Delay-aware optimized scheduling algorithm for high performance wireless sensor networks
Because of the phenomenal expansion of Internet of Things (IoT) devices around the world, Wireless Sensor Networks (WSN) have become increasingly important among the technical community, and research in this area has been growing exponentially. Researchers have used a variety of WSN technologies to...
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| Veröffentlicht in: | Automatika Jg. 65; H. 1; S. 92 - 97 |
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02.01.2024
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| Abstract | Because of the phenomenal expansion of Internet of Things (IoT) devices around the world, Wireless Sensor Networks (WSN) have become increasingly important among the technical community, and research in this area has been growing exponentially. Researchers have used a variety of WSN technologies to address issues such as processing power constraints, bandwidth-limited connections, delays and energy consumption outlines that arise with sensor networks. However, in terms of delay optimization, affordability and effective energy consumption, WSN is the most suitable and alluring technology. This paper uses an Enhanced Scheduling Algorithm (ESA) with a probabilistic approach called Random Classical Game Theory (RCGT) to reduce the delay in WSN. Retransmissions are minimized when ESA and RCGT are used, which improve WSN delay. The idea is to improve the scheduling algorithm by using RCGT to lengthen the lifespan of the entire network. It has been demonstrated that the improved technique outperforms the existing algorithms in terms of throughput, energy consumption, hop count, delay and lifespan ratio. |
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| AbstractList | Because of the phenomenal expansion of Internet of Things (IoT) devices around the world, Wireless Sensor Networks (WSN) have become increasingly important among the technical community, and research in this area has been growing exponentially. Researchers have used a variety of WSN technologies to address issues such as processing power constraints, bandwidth-limited connections, delays and energy consumption outlines that arise with sensor networks. However, in terms of delay optimization, affordability and effective energy consumption, WSN is the most suitable and alluring technology. This paper uses an Enhanced Scheduling Algorithm (ESA) with a probabilistic approach called Random Classical Game Theory (RCGT) to reduce the delay in WSN. Retransmissions are minimized when ESA and RCGT are used, which improve WSN delay. The idea is to improve the scheduling algorithm by using RCGT to lengthen the lifespan of the entire network. It has been demonstrated that the improved technique outperforms the existing algorithms in terms of throughput, energy consumption, hop count, delay and lifespan ratio. Because of the phenomenal expansion of Internet of Things (IoT) devices around the world, Wireless Sensor Networks (WSN) have become increasingly important among the technical community, and research in this area has been growing exponentially. Researchers have used a variety of WSN technologies to address issues such as processing power constraints, bandwidthlimited connections, delays and energy consumption outlines that arise with sensor networks. However, in terms of delay optimization, affordability and effective energy consumption, WSN is the most suitable and alluring technology. This paper uses an Enhanced Scheduling Algorithm (ESA) with a probabilistic approach called Random Classical Game Theory (RCGT) to reduce the delay in WSN. Retransmissions are minimized when ESA and RCGT are used, which improve WSN delay. The idea is to improve the scheduling algorithm by using RCGT to lengthen the lifespan of the entire network. It has been demonstrated that the improved technique outperforms the existing algorithms in terms of throughput, energy consumption, hop count, delay and lifespan ratio. |
| Author | Govindaraj, Annalakshmi Jyoshna, B. S, Soundararajan S, Lalitha |
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| Cites_doi | 10.1109/ACCESS.2018.2809556 10.1002/9781119778868.ch12 10.1007/s11276-017-1558-2 10.1109/TWC.2016.2615296 10.1016/j.comcom.2021.02.007 10.3390/s18051464 10.1016/j.phycom.2023.102038 10.1109/MASS.2013.44 10.1109/TNET.2009.2032294 10.1007/s11277-021-08312-7 10.1109/TMC.2010.42 10.1109/JSEN.2013.2240617 10.1016/j.future.2019.10.001 10.1016/j.comnet.2021.108250 10.1007/s12083-019-00753-z 10.1109/COMST.2014.2363950 10.1002/dac.4440 10.1155/2017/7507625 10.1109/TVT.2019.2914586 |
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| References | e_1_3_2_16_1 e_1_3_2_9_1 e_1_3_2_17_1 e_1_3_2_18_1 e_1_3_2_7_1 e_1_3_2_19_1 Chen Y (e_1_3_2_8_1) 2017; 2017 e_1_3_2_2_1 e_1_3_2_20_1 e_1_3_2_10_1 e_1_3_2_21_1 e_1_3_2_11_1 e_1_3_2_6_1 e_1_3_2_12_1 e_1_3_2_5_1 e_1_3_2_13_1 e_1_3_2_14_1 e_1_3_2_3_1 e_1_3_2_15_1 Kanhere SK (e_1_3_2_4_1) 2012; 1 |
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| SubjectTerms | Algorithms Delay Delay optimization Energy consumption enhanced scheduling algorithm Game theory Gaussian mixture model Internet of Things Life span Scheduling Sensors Wireless sensor networks |
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| Title | Delay-aware optimized scheduling algorithm for high performance wireless sensor networks |
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