{"title":"分析消防应用中节点密度和速度对路由协议性能的影响","authors":"Inam Ullah, Tariq Hussain, Aamir Khan, Iqtidar Ali, Farhad Ali, Chang Choi","doi":"10.1186/s42408-023-00220-4","DOIUrl":null,"url":null,"abstract":"Abstract Background Mobile ad hoc networks have piqued researchers’ interest in various applications, including forest fire detection. Because of the massive losses caused by this disaster, forest fires necessitate regular monitoring, good communication, and technology. As a result, disaster response and rescue applications are mobile ad hoc network’s primary applications. However, quality of service becomes a significant and difficult issue, and the capabilities of the basic routing protocol limit mobile ad hoc network’s ability to deliver reasonable quality of service. Results The proposed research is for disaster-related scenarios, with nodes representing firefighters and vehicles (ambulances). Mobile nodes moving at 10 m/s are thought to be firefighters, while nodes moving at 20 m/s are thought to be vehicles (ambulances) delivering emergency healthcare. The NS-2 simulator is used in this research for the performance assessment of the two routing protocols, such as Optimized Link State Routing (OLSR) and Temporally Order Routing Algorithm (TORA), in terms of average latency, average throughput, and average packet drop. The simulation was run with varying node velocities and network densities to examine the impact of scalability on the two mobile ad hoc network routing protocols. Conclusions This work presents two main protocols: TORA (for reactive networks) and OLSR (for proactive networks). The proposed methods had no impact on the end-to-end bandwidth delay or the packet delivery delay. The performance is evaluated in terms of varying network density and node speed (firefighter speed), i.e., varying network density and mobility speed. The simulation revealed that in a highly mobile network with varying network densities, OLSR outperforms TORA in terms of overall performance. TORA’s speed may have been enhanced by adding more nodes to the 20 nodes that used a significant amount of transmission control protocol traffic.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"45 26 1","pages":"0"},"PeriodicalIF":3.6000,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing the impacts of node density and speed on routing protocol performance in firefighting applications\",\"authors\":\"Inam Ullah, Tariq Hussain, Aamir Khan, Iqtidar Ali, Farhad Ali, Chang Choi\",\"doi\":\"10.1186/s42408-023-00220-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Background Mobile ad hoc networks have piqued researchers’ interest in various applications, including forest fire detection. Because of the massive losses caused by this disaster, forest fires necessitate regular monitoring, good communication, and technology. As a result, disaster response and rescue applications are mobile ad hoc network’s primary applications. However, quality of service becomes a significant and difficult issue, and the capabilities of the basic routing protocol limit mobile ad hoc network’s ability to deliver reasonable quality of service. Results The proposed research is for disaster-related scenarios, with nodes representing firefighters and vehicles (ambulances). Mobile nodes moving at 10 m/s are thought to be firefighters, while nodes moving at 20 m/s are thought to be vehicles (ambulances) delivering emergency healthcare. The NS-2 simulator is used in this research for the performance assessment of the two routing protocols, such as Optimized Link State Routing (OLSR) and Temporally Order Routing Algorithm (TORA), in terms of average latency, average throughput, and average packet drop. The simulation was run with varying node velocities and network densities to examine the impact of scalability on the two mobile ad hoc network routing protocols. Conclusions This work presents two main protocols: TORA (for reactive networks) and OLSR (for proactive networks). The proposed methods had no impact on the end-to-end bandwidth delay or the packet delivery delay. The performance is evaluated in terms of varying network density and node speed (firefighter speed), i.e., varying network density and mobility speed. The simulation revealed that in a highly mobile network with varying network densities, OLSR outperforms TORA in terms of overall performance. TORA’s speed may have been enhanced by adding more nodes to the 20 nodes that used a significant amount of transmission control protocol traffic.\",\"PeriodicalId\":12273,\"journal\":{\"name\":\"Fire Ecology\",\"volume\":\"45 26 1\",\"pages\":\"0\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2023-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fire Ecology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s42408-023-00220-4\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fire Ecology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s42408-023-00220-4","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Analyzing the impacts of node density and speed on routing protocol performance in firefighting applications
Abstract Background Mobile ad hoc networks have piqued researchers’ interest in various applications, including forest fire detection. Because of the massive losses caused by this disaster, forest fires necessitate regular monitoring, good communication, and technology. As a result, disaster response and rescue applications are mobile ad hoc network’s primary applications. However, quality of service becomes a significant and difficult issue, and the capabilities of the basic routing protocol limit mobile ad hoc network’s ability to deliver reasonable quality of service. Results The proposed research is for disaster-related scenarios, with nodes representing firefighters and vehicles (ambulances). Mobile nodes moving at 10 m/s are thought to be firefighters, while nodes moving at 20 m/s are thought to be vehicles (ambulances) delivering emergency healthcare. The NS-2 simulator is used in this research for the performance assessment of the two routing protocols, such as Optimized Link State Routing (OLSR) and Temporally Order Routing Algorithm (TORA), in terms of average latency, average throughput, and average packet drop. The simulation was run with varying node velocities and network densities to examine the impact of scalability on the two mobile ad hoc network routing protocols. Conclusions This work presents two main protocols: TORA (for reactive networks) and OLSR (for proactive networks). The proposed methods had no impact on the end-to-end bandwidth delay or the packet delivery delay. The performance is evaluated in terms of varying network density and node speed (firefighter speed), i.e., varying network density and mobility speed. The simulation revealed that in a highly mobile network with varying network densities, OLSR outperforms TORA in terms of overall performance. TORA’s speed may have been enhanced by adding more nodes to the 20 nodes that used a significant amount of transmission control protocol traffic.
期刊介绍:
Fire Ecology is the international scientific journal supported by the Association for Fire Ecology. Fire Ecology publishes peer-reviewed articles on all ecological and management aspects relating to wildland fire. We welcome submissions on topics that include a broad range of research on the ecological relationships of fire to its environment, including, but not limited to:
Ecology (physical and biological fire effects, fire regimes, etc.)
Social science (geography, sociology, anthropology, etc.)
Fuel
Fire science and modeling
Planning and risk management
Law and policy
Fire management
Inter- or cross-disciplinary fire-related topics
Technology transfer products.