OCSO-CA: opposition based competitive swarm optimizer in energy efficient IoT clustering
Arpita BISWAS, Abhishek MAJUMDAR, Soumyabrata DAS, Krishna Lal BAISHNAB
OCSO-CA: opposition based competitive swarm optimizer in energy efficient IoT clustering
With the advent of modern technologies, IoT has become an alluring field of research. Since IoT connects everything to the network and transmits big data frequently, it can face issues regarding a large amount of energy loss. In this respect, this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and clouds. Consequently, a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency, network lifetime and latency. Furthermore, an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device network. The proposed strategy will help in managing intra-cluster and intercluster data communications in an energy-efficient way. Also, a comparative analysis of the proposed approach with the stateof-the-art optimization algorithms for clustering has been performed.
competitive swarm optimization / cloud computing / clustering / IoT
[1] |
Hui T K, Sherratt R S, Sanchez D D. Major require-’ments for building Smart Homes in Smart Cities based on Internet of Things technologies. Future Generation Computer Systems, 2017, 76: 358–369
CrossRef
Google scholar
|
[2] |
He W, Yan G, Xu L D. Developing vehicular data cloud services in the IoT environment. IEEE Transactions on Industrial Informatics, 2014, 10(2): 1587–1595
CrossRef
Google scholar
|
[3] |
Zhou G, Liu Z, Shu W, Bao T, Mao L, Wu D. Smart savings on private carpooling based on internet of vehicles. Journal of Intelligent & Fuzzy Systems, 2017, 32(5): 3785–3796
CrossRef
Google scholar
|
[4] |
Verma P, Sood S K. Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet of Things Journal, 2018, 5(3): 1789–1796
CrossRef
Google scholar
|
[5] |
Majumdar A, Debnath T, Sood S K, Baishnab K L. Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment. Journal of Medical Systems, 2018, 42(10): 187
CrossRef
Google scholar
|
[6] |
Anagnostopoulos T, Zaslavsky A, Kolomvatsos K, Medvedev A, Amirian P, Morley J, Hadjieftymiades S. Challenges and opportunities of waste management in IoT-enabled smart cities: a survey. IEEE Transactions on Sustainable Computing, 2017, 2(3): 275–289
CrossRef
Google scholar
|
[7] |
Shrouf F, Miragliotta G. Energy management based on Internet of Things: practices and framework for adoption in production management. Journal of Cleaner Production, 2015, 100: 235–246
CrossRef
Google scholar
|
[8] |
Ray P P. Internet of things for smart agriculture: technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 2017, 9(4): 395–420
CrossRef
Google scholar
|
[9] |
Kanungo T, Mount D M, Netanyahu N S, Piatko C D, Silverman R, Wu A Y. An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 881–892
CrossRef
Google scholar
|
[10] |
Van der Merwe D W, Engelhrecht A P. Data clustering using particle swarm optimization. In: Proceedings of the 2003 Congress on Evolutionary Computation. 2003, 215–220
|
[11] |
Latiff N M A, Tsimenidis C C, Sharif B S. Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: Proceedings of the 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. 2007, 1–5
CrossRef
Google scholar
|
[12] |
Hoque M A, Siekkinen M, Nurminen J K. Energy efficient multi-media streaming to mobile devices—a survey. IEEE Communications Surveys and Tutorials, 2014, 16(1): 579–597
CrossRef
Google scholar
|
[13] |
Russell E, Kennedy J. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1995, 1942–1948
|
[14] |
Das S, Malakar T. An emission constraint capacitor placement and sizing problem in radial distribution systems using modified competitive swarm optimiser approach. International Journal of Ambient Energy. 2021, 42(11): 1228–1251
CrossRef
Google scholar
|
[15] |
Muruganathan S D, Ma D C, Bhasin R I, Fapojuwo A O. A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 2005, 43(3): S8–13
CrossRef
Google scholar
|
[16] |
Aslam N, Phillips W, Robertson W, Sivakumar S. A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks. Information Fusion, 2009, 12(3): 202–212
CrossRef
Google scholar
|
[17] |
Sun S, Wang Y Z. K-nearest neighbor clustering algorithm based on kernel methods. Second WRI Global Congress on Intelligent Systems, 2010, 3: 335–338
CrossRef
Google scholar
|
[18] |
Senthilnath J, Omkar S N, Mani V. Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 2011, 1(3): 164–171
CrossRef
Google scholar
|
[19] |
Liang J M, Chen J J, Cheng H H, Tseng Y C. An energy-efficient sleep scheduling with QoS consideration in 3GPP lTE-advanced networks for internet of things. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2013, 3(1): 13–22
CrossRef
Google scholar
|
[20] |
Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, 29(7): 1645–1660
CrossRef
Google scholar
|
[21] |
Zhou Z, Tang J, Zhang L J, Ning K, Wang Q. EGF-Tree: an energyefficient index tree for facilitating multi-region query aggregation in the Internet of Things. Personal and Ubiquitous Computing, 2014, 18(4): 951–966
CrossRef
Google scholar
|
[22] |
Tang J, Zhou Z, Niu J,Wang Q. An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things. Journal of Network and Computer Applications, 2014, 40: 1–11
CrossRef
Google scholar
|
[23] |
Das K N, Singh T K. Drosophila food-search optimization. Applied Mathematics and Computation, 2014, 231: 566–580
CrossRef
Google scholar
|
[24] |
Niu B, Duan Q, Tan L, Liu C, Liang P. A population-based clustering technique using particle swarm optimization and K-means. In: Proceedings of International Conference in Swarm Intelligence. 2015, 145–152
CrossRef
Google scholar
|
[25] |
Rani S, Talwar R, Malhotra J, Ahmed S H, Sarkar M, Song H. A novel scheme for an energy efficient internet of things based on wireless sensor networks. Sensors, 2015, 15(11): 28603–28626
CrossRef
Google scholar
|
[26] |
Akgül Ö U, Canberk B. Self-organized things (SoT): an energy efficient next generation network management. Computer Communications, 2016, 74: 52–62
CrossRef
Google scholar
|
[27] |
Orsino A, Araniti G, Militano L, Alonso-Zarate J, Molinaro A, Iera A. Energy efficient IoT data collection in smart cities exploiting D2D communications. Sensors, 2016, 16(6): 836
CrossRef
Google scholar
|
[28] |
Kaur N, Sood S K. An energy-efficient architecture for the Internet of Things (IoT). IEEE Systems Journal, 2017, 11(2): 796–805
CrossRef
Google scholar
|
[29] |
Song L, Chai K K, Chen Y, Schormans J, Loo J, Vinel A. QoS-aware energy-efficient cooperative scheme for cluster-based IoT systems. IEEE Systems Journal, 2017, 11(3): 1447–1455
CrossRef
Google scholar
|
[30] |
Yaqoob I, Ahmed E, Hashem I A T, Ahmed A I A, Gani A, Imran M, Guizani M. Internet of Ihings architecture: recent advances, taxonomy, requirements, and open challenges. IEEEWireless Communications, 2017, 24(3): 10–16
CrossRef
Google scholar
|
[31] |
Jadhav A R, Shankar T. Whale optimization based energy-efficient cluster head selection algorithm for wireless sensor networks. 2017, arXiv preprint arXiv:1711.09389
|
[32] |
Kiran M S. Particle swarm optimization with a new update mechanism. Applied Soft Computing, 2017, 60: 670–678
CrossRef
Google scholar
|
[33] |
Cheng R, Jin Y. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics, 2014, 45(2): 191–204
CrossRef
Google scholar
|
[34] |
Majumdar A, Laskar NM, Biswas A, Sood S K, Baishnab K L. Energy efficient e-healthcare framework using HWPSO-based clustering approach. Journal of Intelligent & Fuzzy Systems, 2019, 36(5): 3957–3969
CrossRef
Google scholar
|
[35] |
Saaty T L. The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. In: Multiple Criteria Decision Analysis: State of the Art Surveys. Springer, New York, 2005, 345–405
CrossRef
Google scholar
|
[36] |
Wangikar S S, Patowari P K, Misra R D. Effect of process parameters and optimization for photochemical machining of brass and german silver. Materials and Manufacturing Processes, 2017, 32(15): 1747–1755
CrossRef
Google scholar
|
[37] |
Singh A K, Patowari P K, Deshpande N V. Experimental analysis of reverse micro-EDM for machining microtool. Materials and Manufacturing Processes, 2016, 31(4): 530–540
CrossRef
Google scholar
|
[38] |
Roy R K. Multiple criteria of evaluations for designed experiments. See Nutekus.com website, 2018
|
[39] |
Roy R K. Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement. John Wiley and Sons Press, 2001
|
[40] |
Kennedy J, Eberhart R C. Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1995
|
[41] |
Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016, 95: 51–67
CrossRef
Google scholar
|
[42] |
Hao L, Gang X, Gui Y D, Yu B S. Human behavior-based particle swarm optimization. The Scientific World Journal, 2014, 2014: 194706
CrossRef
Google scholar
|
[43] |
Holland J H. Genetic algorithms. Scientific American, 1992, 267(1): 66–73
CrossRef
Google scholar
|
[44] |
Mirjalili S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective. discrete, and multi-objective problems. Neural Computing and Applications, 2016, 27(4): 1053–1073
CrossRef
Google scholar
|
[45] |
Majumdar A, Debnath T, Biswas A, Sood S K, Baishnab K L. An energy efficient e-healthcare framework supported by novel EO-μGA (extremal optimization tuned micro-genetic algorithm). Information Systems Frontiers, 2020, DOI: 10.1007/s10796-020-10016-5
CrossRef
Google scholar
|
[46] |
Derrac J, García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 2011, 1(1): 3–18
CrossRef
Google scholar
|
/
〈 | 〉 |