Network-aware positioning in sensor networks
ABSTRACT Out of its importance to various applications and services, the geographical location of the sensed event is to be associated with the event itself being reported. Despite the numerous number of localization algorithms proposed, very few of them are really ad-hoc methods that are appropriate for sensor networks. In this paper, our contribution is double-folded. First, we design an experimental framework to evaluate localization methods for sensor networks. We use this framework to evaluate three localization methods: ad-hoc positioning system (APS), multi-dimensional scaling (MDS), and semi-definite programming (SDP). Using this evaluation, we identify five network properties that affect the localization accuracy. Second, we propose an adaptive localization method that we refer to as: network-aware positioning (NAP). NAP starts by assuming known network properties. Given these properties, NAP determines the best localization algorithm to use. Simulation results show that NAP performs the best among the three algorithms under all network conditions
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ABSTRACT: This paper introduces a semi-distributed cooperative localization technique realized via multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) optimal fusion: Each base-node estimates the position of target-nodes by joint TOA- DOA evaluation, and then, the target-node position estimation error is minimized by TOA-DOA optimal fusion across multiple base-nodes. The performance of the proposed technique is studied and compared to two GPS-based positioning techniques, i.e., GPS-aided TOA fusion and GPS-aided DOA fusion. The circular error probability (CEP) is derived theoretically and verified via simulations. The results confirm the superiority of the proposed localization technique in moderate scale mobile ad- hoc networks (MANETs) compared to the two GPS-based fusion schemes. Thus, while the proposed technique is applicable to MANETs in GPS-denied environments, it is also suitable for GPS available environments. Finally, compared to the centralized scheme, the positioning updating rate of the semi-distributed technique is higher and its power consumption in the reference base node is considerably lower.Wireless Communications and Networking Conference, 2008. WCNC 2008. IEEE; 05/2008
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ABSTRACT: 1 - This paper presents a multi-node 2-Dimensional (2D) time-of-arrival (TOA) and direction-of-arrival (DOA) optimal fusion technique. This technique can be applied in ad-hoc networks, especially suitable for the application in the mobile ad-hoc networks (MANETs). In this work, positioning error in MANETs would be optimized via TOA-DOA joint estimation and fusion across multiple nodes. In the proposed MANET, we assume two categories of nodes: Those equipped with antenna arrays (base-nodes) and those equipped with omni-directional antennas (target-nodes). All nodes are capable of communicating with other nodes. Base-nodes are capable of positioning (TOA-DOA estimation) other nodes located in their coverage area. A fusion method is proposed to minimize the mean square of the positioning error of a target-node, when more than one base-node estimates its position. The fusion scheme is derived theoretically and compared with simulation results. This paper depicts the capability of the proposed 2D fusion algorithm to considerably reduce the positioning error. The proposed technique has important applications in GPS-denied environments.
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ABSTRACT: This paper introduces a line-of-sight (LOS) and non-LOS (NLOS) separation technique based on the statistics of the phase difference of two received signals. The phase difference is achieved via a co-installed synchronized two-receiver system. The variance of the phase difference is used to separate LOS and NLOS. The probability-density-function (PDF) of the received signal phase generated by NLOS component is theoretically derived. The variance of the phase difference is calculated using the derived PDF numerically and verified via simulations. The LOS and NLOS separation performance versus signal power ratio of LOS to NLOS is evaluated via simulations.