Distributed nonparametric sequential spectrum sensing under electromagnetic interference

Sahasranand, K. R.; Sharma, Vinod

doi:10.1109/icc.2015.7249529

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Wireless sensor networks are now used in a wide range of applications in medicine, telecommunications, and environmental domains, see [2] for a survey. For instance, they are employed for human health monitoring [3], activity recognition on home environments [4], spectrum sensing in cognitive radio [5],…”

Section: Introductionmentioning

confidence: 99%

Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters

Dupraz,

Pastor,

Socheleau

2018

Preprint

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In sensor networks, it is not always practical to set up a fusion center. Therefore, there is need for fully decentralized clustering algorithms. Decentralized clustering algorithms should minimize the amount of data exchanged between sensors in order to reduce sensor energy consumption. In this respect, we propose one centralized and one decentralized clustering algorithm that work on compressed data without prior knowledge of the number of clusters. In the standard K-means clustering algorithm, the number of clusters is estimated by repeating the algorithm several times, which dramatically increases the amount of exchanged data, while our algorithm can estimate this number in one run.The proposed clustering algorithms derive from a theoretical framework establishing that, under asymptotic conditions, the cluster centroids are the only fixed-point of a cost function we introduce. This cost function depends on a weight function which we choose as the p-value of a Wald hypothesis test. This p-value measures the plausibility that a given measurement vector belongs to a given cluster.Experimental results show that our two algorithms are competitive in terms of clustering performance with respect to K-means and DB-Scan, while lowering by a factor at least 2 the amount of data exchanged between sensors.

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Section: Introductionmentioning

confidence: 99%

Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters

Dupraz,

Pastor,

Socheleau

2018

Preprint

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show abstract

“…Networks of sensors are now used in a wide range of applications in medicine, in telecommunications, or in environmental domains [1]. They are employed, for example, for human health monitoring [2], activity recognition on home environments [3], spectrum sensing in cognitive radio [4], and so forth.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Clustering based on Robust Estimation and Hypothesis Testing

Pastor¹,

Dupraz²,

Socheleau³

2017

Preprint

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This paper considers a network of sensors without fusion center that may be difficult to set up in applications involving sensors embedded on autonomous drones or robots. In this context, this paper considers that the sensors must perform a given clustering task in a fully decentralized setup. Standard clustering algorithms usually need to know the number of clusters and are very sensitive to initialization, which makes them difficult to use in a fully decentralized setup. In this respect, this paper proposes a decentralized model-based clustering algorithm that overcomes these issues. The proposed algorithm is based on a novel theoretical framework that relies on hypothesis testing and robust M-estimation. More particularly, the problem of deciding whether two data belong to the same cluster can be optimally solved via Wald's hypothesis test on the mean of a Gaussian random vector. The p-value of this test makes it possible to define a new type of score function, particularly suitable for devising an M-estimation of the centroids. The resulting decentralized algorithm efficiently performs clustering without prior knowledge of the number of clusters. It also turns out to be less sensitive to initialization than the already existing clustering algorithms, which makes it appropriate for use in a network of sensors without fusion center.

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Performance of spectrum sensing algorithms under fading, electromagnetic interference and outliers

Sahasranand

Sharma

2015

2015 Twenty First National Conference on Communications (NCC)

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Distributed nonparametric sequential spectrum sensing under electromagnetic interference

Cited by 4 publications

References 41 publications

Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters

Decentralized Clustering on Compressed Data without Prior Knowledge of the Number of Clusters

Decentralized Clustering based on Robust Estimation and Hypothesis Testing

Performance of spectrum sensing algorithms under fading, electromagnetic interference and outliers

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