无线传感器网络中位置感知的若干关键问题研究
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摘要
无线传感器网络集中了微机电系统、感知技术、嵌入式计算技术、信息处理技术和无线通信技术,孕育出一种全新的信息获取和处理模式。位置信息对于无线传感器网络至关重要,只有知道了位置信息,传感器网络感知到的各种上下文信息才变得更有价值。本文针对无线传感器网络的位置感知这一研究热点展开论述,具体内容如下：
在分析总结了现有的无线传感器网络节点自身定位算法的基础上,提出了一种高精度节点自定位算法CoTOA。在集中式应用环境中,基于时间到达测距技术,运用约束最优化方法,通过乘子法进行迭代计算,巧妙地将定位误差逐渐降到最小,最终求得未知位置的节点的最优坐标。定位的计算过程由网关sink节点来执行,传感器节点不必承担大量计算。此定位方法的架构设计大大节约了无线传感器网络普通感知节点的计算开销。文中采用软件仿真的方法对定位性能进行了分析,仿真结果显示此定位算法可以获得高精度的定位结果,并且此算法的能量消耗很少,符合无线传感器网络的设计要求。
目前的路由研究热点主要集中在保证信息传送成功的情况下尽可能节约无线传感器网络的能量上面。本文从这一关键点入手,提出了一种应用于集中式应用环境下基于位置的能量有效路由算法SEGR。该算法假设传感器节点位置已知,采用集中式无线传感器网络架构,整个网络的路由由网关sink节点来计算、更新并分发。网关sink根据节点的位置信息及剩余能量信息,利用Dijkstra算法计算出最佳路径,然后将每个节点的最佳下一跳信息发给相应节点,开始新一轮的数据传送。该架构将路由计算及发布的能量消耗尽可能地转移到网关节点sink上,很大程度上减轻了普通传感器节点的计算负担和通信负担。从而达到节约传感器节点的能量、延长整个网络生命周期的目的。
就本文所重点关注的传感器节点自身定位和路由协议来说,目前这两个领域结合的成果非常少,单一技术的研究目的是为实现一个完整系统打基础的。本文将无线传感器网络节点自定位和无线传感器网络路由协议两项技术结合在一起,提出一个协同设计的模式,在设计初期就让两个技术采用一样的模式、一样的参数、一样的规则等,尤其是在节点自定位和计算基于位置的路由阶段去掉了中间的冗余环节,为无线传感器网络节约了不必要的能量消耗。随后对协同设计的效果进行了软件仿真分析。仿真结果证明,协同设计最终达到了节约能量、减少整合两个系统的工作量、提高效率、进一步延长网络生命周期的目的,协同设计的性能也完全可以满足基本应用的需求,为建立位置感知应用系统打下良好基础。
在当今越来越普及的社交活动中,大们经常要出席会议、参观展览等。往往很多地方是第一次去,如果当地的服务人员服务不周到的话,用户需要花费大量精力才能找到准确地点。如果系统能准确预测出用户的下一时刻位置,那么根据当时的上下文环境就可以为用户主动提供导向等服务。本文利用无线传感器网络位置感知技术,提出一种在室内环境下的位置预测算法。该算法将大楼内的房间及走廊等建筑拓扑映射成室内地图,在此基础上,利用机器学习、分类等知识,将用户的历史上下文信息和当前上下文信息作为回归模型中的维进行求解,最后根据判别函数的结果预测用户下一时刻的位置。之后文章举例说明了该预测算法的应用,并对模型的可扩展、可定制的灵活特性进行了分析。
本文以传感器网络为基础设施,将传感器网络节点自定位、基于自定位结果的路由策略、室内目标定位、目标位置预测算法等各项关键技术协同起来,提出一个室内位置预测的服务架构。并以医院为应用环境,按传感器网络节点部署、节点自定位、基于位置的路由协议、用户注册、用户服务和用户退出这一整套流程,对该应用模型做了详细介绍。为实现室内位置感知预测应用系统提供强有力的技术支持和理论指导。
A wireless sensor network involves micro-elector-mechanism system, sensing technology, embedded computing, distributed information processing and wireless communication. It brings up a new pattern for information obtaining and processing. Location data is very important to wireless sensor networks in that contexts captured by wireless sensor networks would be much less valuable without relative location data. In this dissertation, we perform research on essential problems of location awareness in wireless sensor networks.
Firstly, we analyze existing node self-localization algorithms in wireless sensor networks, and propose a new one which provides higher accuracy. In a centralized application system, our algorithm utilizes Time-Of-Arrival (TOA) measurements and constrained optimization method to minimize location errors, and applies multiplier method to compute the optimal node locations. Notice that location computations are performed on sink nodes, rather than sensors. By doing this, our algorithm dramatically reduces computational costs on wireless sensor nodes. Finally, we provide simulation results of our localization algorithm. It shows that, compared to traditional TOA and DV-distance locating methods, our algorithm not only achieves higher location accuracy but also requires less sensor loads, which is a key requirement in wireless sensor networks.
Most existing work on routing protocol design in wireless sensor networks focus on minimizing energy consumption while preserving the network connectivity. We propose to work on a centralized routing algorithm which is energy-efficient. Given locations of all sensors nodes, our algorithm performs routing calculating, updating and dispatching on sink nodes and can be applied to centralized wireless sensor networks. Based on sensor locations and energy consumption, sink nodes computes an optimal path using Dijkstra algorithm, passes next-hop information to each network node, and then starts a new round of data transmission. Our algorithm performs most of computation and dispatching on sink nodes. Hence, it reduces workload and energy consumption on sensors and thus prolong network lifetime.
We consider node self-localization and routing algorithms in wireless sensor networks. Although numerous work has been done in these two individual areas, as far as we know, problems in the combined area have not yet been studied well. In this dissertation, we discuss problems in the combined area and present a cooperative design. We consider the self-localization and routing algorithms in the same manner and using the same parameters and rules. Furthermore, we erase the redundant steps in calculating node localization and location-based routing, which then dramatically reduces unnecessary energy consumption in wireless sensor networks. We also provide simulation results to evaluate our cooperation design. It shows that our design can minimize energy consumption on sensors, reduce system workload, and thus improve the efficiency and prolong the network lifetime. In addition, its performance can satisfy the demand of basic applications. This cooperation design can be used as the basis of a location-aware application system.
Nowadays, people spend more time on social activities, such as conferences, exhibitions, etc. When people go to a strange city where local service is insufficient, it would take them a lot of time and effort to find the exact location. In such situations, if we could provide a system that accurately predicts customers' destinations, we could offer customers more timely services based on the information. With location-aware technology in wireless sensor networks, we present a location prediction algorithm in indoor environments. Our algorithm abstracts the topology of rooms and corridors into a map. Based on this map, it introduces historical and current contexts into a regression model and then predicts customer's next position using discriminant functions. We also provide some application examples of this prediction model and analyze the scalability, customization and flexibility of this model.
In this dissertation, we combine node self-localization, location-based routing strategy, indoor object localization and location prediction technology, and propose an indoor location prediction service model in wireless sensor networks. We then introduce an application example, a wireless sensor network in a hospital, to describe the process of network nodes deployment, node self-localization, location-based routing protocol, user registration, user services and exit. We provide technical and theoretical guidance for indoor location-aware prediction application systems.
在分析总结了现有的无线传感器网络节点自身定位算法的基础上,提出了一种高精度节点自定位算法CoTOA。在集中式应用环境中,基于时间到达测距技术,运用约束最优化方法,通过乘子法进行迭代计算,巧妙地将定位误差逐渐降到最小,最终求得未知位置的节点的最优坐标。定位的计算过程由网关sink节点来执行,传感器节点不必承担大量计算。此定位方法的架构设计大大节约了无线传感器网络普通感知节点的计算开销。文中采用软件仿真的方法对定位性能进行了分析,仿真结果显示此定位算法可以获得高精度的定位结果,并且此算法的能量消耗很少,符合无线传感器网络的设计要求。
目前的路由研究热点主要集中在保证信息传送成功的情况下尽可能节约无线传感器网络的能量上面。本文从这一关键点入手,提出了一种应用于集中式应用环境下基于位置的能量有效路由算法SEGR。该算法假设传感器节点位置已知,采用集中式无线传感器网络架构,整个网络的路由由网关sink节点来计算、更新并分发。网关sink根据节点的位置信息及剩余能量信息,利用Dijkstra算法计算出最佳路径,然后将每个节点的最佳下一跳信息发给相应节点,开始新一轮的数据传送。该架构将路由计算及发布的能量消耗尽可能地转移到网关节点sink上,很大程度上减轻了普通传感器节点的计算负担和通信负担。从而达到节约传感器节点的能量、延长整个网络生命周期的目的。
就本文所重点关注的传感器节点自身定位和路由协议来说,目前这两个领域结合的成果非常少,单一技术的研究目的是为实现一个完整系统打基础的。本文将无线传感器网络节点自定位和无线传感器网络路由协议两项技术结合在一起,提出一个协同设计的模式,在设计初期就让两个技术采用一样的模式、一样的参数、一样的规则等,尤其是在节点自定位和计算基于位置的路由阶段去掉了中间的冗余环节,为无线传感器网络节约了不必要的能量消耗。随后对协同设计的效果进行了软件仿真分析。仿真结果证明,协同设计最终达到了节约能量、减少整合两个系统的工作量、提高效率、进一步延长网络生命周期的目的,协同设计的性能也完全可以满足基本应用的需求,为建立位置感知应用系统打下良好基础。
在当今越来越普及的社交活动中,大们经常要出席会议、参观展览等。往往很多地方是第一次去,如果当地的服务人员服务不周到的话,用户需要花费大量精力才能找到准确地点。如果系统能准确预测出用户的下一时刻位置,那么根据当时的上下文环境就可以为用户主动提供导向等服务。本文利用无线传感器网络位置感知技术,提出一种在室内环境下的位置预测算法。该算法将大楼内的房间及走廊等建筑拓扑映射成室内地图,在此基础上,利用机器学习、分类等知识,将用户的历史上下文信息和当前上下文信息作为回归模型中的维进行求解,最后根据判别函数的结果预测用户下一时刻的位置。之后文章举例说明了该预测算法的应用,并对模型的可扩展、可定制的灵活特性进行了分析。
本文以传感器网络为基础设施,将传感器网络节点自定位、基于自定位结果的路由策略、室内目标定位、目标位置预测算法等各项关键技术协同起来,提出一个室内位置预测的服务架构。并以医院为应用环境,按传感器网络节点部署、节点自定位、基于位置的路由协议、用户注册、用户服务和用户退出这一整套流程,对该应用模型做了详细介绍。为实现室内位置感知预测应用系统提供强有力的技术支持和理论指导。
A wireless sensor network involves micro-elector-mechanism system, sensing technology, embedded computing, distributed information processing and wireless communication. It brings up a new pattern for information obtaining and processing. Location data is very important to wireless sensor networks in that contexts captured by wireless sensor networks would be much less valuable without relative location data. In this dissertation, we perform research on essential problems of location awareness in wireless sensor networks.
Firstly, we analyze existing node self-localization algorithms in wireless sensor networks, and propose a new one which provides higher accuracy. In a centralized application system, our algorithm utilizes Time-Of-Arrival (TOA) measurements and constrained optimization method to minimize location errors, and applies multiplier method to compute the optimal node locations. Notice that location computations are performed on sink nodes, rather than sensors. By doing this, our algorithm dramatically reduces computational costs on wireless sensor nodes. Finally, we provide simulation results of our localization algorithm. It shows that, compared to traditional TOA and DV-distance locating methods, our algorithm not only achieves higher location accuracy but also requires less sensor loads, which is a key requirement in wireless sensor networks.
Most existing work on routing protocol design in wireless sensor networks focus on minimizing energy consumption while preserving the network connectivity. We propose to work on a centralized routing algorithm which is energy-efficient. Given locations of all sensors nodes, our algorithm performs routing calculating, updating and dispatching on sink nodes and can be applied to centralized wireless sensor networks. Based on sensor locations and energy consumption, sink nodes computes an optimal path using Dijkstra algorithm, passes next-hop information to each network node, and then starts a new round of data transmission. Our algorithm performs most of computation and dispatching on sink nodes. Hence, it reduces workload and energy consumption on sensors and thus prolong network lifetime.
We consider node self-localization and routing algorithms in wireless sensor networks. Although numerous work has been done in these two individual areas, as far as we know, problems in the combined area have not yet been studied well. In this dissertation, we discuss problems in the combined area and present a cooperative design. We consider the self-localization and routing algorithms in the same manner and using the same parameters and rules. Furthermore, we erase the redundant steps in calculating node localization and location-based routing, which then dramatically reduces unnecessary energy consumption in wireless sensor networks. We also provide simulation results to evaluate our cooperation design. It shows that our design can minimize energy consumption on sensors, reduce system workload, and thus improve the efficiency and prolong the network lifetime. In addition, its performance can satisfy the demand of basic applications. This cooperation design can be used as the basis of a location-aware application system.
Nowadays, people spend more time on social activities, such as conferences, exhibitions, etc. When people go to a strange city where local service is insufficient, it would take them a lot of time and effort to find the exact location. In such situations, if we could provide a system that accurately predicts customers' destinations, we could offer customers more timely services based on the information. With location-aware technology in wireless sensor networks, we present a location prediction algorithm in indoor environments. Our algorithm abstracts the topology of rooms and corridors into a map. Based on this map, it introduces historical and current contexts into a regression model and then predicts customer's next position using discriminant functions. We also provide some application examples of this prediction model and analyze the scalability, customization and flexibility of this model.
In this dissertation, we combine node self-localization, location-based routing strategy, indoor object localization and location prediction technology, and propose an indoor location prediction service model in wireless sensor networks. We then introduce an application example, a wireless sensor network in a hospital, to describe the process of network nodes deployment, node self-localization, location-based routing protocol, user registration, user services and exit. We provide technical and theoretical guidance for indoor location-aware prediction application systems.
引文
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