{"title":"基于生命周期规划的无线传感器网络QoS改进","authors":"Mohamed Abdelaal, Peilin Zhang, Oliver E. Theel","doi":"10.1109/MSN.2015.13","DOIUrl":null,"url":null,"abstract":"Energy efficiency is an important goal for Wireless Sensor Network (WSN) designers. However, successful implementations of such networks are highly dependent on the enabling technologies, as well as on the provisioning of Quality of Service (QoS) in the network. In this paper, we propose a novel strategy, referred to as the lifetime planning for achieving best-effort QoS. Simultaneously, an adequate lifetime required to complete the assigned task is reached. The core idea is to sidestep lifetime maximization strategies in which sensor nodes continue functioning even after the fulfillment of the required task. In these cases, we could deliberately bound the operational lifetime to the expected task lifetime. As a result, more energy can be spent throughout the entire task lifetime for enhancing the provided service qualities. An analytical QoS model is engineered to validate the QoS's \"conflicts-free\" nature of lifetime planning. The proposed strategy is feasible via the design of QoS boundaries at design-time. During run-time, the controllable parameters are modulated by a proactive adaptation mechanism. To demonstrate the effectiveness of our design, we conduct an intensive performance evaluation using an office monitoring scenario in a cluster-tree WSN topology. The scenario has been designed in the Contiki network simulator Cooja using Tmote sky motes. Furthermore, we examine the profit of adopting our strategy relative to fixed heuristics and blind adaptation.","PeriodicalId":363465,"journal":{"name":"2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"QoS Improvement with Lifetime Planning in Wireless Sensor Networks\",\"authors\":\"Mohamed Abdelaal, Peilin Zhang, Oliver E. Theel\",\"doi\":\"10.1109/MSN.2015.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy efficiency is an important goal for Wireless Sensor Network (WSN) designers. However, successful implementations of such networks are highly dependent on the enabling technologies, as well as on the provisioning of Quality of Service (QoS) in the network. In this paper, we propose a novel strategy, referred to as the lifetime planning for achieving best-effort QoS. Simultaneously, an adequate lifetime required to complete the assigned task is reached. The core idea is to sidestep lifetime maximization strategies in which sensor nodes continue functioning even after the fulfillment of the required task. In these cases, we could deliberately bound the operational lifetime to the expected task lifetime. As a result, more energy can be spent throughout the entire task lifetime for enhancing the provided service qualities. An analytical QoS model is engineered to validate the QoS's \\\"conflicts-free\\\" nature of lifetime planning. The proposed strategy is feasible via the design of QoS boundaries at design-time. During run-time, the controllable parameters are modulated by a proactive adaptation mechanism. To demonstrate the effectiveness of our design, we conduct an intensive performance evaluation using an office monitoring scenario in a cluster-tree WSN topology. The scenario has been designed in the Contiki network simulator Cooja using Tmote sky motes. Furthermore, we examine the profit of adopting our strategy relative to fixed heuristics and blind adaptation.\",\"PeriodicalId\":363465,\"journal\":{\"name\":\"2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSN.2015.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSN.2015.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QoS Improvement with Lifetime Planning in Wireless Sensor Networks
Energy efficiency is an important goal for Wireless Sensor Network (WSN) designers. However, successful implementations of such networks are highly dependent on the enabling technologies, as well as on the provisioning of Quality of Service (QoS) in the network. In this paper, we propose a novel strategy, referred to as the lifetime planning for achieving best-effort QoS. Simultaneously, an adequate lifetime required to complete the assigned task is reached. The core idea is to sidestep lifetime maximization strategies in which sensor nodes continue functioning even after the fulfillment of the required task. In these cases, we could deliberately bound the operational lifetime to the expected task lifetime. As a result, more energy can be spent throughout the entire task lifetime for enhancing the provided service qualities. An analytical QoS model is engineered to validate the QoS's "conflicts-free" nature of lifetime planning. The proposed strategy is feasible via the design of QoS boundaries at design-time. During run-time, the controllable parameters are modulated by a proactive adaptation mechanism. To demonstrate the effectiveness of our design, we conduct an intensive performance evaluation using an office monitoring scenario in a cluster-tree WSN topology. The scenario has been designed in the Contiki network simulator Cooja using Tmote sky motes. Furthermore, we examine the profit of adopting our strategy relative to fixed heuristics and blind adaptation.