Enhanced Dependable Multi-Path Assortment Based on Energy Competent Model with Proficient Data Communication
Keywords:
Wireless sensor network, Path selection, Energy efficiency, Data Transmission, Network lifetime.Abstract
This study suggests a multi-path direction-finding technique for WSNs that is power-competent. The main problems limiting the lifespan of sensor associations are the characteristics of sensor nodes, which include limited battery capacity and ineffective protocols. This work aims to extend an improved direction-finding technique that may be applied in a wireless sensor network. The main achievement of the proposed protocol is reducing the disproportionate overhead that is typically experienced in the majority direction-finding procedures by using predefined clustering and reducing the number of CH transforms by using fixed CHs, including idle CHs. The performance analysis shows that because energy-efficient protocols can lower sensor node power consumption, lowering overhead greatly extends sensor node lifetime. Consequently, a WSN's scalability can be increased. Additionally, the deployment of transmit nodes has a positive impact on network power indulgence.
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References
G. Singh, and F. Al-Turjman, “Learning Data Delivery Paths in QoIAware Information-Centric Sensor Networks”, IEEE Internet of Things Journal, vol. 3, no. 4, 2016, pp. 572 – 580.
A. M. S. Saleh, B. M. Ali, M. F. A. Rasid, and A. Ismail, “A survey on energy awareness mechanisms in routing protocols for wireless sensor networks using optimization methods”, Transaction on Emerging Telecommunication Technolology, vol. 25, no. 12, Dec. 2014, pp. 1184–1207.
T. Rault, A. Bouabdallah, and Y. Challal, “Energy efficiency in wireless sensor networks: A top-down survey”, Computer Networks, vol. 67, Jul. 2014, pp. 104–122.
T. S. Panag and J. Dhillon, “Dual head static clustering algorithm for wireless sensor networks”, AEU-International Journal of Electronics Communications, vol. 88, pp. 148–156, May 2018.
Jagriti and D. Lobiyal, ‘‘Energy consumption reduction in S-MAC protocol for wireless sensor network”, Procedia Computer Science, vol. 143, pp. 757–764, 2018.
V. Singh, V. Thakkar, and V. Goswami, ‘‘SEESH: Sleep-awake energy efficient super heterogeneous routing protocol for wireless sensor networks,’’ in Proceedings of 3rd International Conference on Advanced Computing, Communication, Automation. (ICACCA), 2017, pp. 1–6.
S. Ramesh and C. Yaashuwanth, ‘‘Enhanced approach using trust based decision making for secured wireless streaming video sensor networks”, Multimedia Tools Appl, Apr. 2019, pp. 1–20,
D. Sharma, A. Goap, A. Shukla, and A. P. Bhondekar, ‘‘Traffic heterogeneity analysis in an energy heterogeneous WSN routing algorithm, in Proceedings of 2nd International Conference Communication, Computer. Networks, 2019, pp. 335–343.
G. Han, J. Jiang, M. Guizani, and J. J. P. C. Rodrigues, “Green routing protocols for wireless multimedia sensor networks”, IEEE Wireless Communication, vol. 23, no. 6, Dec. 2016, pp. 140–146.
D. Sharma and A. P. Bhondekar, ‘‘Traffic and energy aware routing for heterogeneous wireless sensor networks,’’ IEEE Communication Letters, vol. 22, no. 8, Aug. 2018, pp. 1608–1611.
S. Lalitha A, M. Sundararajan B , B. Karthik, “Reliable Multi-Path Route Selection Strategy Based On Evidence Theory For Internet Of Things Enabled Networks”, Measurement: Sensors, 100795, Vol. 27, 2023, pp. 01 – 07.
Nura Modi Shagari , Mohd Yamani Idna Idris, Rosli Bin Salleh, Ismail Ahmedy, Ghulam Murtaza, Hisham A. Shehadeh, “Heterogeneous Energy And Traffic Aware Sleep-Awake Cluster-Based Routing Protocol For Wireless Sensor Network”, Special Section On Green Communications On Wireless Networks , IEEE Access. Vol. 08, 2020, pp. 12232 – 12252.
Korhan Cengiz, Tamer Dag, “Energy Aware Multi-Hop Routing Protocol for WSNs”, IEEE Access, Vol. 6, 2018, pp. 2622-2633.
F. Al-Turjman, "Energy-Aware Data Delivery Framework for Safety-Oriented Mobile IoT," In IEEE Sensors Journal, Vol. 18, No. 1, pp. 470-478.
Ahmed Ibrahim Hassan, Maha Elsabrouty, Salwa El-Ramly, “Energy-efficient reliable packet delivery in variable-power wireless sensor networks”, Ain Shams Engineering Journal, Vol. 2, No. 2, June 2011, pp. 87-98.
Venkateswara Rao M, Srinivas Malladi, “Improving Packet Delivery Ratio in Wireless Sensor Network with Multi Factor Strategies”, International Journal of Advanced Computer Science and Applications, Vol. 12, No. 5, 2021, pp. 627 - 634.
M.Khaeel Ullah Khan, K.S.Ramesh, “Effect on Packet Delivery Ratio (PDR) & Throughput in Wireless Sensor Networks Due to Black Hole Attack”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, No. 12S, October 2019, pp. 428 - 432.
M. F. Khan, E. A. Felemban, S. Qaisar and S. Ali, "Performance Analysis on Packet Delivery Ratio and End-to-End Delay of Different Network Topologies in Wireless Sensor Networks (WSNs)," 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, Dalian, China, 2013, pp. 324-329.
Suresh Kumar, M. and Sathish Kumar, G.A., “Enhanced Ant Colony Optimization Algorithm for Packet Delivery with Improved Energy Efficiency in Wireless Sensor Networks”, Vol.1, January 2023, pp. 7909 – 7917.
Dr. F. Rahman, Omprakash Dewangan, “An Energy-Efficient and Secured Routing Protocol in Wireless Sensor Network Using Machine Learning Algorithm”, Nanotechnology Perceptions, ISSN: 1660-6795, Vol. 20, No.S4, 2024, pp. 73 – 84.
Ashfauk Ahamed A.K.,Manogar E.,Jeevitha S.,Sujithra L. R., Boopathi Kumar E, (2024) Prediction Of The Growing Stock In Stock Market On Analysis Of The Opinions Using Sentiment Lexicon Extraction And Deep Learning Architectures. Frontiers in Health Informatics, 13 (3), 1382-1392.
Prakash, G., P. Logapriya, and A. Sowmiya. "Smart Parking System Using Arduino and Sensors." NATURALISTA CAMPANO 28.1 (2024): 2903-2911.
Navatha, S., et al. "Multitask Learning Architecture For Vehicle Over Speed As Traffic Violations Detection And Automated Safety Violation Fine Ticketing Using Convolution Neural Network And Yolo V4 Techniques." Chinese Journal of Computational Mechanics 5 (2023): 431-435.
Kumar, E., et al. "Predicting the Fake Review of Products Using Graph Recurrent Neural Network." International Journal of Interdisciplinary Organizational Studies 18.1 (2023).
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