{"title":"拥塞和吞吐量优化协议,提供更好的服务质量和体验","authors":"Sathya Vijaykumar, Shiva Prakash Thyagaraj","doi":"10.11591/ijai.v13.i2.pp2364-2373","DOIUrl":null,"url":null,"abstract":"Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Congestion and throughput optimization protocol for providing better quality of service and experience\",\"authors\":\"Sathya Vijaykumar, Shiva Prakash Thyagaraj\",\"doi\":\"10.11591/ijai.v13.i2.pp2364-2373\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.\",\"PeriodicalId\":507934,\"journal\":{\"name\":\"IAES International Journal of Artificial Intelligence (IJ-AI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IAES International Journal of Artificial Intelligence (IJ-AI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11591/ijai.v13.i2.pp2364-2373\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence (IJ-AI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v13.i2.pp2364-2373","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Congestion and throughput optimization protocol for providing better quality of service and experience
Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.