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RETRACTED ARTICLE: Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection

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This article was retracted on 30 May 2022

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Abstract

In a modern approach, a smart grid requires an innovation on various fronts such as a wireless sensor network which is an important component of smart grid applicability. To fulfill the Quality of Service (QoS) needs in smart grid network, a structure of network and topology should be optimized especially in the urban areas. The clustering methodologies are useful technique for optimizing network topologies. In clustering, the clustering process consists of cluster head node selection and rotation which is based on Residual Energy, Distance of node from base station etc. However, the impact of temperature rise has not been considered so far. Based on the previous related works, this paper proposes a Thermal Aware solution based on combining Eigen Centrality Fuzzy Cluster size Control and Spider Optimization Algorithm. Furthermore, an influence of temperature can be realized through Received Signal Strength (RSS) and the number of packets received. The proposed algorithm is simulated in MATLAB and implemented in hardware test bed using Zigbee and PIC microcontroller. Consequently, the result confirms the impact of thermal heat on Cluster Head selection control and also the prediction of number of rounds.

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References

  • AbdelSalam AS, Olariu S (2012) BEES bioinspired backbone E selection in wireless sensor networks. IEEE Transact Parallel Distrib Syst 23(1):44–51

    Article  Google Scholar 

  • Al-Anbagi I, Erol-Kantarci M, Mouftah HT (2013) QoS-aware inter-cluster head scheduling in WSNs for high data rate smart grid applications. In: IEEE global communications conference (GLOBECOM)

  • Arora VK, Sharma V, Sachdeva M (2019) ACO optimized self-organized tree-based energy balance algorithm for wireless sensor network. J Ambient Intell Human Comput 10:4963–4975

    Article  Google Scholar 

  • Bag A, Bassiouni MA (2008) Hotspot preventing routing algorithm for delay-sensitive applications of in vivo biomedical sensor networks. Inf Fusion 9:389–398

    Article  Google Scholar 

  • Bannister K, Giorgetti G, Gupta SK (2008) Wireless sensor networking for “hot” applications: effects of temperature on signal strength, data collection and localization. In: Proceedings of the ACM 15th workshop on embedded networked sensors, Charlottesville, 2–3 June

  • Boanoy CA, Wennerströmx H, Zúñiga MA (2013) Hot packets: a systematic evaluation of the effect of temperature on low power wireless transceivers. In: 5th extreme conference on communication (ExtremeCom).

  • Buttyan L, Schaffer P (2007) Panel: Position-based aggregato rnode election in wireless sensor networks. In: 2007 IEEE Inter-national conference on mobile adhoc and sensor systems, pp 1–9

  • Cai X, Duan Y, He Y (2015) Bee-Sensor-C: an energy-efficient and scalable multipath routing protocol for wireless sensor networks. Int J Distrib Sens Netw

  • Ebrahimi MS, Daraei MH, Behzadan V, Khajooeizadeh A (2011) A novel utilization of cluster-tree wireless sensor networks for situation awareness in smart grids. In: IEEE PES Innovative Smart Grid Technologies, pp 13–16

  • Elhabyan RS, Yagoub MCE (2014) PSO-HC: particle swarm optimization protocol for hierarchical clustering in wireless sensor networks. In: 10th IEEE international conference on collaborative computing: networking, applications and worksharing

  • Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks, in System Sciences, 2000. In: Proceedings of the 33rd Annual Hawaii International Conference on, vol 2. p 10

  • Ho CK, Ewe HT (2005) Performance of an ant colony optimization (ACO) algorithm on the dynamic load-balanced clustering problem in ad hoc networks. In: International conference on computational and information science CIS: computational intelligence and security, pp 622–629

  • Hong J, KookJ LS, Kwon D, Yi S (2008) T-leach: the method of threshold-based cluster head replacement for wireless sensor networks. Inf Syst Front 11(5):513–521

    Article  Google Scholar 

  • Jain A, Ramana Reddy BV (2015) Eigenvector centrality based cluster size control in randomly deployed wireless sensor networks. Expert Syst Appl 42(5):2657–2669

    Article  Google Scholar 

  • Jiang C, Shi W, Xiang M (2010) Energy-balanced unequal clustering routing protocol for wireless sensor networks. J China Univ Posts Telecommun 17(4):94–99

    Article  Google Scholar 

  • Jin Y, Wang L, Kim Y, Yang X (2008) Eemc: an energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks. Comput Netw 52(3):542–562

    Article  Google Scholar 

  • Lee J, Cheng W (2012) Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sens J 12(9):2891–2897

    Article  Google Scholar 

  • Lindsey S, Raghavendra C (2002) Pegasis: power-efficient gathering in sensor information systems. In: Aerospace conference proceedings, IEEE, vol 3, 2. pp 3–1125–3–1130

  • Manjeshwar A, Agrawal DP (2001) Teen: a routing protocol for enhanced efficiency in wireless sensor networks, in parallel and distributed processing symposium. In: Proceedings 15th International, pp 2009–2015

  • Monowar MM, Bajaber F (2015) On designing thermal-aware localized QoS routing protocol for in-vivo sensor nodes in wireless body area networks. Sensors 5:14036–14044

    Google Scholar 

  • Ni Q, Pan Q, Du H (2015) A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization. In: IEEE/ACM transactions on computational biology and bioinformatics

  • Nokhanji N, Hanapi ZM, Subramaniam S, Mohamed MA (2015) An energy aware distributed clustering algorithm using fuzzy logic for wireless sensor networks with non-uniform node distribution. Wirel Personal Commun 84:395–419

    Article  Google Scholar 

  • Singh M, Soni SK (2019) Fuzzy based novel clustering technique by exploiting spatial correlation in wireless sensor network. J Ambient Intell Human Comput 10:1361–1378

    Article  Google Scholar 

  • Taheri H, Neamatollahi P, Younis O, Naghibzadeh S, Yaghmaee M (2012) An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Netw 10:1469–1481

    Article  Google Scholar 

  • Ye M, Li C, Chen G, Wu J (2005) Eecs: an energy efficient clustering scheme in wireless sensor networks in PCCC 2005. In: 24th IEEE international performance, computing, and communications conference, April 2005, pp 535–540

  • Younis O, Fahmy S (2004) Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transact Mob Comput 3(4):366–379

    Article  Google Scholar 

Download references

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Correspondence to R. Karpaga Priya.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03983-x"

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Priya, R.K., Venkatanarayanan, S. RETRACTED ARTICLE: Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection. J Ambient Intell Human Comput 12, 5245–5255 (2021). https://doi.org/10.1007/s12652-020-02002-1

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  • DOI: https://doi.org/10.1007/s12652-020-02002-1