Neighbor discovery in wireless networks and the coupon collector's problem

Vasudevan, Subramanyan; Towsley, D.; Goeckel, Dennis; Khalili, Ramin

doi:10.1145/1614320.1614341

Cited by 150 publications

(197 citation statements)

References 17 publications

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“…major work in this category [15], [18], [22], [27], [31]) adopt probabilistic strategies at each node. Specifically, each node remains active or asleep with different probabilities.…”

Section: Related Workmentioning

confidence: 99%

Never Live Without Neighbors: From Single- to Multi-Channel Neighbor Discovery for Mobile Sensing Applications

Chen¹,

Bian²,

Zheng³

2016

IEEE/ACM Trans. Networking

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Abstract-Neighbor discovery is of paramount importance in mobile sensing applications and is particularly challenging if the operating frequencies of mobile devices span multiple channels. In this paper, we formulate the multi-channel neighbor discovery problem and establish a theoretical framework of it, under which we derive the performance bound of any neighbor discovery protocol guaranteeing discovery. We then develop a multi-channel discovery protocol that achieves guaranteed discovery with orderminimum worst-case discovery delay and fine-grained control of energy conservation levels.

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“…major work in this category [15], [18], [22], [27], [31]) adopt probabilistic strategies at each node. Specifically, each node remains active or asleep with different probabilities.…”

Section: Related Workmentioning

confidence: 99%

Never Live Without Neighbors: From Single- to Multi-Channel Neighbor Discovery for Mobile Sensing Applications

Chen¹,

Bian²,

Zheng³

2016

IEEE/ACM Trans. Networking

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“…An execution of the system is divided into slots. In each slot, a node u can be in exactly one of the following three modes: (1) transmit mode on some channel in A(u), (2) receive mode on some channel in A(u), or (3) quiet mode when the transceiver is switched off. Hereafter, we use the terms "node" and "cognitive radio node" interchangeably.…”

Section: Problem Statementmentioning

confidence: 99%

“…Existing approaches to the neighbor discovery problem when nodes use omni-directional antennas can be classified into three categories, namely, randomized [2]- [5], deterministic [6]- [9], and multi-user detection-based [10], [11]. Some of these solutions require a priori knowledge of the number of neighbors [3]- [5] and can provide only probabilistic guarantees, while we are focused on designing a deterministic algorithm with 100% discovery guarantee without knowing anything about network size or diameter beforehand.…”

Section: Introductionmentioning

confidence: 99%

Fast Neighbor Discovery with Lightweight Termination Detection in Heterogeneous Cognitive Radio Networks

Mittal

Venkatesan

Chandrasekaran

2010

2010 Ninth International Symposium on Parallel and Distributed Computing

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Abstract-An important step in the initialization of a wireless ad hoc network is neighbor discovery in which every node attempts to determine the set of nodes it can communicate with in one wireless hop. In recent years, cognitive radio technology has gained attention as an attractive approach to alleviate the congestion within the frequency spectrum. A cognitive radio transceiver can operate over a wide range of frequencies possibly spanning multiple frequency bands. A cognitive radio node can scan the frequency spectrum and dynamically identify frequencies that it can use for communication without interference from other wireless devices in its vicinity.When multiple cognitive radio nodes come together to form an ad hoc network, due to spatial variations in frequency usage and hardware variations in radio transceivers, different nodes in the network may perceive different subsets of frequencies available to them for communication. This heterogeneity in the available channel sets across the network increases the complexity of conducting neighbor discovery in a cognitive radio network. In this paper, we propose a new deterministic neighbor discovery algorithm for heterogeneous multi-hop cognitive radio networks with termination detection capability. Our algorithm allows nodes to detect termination of the neighbor discovery process. Our simulation results indicate that our algorithm is much faster compared to a recently proposed deterministic neighbor discovery algorithm (a reduction of 97-98% in time complexity ) and termination detection is lightweight (at most 3% of the total time complexity).

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“…This question has been addressed over the past years mainly with proposals on fully distributed device discovery schemes [9]. Examples include systems based on Wi-Fi technology such as Wi-Fi direct and Nokia Instant Community (NIC) running on unlicensed without infrastructure support [10,11].…”

Section: Introductionmentioning

confidence: 99%

Clustering Schemes for D2D Communications under Partial/No Network Coverage

Lu¹,

Miao²,

Fodor

et al. 2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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Abstract-Device-to-Device (D2D) communication has been proposed to deal with the significant amount of the local traffic by enabling devices to directly connect to each other when being in proximity. In order to start direct communications, a device must first discover that other devices are in the proximity and that they are relevant for a certain context (i.e. can offer relaying to the network). This task becomes particularly challenging in out-of-network coverage. In this paper, we propose three clustering schemes that assist a device discovering and associating with other devices considered relevant in a given context. We compare the efficiency of the proposed approaches in terms of discovery ratio, clustering time and energy consumption and study the tradeoff between these performance metrics that need to be considered when selecting one of the grouping schemes. In particular, the simulation results prove that grouping devices based on their capabilities taking into account the exchanged radio signal strengths ensure, in most of the scenarios, the balance between high discovery ratio, short latency and energy efficiency.

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Neighbor discovery in wireless networks and the coupon collector's problem

Cited by 150 publications

References 17 publications

Never Live Without Neighbors: From Single- to Multi-Channel Neighbor Discovery for Mobile Sensing Applications

Never Live Without Neighbors: From Single- to Multi-Channel Neighbor Discovery for Mobile Sensing Applications

Fast Neighbor Discovery with Lightweight Termination Detection in Heterogeneous Cognitive Radio Networks

Clustering Schemes for D2D Communications under Partial/No Network Coverage

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