A cluster-based selective cooperative spectrum sensing scheme in cognitive radio

Nguyen-Thanh, Nhan; Koo, Insoo

doi:10.1186/1687-1499-2013-176

Cited by 37 publications

(17 citation statements)

References 15 publications

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“…These nodes in a cluster can be classified as cluster members (CM) who are the communicating nodes in a cluster or cluster head (CH) coordinating a cluster's operations. This separate classification of CM and CH offers a framework for fair communication [1], [5], [7], [10].…”

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confidence: 99%

Improved Cluster Stability in the Ad Hoc Vehicle Network

Ramakrishnan*¹,

Suganthi²

2019

IJITEE

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Traffic management has been and remains a significant issue, particularly in urban regions with elevated vehicle density. Adoption of an intelligent transport system (ITS) has extensively experimented to curb the traffic threat with blended experiences as a result. By communicating with other vehicles traveling on the same road in the form of clusters, an ad hoc vehicle network (VANET) forms and ITS that can allow vehicles with less human input to cooperate. By considering the elevated mobility nature of cars in VANET, this article provides a solution to the primary threat of VANET clustering by embracing the flexibility of fuzzy logics for cluster formation on a multilane urban high way. It also demonstrates that cluster stability is enhanced by performing the cluster head (CH) selection process based on a mixture of fuzzy logic, lane weighting, and utility function with the fuzzy membership function adjusted to boost cluster stability.

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confidence: 99%

Improved Cluster Stability in the Ad Hoc Vehicle Network

Ramakrishnan*¹,

Suganthi²

2019

IJITEE

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“…They first propose a voting scheme based on SUs' own confidence then develop a cluster-collectforward scheme to save energy spent on vote-collection and information exchange. In [15], to reduce reporting time and bandwidth requirement, a dynamic CH selection scheme is proposed based on the sensing qualities of SUs. Kozal et al propose a multi-hop reporting scheme to reduce the reporting power consumption.…”

Section: B Related Workmentioning

confidence: 99%

Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

Çelik

Kamal

2016

IEEE Trans. Cogn. Commun. Netw.

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In this study, we address energy efficient (EE) cooperative spectrum sensing (CSS) policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels (PCs) and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives; Macro perspective groups SUs into clusters with the objectives: 1) Total energy consumption minimization, 2) Total throughput maximization, and 3) Inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) The best CH which gives the minimum total multi-hop error rate, and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.

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“…In [75], a clustering technique is adopted to save energy consumed in reporting results and exchanging information. In [76], to reduce reporting time and bandwidth requirement, a dynamic CH selection scheme is proposed based on the sensing qualities of SUs. Kozal et al propose a multihop reporting scheme to reduce the reporting power consumption.…”

Section: Related Workmentioning

confidence: 99%