Location Based Distributed Spectral Clustering for Wireless Sensor Networks

Muniraju, Gowtham; Zhang, Sai; Tepedelenlioğlu, Cihan; Banavar, Mahesh K.; Spanias, Andreas; Vargas-Rosales, César; Villalpando-Hernández, Rafaela

doi:10.1109/sspd.2017.8233241

Cited by 24 publications

(6 citation statements)

References 20 publications

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“…Examples of the application of spectral clustering in the context of IoT can be found in refs. [10][11][12]. For instance, in ref.…”

Section: Spectral Clusteringmentioning

confidence: 99%

“…These connected objects generate a lot data, which can be analyzed to identify trends and information for various purposes. This is where clustering for IoT [5][6][7][8][9][10][11][12][13][14][15][16][17][18] becomes highly demanded. The advantages that clustering provides are numerous, such as the fact that it enables the scalability of the IoT network and reduces the routing overhead by managing the routing decisions on the elected cluster-heads (CHs) [19,20].…”

Section: Introducing Clustering For Iotmentioning

confidence: 99%

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Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things

Guyeux

Chrétien

Tayeh

et al. 2019

JSAN

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The use of wireless sensor networks, which are the key ingredient in the growing Internet of Things (IoT), has surged over the past few years with a widening range of applications in the industry, healthcare, agriculture, with a special attention to monitoring and tracking, often tied with security issues. In some applications, sensors can be deployed in remote, large unpopulated areas, whereas in others, they serve to monitor confined busy spaces. In either case, clustering the sensor network’s nodes into several clusters is of fundamental benefit for obvious scalability reasons, and also for helping to devise maintenance or usage schedules that might greatly improve the network’s lifetime. In the present paper, we survey and compare popular and advanced clustering schemes and provide a detailed analysis of their performance as a function of scale, type of collected data or their heterogeneity, and noise level. The testing is performed on real sensor data provided by the UCI Machine Learning Repository, using various external validation metrics.

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“…Examples of the application of spectral clustering in the context of IoT can be found in refs. [10][11][12]. For instance, in ref.…”

Section: Spectral Clusteringmentioning

confidence: 99%

Section: Introducing Clustering For Iotmentioning

confidence: 99%

Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things

Guyeux

Chrétien

Tayeh

et al. 2019

JSAN

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“…Among the previously cited works, [27]- [29] present a similar approach to the one considered in this paper. Specifically, in [27] the authors propose a distributed spectral clustering algorithm but they do not consider weights neither in the nodes nor in the edges. In [28], the authors propose a distributed spectral clustering algorithm but they only consider an edge-weighted graph.…”

Section: Introductionmentioning

confidence: 97%

“…In this paper, we focus on spectral clustering techniques because they are easy to implement and have been shown to be more effective in finding clusters than some traditional algorithms such as k-means [30]. Among the previously cited works, [27]- [29] present a similar approach to the one considered in this paper. Specifically, in [27] the authors propose a distributed spectral clustering algorithm but they do not consider weights neither in the nodes nor in the edges.…”

Section: Introductionmentioning

confidence: 99%

Distributed Clustering Algorithm for Adaptive Pandemic Control

Insausti¹,

Zárraga-Rodríguez

Nolasco-Ferencikova

et al. 2021

IEEE Access

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The COVID-19 pandemic has had severe consequences on the global economy, mainly due to indiscriminate geographical lockdowns. Moreover, the digital tracking tools developed to survey the spread of the virus have generated serious privacy concerns. In this paper, we present an algorithm that adaptively groups individuals according to their social contacts and their risk level of severe illness from COVID-19, instead of geographical criteria. The algorithm is fully distributed and therefore, individuals do not know any information about the group they belong to. Thus, we present a distributed clustering algorithm for adaptive pandemic control.

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“…Estimating the statistics of sensor measurements in WSNs is necessary in detecting anomalous sensors, supporting the nodes with insufficient resources, network area estimation [2], and spectrum sensing [3] for cognitive radio applications, just to name a few. Knowledge of extremes are often used in algorithms for outlier detection, clustering [4], classification [5], and localization [6]. However, several factors [7] such as additive noise in wireless channels, random link failures, packet loss and delay of arrival significantly degrade the performance of distributed algorithms.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of Robust Max Consensus for Wireless Sensor Networks

Muniraju

Tepedelenlioğlu

Spanias

2019

IEEE Trans. on Signal and Inf. Process. over Networks

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A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus algebra is used as a tool to study this ergodic process. The subadditive ergodic theorem is invoked to establish a constant growth rate for the state values due to noise, which is studied by analyzing the max-plus Lyapunov exponent of the product of noise matrices in a max-plus semiring. The growth rate of the state values is upper bounded by a constant which depends on the spectral radius of the network and the noise variance. Upper and lower bounds are derived for both fixed and random graphs. Finally, a two-run algorithm robust to additive noise in the network is proposed and its variance is analyzed using concentration inequalities. Simulation results supporting the theory are also presented.Index Terms-Max consensus, spectral radius, max-plus algebra, wireless sensor networks.

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Location Based Distributed Spectral Clustering for Wireless Sensor Networks

Cited by 24 publications

References 20 publications

Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things

Introducing and Comparing Recent Clustering Methods for Massive Data Management in the Internet of Things

Distributed Clustering Algorithm for Adaptive Pandemic Control

Analysis and Design of Robust Max Consensus for Wireless Sensor Networks

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