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PALES: : A Predictive Approach for the election of semantic cluster LEaders in wireless Sensor networks

Published: 01 January 2019 Publication History
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  • Abstract

    Recently, semantic clustering has been proposed to save energy in wireless sensor networks. Semantic clustering organizes the topology in clusters composed of semantically correlated nodes whose leader (collector) is periodically elected. Since collectors’ energy depletion is faster than the inner cluster nodes, suitable election mechanisms are required to avoid the energy hole problem. A potential drawback in adopting traditional election mechanisms is their reactive features since they usually wait for the leader nodes death to then elect a new leader. This behavior may cause holes in the network. The semantic clustering has presented better energy efficiency than other classical clustering methods. We propose PALES to reduce power consumption of Wireless Sensor and Actuator Network (WSAN) through a predictive election of semantic collectors using the ARIMA method. PALES extends an existing decentralized semantic clustering mechanism, inheriting its properties of self-adaptation, self-reconfiguration, and self-organization through a collaborative process. Results show that the PALES election increased up to 73% the collector battery saving in comparison to reactive election methods.

    References

    [1]
    M. Afsar, M.-H. Tayarani-N. and M. Aziz, An adaptive competition-based clustering approach for wireless sensor networks, Telecommunication Systems 61(1) (2016), 181–204.
    [2]
    V.V. Deshpande and A.R. Bhagat Patil, Energy efficient clustering in wireless sensor network using cluster of cluster heads, in: Wireless and Optical Communications Networks (WOCN), 2013 Tenth International Conference on, 2013, pp. 1–5, ISSN 2151-7681.
    [3]
    I. Dietrich and F. Dressler, On the lifetime of wireless sensor networks, ACM Trans. Sen. Netw. 5(1) (2009), 5:1–5:39.
    [4]
    A. Dunkels, B. Gronvall and T. Voigt, Contiki – a lightweight and flexible operating system for tiny networked sensors, in: Local Computer Networks, 2004. 29th Annual IEEE International Conference on, 2004, pp. 455–462, ISSN 0742-1303.
    [5]
    FIT IoT-LAB, IoT-LAB: A very large scale open testbed, 2016, https://www.iot-lab.info/.
    [6]
    N. Fournel, A. Fraboulet and P. Feautrier, eSimu: A fast and accurate energy consumption simulator for real embedded system, in: 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2007, pp. 1–6.
    [7]
    D.G. Gomes and A. Forster, Introduction to the special issue on Green engineering: Towards sustainable smart cities, Computers and Electrical Engineering 45 (2015), 141–142, http://www.sciencedirect.com/science/article/pii/S0045790615002396.
    [8]
    R.T. Hermeto, D.S. Kridi, A.R. Rocha and D.G. Gomes, A distributed algorithm for semantic collectors election in wireless sensors networks, Journal of Applied Computing Research 3(12) (2013), 1–10, http://www.revistas.unisinos.br/index.php/jacr/article/view/5830.
    [9]
    D. Izadi, J. Abawajy and S. Ghanavati, An alternative clustering scheme in WSN, IEEE Sensors Journal 15(7) (2015), 4148–4155.
    [10]
    Y. Liao, H. Qi and W. Li, Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks, IEEE Sensors Journal 13(5) (2013), 1498–1506.
    [11]
    X. Liu, A survey on clustering routing protocols in wireless sensor networks, 12 (2012), 11113–11153.
    [12]
    X. Liu and H. Xin, Variable weight based clustering approach for load balancing in wireless sensor networks, Communications in Computer and Information Science (2017).
    [13]
    M. Lukic, B. Pavkovic, N. Mitton and I. Stojmenovic, Greedy geographic routing algorithms in real environment, in: Mobile Ad-Hoc and Sensor Networks, 2009. MSN’09. 5th International Conference on, 2009, pp. 86–93.
    [14]
    M. Mirzaie and S.M. Mazinani, Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network, Computer Communications 111(Supplement C) (2017), 56–67, http://www.sciencedirect.com/science/article/pii/S0140366416303954.
    [15]
    M. Mónton and M. Picone, An open-source cloud architecture for big stream IoT applications, Interoperability and Open-Source Solutions for the Internet of Things, International Workshop, FP7 OpenIoT Project, Held in Conjunction with SoftCOM 2014, Split, Croatia, September 18, 2014, Invited Papers 9001 (2015), 73.
    [16]
    L.O. Moreira, V.A.E. Farias, F.R.C. Sousa, G.A.C. Santos, J.G.R. Maia and J.C. Machado, Towards improvements on the quality of service for multi-tenant RDBMS in the cloud, in: Data Engineering Workshops (ICDEW), 2014 IEEE 30th International Conference on, 2014, pp. 162–169.
    [17]
    M. Moreira Neto, L.O. Moreira and D.G. Gomes, FLECHA: A Forecasting eLEction meCHAnism for semantic collectors sensor nodes, in: XV Workshop em Desempenho de Sistemas Computacionais e de Comunicação (WPerformance), 2016, pp. 2851–2862, http://ebooks.pucrs.br/edipucrs/anais/csbc/#/evento/15wperformance.
    [18]
    L. Remy, Smart gateway for low-power lossy networks, in: Proceedings of the 2015 on MobiSys PhD Forum, PhDForum’15, ACM, New York, NY, USA, 2015, pp. 13–14. ISBN 978-1-4503-3497-6.
    [19]
    B.D. Ripley, The R project in statistical computing, MSOR Connections (2001).
    [20]
    A.R. Rocha, F.C. Delicato, L. Pirmez, D.G. Gomes and J.N. de Souza, A fully-decentralized semantic mechanism for autonomous wireless sensor nodes, Journal of Network and Computer Applications 61 (2016), 142–160.
    [21]
    A.R. Rocha, L. Pirmez, F.C. Delicato, É. Lemos, I. Santos, D.G. Gomes and J.N. de Souza, Wireless sensor networks clustering based on semantic neighborhood relationships, Computer Networks 56(5) (2012), 1627–1645, http://www.sciencedirect.com/science/article/pii/S1389128612000382.
    [22]
    J. Rodway and P. Musilek, Wireless sensor networks with pressure-based energy forecasting: A simulation study, in: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2016, pp. 1–4.
    [23]
    G.A.C. Santos, J.G.R. Maia, L.O. Moreira, F.R.C. Sousa and J.C. Machado, Scale-space filtering for workload analysis and forecast, in: 2013 IEEE Sixth International Conference on Cloud Computing, 2013, pp. 677–684, ISSN 2159-6182.
    [24]
    S.P. Singh and S.C. Sharma, Genetic-algorithm-based energy-efficient clustering (GAEEC) for homogenous wireless sensor networks, IETE Journal of Research 64(5) (2018), 648–659.
    [25]
    A. Somov and R. Giaffreda, Powering IoT devices: Technologies and opportunities, IEEE Internet of Things Newslettter (2015).
    [26]
    Z. Xu, Y. Yin, J. Wang and J.-U. Kim, An density-based energy-efficient routing algorithm in wireless sensor networks using game theory, International Journal of Future Generation Communication and Networking 5(4) (2012).

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            Published In

            cover image Journal of Ambient Intelligence and Smart Environments
            Journal of Ambient Intelligence and Smart Environments  Volume 11, Issue 4
            2019
            71 pages

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            IOS Press

            Netherlands

            Publication History

            Published: 01 January 2019

            Author Tags

            1. Clustering algorithms
            2. clustering methods
            3. prediction methods
            4. semantics
            5. wireless sensor networks

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