基于多尺度分析技术的无线传感器网络定位算法研究
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摘要
无线传感器网络是由大量无处不在的、具有通信与计算能力的微小传感器节点以多跳无线通信方式构成的自组织网络系统。无线传感器网络带来了一种全新的信息获取与信息处理模式,在军事、环境监测、灾难救援及商业领域等领域有着广阔的应用前景。
节点定位问题是无线传感器网络应用的的关键技术之一。由于无线传感器网络节点数量众多,且受自身资源、能量等限制,导致节点定位十分困难。因此,研究有效的定位算法对于推动无线传感器网络技术的应用和发展具有重要意义。
多尺度分析技术(Multidimensional Scaling, MDS)源自心理测量学,已成为一种在许多领域中广泛使用的通用数据分析技术。自2003年Shang Y等人提出MDS-MAP定位算法,MDS被用来解决无线传感器网络节点定位问题,已经取得了较大进展。MDS的利用传感器网络各节点间诸如连通度、距离等信息来构建其相异(似)性矩阵,通过一系列变换来获取各节点的相对坐标来满足其胁强函数最小。
针对集中式算法对传感器网络节点要求高、MDS-MAP只有在测距情况下才能精确定位等不足,提出了一种基于非度量多尺度分析技术的分布式定位算法NMDS-AC。该算法以各锚节点为簇头将传感器网络分簇成若干子网络,再在子网络中利用RSSI测距技术构建网络的相异性矩阵,进而采用非度量多尺度分析技术进行局部定位获得局部相对坐标,再利用簇头节点信息将局部相对坐标转化为局部绝对坐标,然后对各子网络绝对坐标融合得到整个网络的绝对坐标。相比集中式MDS算法,NMDS-AC算法降低了对节点计算能力的要求,利用非度量多尺度进行定位,有效地降低测距误差的影响,提高了定位精度。
针对当网络规模较大时,误差累积的影响造成的定位误差较大的问题,利用信号处理技术中的恒模算法(Constant Modulal Algorithm, CMA),在NMDS-AC(O)定位算法的基础上提出了一种改进算法NMDS-AC(O)。该算法首先利用NMDS-AC算法估计出未知节点的坐标,然后将其作为CMA的初始值对节点坐标进行迭代优化,从而提高定位精度。相比典型的定位算法,NMDS-AC(O)对测距误差具有较好的鲁棒性和较高的定位精度,更适于节点大规模部署的传感器网络。
本文开展了无线传感器网络定位问题的研究,利用MDS技术,研究提出了NMDS-AC算法和NMDS-AC(O)算法。仿真实验结果充分表明了算法的有效性。进一步研究将从提升算法综合性能和增强实用性进行。
Wireless sensor networks (WSN), which consist of a large number of ubiquitous sensors, is a self-organizing network system. The sensor with communication and computing ability connet each other in multi-hop wireless communicaiton way. WSN is a new informaiton acquisiton and processing technology, and have broad application prospects in military, environmental monitoring,disaster relief and business fields etc.
Node localization is one of the key technologies of the application of WSN. Due to the large number of wireless sensor network nodes, and its restrictions on resources and energy, location information of nodes is very difficult to know. Therefore, the study of effective algorithm for promoting the application of wireless sensor network technology and development have the significant meaning.
Multidimensional scaling (MDS), adapted from psychometric, is general data analysis technology amd have widely used in many fields. Since Shang Y. et al proposed MDS-MAP algorithm in 2003, MDS technique is used to solve node localization problem of wireless sensor networks, and have has made great progress. Connectivity information and distances between nodes are used to construct dissimilarity matrix. Relative coordinates is estimated by minimizing stress function through s series tranformation.
Centralized algorithm requires strictly for sensor itself in WSN and MDS-MAP algorithm can localize accurately only if the accurate ranging. To solve these problem, an nonmeric MDS-basesd distributd algorithm, NMDS-AC, is proposed. In NMDS-AC localization algorithm, WSN is clustered to several sub-networks by selecting anchor node as cluster head. Through gathering radio Received Signal Strength Indication (RSSI) of pairwise nodes, dissimilarity matrix is constructed. Then, relative map of nodes is computed by NMDS in a cluster. All relative maps are merged into a general map. In this way, all coordinates of unknown nodes is estimated. Compare to Centralized NMDS-AC , NMDS-AC(O) reduces the requirment of computing ability of nodes. Besides, impact of ranging error is effectively reduced and computational complexity and localization accuracy is improved.
For positioning error problem with error accumulation when the network size is large, an improved algorithm, MDS-AC(O), is proposed by using constant modulus algorithm of signal processing area. The proposed algorithm using NMDS-AC algorithm to estimate coordinates of unknown node, then selected as the initial value of CMA to iteratively optimize the coordinates. Positioning accuracy is improved in this way. Compared to the typical exsisting location algorithm, NMDS-AC(O) is more robuster to the ranging error and gains higher positioning accuracy. Thus it is more suitable for WSN with large-scale deployment.
Thesis focuses on sensor localizaiton in wireless sensor networks. Two localization algorithms based on multidimensional scaling, NMDS-AC and NMDS-AC(O) are proposed. Simulation result show that the proposed algorithms are valid. Further research would be conducted to improve localization algorithm complex performance practicality.
节点定位问题是无线传感器网络应用的的关键技术之一。由于无线传感器网络节点数量众多,且受自身资源、能量等限制,导致节点定位十分困难。因此,研究有效的定位算法对于推动无线传感器网络技术的应用和发展具有重要意义。
多尺度分析技术(Multidimensional Scaling, MDS)源自心理测量学,已成为一种在许多领域中广泛使用的通用数据分析技术。自2003年Shang Y等人提出MDS-MAP定位算法,MDS被用来解决无线传感器网络节点定位问题,已经取得了较大进展。MDS的利用传感器网络各节点间诸如连通度、距离等信息来构建其相异(似)性矩阵,通过一系列变换来获取各节点的相对坐标来满足其胁强函数最小。
针对集中式算法对传感器网络节点要求高、MDS-MAP只有在测距情况下才能精确定位等不足,提出了一种基于非度量多尺度分析技术的分布式定位算法NMDS-AC。该算法以各锚节点为簇头将传感器网络分簇成若干子网络,再在子网络中利用RSSI测距技术构建网络的相异性矩阵,进而采用非度量多尺度分析技术进行局部定位获得局部相对坐标,再利用簇头节点信息将局部相对坐标转化为局部绝对坐标,然后对各子网络绝对坐标融合得到整个网络的绝对坐标。相比集中式MDS算法,NMDS-AC算法降低了对节点计算能力的要求,利用非度量多尺度进行定位,有效地降低测距误差的影响,提高了定位精度。
针对当网络规模较大时,误差累积的影响造成的定位误差较大的问题,利用信号处理技术中的恒模算法(Constant Modulal Algorithm, CMA),在NMDS-AC(O)定位算法的基础上提出了一种改进算法NMDS-AC(O)。该算法首先利用NMDS-AC算法估计出未知节点的坐标,然后将其作为CMA的初始值对节点坐标进行迭代优化,从而提高定位精度。相比典型的定位算法,NMDS-AC(O)对测距误差具有较好的鲁棒性和较高的定位精度,更适于节点大规模部署的传感器网络。
本文开展了无线传感器网络定位问题的研究,利用MDS技术,研究提出了NMDS-AC算法和NMDS-AC(O)算法。仿真实验结果充分表明了算法的有效性。进一步研究将从提升算法综合性能和增强实用性进行。
Wireless sensor networks (WSN), which consist of a large number of ubiquitous sensors, is a self-organizing network system. The sensor with communication and computing ability connet each other in multi-hop wireless communicaiton way. WSN is a new informaiton acquisiton and processing technology, and have broad application prospects in military, environmental monitoring,disaster relief and business fields etc.
Node localization is one of the key technologies of the application of WSN. Due to the large number of wireless sensor network nodes, and its restrictions on resources and energy, location information of nodes is very difficult to know. Therefore, the study of effective algorithm for promoting the application of wireless sensor network technology and development have the significant meaning.
Multidimensional scaling (MDS), adapted from psychometric, is general data analysis technology amd have widely used in many fields. Since Shang Y. et al proposed MDS-MAP algorithm in 2003, MDS technique is used to solve node localization problem of wireless sensor networks, and have has made great progress. Connectivity information and distances between nodes are used to construct dissimilarity matrix. Relative coordinates is estimated by minimizing stress function through s series tranformation.
Centralized algorithm requires strictly for sensor itself in WSN and MDS-MAP algorithm can localize accurately only if the accurate ranging. To solve these problem, an nonmeric MDS-basesd distributd algorithm, NMDS-AC, is proposed. In NMDS-AC localization algorithm, WSN is clustered to several sub-networks by selecting anchor node as cluster head. Through gathering radio Received Signal Strength Indication (RSSI) of pairwise nodes, dissimilarity matrix is constructed. Then, relative map of nodes is computed by NMDS in a cluster. All relative maps are merged into a general map. In this way, all coordinates of unknown nodes is estimated. Compare to Centralized NMDS-AC , NMDS-AC(O) reduces the requirment of computing ability of nodes. Besides, impact of ranging error is effectively reduced and computational complexity and localization accuracy is improved.
For positioning error problem with error accumulation when the network size is large, an improved algorithm, MDS-AC(O), is proposed by using constant modulus algorithm of signal processing area. The proposed algorithm using NMDS-AC algorithm to estimate coordinates of unknown node, then selected as the initial value of CMA to iteratively optimize the coordinates. Positioning accuracy is improved in this way. Compared to the typical exsisting location algorithm, NMDS-AC(O) is more robuster to the ranging error and gains higher positioning accuracy. Thus it is more suitable for WSN with large-scale deployment.
Thesis focuses on sensor localizaiton in wireless sensor networks. Two localization algorithms based on multidimensional scaling, NMDS-AC and NMDS-AC(O) are proposed. Simulation result show that the proposed algorithms are valid. Further research would be conducted to improve localization algorithm complex performance practicality.
引文
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