The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load. Since CMs only communicate with their CH and have a limited range, they avoid redundant information. However, the CH's routing, compression, and aggregation functions consume power quickly compared to other protocols, like TPGF, LQEAR, MPRM, and P-LQCLR. To address energy usage in wireless sensor networks (WSNs), heterogeneous high-power nodes (HPN) are used to balance energy consumption. CHs close to the base station require effective algorithms for improvement. The cluster-based glow-worm optimization technique utilizes random clustering, distributed cluster leader selection, and link-based routing. The cluster head routes data to the next group leader, balancing energy utilization in the WSN. This algorithm reduces energy consumption through multi-hop communication, cluster construction, and cluster head election. The glow-worm optimization technique allows for faster convergence and improved multi-parameter selection. By combining these methods, a new routing scheme is proposed to extend the network's lifetime and balance energy in various environments. However, the proposed model consumes more energy than TPGF, and other protocols for packets with 0 or 1 retransmission count in a 260-node network. This is mainly due to the short INFO packets during the neighbor discovery period and the increased hop count of the proposed derived pathways. Herein, simulations are conducted to evaluate the technique's throughput and energy efficiency.

Department of Biosciences Saveetha School of Engineering Saveetha Nagar Thandalam 602105 India

Department of Computer Science Aligarh Muslim University Aligarh 202001 India

Department of Computer Science and Engineering BMS Institute of Technology and Management Yelahanka Bengaluru 560064 India

Department of Computer Science and Engineering Sharnbasva University Kalaburagi 585105 India

Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Chennai 600062 India

Department of Control Systems and Instrumentation Faculty of Mechanical Engineering VSB Technical University of Ostrava 17 Listopadu 2172 15 70800 Ostrava Czech Republic

Department of R and D Bond Marine Consultancy London EC1V 2NX UK

School of Computing Mohan Babu University Tirupati 517102 India

Zobrazit více v PubMed

Chen J., Wang Q., Peng W., Xu H., Li X., Xu W. Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Trans. Intell. Transp. Syst. 2022;23:18855–18863. doi: 10.1109/TITS.2022.3161977. DOI

Li B., Zhang M., Rong Y., Han Z. Transceiver optimization for wireless powered time-division duplex MU-MIMO systems: Non-robust and robust designs. IEEE Trans. Wirel. Commun. 2021;21:4594–4607. doi: 10.1109/TWC.2021.3131595. DOI

Huang C., Han Z., Li M., Wang X., Zhao W. Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis. Australas. J. Educ. Technol. 2021;37:81–95. doi: 10.14742/ajet.6749. DOI

Ghaffari A. An Energy Efficient Routing Protocol for Wireless Sensor Networks using A-star Algorithm. J. Appl. Res. 2014;12:815–822. doi: 10.1016/S1665-6423(14)70097-5. DOI

Mukherjee S., Amin R., Biswas G.P. Design of Routing Protocol for Multi-Sink Based Wireless Sensor Networks. J. Wirel. Netw. 2019;25:4331–4347. doi: 10.1007/s11276-019-02095-3. DOI

Jiang H., Xiao Z., Li Z., Xu J., Zeng F., Wang D. An Energy-Efficient Framework for Internet of Things Underlaying Heterogeneous Small Cell Networks. IEEE Trans. Mob. Comput. 2022;21:31–43. doi: 10.1109/TMC.2020.3005908. DOI

El-Fouly F.H., Ramadan R.A. Energy Efficient Environment-Aware Fusion Based Reliable Routing in Wireless Sensor Networks. IEEE Access. 2020;8:112145–112159. doi: 10.1109/ACCESS.2020.3003155. DOI

El-Fouly F.H., Ramadan R.A. Real-Time Energy-Efficient Reliable Traffic Aware Routing for Industrial Wireless Sensor Networks. IEEE Access. 2020;8:58130–58145. doi: 10.1109/ACCESS.2020.2980682. DOI

Chen Y., Zhu L., Hu Z., Chen S., Zheng X. Risk Propagation in Multilayer Heterogeneous Network of Coupled System of Large Engineering Project. J. Manag. Eng. 2022;38:4022003. doi: 10.1061/(ASCE)ME.1943-5479.0001022. DOI

Zhang W., Liu Y., Han G., Feng Y., Zhao Y. An Energy Efficient and QoS Aware Routing Algorithm Based on Data Classification for Industrial Wireless Sensor Networks. IEEE Access. 2018;6:46495–46504. doi: 10.1109/ACCESS.2018.2866165. DOI

Tavallaie O., Naji H.R., Sabaei M., Arastouie N.R.-T., Aware E. Routing for Industrial Wireless Sensor Networks. Wireless Pers. Commun. 2017;95:4601–4621. doi: 10.1007/s11277-017-4109-3. DOI

Liu G. Data Collection in MI-Assisted Wireless Powered Underground Sensor Networks: Directions, Recent Advances, and Challenges. IEEE Commun. Mag. 2021;59:132–138. doi: 10.1109/MCOM.001.2000921. DOI

Chen S., Huang Q., Zhang Y., Li X. Double Sink Energy Hole Avoidance Strategy for Wireless Sensor Network. EURASIP J. Wirel. Commun. Netw. 2020;2020:226. doi: 10.1186/s13638-020-01837-8. DOI

Ni Q., Guo J., Wu W., Wang H. Influence-Based Community Partition with Sandwich Method for Social Networks. IEEE Trans. Comput. Soc. Syst. 2022;10:819–830. doi: 10.1109/TCSS.2022.3148411. DOI

Fu X., Yang Y., Postolache O. Sustainable Multipath Routing Protocol for Multi-Sink Wireless Sensor Networks in Harsh Environments. IEEE Trans. Sustain. Comput. 2020;6:168–181. doi: 10.1109/TSUSC.2020.2976096. DOI

Shi Y., Hu J., Wu Y., Ghosh B.K. Intermittent output tracking control of heterogeneous multi-agent systems over wide-area clustered communication networks. Nonlinear Anal. Hybrid Syst. 2023;50:101387. doi: 10.1016/j.nahs.2023.101387. DOI

Li D., Ge S.S., Lee T.H. Fixed-Time-Synchronized Consensus Control of Multiagent Systems. IEEE Trans. Control Netw. Syst. 2021;8:89–98. doi: 10.1109/TCNS.2020.3034523. DOI

Fu X., Fortino G., Pace P., Aloi G., Li W. Environment-fusion multipath routing protocol for wireless sensor networks. Inf. Fusion. 2020;53:4–19. doi: 10.1016/j.inffus.2019.06.001. DOI

Rajendran S., N. G., Čep R., R. C. N., Pal S., Kalita K. A conceptual comparison of six nature-inspired metaheuristic algorithms in process optimization. Processes. 2022;10:197. doi: 10.3390/pr10020197. DOI

Shankar R., Ganesh N., Čep R., Narayanan R.C., Pal S., Kalita K. Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization. Processes. 2022;10:616. doi: 10.3390/pr10030616. DOI

Joshi M., Kalita K., Jangir P., Ahmadianfar I., Chakraborty S. A Conceptual Comparison of Dragonfly Algorithm Variants for CEC-2021 Global Optimization Problems. Arab. J. Sci. Eng. 2022;48:1563–1593. doi: 10.1007/s13369-022-06880-9. DOI

McCune R., Madey G. Control of Artifial Swarms with DDDAS. Procedia Comput. Sci. 2014;29:1171–1181. doi: 10.1016/j.procs.2014.05.105. DOI

Nayyar A., Singh R. Ant Colony Optimization (ACO) based Routing Protocols for Wireless Sensor Networks: A survey. Int. J. Adv. 2017;8:148–155. doi: 10.14569/IJACSA.2017.080220. DOI

Zhong Q., Han S., Shi K., Zhong S., Kwon O.-M. Co-Design of Adaptive Memory Event-Triggered Mechanism and Aperiodic Intermittent Controller for Nonlinear Networked Control Systems. IEEE Trans. Circuits Syst. II Express Briefs. 2022;69:4979–4983. doi: 10.1109/TCSII.2022.3188036. DOI

Zenggang X., Mingyang Z., Xuemin Z., Sanyuan Z., Fang X., Xiaochao Z., Yunyun W., Xiang L. Social Similarity Routing Algorithm based on Socially Aware Networks in the Big Data Environment. J. Signal Process. Syst. 2022;94:1253–1267. doi: 10.1007/s11265-022-01790-3. DOI

Xiong Z., Li X., Zhang X., Deng M., Xu F., Zhou B., Zeng M. A Comprehensive Confirmation-based Selfish Node Detection Algorithm for Socially Aware Networks. J. Signal Process. Syst. 2023:1–19. doi: 10.1007/s11265-023-01868-6. DOI

Li B., Tan Y., Wu A.-G., Duan G.-R. A Distributionally Robust Optimization Based Method for Stochastic Model Predictive Control. IEEE Trans. Autom. Control. 2021;67:5762–5776. doi: 10.1109/TAC.2021.3124750. DOI

Mottola L., Picco G.P. MUSTER: Adaptive Energy-Aware Multisink Routing in Wireless Sensor Networks. IEEE Trans. Mob. Comput. 2010;10:1694–1709. doi: 10.1109/TMC.2010.250. DOI

Tan D.D., Kim D.-S. Dynamic traffic-aware routing algorithm for multi-sink wireless sensor networks. Wirel. Networks. 2013;20:1239–1250. doi: 10.1007/s11276-013-0672-z. DOI

Cao K., Ding H., Li W., Lv L., Gao M., Gong F., Wang B. On the Ergodic Secrecy Capacity of Intelligent Reflecting Surface Aided Wireless Powered Communication Systems. IEEE Wirel. Commun. Lett. 2022;11:2275–2279. doi: 10.1109/LWC.2022.3199593. DOI

Cao B., Zhao J., Gu Y., Fan S., Yang P. Security-Aware Industrial Wireless Sensor Network Deployment Optimization. IEEE Trans. Ind. Inform. 2020;16:5309–5316. doi: 10.1109/TII.2019.2961340. DOI

Wang Z., Zhao D., Guan Y. Flexible-constrained time-variant hybrid reliability-based design optimization. Struct. Multidiscip. Optim. 2023;66:89. doi: 10.1007/s00158-023-03550-8. DOI

Tshiamo S., Yim F.H., Misfa S. Energy-efficient 5G cloud RAN with virtual BBU server consolidation and base station sleeping. Comput. Netw. 2020;177:2.

Muhammad K.S., Luca R., Stefan W., Mohamad A. ML-Based Massive MIMO Channel Prediction: Does It Work on Real-World Data? IEEE Wirel. Commun. Lett. 2022;11:811–815.

Li Z., Kong Y., Jiang C. A Transfer Double Deep Q Network Based DDoS Detection Method for Internet of Vehicles. IEEE Trans. Veh. Technol. 2023;72:5317–5331. doi: 10.1109/TVT.2022.3233880. DOI

Namrata S., Swapnil A., Tanya A., Pavan K.M.-V. Based Resource Allocation Scheme for 5G CRAN Networks; Proceedings of the 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE); Jaipur, India. 22–25 November 2018; pp. 22–25.

Bajracharya R., Shrestha R., Ali R., Musaddiq A., Kim S.W. LWA in 5G: State-of-the-Art Architecture, Opportunities, and Research Challenges. IEEE Commun. Mag. 2018;56:134–141. doi: 10.1109/MCOM.2018.1701177. DOI

Mao Y., Zhu Y., Tang Z., Chen Z. A Novel Airspace Planning Algorithm for Cooperative Target Localization. Electronics. 2022;11:2950. doi: 10.3390/electronics11182950. DOI

Liu Q., Han T., Ansari N. Energy-Efficient On-Demand Resource Provisioning in Cloud Radio Access Networks. IEEE Trans. Green Commun. Netw. 2019;3:1142–1151. doi: 10.1109/TGCN.2019.2926287. DOI

Zhao Z., Verma G., Rao C., Swami A., Segarra S. Link Scheduling using Graph Neural Networks. arXiv. 2022 doi: 10.1109/TWC.2022.3222781.2109.05536 DOI

Zhang X., Jia M., Gu X., Guo Q. An Energy-Efficient Resource Allocation Scheme Based on Cloud-Computing in H-CRAN. IEEE Internet Things J. 2019;6:4968–4976. doi: 10.1109/JIOT.2019.2894000. DOI

Lin X., Wang S. Efficient remote radio head switching scheme in cloud radio access network: A load balancing perspective; Proceedings of the IEEE INFOCOM 2017-IEEE Conference on Computer Communications; Atlanta, GA, USA. 1–4 May 2017; pp. 1–9.

Kumar N., Ahmad A. Cooperative evolution of support vector machine empowered knowledge-based radio resource management for 5G C-RAN. Ad Hoc Netw. 2022;136:10296. doi: 10.1016/j.adhoc.2022.102960. DOI

Naveed A.C., Mudassar A., Saad Q., Muhammad I., Muhammad N., Qamar F. Energy Efficient Resource Allocation for Energy Harvesting Aided H-CRAN. IEEE Access. 2018;6:43990–44001.

Li Y. Predicting potential customers of 5G services via ADTree; Proceedings of the 6th International Conference on Intelligent Computing and Signal Processing (ICSP); Xi’an, China. 9–11 April 2021; pp. 9–11.

Xenofon F., Bozidar R.C. Teaching the 5G vRAN to share compute; Proceedings of the ACM SIGCOMM; New York, NY, USA. 23–27 August 2021; pp. 23–27.

Asokan R., Preethi P. Deep Learning Applications and Intelligent Decision Making in Engineering. IGI Global; Hershey, PA, USA: 2021. Deep learning with conceptual view in meta data for content categorization; pp. 176–191.

Preethi P., Asokan R. Neural network oriented roni prediction for embedding process with hex code encryption in dicom images; Proceedings of the 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN); Greater Noida, India. 18–19 December 2020; pp. 18–19.

Preethi P., Asokan R. A High Secure Medical Image Storing and Sharing in Cloud Environment Using Hex Code Cryptography Method—Secure Genius. J. Med. Imaging Heal. Inform. 2019;9:1337–1345. doi: 10.1166/jmihi.2019.2757. DOI

Bai D.P., Preethi P. Security Enhancement of Health Information Exchange Based on Cloud Computing System. Int. J. Sci. Eng. Res. 2016;4:79–82.

Xin C., Xie B., Shen C.-C. A novel layered graph model for topology formation and routing in dynamic spectrum access networks; Proceedings of the First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005 (DySPAN 2005); Baltimore, MD, USA. 8–11 November 2005.

Loyola-González O. Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses from A Practical Point of View. IEEE Access. 2019;7:154096–154113. doi: 10.1109/ACCESS.2019.2949286. DOI

Rodríguez-Ruiz J., Monroy R., Medina-Pérez M.A., Loyola-González O., Cervantes B. Cluster validation in clustering-based one-class classification. Expert Syst. 2019;36:e12475. doi: 10.1111/exsy.12475. DOI