International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Localization and Deployment Considerations into Quality of Service Optimization for Energy-Efficient Wireless Sensor Networks


Affiliations
1 Department of Computer Science, KSG College of Arts and Science, Coimbatore, Tamil Nadu, India
 

Wireless Sensor Networks (WSNs) have been more popular for a wide range of applications due to research ability to monitor and gather data from a variety of situations. However, it remains challenging to achieve Quality of Service (QoS) while maintaining energy efficiency. In the context of QoS optimization for energy-efficient WSNs, this study investigates the crucial issues of localization and deployment concerns. Localization the precise positions of sensor nodes are crucial for effective data fusion and routing algorithms that rely on localization. This study compares and contrasts several localization methods, including range-based and range-free approaches, and explains benefits and drawbacks. The study also investigates the effects on QoS and energy savings of various deployment strategies, including optimizing node location, boosting coverage, and increasing node density. The goal of this research is to find out how to optimize QoS in low-power wireless networks by including latency, throughput, and stability, among other quality of service characteristics, into the design of routing algorithms. Current routing protocols, like Low-Energy Adaptive Clustering Hierarchy (LEACH), are assessed for ability to optimize quality of service while minimizing energy consumption. In addition, this study explores several approaches that might help enhance QoS while reducing energy consumption, such as energy-aware routing, adaptive duty cycling, and data aggregation methods. By thoroughly examining and evaluating localization algorithms, deployment concerns, and routing protocols, this study offers practical and theoretical insights for researchers and practitioners aiming to optimize quality of service in energy-efficient WSNs. Useful and dependable WSN deployments in a wide variety of domains possible with the help of the presented results and suggestions.

Keywords

LEACH, Node Density, Quality of Service, Range-Based Localization, Routing Protocols, Wireless Sensor Networks.
User
Notifications
Font Size

  • Abella, C. S., Bonina, S., Cucuccio, A., D’Angelo, S., Giustolisi, G., Grasso, A. D., Scuderi, A. (2019). Autonomous Energy-Efficient Wireless Sensor Network Platform for Home/Office Automation. IEEE Sensors Journal, 19(9), 3501–3512. doi:10.1109/jsen.2019.2892604

  • Alghamdi, T. A. (2020). Energy efficient protocol in wireless sensor network: optimized cluster head selection model. Telecommunication Systems, 74(3), 331–345. doi:10.1007/s11235-020-00659-9

  • Amutha, J., Sharma, S., & Nagar, J. (2020). WSN Strategies Based on Sensors, Deployment, Sensing Models, Coverage and Energy Efficiency: Review, Approaches and Open Issues. Wireless Personal Communications. 111(4), 1089-1115. doi:10.1007/s11277-019-06903-z

  • Ben-Ghorbel, M., Rodriguez-Duarte, D., Ghazzai, H., Hossain, M. J., &Menouar, H. (2019). Joint Position and Travel Path Optimization for Energy Efficient Wireless Data Gathering using Unmanned Aerial Vehicles. IEEE Transactions on Vehicular Technology, 68(3), 2165-2175. doi:10.1109/tvt.2019.2893374

  • Ekpenyong, M. E., Asuquo, D. E., &Umoren, I. J. (2019). Evolutionary Optimisation of Energy-Efficient Communication in Wireless Sensor Networks. International Journal of Wireless Information Networks, 26(40), 344–366. https://doi.org/10.1007/s10776-019-00450-x

  • Jaiswal, K., &Anand, V. (2019). EOMR: An Energy-Efficient Optimal Multi-path Routing Protocol to Improve QoS in Wireless Sensor Network for IoT Applications. Wireless Personal Communications, 111(4), 2493–2515. doi:10.1007/s11277-019-07000-x

  • Kaur, A., Kumar, P., & Gupta, G. P. (2019). A weighted centroid localization algorithm for randomly deployed wireless sensor networks. Journal of King Saud University - Computer and Information Sciences. 31(1), 82-91. doi:10.1016/j.jksuci.2017.01.007

  • Lee, J.-H., & Moon, I. (2014). Modeling and optimization of energy efficient routing in wireless sensor networks. Applied Mathematical Modelling, 38(7-8), 2280–2289. doi:10.1016/j.apm.2013.10.044

  • Mittal, N. (2018). Moth Flame Optimization Based Energy Efficient Stable Clustered Routing Approach for Wireless Sensor Networks. Wireless Personal Communications .104(1), 677-694. doi:10.1007/s11277-018-6043-4

  • Parvin, J. R., &Vasanthanayaki, C. (2019). Particle Swarm Optimization-based Energy Efficient Target Tracking in Wireless Sensor Network. Measurement, 147, 106882. doi:10.1016/j.measurement.2019.106882

  • Ramesh, M. V. (2014). Design, development, and deployment of a wireless sensor network for detection of landslides. Ad Hoc Networks, 13(A), 2–18. doi:10.1016/j.adhoc.2012.09.002

  • Rao, P. C. S., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005–2020. doi:10.1007/s11276-016-1270-7

  • Rathee, M., Kumar, S., Gandomi, A. H., Dilip, K., Balusamy, B., &Patan, R. (2019). Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks. IEEE Transactions on Engineering Management, 68(1), 170-182. doi:10.1109/tem.2019.2953889

  • Reddy, D. L., C., P., & Suresh, H. N. (2021). Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in Wireless Sensor Network. Pervasive and Mobile Computing, 71, 101338. doi:10.1016/j.pmcj.2021.101338

  • Sahoo, B. M., Amgoth, T., &Pandey, H. M. (2020). Particle Swarm Optimization Based Energy Efficient Clustering and Sink Mobility in Heterogeneous Wireless Sensor Network. Ad Hoc Networks, 106, 102237. doi:10.1016/j.adhoc.2020.102237

  • Sharma, V., & Grover, A. (2016). A modified ant colony optimization algorithm (mACO) for energy efficient wireless sensor networks. Optik - International Journal for Light and Electron Optics, 127(4), 2169–2172. doi:10.1016/j.ijleo.2015.11.117

  • Singh, O., Rishiwal, V., &Yadav, M. (2021). Multi-objective lion optimization for energy-efficient multi-path routing protocol for wireless sensor networks. International Journal of Communication Systems. 34(17), 4969. doi:10.1002/dac.4969

  • Srinivas, M., &Amgoth, T. (2020). EE-hHHSS: Energy-efficient wireless sensor network with mobile sink strategy using hybrid Harris hawk-salp swarm optimization algorithm. International Journal of Communication Systems, 33(16), e4569. doi:10.1002/dac.4569

  • Tuna, G., &Gungor, V. C. (2017). A survey on deployment techniques, localization algorithms, and research challenges for underwater acoustic sensor networks. International Journal of Communication Systems, 30(17), e3350. doi:10.1002/dac.3350

  • Zhang, W., Wei, X., Han, G., & Tan, X. (2018). An Energy-Efficient Ring Cross-Layer Optimization Algorithm for Wireless Sensor Networks. IEEE Access, 6, 16588–16598. https://doi.org/10.1109/ACCESS.2018.2809663

  • Gou, P., Guo, B., Guo, M., & Mao, S. (2023). VKECE-3D: Energy-Efficient coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks based on 3D-Voronoi and K-means Algorithm. Sensors, 23(2), 573. doi:10.3390/s23020573

  • Muthurajkumar, S., Ganapathy, S., Vijayalakshmi, M., & Kannan, A. (2017). An Intelligent Secured and Energy Efficient Routing Algorithm for MANETs. Wireless Personal Communications, 96(2), 1753–1769. doi.10.1007/s11277-017-4266-4

  • Amarlingam, M., Mishra, P. K., Rajalakshmi, P., Channappayya, S. S., & Sastry, C. S. (2018). Novel Light Weight Compressed Data Aggregation using sparse measurements for IoT networks. Journal of Network and Computer Applications. 121(C), 119-134. doi:10.1016/j.jnca.2018.08.004

  • Zhang, W., Wang, J., Han, G., Zhang, X., & Feng, Y. (2019). A cluster sleep-wake scheduling algorithm based on 3D Topology control in underwater sensor networks. Sensors, 19(1), 156. https://doi.org/10.3390/s19010156

  • Peruzzi, G., &Pozzebon, A. (2020). A review of Energy Harvesting Techniques for Low Power Wide Area Networks (LPWANs). Energies, 13(13), 3433. https://doi.org/10.3390/en13133433

  • Khalid, S., Hwang, H., & Kim, H. S. (2021). Real-world data-driven machine-learning-based optimal sensor selection approach for equipment fault detection in a thermal power plant. Mathematics, 9(21), 2814. https://doi.org/10.3390/math9212814

  • Kevin P., Dian viely., Samarakoon UT. (2019). Performance analysis of wireless sensor network localization algorithms. International Journal of Computer Networks and Applications (IJCNA). 2019; Dec: 6(6), 92-99. doi:10.22247/ijcna/2019/189009

  • Mageid SA. (2017). Connectivity based positioning system for underground vehicular Ad Hoc networks. International Journal of Computer Networks and Applications (IJCNA). 2017; 4(1):1-14. doi:10.22247/ijcna/2017/41285

  • N. Kumar, P. Rani, V. Kumar, S. V. Athawale and D. Koundal. (2022). THWSN: Enhanced Energy-Efficient Clustering Approach for Three-Tier Heterogeneous Wireless Sensor Networks, IEEE Sensors Journal, 22(20) , 20053-20062. doi: 10.1109/JSEN.2022.3200597.

  • Z. Yao, Y. Desmouceaux, J. -A. Cordero-Fuertes, M. Townsley and T. Clausen. (2022). HLB: Toward Load-Aware Load Balancing, IEEE/ACM Transactions on Networking, 30(6), 2658-2673, https://doi.org/10.1109/TNET.2022.3177163.

  • Nagarajan, M. (2014). A New Approach to Improve Life Time Using Energy Based Routing in Wireless Sensor Network. International Journal of Science and Research (IJSR). 3(7), 1734-1738.

  • Nagarajan, M., and S. Karthikeyan. (2012). A new approach to increase the life time and efficiency of wireless sensor network. International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012). IEEE, 2012. 231-235. https://doi.org/10.1109/ICPRIME.2012.6208349.


Abstract Views: 49

PDF Views: 1




  • Localization and Deployment Considerations into Quality of Service Optimization for Energy-Efficient Wireless Sensor Networks

Abstract Views: 49  |  PDF Views: 1

Authors

Jeya Rani D.
Department of Computer Science, KSG College of Arts and Science, Coimbatore, Tamil Nadu, India
Nagarajan Munusamy
Department of Computer Science, KSG College of Arts and Science, Coimbatore, Tamil Nadu, India

Abstract


Wireless Sensor Networks (WSNs) have been more popular for a wide range of applications due to research ability to monitor and gather data from a variety of situations. However, it remains challenging to achieve Quality of Service (QoS) while maintaining energy efficiency. In the context of QoS optimization for energy-efficient WSNs, this study investigates the crucial issues of localization and deployment concerns. Localization the precise positions of sensor nodes are crucial for effective data fusion and routing algorithms that rely on localization. This study compares and contrasts several localization methods, including range-based and range-free approaches, and explains benefits and drawbacks. The study also investigates the effects on QoS and energy savings of various deployment strategies, including optimizing node location, boosting coverage, and increasing node density. The goal of this research is to find out how to optimize QoS in low-power wireless networks by including latency, throughput, and stability, among other quality of service characteristics, into the design of routing algorithms. Current routing protocols, like Low-Energy Adaptive Clustering Hierarchy (LEACH), are assessed for ability to optimize quality of service while minimizing energy consumption. In addition, this study explores several approaches that might help enhance QoS while reducing energy consumption, such as energy-aware routing, adaptive duty cycling, and data aggregation methods. By thoroughly examining and evaluating localization algorithms, deployment concerns, and routing protocols, this study offers practical and theoretical insights for researchers and practitioners aiming to optimize quality of service in energy-efficient WSNs. Useful and dependable WSN deployments in a wide variety of domains possible with the help of the presented results and suggestions.

Keywords


LEACH, Node Density, Quality of Service, Range-Based Localization, Routing Protocols, Wireless Sensor Networks.

References





DOI: https://doi.org/10.22247/ijcna%2F2024%2F224438