Abstract
Wireless sensor networks are applied in different domains such as agriculture, military, healthcare, and defense. The most critical issue in wireless sensor networks is achieving greater energy efficiency fairness measurement. This research proposes new evolutionary modeling using genetic algorithms with an energy-efficient clustering routing strategy to resolve this critical issue in healthcare networks of hospitals. This article develops a new model of homogeneous and heterogeneous network structures with the novelty of combining the consumption model of original energy to construct new evolutionary modeling to predict the optimal clusters count for minimizing the consumption of the total energy in wireless sensor healthcare applications. The simulation results show that the lifetime is maximum when the routing factor, α = 0.1 compared to other values of α. For the homogeneous healthcare networks instances, the death round value of the first node is for a smaller value of α. The better routing factor is obtained as α ranging from 0.2 to 0.5 for homogeneous and heterogeneous healthcare networks instances. For the range of energy levels that ranges from 0.4 to 0.8, the proposed evolutionary model achieves better measures for the rounds from 1800 up to 2400 compared to the recent methods. The proposed model evaluates optimal clusters based on the structure of the healthcare network, and then the clusters head is identified based on the clustering locations. The proposed method achieves a higher throughput percentage in all cases of homogeneous and heterogeneous instances where the energy lies between (0.4, 0.8). Simulation outcomes of the proposed evolutionary model result in the performance metrics mainly significant reduction of energy consumption decay rate of the network while maximizing the healthcare network’s lifetime by improving the network’s throughput compared to the state-of-the-art methods.
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References
Akram J, Munawar HS, Kouzani AZ, Mahmud MAP (2022) Using adaptive sensors for optimised target coverage in wireless sensor networks. Sensors 22(3):1083. https://doi.org/10.3390/s22031083
Astorino A, Gaudioso M, Miglionico G (2021) A Lagrangean relaxation approach to lifetime maximization of directional sensor networks. Networks. Wiley, Hoboken. https://doi.org/10.1002/net.22017
Atiq HU, Ahmad Z, uz Zaman SK, Khan MA, Shaikh AA, Al-Rasheed A (2023) Reliable resource allocation and management for IoT transportation using fog computing. Electronics 12:1452. https://doi.org/10.3390/electronics12061452
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Boyd S, Xiao L, Mutapcic A (2014) Subgradient methods; technical report EE364b; Stanford Univ: Stanford, CA, USA
Chai G, Wu W, Yang Q, Liu R, Kwak KS (2021) Learning to optimize for resource allocation in LTE-U networks. China Commun 18(3):142–154. https://doi.org/10.23919/JCC.2021.03.012
Du B, Pan C, Zhang W, Chen M (2014) Distributed energy-efficient power optimization for CoMP systems with max–min fairness. IEEE Commun Lett 18(6):999–1002. https://doi.org/10.1109/LCOMM.2014.2317734
Gautam V, Tiwari RG, Jain AK, Agarwal A (2022) Research pattern of internet of things and its impact on cyber security. In:11th International conference on system modeling & advancement in research trends (SMART). IEEE, pp 260–263
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the international conference on artificial intelligence and statistics, sardinia, Italy, pp 249–256
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press, Cambridge
He S, Huang Y, Jin S, Yu F, Yang L (2013) Max–min energy efficient beamforming for multicell multiuser joint transmission systems. IEEE Commun Lett 17(10):1956–1959. https://doi.org/10.1109/LCOMM.2013.082613.131540
He S, Huang Y, Yang L, Ottersten B (2014) Coordinated multicell multiuser precoding for maximizing weighted sum energy efficiency. IEEE Trans Signal Process 62(3):741–751. https://doi.org/10.1109/TSP.2013.2294595
Hong M, Razaviyayn M, Luo Z-Q, Pang J-S (2016) A unifined algorithmic framework for block-structured optimization involving big data: with applications in machine learning and signal processing. IEEE Signal Process Mag 33:57–77
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internalcovariance shift. Proc Int Conf Mach Learn Lille Fr 6–11:448–456
Isheden C, Chong Z, Jorswieck E, Fettweis G (2012) Framework for link-level energy efficiency optimization with informed transmitter. IEEE Trans Wirel Commun 11(8):2946–2957. https://doi.org/10.1109/TWC.2012.060412.111829
Khan MA, Khan J, Mahmood K, Bari I, Ali H, Jan N, Ghoniem RM (2022) Algorithm for increasing network lifetime in wireless sensor networks using jumping and mobile sensor nodes. Electronics 11:2913. https://doi.org/10.3390/electronics11182913
Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: Proceedings of the the 3rd international conference for learning representations. Represent. (ICLR), San Diego, CA, USA
LeCun Y, Yoshua B, Hinton G (2015) Deep learning. Nature 521:436–444
Lee H, Jang HS, Jung BC (2019) Improving energy efficiency fairness of wireless networks: a deep learning approach. Energies 12:4300. https://doi.org/10.3390/en12224300
Lee W, Kim M, Cho D-H (2018a) Deep power control: transmit power control scheme based on convolutional neural network. IEEE Commun Lett 22(6):1276–1279. https://doi.org/10.1109/LCOMM.2018.2825444
Lee W, Kim M, Cho D-H (2019b) Transmit power control using deep neural network for underlaydevice-to-device communication. IEEE Wirel Commun Lett 8:141–144
Lee W, Kim M, Cho D-H (2019c) Deep learning based transmit power control in underlaid device-to-device communication. IEEE Syst J 13:2551–2554
Lee H, Lee I, Lee SH (2018b) Deep learning based transceiver design for multi-colored VLC systems. Opt Express 26:6222–6238
Lee H, Lee I, Quek TQS, Lee SH (2018b) Binary signaling design for visible light communication: a deeplearning framework. Opt Express 26:18131–18142
Lee H, Lee SH, Quek TQS, Lee I (2019a) Deep learning framework for wireless systems: applications tooptical wireless communications. IEEE Commun Mag 57:35–41
Lee W (2018) Resource allocation for multi-channel underlay cognitive radio network based on deep neural network. IEEE Commun Lett 22(9):1942–1945. https://doi.org/10.1109/LCOMM.2018.2859392
Lilhore UK, Simaiya S, Pandey H, Gautam V, Garg A, Ghosh P (2022) Breast cancer detection in the IoT cloud-based healthcare environment using fuzzy cluster segmentation and SVM classifier. In: Ambient communications and computer systems: proceedings of RACCCS 2021. Springer, Singapore, pp 165–179
Liu R, Ma Y, Zhang X, Gao Y (2021) Deep learning-based spectrum sensing in space-air-ground integrated networks. J Commun Inform Netw 6(1):82–90. https://doi.org/10.23919/JCIN.2021.9387707
Lu Z, Pu H, Wang F, Hu Z, Wang L (2017) The expressive power of neural networks: a view from the width. In: Proceedings of the advances in neural information processing systems 30 (NIPS), Long Beach, CA, USA, pp 6231–6239
Lv S, Ji J (2023) Secrecy outage performance and power allocation for three secondary users CR-NOMA networks with transmit antenna selection. Electronics 2023:12. https://doi.org/10.3390/electronics12081896
Manju A (2021) Meta-heuristic based approach with modified mutation operation for heterogeneous networks. Wirel Pers Commun. https://doi.org/10.1007/s11277-021-08935-w
Minani F (2019) Maximization of lifetime for wireless sensor networks based on energy efficient clustering algorithm. Int J Electron Commun Eng 13(6):389–395
Minella J, Orr S (2022) Wireless security architecture: designing and maintaining secure wireless for enterprise. Wiley, Hoboken
Nguyen K-G, Tran L-N, Tervo O, Vu Q-D, Juntti M (2015) Achieving energy efficiency fairness in multicell MISO downlink. IEEE Commun Lett 19(8):1426–1429. https://doi.org/10.1109/LCOMM.2015.2436382
Nguyen K-G, Vu Q-D, Juntti M, Tran L-N (2017) Distributed solutions for energy efficiency fairness in multicell MISO downlink. IEEE Trans Wirel Commun 16(9):6232–6247. https://doi.org/10.1109/TWC.2017.2721369
O’Shea T, Hoydis J (2017) An introduction to deep learning for the physical layer. IEEE Trans Cognit Commun Netw 3:563–575
Singh S (2021) A clustering-based optimized stable election protocol in wireless sensor networks. Appl Ubiquitous Comput. https://doi.org/10.1007/978-3-030-35280-6_8,(157-176)
Sun H, Chen X, Shi Q, Hong M, Fu X, Sidiropoulos ND (2018) Learning to optimize: training deep neural networks for interference management. IEEE Trans Signal Process 66(20):5438–5453. https://doi.org/10.1109/TSP.2018.2866382
Tervo O, Tran L-N, Juntti M (2015) Optimal energy-efficient transmit beamforming for multi-user MISOdownlink. IEEE Trans Signal Process 63:5574–5588
Wang X, Zhu P, Sheng B, You X (2013) Energy-efficient downlink transmission in multi-cell coordinated beamforming systems. IEEE Wirel Commun Network Conf (WCNC) 2013:2554–2558. https://doi.org/10.1109/WCNC.2013.6554963
Xiong Y, Chen G, Lu M, Wan X, Wu M, She J (2020) A two-phase lifetime-enhancing method for hybrid energy-harvesting wireless sensor network. IEEE Sens J 20(4):1934–1946. https://doi.org/10.1109/JSEN.2019.2948620
Xu Z, Yang C, Li GY, Liu Y, Xu S (2014) Energy-efficient CoMP precoding in heterogeneous networks. IEEE Trans Signal Process 62(4):1005–1017. https://doi.org/10.1109/TSP.2013.2296279
Yu W, Li X, Zeng Z, Luo M (2022) Problem characteristics and dynamic search balance-based artificial bee colony for the optimization of two-tiered WSN lifetime with relay nodes deployment. Sensors 22:8916. https://doi.org/10.3390/s22228916
Zappone A, Jorswieck E (2015) Energy efficiency in wireless networks via fractional programming theory. Found Trends Commun Inf Theory 11:185–396
Zhao Z, Xu K, Hui G, Hu L (2018) An energy-efficient clustering routing protocol for wireless sensor networks based on AGNES with balanced energy consumption optimization. Sensors 18:3938. https://doi.org/10.3390/s18113938
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Marappan, R., Vardhini, P.A.H., Kaur, G. et al. Efficient evolutionary modeling in solving maximization of lifetime of wireless sensor healthcare networks. Soft Comput 27, 11853–11867 (2023). https://doi.org/10.1007/s00500-023-08623-w
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DOI: https://doi.org/10.1007/s00500-023-08623-w