Investigation of radio channel uncertainty in distance estimation in wireless sensor networks

The distance estimation between nodes is a crucial requirement for localization and object tracking. Received signal strength (RSS) measurement is one of the used methods for the distance estimation in wireless networks. Its main advantage is that there are no additional hardware requirements. This paper describes a lateration approach for localization and distance estimation using RSS. For the purpose of investigation of RSS uncertainty, several scenarios were designed for both indoor and outdoor measurements. The first set of RSS measurement scenarios was proposed with the intention of hardware independent investigation of radio channel. For the second set of measurements, we employed IRIS sensor nodes to evaluate the distance estimation with certain devices. The experiments considered also obstacles in the radio channel. The results obtained in the proposed scenarios present usability of the method under different conditions. There is also a signal propagation model constructed from measured data at a node, which subsequently serves for distance determination.     

井下人员无线定位关键技术研究

针对现有井下人员定位系统亟待解决的功耗、漏检率和定位精度等技术问题,从探讨基于ZigBee通信技术的无线传感器网络体系架构出发,对人员无线定位系统中的传感器终端设计、定位机制及无线网拓扑等关键技术进行了较为深入的研究,给出了传感器节点设计、定位算法以及无线组网等工程实现过程,及各单元模块所采用的技术优化或改进策略,改善了现有系统的综合性能.     

Relative location estimation in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.     

一种基于RSS的环境自适应目标定位算法

目标定位是无线传感器网络的重要应用之一,但是基于接收信号强度(RSS)的定位方法通常因为非合作目标未知其发射功率以及不同环境下难以获取准确的路径衰减指数而无法实现准确定位,得不到广泛应用。提出了一种环境自适应的未知目标定位算法,能够实现对未知信号发射功率的目标进行准确定位,同时不断更新路径衰减指数动态适应环境,从而使提高了算法的适用性。     

A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network

In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also the coordinates of the anchor points (APs). The localization accuracy depends on the errors in the distance estimation with the RSS measurements and the size of the polygon composed of the APs used for the lateration. Since errors are inevitably involved in the RSS measurement, we focus on reducing the size of the polygon to increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon composed of the APs hearing the target. Once the relative position is estimated, only the APs covering the area are used for localization. We implement the localization method and evaluate the accuracy of the proposed method in various radio propagation environments. The experimental results show that the proposed method improves the localization accuracy and is robust against the dynamically changing radio propagation environments over time.     

Locating the nodes: cooperative localization in wireless sensor networks

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.     

Evaluation of impact factors on RSS accuracy for localization and tracking applications in sensor networks

The use of Received Signal Strength Indicator (RSSI), obtained automatically with the received messages in most sensor radios, is a popular way for estimating the location of a mobile wireless object. The great variation of Received Signal Strength (RSS), which may result in inaccurate estimations, is compensated by the fact that RSS does not require any additional hardware, and may reduce the sensor node power consumption, size and cost. The present work investigates the impact of variety of parameters on RSS by experimenting with Tmote Sky nodes in real-field outdoor environments. Besides the operating frequency, the transmitter–receiver distance, the variation of transceivers, the antenna orientation, and the environment specifics were found as important factors for creating accurate models, which would serve in tracking and localization applications.     

非视距环境下基于RSS-TOA的定位算法

在无线传感器网络定位系统中,尤其是在室内定位中,非视距（NLOS）误差的存在使定位性能急剧下降。为克服非视距传播带来的定位误差,提出了一种针对非视距环境下联合接收信号强度（RSS）和到达时间（TOA）的定位算法。该方法首先通过 RSS和 TOA的测量结果建立关于目标位置的非凸优化问题,然后通过二阶锥松弛理论,将原始的非凸优化问题转换为一种凸优化问题,由此能够快速得到原问题的一个次优解。通过计算机模拟仿真验证,新方法的估计精度更高,性能更好。     

A Framework for Improving the Performance of Direct Position Determination by Normal Hedge Algorithm for Emitter Localization in Indoor Applications

Indoor localization is a way to determine the location of transmitter(s) by using wireless network sensors without using GPS. In this paper, it is assumed that a random signal is transmitted by an unknown-position emitter through a noisy and lossy channel and then is received by known-position wireless sensors. Unlike the classical algorithms such as TDOA or AOA, the direct position determination (DPD) approach estimates the position of emitter in one step using the observation signal from all the collector receivers. In this paper a framework of methods is proposed to develop the DPD formulations. Originally, a preprocessing is applied on signals and later, a tree search algorithm and a fine localization algorithm will be proposed in order to achieve a higher resolution and with a less volume of computing. Furthermore, an approach is proposed to increase the performance of this algorithm. Using Normal Hedge algorithm with a proposed loss function, fuses the estimation results of several runs of the fine DPD algorithm. The simulation results are shown that the proposed framework would eventually improve the performance of the DPD algorithm.     

Comparative Performance Evaluation of RF Localization for Wireless Capsule Endoscopy Applications

This paper presents an assessment of the accuracy of cooperative localization of a wireless capsule endoscope (WCE) using radio frequency (RF) signals with particular emphasis on localization inside the small intestine. We derive the Cramer–Rao lower bound (CRLB) for cooperative location estimators using the received signal strength (RSS) or the time of arrival (TOA) of the RF signal. Our derivations are based on a three-dimension human body model, an existing model for RSS propagation from implanted organs to the body surface and a new TOA ranging error model for the effects of non-homogeneity of the human body on TOA of the RF signals. Using models for RSS and TOA errors, we first calculate the 3D CRLB bounds for cooperative localization of the WCE in three major digestive organs in the path of GI tract: the stomach, the small intestine and the large intestine. Then we analyze the performance of localization techniques on a typical path inside the small intestine. Our analysis includes the effects of the number of external sensors, the external sensor array topology, number of WCEs used in cooperation and the random variations in the transmitted power from the capsule.     

A factor graph approach to link loss monitoring in wireless sensor networks

The highly stochastic nature of wireless environments makes it desirable to monitor link loss rates in wireless sensor networks. In a wireless sensor network, link loss monitoring is particularly supported by the data aggregation communication paradigm of network traffic: the data collecting node can infer link loss rates on all links in the network by exploiting whether packets from various sensors are received, and there is no need to actively inject probing packets for inference purposes. In this paper, we present a low complexity algorithmic framework for link loss monitoring based on the recent modeling and computational methodology of factor graphs. The proposed algorithm iteratively updates the estimates of link losses upon receiving (or detecting the loss of) recently sent packets by the sensors. The algorithm exhibits good performance and scalability, and can be easily adapted to different statistical models of networking scenarios. In particular, due to its low complexity, the algorithm is particularly suitable as a long-term monitoring facility.     

WSN Deployment and Localization Using a Mobile Agent

To address the problem the sensors were typically deployed in fixed positions, but the robots can be used to calibrate, deploy and maintain the surrounding wireless sensor network (WSN) in disaster relief applications, a novel framework was proposed to obtain a wide coverage of the unknown environment by the sensors, which can help the robot during the disaster recovery activities, for the concurrent deployment and localization of a WSN by means of a mobile robot. During the mission, the robot explored an unknown environment, and was equipped with both proprioceptive sensors, range finders and wireless antennas. Moreover, the robot carried a set of nodes, and it can deploy them while exploring the unknown environment. Variou experimental results showd the proposed algorithm can outperform trilateration method in unknown environment exploration and network coverage problems.     

Manifold‐based canonical correlation analysis for wireless sensor network localization

Signal‐strength‐based location estimation in wireless sensor networks is to locate the physical positions of unknown sensors via the received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly‐proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimation—LPCCA (LE—LPCCA) and location estimation—LCA (LE—LCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

An Improved Hybrid RSS/TDOA Wireless Sensors Localization Technique Utilizing Wi-Fi Networks

This paper presents a hybrid localization algorithm for wireless sensor networks (WSNs) that simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurements. The accuracy and convergence reliability of the proposed hybrid scheme are also enhanced by incorporating RSS measurements from Wi-Fi networks via cooperative communications between Wi-Fi and sensor networks. To this end, two different types of estimators based on Taylor-series (TS) expansion and maximum-likelihood (ML) estimation are first proposed to solve the set of nonlinear RSS/TDOA equations taking into account measurement errors. The corresponding Cramér-Rao lower bound (CRLB) for the established scheme is then derived and utilized as a performance measure for the two estimators. Simulation results show that the proposed hybrid positioning approach significantly outperforms the previously considered localization solutions in WSNs, thanks to the joint process of the received signals’ power and time difference of arrival. The advantages of the proposed scheme in providing high location accuracy, fast convergence, low complexity implementation, and low power consumption make it an attractive localization solution via WSNs.     

Accuracy of RSS-Based RF Localization in Multi-capsule Endoscopy

In this paper, we derive and analyze cooperative localization bounds for endoscopic wireless capsule as it passes through the human gastrointestinal (GI) tract. We derive the Cramer-Rao bound (CRB) variance limits on location estimators which use measured received signal strength (RSS). Using a three-dimension human body model from a full wave simulation software and log-normal models for RSS propagation from implant organs to body surface, we calculate bounds on location estimators in three digestive organs: stomach, small intestine and large intestine. We provide analysis of the factors affecting localization accuracy, including various organ environments, external sensor array topology, number of pills in cooperation and the random variations in transmit power of sensor nodes. We also do localization accuracy analysis for the case when transmit power of the sensor is random with known priori distribution. The simulation results show that the number of receiver sensors on body surface has more influence on the accuracy of localization than the number of pills in cooperation inside the GI tract, The large intestine is affected the most with the transmit power randomness.     

基于无线电相干的传感器网络节点定位

目前无线传感器网络节点定位算法中,能够兼顾高精度和远距离定位的算法只有RIPS方法,然而该方法利用汇聚节点进行集中定位。提出了一种基于无线电相干的角度估计算法,并分布式定位节点,在高精度、远距离定位节点的同时,可大规模应用该算法,且定位速度快。实验表明,该方法平均方位估计误差是3.20,90%的测量值误差在6.4度以内。     

Sequential Monte Carlo localization in mobile sensor networks

Node localization in wireless sensor networks is essential to many applications such as routing protocol, target tracking and environment surveillance. Many localization schemes have been proposed in the past few years and they can be classified into two categories: range-based and range-free. Since range-based techniques need special hardware, which increases the localization cost, many researchers now focus on the range-free techniques. However, most of the range-free localization schemes assume that the sensor nodes are static, the network topology is known in advance, and the radio propagation is perfect circle. Moreover, many schemes need densely distributed anchor nodes whose positions are known in advance in order to estimate the positions of the unknown nodes. These assumptions are not practical in real network. In this paper, we consider the sensor networks with sparse anchor nodes and irregular radio propagation. Based on Sequential Monte Carlo method, we propose an alterative localization method—Sequential Monte Carlo Localization scheme (SMCL). Unlike many previously proposed methods, our work takes the probabilistic approach, which is suitable for the mobile sensor networks because both anchors and unknown nodes can move, and the network topology need not be formed beforehand. Moreover, our algorithm is scalable and can be used in large-scale sensor networks. Simulation results show that SMCL has better localization accuracy and it can localize more sensor nodes when the anchor density is low. The communication overhead of SMCL is also lower than other localization algorithms.

改进的自适应物联网技术节能优化控制仿真

研究异构传感网节能优化拓扑控制优化问题.在异构传感器网络中,每个传感器节点普遍存在初始能量异构,节点在无线通信过程中通信链路异构等异构现象.为了延长网络的生存期,提出一种自适应优化异构无线传感器网络拓扑结构控制算法.算法主要难点技术问题在于对参数E的选择控制问题.该算法基于传输数据跳数和相邻传感器之间通信距离,依据相似三角形几何原理,结合具体应用场景对传感器节点的分簇、成簇等操作进行自适应优化控制.仿真实验表明,改进的算法可以高效控制给定数据采集监测区域所有节点的网络拓扑同时极大地延长了异构传感网的生命周期.     

Collaborative sensor networking towards real-time acoustical beamforming in free-space and limited reverberance

Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.     

CRWSNP: cooperative range-free wireless sensor network positioning algorithm

Sensing events occur in an area without knowing the events locations, is meaningless. Since there is no priorly knowledge about the locations of most of the sensors which scattered randomly in an area, wireless sensor network localization methods try to find out where sensors are located. A new cooperative and distributed range-free localization algorithm, based on only connectivity information is proposed in this paper. The method first uses convex optimization techniques to find primitive target nodes locations estimation, then nodes cooperate with each other in several iterations to improve the whole network location estimation. CRWSNP converges after a finite number of iterations because of applying two novel heuristic location correction techniques. As well as, results of the algorithm have been compared with six range-free based methods like CPE, DV-hop, APIT; and CRWSNP algorithm provides more accurate results over 50 random topologies for the network, in mean error and maximum error metrics.