Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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Subject: search.filters.subject.Clustering

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 5
    Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks
    (Springer, 2021) Al-Qamaji, Ali; Atakan, Barış
    Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS). In WSNs, due to the dense deployment, sensor readings can be spatially correlated and it is nonessential to transmit all their readings to the BS. Therefore, for more energy efficient, it is vital to choose which sensor node should report their sensed readings to the BS. In this paper, the event distortion-based clustering (EDC) algorithm is proposed for the spatially correlated sensor nodes. Here, the sensor nodes are assumed to harvest energy from ambient electromagnetic radiation source. The EDC algorithm allows the energy-harvesting sensor nodes to select and eliminate nonessential nodes while maintain an acceptable level of distortion at the BS. To measure the reliability, a theoretical framework of the distortion function is first derived for both single-hop and two-hop communication scenarios. Then, based on the derived theoretical framework, the EDC algorithm is introduced. Through extensive simulations, the performance of the EDC algorithm is evaluated in terms of achievable distortion level, number of alive nodes and harvested energy levels. As a result, EDC algorithm can successfully exploit both the spatial correlation and energy harvesting to improve the energy efficiency while preserving an acceptable level of distortion. Furthermore, the performance comparisons reveal that the two-hop communication model outperforms the single-hop model in terms of the distortion and energy-efficiency.
  • Article
    Citation - Scopus: 1
    Annealing-Based Model-Free Expectation Maximisation for Multi-Colour Flow Cytometry Data Clustering
    (Inderscience Enterprises Ltd., 2016) Köktürk, Başak Esin; Karaçalı, Bilge
    This paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carriedout using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learningalgorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions arecontinued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometrydatasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters ortheir distribution models.