Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

被引：0
作者：
Santhanaraj R.K. [1 ]
Rajendran S. [2 ]
Romero C.A.T. [3 ]
Murugaraj S.S. [4 ]
机构：
[1] Department of Information Technology, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai
[2] Center for Artificial Intelligence and Research (CAIR), Chennai Institute of Technology, Chennai
[3] COMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de Cali, Cali
[4] Department of Emerging Technologies, Guru Nanak Institute of Technology, Telangana, Ibrahipatnam
来源：
Comput Syst Sci Eng | / 2卷 / 1561-1576期
关键词：
clustering; disaster management; wireless sensor networks; Internet of things; real time applications; routing;
D O I：
10.32604/csse.2023.029463
中图分类号：
学科分类号：
摘要：
Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region. To achieve this, EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering (YSGF-C) technique to elect cluster heads (CHs) and organize clusters. In addition, enhanced cockroach swarm optimization (ECSO) based multihop routing (ECSO-MHR) approach was derived for optimal route selection. The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime. The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work. For examining the improved outcomes of the EAMCR-RTDM system, a wide range of simulations were performed and the extensive results are assessed in terms of different measures. The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches. © 2023 CRL Publishing. All rights reserved.
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页码：1561 / 1576
页数：15
相关论文
共 24 条
  • [1] Shah S. A., Seker D. Z., Hameed S., Draheim D., The rising role of big data analytics and IoT in disaster management: Recent advances, taxonomy and prospects, IEEE Access, 7, pp. 54595-54614, (2019)
  • [2] Liu J., Chen Y., Chen Y., Emergency and disaster management-crowd evacuation research, Journal of Industrial Information Integration, 21, (2021)
  • [3] Thomas A., Raja G., FINDER: A D2D based critical communications framework for disaster management in 5G, Peer-to-Peer Networking and Applications, 12, 4, pp. 912-923, (2019)
  • [4] Sharma K., Anand D., Sabharwal M., Tiwari P. K., Cheikhrouhou O., Et al., A disaster management framework using internet of things-based interconnected devices, Mathematical Problems in Engineering, 2021
  • [5] Zafar U., Shah M. A., Wahid A., Akhunzada A., Arif S., Exploring IoT applications for disaster management: Identifying key factors and proposing future directions, Recent Trends and Advances in Wireless and IoTEnabled Networks, pp. 291-309, (2019)
  • [6] Masaracchia A., Nguyen L. D., Duong T. Q., Nguyen M. N., An energy-efficient clustering and routing framework for disaster relief network, IEEE Access, 7, pp. 56520-56532, (2019)
  • [7] Palani U., Amuthavalli G., Alamelumangai V., Secure and load-balanced routing protocol in wireless sensor network or disaster management, IET Information Security, 14, 5, pp. 513-520, (2020)
  • [8] Waghole D., Deshpande V., Midhunchakkaravarthy D., Jadhav M., Position aware congestion control (PACC) algorithm for disaster management system using WSN to improve QoS, Design Engineering, 7, pp. 11470-11478, (2021)
  • [9] Vaiyapuri T., Parvathy V. S., Manikandan V., Krishnaraj N., Gupta D., Et al., A novel hybrid optimization for cluster-based routing protocol in information-centric wireless sensor networks for IoT based mobile edge computing, Wireless Personal Communications, pp. 1-24, (2021)
  • [10] Rajendran S., Khalaf O. I., Alotaibi Y., Alghamdi S., MapReduce-Based big data classification model using feature subset selection and hyperparameter tuned deep belief network, Scientific Reports, 11, (2021)
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