little or non-existing information on the network topology. In particular, this paper assumes that each node only knows its unique identifier. The aim is to estimate the cardinality of the graph and the neighbours of each node by querying a small portion of them. While the former allows the design of more efficient coding schemes for the network, the second provides a reliable way for routing packets. As a reference for comparison, this work considers the Best Linear Unbiased Estimators (BLUE). For dense graphs and specific running times, the proposed algorithm produces a cardinality estimate proportional to the BLUE. Furthermore, for an arbitrary number of iterations, the estimate converges to the BLUE as the number of queried nodes tends to the total number of nodes in the network. Simulation results confirm the theoretical results by revealing that, for a moderate running time, asking a small group of nodes is sufficient to perform an estimation of 95% of the whole network.

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Lightweight neighborhood cardinality estimation in dynamic wireless networks

Cited by 12 publications

References 25 publications

Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

Efficient Density Estimation Algorithm for Ultra Dense Wireless Networks

Neighborhood View Consistency in Wireless Sensor Networks

Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

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