Augmented consensus filter for simultaneous localization and tracking with limited sense range

In this paper, we investigate how to exploit distributed average consensus fusion for conducting simultaneous localization and tracking (SLAT) by using wireless sensor networks. To this end, we commence by establishing a limited sense range (LSR) nonlinear system that characterizes the coupling of target state and sensor localization with respect to each sensor. We then employ an augmented extended Kalman filter to estimate the sensor and target states of our system. Furthermore, we adopt a consensus filtering scheme which fuses the information from neighboring sensors. We thus obtain a two-stage distributed filtering framework that not only obtains updated sensor locations trough augment filtering but also provides an accurate target state estimate in consensus filtering. Additionally, our framework is computationally efficient because it only requires neighboring sensor communications. The simulation results reveal that the proposed filtering framework is much more robust than traditional information fusion methods in limited ranging conditions.     

Distributed State Estimation for Discrete-Time Nonlinear System with Unknown Inputs

This paper investigated the problem of distributed estimation for a class of discrete-time nonlinear systems with unknown inputs in a sensor network. A modification scheme to the derivative-free versions of nonlinear robust two-stage Kalman filter (DNRTSKF) is first introduced based on recently developed cubature Kalman filter (CKF) technique. Afterward, a novel information filter is proposed by expressing the recursion in terms of the information matrix based upon DNRTSKF. In the end, distributed DNRTSKF is developed by applying a new information consensus filter to diffuse local statistics over the entire sensor network. In the implementation procedure, each sensor node only fuses the local observation instead of the global information and updates its local information state and matrix from its neighbors’ estimates using Average-Consensus Algorithm. Simulation results illustrate that the proposed distributed filter reveals the performance comparable to centralized DNRTSKF and better than distributed CKF.     

Decentralized target positioning and tracking based on a weighted extended Kalman filter for wireless sensor networks

This paper presents a decentralized positioning and tracking method based on recursive weighted least-squares optimization for wireless sensor networks. The proposed algorithm—weighted extended Kalman filter—is derived by minimizing a recursive-in-time objective function and then applying it in an iterative decentralized manner. The target location is calculated iteratively by taking a weighted average of the local estimates based on the participating sensor nodes’ reliability, where a participating sensor node computes the newest location estimate according to its own observation and the most recent local estimate passed from the previous participating sensor node. A convergence analysis is given to show the convergence behavior of the proposed algorithm. To track the target in the network, a message-passing algorithm is proposed for adaptively selecting the participating sensor nodes as the target moves around the area. During each iteration, the current participating sensor node computes the local estimate and passes it on to the next participating sensor node for further processing. The update process is circulated only among the selected participating sensor nodes that surround the target. Computer simulation results show that our proposed algorithm outperforms previous related methods.     

A Distributed Node Localization Scheme for Wireless Sensor Networks

A distributed node localization scheme for wireless sensor networks (WSNs) is presented in this paper, and it includes three generic phases: (1) determine node-beacon distances, (2) compute node positions, and (3) refine the positions. Different from previous researches, we propose an algorithm combination Min–max + LI for the position derivation and SD method for the refinement in our scheme. Simulation shows that our proposed scheme can perform more robust than some representative distributed node localization schemes presented in previous researches in terms of the trade-off among accuracy, coverage, computation cost, and communication overhead.     

Hybrid area exploration–based mobility‐assisted localization with sectored antenna in wireless sensor networks

In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location‐based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max‐gain and cost‐utility–based frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area exploration–based localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area exploration–based localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna.     

VN-APIT: virtual nodes-based range-free APIT localization scheme for WSN

As an important supporting technology, the localization technology has become the basis of the practical applications of wireless sensor networks for information acquisition and processing. APIT (approximate Point-In-Triangulation) localization scheme is widely used for localization estimation in wireless sensor networks due to the advantages of the high localization accuracy and easy to deploy. However, there are some inherent defects caused by the uneven distribution of sensor nodes in APIT scheme. To overcome the problem, in this paper a novel virtual nodes-based range-free localization scheme, i.e., VN-APIT, is proposed to improve the APIT scheme. By rationally deploying virtual nodes in sensor network according to the proposed VN-APIT test theory, VN-APIT localization scheme can determine independently that whether a target node is inside or outside the triangle formed by three certain anchor nodes. Therefore, it will not subject to the effect of the density and distribution of sensor nodes and there is no the problem of the In-to-Out error and the Out-to-In error in VN-APIT scheme. Simulation evaluation shows that the proposed VN-APIT scheme is more robust and has a lower average localization error (ALE) than the conventional APIT scheme.     

Location estimation and trajectory prediction for cellular networks with mobile base stations

This paper provides mobility estimation and prediction for a variant of the GSM network that resembles an ad hoc wireless mobile network in which base stations and users are both mobile. We propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user's next mobile base station from the user's location, heading, and altitude, to improve connection reliability and bandwidth efficiency of the underlying system. Our analysis demonstrates that our algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.     

Distributed detection of mobile malicious node attacks in wireless sensor networks

In wireless sensor networks, sensor nodes are usually fixed to their locations after deployment. However, an attacker who compromises a subset of the nodes does not need to abide by the same limitation. If the attacker moves his compromised nodes to multiple locations in the network, such as by employing simple robotic platforms or moving the nodes by hand, he can evade schemes that attempt to use location to find the source of attacks. In performing DDoS and false data injection attacks, he takes advantage of diversifying the attack paths with mobile malicious nodes to prevent network-level defenses. For attacks that disrupt or undermine network protocols like routing and clustering, moving the misbehaving nodes prevents them from being easily identified and blocked. Thus, mobile malicious node attacks are very dangerous and need to be detected as soon as possible to minimize the damage they can cause. In this paper, we are the first to identify the problem of mobile malicious node attacks, and we describe the limitations of various naive measures that might be used to stop them. To overcome these limitations, we propose a scheme for distributed detection of mobile malicious node attacks in static sensor networks. The key idea of this scheme is to apply sequential hypothesis testing to discover nodes that are silent for unusually many time periods—such nodes are likely to be moving—and block them from communicating. By performing all detection and blocking locally, we keep energy consumption overhead to a minimum and keep the cost of false positives low. Through analysis and simulation, we show that our proposed scheme achieves fast, effective, and robust mobile malicious node detection capability with reasonable overhead.     

Autonomic Group Key Management in Deep Space DTN

In deep space delay tolerant networks rekeying expend vast amounts of energy and delay time as a reliable end-to-end communication is very difficult to be available between members and key management center. In order to deal with the question, this paper puts forwards an autonomic group key management scheme for deep space DTN, in which a logical key tree based on one-encryption-key multi-decryption-key key protocol is presented. Each leaf node with a secret decryption key corresponds to a network member and each non-leaf node corresponds to a public encryption key generated by all leaf node’s decryption keys that belong to the non-leaf node’s sub tree. In the proposed scheme, each legitimate member has the same capability of modifying public encryption key with himself decryption key as key management center, so rekeying can be fulfilled successfully by a local leaving or joining member in lack of key management center support. In the security aspect, forward security and backward security are guaranteed. In the efficiency aspect, our proposed scheme’s rekeying message cost is half of LKH scheme when a new member joins, furthermore in member leaving event a leaving member makes tradeoff between computation cost and message cost except for rekeying message cost is constant and is not related to network scale. Therefore, our proposed scheme is more suitable for deep space DTN than LKH and the localization of rekeying is realized securely.     

Range‐free intersecting chord‐based geometric localization scheme for wireless sensor networks

Recent advancement in wireless sensor network has contributed greatly to the emerging of low‐cost, low‐powered sensor nodes. Even though deployment of large‐scale wireless sensor network became easier, as the power consumption rate of individual sensor nodes is restricted to prolong the battery lifetime of sensor nodes, hence the heavy computation capability is also restricted. Localization of an individual sensor node in a large‐scale geographic area is an integral part of collecting information captured by the sensor network. The Global Positioning System (GPS) is one of the most popular methods of localization of mobile terminals; however, the use of this technology in wireless sensor node greatly depletes battery life. Therefore, a novel idea is coined to use few GPS‐enabled sensor nodes, also known as anchor nodes, in the wireless sensor network in a well‐distributed manner. Distances between anchor nodes are measured, and various localization techniques utilize this information. A novel localization scheme Intersecting Chord‐Based Geometric Localization Scheme (ICBGLS) is proposed here, which loosely follows geometric constraint‐based algorithm. Simulation of the proposed scheme is carried out for various communication ranges, beacon broadcasting interval, and anchor node traversal techniques using Omnet++ framework along with INET framework. The performance of the proposed algorithm (ICBGLS), Ssu scheme, Xiao scheme, and Geometric Constraint‐Based (GCB) scheme is evaluated, and the result shows the fact that the proposed algorithm outperforms the existing localization algorithms in terms of average localization error. The proposed algorithm is executed in a real‐time indoor environment using Arduino Uno R3 and shows a significant reduction in average localization time than GCB scheme and similar to that of the SSU scheme and Xiao scheme.     

Improvement of Positioning Technology Based on RSSI in ZigBee Networks

In this paper, the node localization methods of ZigBee wireless sensor networks were studied. There are two key issues affecting the positioning accuracy: accuracy of RSSI value and optimization of localization algorithm. For the first issue, the effects of two kinds of environmental disturbance on RSSI values were analyzed, and then RSSI values were pretreated using Kalman filter. For the second, the RSSI-based localization algorithm were introduced in detail, and a new algorithm-triangle centroid localization algorithm based on weighted feature points-was proposed. MATLAB simulation and actual network tests were carried out. The simulation and experimental results all showed that our pretreatment strategy of RSSI and optimization of localization algorithm had great effects on positioning accuracy.     

DFMAC: DTN-Friendly Medium Access Control for Wireless Local Area Networks Supporting Voice/Data Services

In this paper, we consider a wireless communication network for both local nodes residing in densely populated hot spots and nomadic nodes roaming in a large area. Wireless local area networks (WLANs) are deployed in the hot spots, while a delay/ disruption tolerant network (DTN) provides services to nomadic nodes in the large area with low node density. We investigate the radio resource allocation for a DTN/WLAN integrated network, and propose a DTN-friendly medium access control (DFMAC) scheme for the hot spots supporting voice/data services. Analytical models are established to characterize the interactions between a DTN and WLANs under the proposed DFMAC. Numerical and simulation results demonstrate that our proposed MAC scheme can provide a deterministic delay guarantee for the voice services of local nodes, while achieving an efficient performance tradeoff for the data services between nomadic nodes and local nodes.     

DTN 网络环境下动态随机网络编码方法简

容迟容断网络的移动性、间歇连通性和动态拓扑等动态特性使得当前应用于静态网络拓扑和固定多播容量的静态随机网络编码难以适应DTN网络环境的网络编码传输,为此提出了一种DTN网络环境下动态随机网络编码传输方法。该方法以马尔科夫信道模型为基础,根据节点的数据状态动态监测信道速率,在信源节点构造了带信道容量的网络流图,并计算和预测当前网络多播容量,最后根据多播容量的变化动态扩展和裁剪随机网络编码方案,实现DTN网络环境下数据的动态网络编码传输。仿真结果表明,相比传统的固定多播率编码方法,动态随机网络编码方法降低了数据的平均传递延迟,提高了数据投递率。     

Sensor Position Determination with Flying Anchors in Three-Dimensional Wireless Sensor Networks

This paper presents a range-free position determination (localization) mechanism for sensors in a three-dimensional wireless sensor network based on the use of flying anchors. In the scheme, each anchor is equipped with a GPS receiver and broadcasts its location information as it flies through the sensing space. Each sensor node in the sensing area then estimates its own location by applying basic geometry principles to the location information it receives from the flying anchors. The scheme eliminates the requirement for specific positioning hardware, avoids the need for any interaction between the individual sensor nodes, and is independent of network densities and topologies. The performance of the localization scheme is evaluated in a series of simulations performed using ns-2 software and is compared to that of the Centroid and Constraint range-free mechanisms. The simulation results demonstrate that the localization scheme outperforms both Centroid and Constraint in terms of a higher location accuracy, a reduced localization time, and a lower beacon overhead. In addition, the localization scheme is implemented on the Tmote Sky for validating the feasibility of the localization scheme.     

Wi‐Fi‐based modeling and hybrid routing scheme for delay‐tolerant public bus network

Delay‐ or Disruption‐Tolerant Networking (DTN) is a communications approach that is utilized in easily disrupted or delayed networks. Examples of such networks are often found in heterogeneous networks, mobile or extreme terrestrial networks, and planned networks in space. In this paper, we examine the metropolitan bus network as a research target of DTN for a public transport network. We analyze the metropolitan bus network through spatial and temporal modeling using an existing Bus Information System (BIS) database. On the basis of the results of our analysis, we propose and design an appropriate DTN routing scheme called Hybrid Position‐based DTN Routing. This scheme uses position‐based routing instead of address‐based routing by soliciting infrastructural help from nearby Access Points for the real‐time BIS location service. We simulated our scheme using a WLAN for the wideband DTN communication and evaluated it by comparing it with traditional Ad hoc flooding, Epidemic routing, and strategic protocol steps in our own algorithm. The results indicate that our scheme achieves reasonably high performance in terms of packet delivery ratio, latency, and resource usage. Copyright © 2013 John Wiley & Sons, Ltd.     

容积卡尔曼一致滤波

针对卡尔曼一致滤波的应用受限于被估计系统需 满足线性条件的问题,通过容积卡尔曼滤波(CKF)和一致性策 略的动态结合,提出一种容积卡尔曼一致滤波(CKCF)算法。算法采用分布式融合机制, 传感器节点采集可通信相邻 节点的信息,并作为自身节点的量测信息应用于CKF,获取局部状态估计 值。在此基础上,利用一 致性策略实现对整个量测系统中传感器节点局部估计值的优化,进而通过增强传感器节点估 计值一致性实现目标 状态估计精度的提升。相对于标准卡尔曼一致滤波,本文算法将一致性策略推广到非线性系 统估计领域。理论分析 与仿真实验验证了算法的可行性与有效性。     

On localization in sensor network with inaccurate and incomplete distance measurements

The sensor network localization problem has received a lot of attention in recent years because many important applications resort to node position information. In contrast to the many interesting algorithms proposed in the literature, this paper provides a relatively straightforward procedure that can tackle localization problem for sensor network in a Least Squares Euclidean Distance Matrix Approximation （LS-EDMA） framework. Simulation results reveal that our proposed algorithm is more robust than another popular Multi-Dimensional Scaling （MDS） and Semi-Definite Programming （SDP） based localization techniques, especially with inaccurate and incomplete distance measurements.     

An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network

In many applications of wireless sensor network, the position of the sensor node is useful to identify the actuating response of the environment. The main idea of the proposed localization scheme is similar with most of the existing localization schemes, where a mobile beacon with global positioning system broadcast its current location coordinate periodically. The received information of the coordinates help other unknown nodes to localize themselves. In this paper, we proposed a localization scheme using mobile beacon points based on analytical geometry. Sensor node initially choose two distant beacon points, in-order to minimize its residence area. Later using the residence area, sensor node approximate the radius and half length of the chord with reference to one of the distant beacon point. Then the radius and half length of the chord are used to estimate the sagitta of an arc. Later, sensor node estimate its position using radius, half length of the chord, and sagitta of an arc. Simulation result shows the performance evaluation of our proposed scheme on various trajectories of mobile beacon such as CIRCLE, SPIRAL, S-CURVE, and HILBERT.     

Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation

In this paper, we proposed an enhanced pedestrian dead reckoning (PDR) system based on sensor fusion schemes using a smartphone. PDR is an effective technology for 3D indoor navigation. However, still, there are some obstacles to be overcome in its practical application. To track and simulate pedestrian’s position, which is confronted by environmental errors, walls, Bayesian errors, and other obstacles, our proposed PDR system enables estimation of stride based on the vertical accelerometer data and orientation from sensor fusion technique of magnetic angular rate and gravity sensor data by Madgwick filter. This localization system is independent of the received signal strength-based fingerprinting system. In addition, to estimate the current floor level, we make use of barometer information. To collect ground truth accurately and efficiently a prototype is implemented with the benchmark. We perform the same distance estimation for four different pedestrians to evaluate the accuracy of the proposed system. The real indoor experimental results demonstrate that the proposed system performs well while tracking the test subject in a 2D scenario with low estimation error (< 2 m). The 3D evaluation of the system inside a multi-story building shows that high accuracy can be achieved for a short range of time without position update from external sources. Then we compared localization performance between our proposed system and an existing (extended Kalman filter based) system.

     

Robust Tracking and Geolocation for Wireless Networks in NLOS Environments

We address the problem of robust tracking and geolocation using time of arrival estimates in wireless networks. Especially in urban or indoor environments and hilly terrains, these estimates are often contaminated by interference due to non-line-of-sight (NLOS) propagation. Standard techniques such as least-squares are inadequate as they lead to erroneous position estimates. We propose robust methods for tracking and geolocation based on a semi-parametric approach that does not require specification of the noise density. Unlike conventional, minimax based, robust techniques, we show that our proposed techniques are more robust as they adapt automatically to the interfering environment. Specifically, we propose a robust extended Kalman filter for tracking a mobile terminal based on robust semi-parametric estimators. Numerical studies for different network scenarios illustrate a substantial gain in performance compared to standard robust competitors.