1. Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar 788010, India 2. Department of Computer Science & Engineering, Karunya Institute of Technology & Sciences, Coimbatore 641114, India 3. Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, India
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.
T K Hui, R S Sherratt, D D Sanchez. Major require-’ments for building Smart Homes in Smart Cities based on Internet of Things technologies. Future Generation Computer Systems, 2017, 76: 358–369 https://doi.org/10.1016/j.future.2016.10.026
2
W He, G Yan, L D Xu. Developing vehicular data cloud services in the IoT environment. IEEE Transactions on Industrial Informatics, 2014, 10(2): 1587–1595 https://doi.org/10.1109/TII.2014.2299233
3
G Zhou, Z Liu, W Shu, T Bao, L Mao, D Wu. Smart savings on private carpooling based on internet of vehicles. Journal of Intelligent & Fuzzy Systems, 2017, 32(5): 3785–3796 https://doi.org/10.3233/JIFS-169311
4
P Verma, S K Sood. Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet of Things Journal, 2018, 5(3): 1789–1796 https://doi.org/10.1109/JIOT.2018.2803201
5
A Majumdar, T Debnath, S K Sood, K L Baishnab. Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment. Journal of Medical Systems, 2018, 42(10): 187 https://doi.org/10.1007/s10916-018-1041-3
6
T Anagnostopoulos, A Zaslavsky, K Kolomvatsos, A Medvedev, P Amirian, J Morley, S Hadjieftymiades. Challenges and opportunities of waste management in IoT-enabled smart cities: a survey. IEEE Transactions on Sustainable Computing, 2017, 2(3): 275–289 https://doi.org/10.1109/TSUSC.2017.2691049
7
F Shrouf, G Miragliotta. Energy management based on Internet of Things: practices and framework for adoption in production management. Journal of Cleaner Production, 2015, 100: 235–246 https://doi.org/10.1016/j.jclepro.2015.03.055
8
P P Ray. Internet of things for smart agriculture: technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments, 2017, 9(4): 395–420 https://doi.org/10.3233/AIS-170440
9
T Kanungo, D M Mount, N S Netanyahu, C D Piatko, R Silverman, A Y Wu. An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 881–892 https://doi.org/10.1109/TPAMI.2002.1017616
10
D W Van der Merwe, A P Engelhrecht. Data clustering using particle swarm optimization. In: Proceedings of the 2003 Congress on Evolutionary Computation. 2003, 215–220
11
N M A Latiff, C C Tsimenidis, B S Sharif. 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 https://doi.org/10.1109/PIMRC.2007.4394521
12
M A Hoque, M Siekkinen, J K Nurminen. Energy efficient multi-media streaming to mobile devices—a survey. IEEE Communications Surveys and Tutorials, 2014, 16(1): 579–597 https://doi.org/10.1109/SURV.2012.111412.00051
13
E Russell, J Kennedy. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1995, 1942–1948
14
S Das, T Malakar. 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 https://doi.org/10.1080/01430750.2019.1587723
15
S D Muruganathan, D C Ma, R I Bhasin, A O Fapojuwo. A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Communications Magazine, 2005, 43(3): S8–13 https://doi.org/10.1109/MCOM.2005.1404592
16
N Aslam, W Phillips, W Robertson, S Sivakumar. A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks. Information Fusion, 2009, 12(3): 202–212 https://doi.org/10.1016/j.inffus.2009.12.005
17
S Sun, Y Z Wang. K-nearest neighbor clustering algorithm based on kernel methods. Second WRI Global Congress on Intelligent Systems, 2010, 3: 335–338 https://doi.org/10.1109/GCIS.2010.272
18
J Senthilnath, S N Omkar, V Mani. Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 2011, 1(3): 164–171 https://doi.org/10.1016/j.swevo.2011.06.003
19
J M Liang, J J Chen, H H Cheng, Y C Tseng. 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 https://doi.org/10.1109/JETCAS.2013.2243631
20
J Gubbi, R Buyya, S Marusic, M Palaniswami. Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, 29(7): 1645–1660 https://doi.org/10.1016/j.future.2013.01.010
21
Z Zhou, J Tang, L J Zhang, K Ning, Q Wang. 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 https://doi.org/10.1007/s00779-013-0710-y
22
J Tang, Z Zhou, J Niu, Q Wang. 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 https://doi.org/10.1016/j.jnca.2013.07.009
B Niu, Q Duan, L Tan, C Liu, P Liang. A population-based clustering technique using particle swarm optimization and K-means. In: Proceedings of International Conference in Swarm Intelligence. 2015, 145–152 https://doi.org/10.1007/978-3-319-20466-6_16
25
S Rani, R Talwar, J Malhotra, S H Ahmed, M Sarkar, H Song. A novel scheme for an energy efficient internet of things based on wireless sensor networks. Sensors, 2015, 15(11): 28603–28626 https://doi.org/10.3390/s151128603
26
Ö U Akgül, B Canberk. Self-organized things (SoT): an energy efficient next generation network management. Computer Communications, 2016, 74: 52–62 https://doi.org/10.1016/j.comcom.2014.07.004
27
A Orsino, G Araniti, L Militano, J Alonso-Zarate, A Molinaro, A Iera. Energy efficient IoT data collection in smart cities exploiting D2D communications. Sensors, 2016, 16(6): 836 https://doi.org/10.3390/s16060836
28
N Kaur, S K Sood. An energy-efficient architecture for the Internet of Things (IoT). IEEE Systems Journal, 2017, 11(2): 796–805 https://doi.org/10.1109/JSYST.2015.2469676
29
L Song, K K Chai, Y Chen, J Schormans, J Loo, A Vinel. QoS-aware energy-efficient cooperative scheme for cluster-based IoT systems. IEEE Systems Journal, 2017, 11(3): 1447–1455 https://doi.org/10.1109/JSYST.2015.2465292
30
I Yaqoob, E Ahmed, I A T Hashem, A I A Ahmed, A Gani, M Imran, M Guizani. Internet of Ihings architecture: recent advances, taxonomy, requirements, and open challenges. IEEEWireless Communications, 2017, 24(3): 10–16 https://doi.org/10.1109/MWC.2017.1600421
31
A R Jadhav, T Shankar. Whale optimization based energy-efficient cluster head selection algorithm for wireless sensor networks. 2017, arXiv preprint arXiv:1711.09389
R Cheng, Y Jin. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics, 2014, 45(2): 191–204 https://doi.org/10.1109/TCYB.2014.2322602
34
A Majumdar, NM Laskar, A Biswas, S K Sood, K L Baishnab. Energy efficient e-healthcare framework using HWPSO-based clustering approach. Journal of Intelligent & Fuzzy Systems, 2019, 36(5): 3957–3969 https://doi.org/10.3233/JIFS-169957
35
T L Saaty. 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 https://doi.org/10.1007/0-387-23081-5_9
36
S S Wangikar, P K Patowari, R D Misra. Effect of process parameters and optimization for photochemical machining of brass and german silver. Materials and Manufacturing Processes, 2017, 32(15): 1747–1755 https://doi.org/10.1080/10426914.2016.1244848
37
A K Singh, P K Patowari, N V Deshpande. Experimental analysis of reverse micro-EDM for machining microtool. Materials and Manufacturing Processes, 2016, 31(4): 530–540 https://doi.org/10.1080/10426914.2015.1070426
38
R K Roy. Multiple criteria of evaluations for designed experiments. See Nutekus.com website, 2018
39
R K Roy. Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement. John Wiley and Sons Press, 2001
40
J Kennedy, R C Eberhart. Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks. 1995
L Hao, X Gang, Y D Gui, B S Yu. Human behavior-based particle swarm optimization. The Scientific World Journal, 2014, 2014: 194706 https://doi.org/10.1155/2014/194706
S Mirjalili. 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 https://doi.org/10.1007/s00521-015-1920-1
45
A Majumdar, T Debnath, A Biswas, S K Sood, K L Baishnab. An energy efficient e-healthcare framework supported by novel EO-μGA (extremal optimization tuned micro-genetic algorithm). Information Systems Frontiers, 2020, https://doi.org/10.1007/s10796-020-10016-5
46
J Derrac, S García, D Molina, F Herrera. 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 https://doi.org/10.1016/j.swevo.2011.02.002