Information Content-Based Sensor Selection and Transmission Power Adjustment for Collaborative Target Tracking

For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy.     

Effective sensor deployment based on field information coverage in precision agriculture

Coverage is an importance issue in wireless sensor networks. In this work, we first propose a novel notion of information coverage, which refers to the coverage efficiency of field information covered by deployed sensor nodes. On the basis of information coverage, we consider an optimization problem of how to partition the given field into multiple parcels and to deploy sensor nodes in some selected parcels such that the field information covered by the deployed sensor nodes meets the requirement. First, we develop two effective polynomial‐time algorithms to determine the deployed locations of source nodes for information 1‐coverage and q‐coverage of the field, respectively, without consideration of communication, where information q‐coverage implies that the field information in terms of information point is covered by at least q source nodes. Also, we prove the upper bound in the theoretical for the approximate solution derived by our proposed method. Second, another polynomial‐time algorithm is presented for deriving the deployed locations of relay nodes. In the theoretical, this proposed algorithm can achieve the minimized number of relay nodes. Further, the related information 1‐coverage algorithms are applied in our wireless sensor network‐based automatic irrigation project in precision agriculture. Experimental results show the major trade‐offs of impact factors in sensor deployment and significant performance improvements achieved by our proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

Cluster head election techniques for coverage preservation in wireless sensor networks

Coverage preservation is one of the basic QoS requirements of wireless sensor networks, yet this problem has not been sufficiently explored in the context of cluster-based sensor networks. Specifically, it is not known how to select the best candidates for the cluster head roles in applications that require complete coverage of the monitored area over long periods of time. In this paper, we take a unique look at the cluster head election problem, specifically concentrating on applications where the maintenance of full network coverage is the main requirement. Our approach for cluster-based network organization is based on a set of coverage-aware cost metrics that favor nodes deployed in densely populated network areas as better candidates for cluster head nodes, active sensor nodes and routers. Compared with using traditional energy-based selection methods, using coverage-aware selection of cluster head nodes, active sensor nodes and routers in a clustered sensor network increases the time during which full coverage of the monitored area can be maintained anywhere from 25% to 4.5×, depending on the application scenario.     

Joint mobility tracking and handoff in cellular networks via sequential Monte Carlo filtering

We consider the application of sequential Monte Carlo (SMC) methodology to the problem of joint mobility tracking and handoff detection in cellular wireless communication networks. Both mobility tracking and handoff detection are based on the measurements of pilot signal strengths from certain base stations. The dynamics of the system under consideration are described by a nonlinear state-space model. Mobility tracking involves an online estimation of the location and velocity of the mobile, whereas handoff detection involves an online prediction of the pilot signal strength at some future time instants. The optimal solutions to both problems are prohibitively complex due to the nonlinear nature of the system. The SMC methods are therefore employed to track the probabilistic dynamics of the system and to make the corresponding estimates and predictions. Both hard handoff and soft handoff are considered and three novel locally optimal (LO) handoff schemes are developed based on different criteria. It is seen that under the SMC framework, optimal mobility tracking and handoff detection can be implemented naturally in a joint fashion, and significant improvement is achieved over existing methods, in terms of both the tracking accuracy and the trade-off between service quality and resource utilization during handoff.     

面向TDOA被动定位的定位节点选择方法

该文主要研究一种面向到达时间差(TDOA)被动定位的定位节点选择方法。首先,通过经典的闭式解析算法将TDOA非线性方程转化为伪线性方程,并使用位置误差的协方差矩阵来度量定位精度。其次,在可用节点数量给定的条件下,在数学上将定位节点选择问题转化为最小化位置误差协方差矩阵的迹这一非凸优化问题。然后,将非凸优化问题凸松弛并化为半正定规划问题,从而快速有效地求解出最优的定位节点组合。仿真结果表明,所提节点优选方法的性能非常接近穷尽搜索方法,而且克服了穷尽搜索方法运算复杂度高、时效性差的不足,从而验证了所提方法的有效性。

     

Distributed multi-target tracking using joint probabilistic data association and average consensus filter

In this paper, a distributed multi-target tracking (MTT) algorithm suitable for implementation in wireless sensor networks is proposed. For this purpose, the Monte Carlo (MC) implementation of joint probabilistic data-association filter (JPDAF) is applied to the well-known problem of multi-target tracking in a cluttered area. Also, to make the tracking algorithm scalable and usable for sensor networks of many nodes, the distributed expectation maximization algorithm is exploited via the average consensus filter, in order to diffuse the nodes’ information over the whole network. The proposed tracking system is robust and capable of modeling any state space with nonlinear and non-Gaussian models for target dynamics and measurement likelihood, since it uses the particle-filtering methods to extract samples from the desired distributions. To encounter the data-association problem that arises due to the unlabeled measurements in the presence of clutter, the well-known JPDAF algorithm is used. Furthermore, some simplifications and modifications are made to MC–JPDAF algorithm in order to reduce the computation complexity of the tracking system and make it suitable for low-energy sensor networks. Finally, the simulations of tracking tasks for a sample network are given.     

基于Web的无线传感器网络可视化管理系统的设计与实现

无线传感器网络广泛部署于工业、农业、医疗及多种场景。面向网络应用的信息管理需求,设计并实现了一种基于Web的无线传感器网络可视化管理系统。传感器节点采集用户感兴趣的对象信息,通过多跳方式汇聚至网关,网关利用以太网或GPRS／CDMA等多种Internet接入方式将信息传送给网络服务器。用户利用终端设备,可跨平台访问此管理系统,执行对IPv6无线传感器网络状态信息和传感信息的动态实时检测,并可对节点设备进行远程控制与管理。     

Emerging techniques for long lived wireless sensor networks

In recent years, sensor networks have transitioned from being objects of academic research interest to a technology that is frequently being deployed in real-life applications and rapidly being commercialized. However, energy consumption continues to remain a barrier challenge in many sensor network applications that require long lifetimes. Battery-operated sensor nodes have limited energy storage capability due to small form-factors, or operate in environments that rule out frequent energy replenishment, resulting in a mismatch between the available energy budget for system operation and the required energy budget to obtain desired lifetimes. This article surveys several techniques that show promise in addressing and alleviating this energy consumption challenge. In addition to describing recent advances in energy-aware platforms for information processing and communication protocols for sensor collaboration, the article also looks at emerging, hitherto largely unexplored techniques, such as the use of environmental energy harvesting and the optimization of the energy consumed during sensing.     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

Group key management scheme for large-scale sensor networks

Wireless sensor networks are inherently collaborative environments in which sensor nodes self-organize and operate in groups that typically are dynamic and mission-driven. Secure communications in wireless sensor networks under this collaborative model calls for efficient group key management. However, providing key management services in wireless sensor networks is complicated by their ad-hoc nature, intermittent connectivity, large scale, and resource limitations. To address these issues, this paper proposes a new energy-efficient key management scheme for networks consisting of a large number of commodity sensor nodes that are randomly deployed. All sensor nodes in the network are anonymous and are preloaded with identical state information. The proposed scheme leverages a location-based virtual network infrastructure and is built upon a combinatorial formulation of the group key management problem. Secure and efficient group key initialization is achieved in the proposed scheme by nodes autonomously computing, without any communications, their respective initial group keys. The key server, in turn, uses a simple location-based hash function to autonomously deduce the mapping of the nodes to their group keys. The scheme enables dynamic setup and management of arbitrary secure group structures with dynamic group membership.     

Hybrid movement strategy in self-orienting directional sensor networks

The coverage problem in directional sensor networks (DSNs) introduces new challenges especially for randomly deployed networks. As many overlapped regions and coverage holes might occur after the initial deployment, self-orientation of the nodes is a necessity for randomly deployed DSNs. There exist two main approaches for the self-orientation of directional sensor nodes in DSNs [1], motility and mobility. Motility refers to the adjustment of the working direction of the nodes, whereas mobility describes the physical movement of the nodes. Most existing studies propose solutions based on the motility capability of the directional sensor nodes. On the other hand, mobility is a powerful feature offering great flexibility. Nevertheless, the high energy consumption of mobility discourages researchers to utilize this approach in their solutions. In this study, we propose a novel approach, a hybrid movement strategy (HMS), where we exploit motility/mobility in a cascaded manner for the coverage improvement in DSNs. The HMS improves the initial coverage up to 47% and achieves up to 7% more coverage than the motility only solution. Besides, it has provided at least 40% energy-saving compared to the mobility only solution in our scenarios.     

Practical and secure localization and key distribution for wireless sensor networks

In many applications of wireless sensor networks, sensor nodes are manually deployed in hostile environments where an attacker can disrupt the localization service and tamper with legitimate in-network communication. In this article, we introduce Secure Walking GPS, a practical and cost effective secure localization and key distribution solution for real, manual deployments of WSNs. Using the location information provided by the GPS and inertial guidance modules on a special master node, Secure Walking GPS achieves accurate node localization and location-based key distribution at the same time. We evaluate our localization solution in real deployments of MicaZ. Our experiments show that 100% of the deployed nodes localize (i.e., have a location position) and that the average localization errors are within 1–2 m, due mainly to the limitations of the existing commercial GPS devices. Our further analysis and simulation results indicate that the Secure Walking GPS scheme makes a deployed WSN resistant to the Dolev-Yao, the wormhole, and the GPS-denial attacks, the scheme is practical for large-scale deployments with resource-constrained sensor nodes and has good localization and key distribution performance.     

Energy Efficient Target Tracking with Collision Avoidance in WSNs

Wireless sensor network (WSN) is one of the most evolving technologies. WSN involves collecting, processing, transferring and storing information about objects with the help of sensor nodes. Tracking and detection of targets is one of the most attractive applications of WSN in surveillance systems. To resolve the problem of target tracking, it is essential to deploy a system model. It has been observed that clustering algorithms play an important role in cluster head selection, but they consume significant amount of energy. In this paper an energy efficient system model is deployed with a novel target tracking algorithm to track the target around the vicinity of the WSN. As there is more possibility of collision proximate to the base station, a new collision avoidance method is introduced. The lifetime of the network on the basis of congestion around the sink node, packet density and path loss are also measured efficiently.

     

Data aggregation in sensor networks using learning automata

One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having almost the same information are aggregated together. For this to occur, each node should forward its packets along a path on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These changes may result from changes occurred in the environment in which the sensor network resides and usually cannot be predicted beforehand. In this paper, a learning automata-based data aggregation method in sensor networks when the environment’s changes cannot be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations have been conducted and the results are compared with the results of three existing methods. The results have shown that the proposed method outperforms all these methods, especially when the environment is highly dynamic.     

渐进扩展卡尔曼滤波器

渐进贝叶斯方法将贝叶斯更新步骤等效为伪时间上的连续演化过程,以实现对状态的后验估计.本文基于渐进贝叶斯框架,导出一种新的高斯型非线性滤波算法.在线性高斯条件下推导了渐进贝叶斯方法的精确解;证明了对于由线性高斯解确定的动态系统,其均值和协方差矩阵满足的微分方程与常数状态估计的Kalman-Bucy滤波器是一致的.对于非线性系统,利用一阶Taylor展开推导了近似解表达式,进而导出渐进扩展卡尔曼滤波器.仿真算例表明新滤波器性能较扩展卡尔曼滤波器有大幅提高,且避免了窄形似然函数带来的滤波性能恶化问题.     

混合高斯噪声背景下基于多目标优化的节点选择方法

为解决非高斯噪声背景下,基于贝叶斯Fisher信息矩阵和基于互信息的节点选择不一致的问题,该文提出一种基于多目标优化的节点选择方法。推导出节点噪声为混合高斯分布时的贝叶斯Fisher信息矩阵和互信息,将节点个数、选择的节点对应的Fisher信息矩阵和互信息共同作为优化的目标函数。提出利用基于分解的多目标优化方法寻找Pareto最优解,并采用与理想解相似的偏好排序技术(TOPSIS)从所有Pareto最优解中选择最终的节点选择方案。仿真实验结果表明,基于多目标优化的节点选择方法选择的节点具有更优更稳健的定位精度。

     

Dynamic node activation in networks of rechargeable sensors

We consider a network of rechargeable sensors, deployed redundantly in a random sensing environment, and address the problem of how sensor nodes should be activated dynamically so as to maximize a generalized system performance objective. The optimal sensor activation problem is a very difficult decision question, and under Markovian assumptions on the sensor discharge/recharge periods, it represents a complex semi-Markov decision problem. With the goal of developing a practical, distributed but efficient solution to this complex, global optimization problem, we first consider the activation question for a set of sensor nodes whose coverage areas overlap completely. For this scenario, we show analytically that there exists a simple threshold activation policy that achieves a performance of at least 3/4 of the optimum over all possible policies. We extend this threshold policy to a general network setting where the coverage areas of different sensors could have partial or no overlap with each other, and show by simulations that the performance of our policy is very close to that of the globally optimal policy. Our policy is fully distributed, and requires the sensor nodes to only keep track of the node activation states in its immediate neighborhood. We also consider the effects of spatial correlation on the performance of the threshold activation policy, and the choice of the optimal threshold.     

A sequential Monte Carlo blind receiver for OFDM systems infrequency-selective fading channels

We consider the application of the sequential Monte Carlo (SMC) methodology to the problem of blind symbol detection in a wireless orthogonal frequency-division multiplexing (OFDM) system over a frequency-selective fading channel. Bayesian inference of the unknown data symbols in the presence of an unknown multipath fading channel is made only from the observations over one OFDM symbol duration. A novel blind SMC detector built on the techniques of importance sampling and resampling is developed for differentially encoded OFDM systems. The performance of different schemes of delayed-weight estimation methods is studied. Furthermore, being soft-input and soft-output in nature, the proposed SMC detector is employed as the first-stage demodulator in a turbo receiver for a coded OFDM system. Such a turbo receiver successively improves the receiver performance by iteratively exchanging the so-called extrinsic information with the maximum a posteriori (MAP) outer channel decoder. Finally, the performance of the proposed sequential Monte Carlo receiver is demonstrated through computer simulations     

Solving inverse problems by Bayesian neural network iterativeinversion with ground truth incorporation

Neural networks have long been applied to inverse parameter retrieval problems. The literature documents a development from the use of neural networks as explicit inverses to neural network iterative inversion (NNII) and, finally, to Bayesian neural network iterative inversion (BNNII), which adds a Bayesian superstructure to NNII. Inverse problems have been often considered ill posed, i.e. the statement of the problem does not thoroughly constrain the solution space. BNNII takes advantage of this lack of information by adding additional informative constraints to the problem solution using Bayesian methodology. This paper extends BNNII, showing how ground truth information, information regarding the particular parameter contour under reconstruction, and information regarding the underlying physical process, can be seamlessly added to the problem solution. Remote sensing problems afford opportunities for inclusion of ground truth information, prior probabilities, noise distributions, and other informative constraints within a Bayesian probabilistic framework. We apply these Bayesian methods to a synthetic remote sensing problem, showing that the addition of ground truth information, which is naturally included through Bayesian modeling, provides a significant performance improvement     

Joint multiple target tracking and classification in collaborative sensor networks

We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. In order to meet the requirements inherent to sensor networks such as distributed processing and low-power consumption, a collaborative signal processing algorithm is presented. At any time, for a given tracked target, only one sensor is active. This leader node is focused on a single target but takes into account the possible existence of other targets. It is assumed that the motion model of a given target belongs to one of several classes. This class-target dynamic association is the basis of our classification criterion. We propose an algorithm based on the sequential Monte Carlo (SMC) filtering of jump Markov systems to track the dynamic of the system and make the corresponding estimates. A novel class-based resampling scheme is developed in order to get a robust classification of the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC target tracking framework. Simulation results are presented to illustrate the excellent performance of the proposed multitarget tracking and classification scheme in a collaborative sensor network.