基于模糊时间序列的传感器网络感知数据预测模型

传感器网络监控系统属于大型复杂系统,由感知节点以一定的时间间隔向sink节点发送感知数据,以实现对应用环境的监控。由于网络本身及应用环境的影响,得到的感知数据往往存在不确定性。此外,周期性报告数据模式影响到实时监控数据的精确性。本文应用时间序列模型预测传感器数据以响应用户查询,可有效降低网络通信量。通过对无线传感器网络的数据分析,引入多属性模糊时间序列预测模型,充分考虑了无线传感器网络时间序列中存在的趋势因素,并提出了适合于传感器网络的修正预测模型。实验结果表明模糊时间序列模型可有效预测传感器网络数据,且能提高预测精度。     

P2P环境下基于WSN和RFID的供应链管理语义监控平台

提出了一种基于语义P2P的无线传感器网络应用架构,在此架构下融合了RFID技术提出了供应链管理的语义监控平台.该平台采用语义P2P技术解决了信息过载通信瓶颈问题,方便了传感器节点部署、使得传感器网络易于扩展,同时采用本体技术实现了供应链管理中的信息语义交互和共享,为供应链中信息的及时、无歧义理解提供了便利.     

The Infinite-Dimensional Optimal Filtering Problem with Mobile and Stationary Sensor Networks

In this article, we introduce a framework to address filtering and smoothing with mobile sensor networks for distributed parameter systems. The main problem is formulated as the minimization of a functional involving the trace of the solution of a Riccati integral equation with constraints given by the trajectory of the sensor network. We prove existence and develop approximation of the solution to the Riccati equation in certain trace-class spaces. We also consider the corresponding optimization problem. Finally, we employ a Galerkin approximation scheme and implement a descent algorithm to compute optimal trajectories of the sensor network. Numerical examples are given for both stationary and moving sensor networks.     

Geometric structure of the joint N‐voM distribution manifold and its applications to sensor networks

In this paper, the geometric structure for normal distribution manifold, von Mises distribution manifold and their joint distribution manifold are firstly given by the metric, curvature, and divergence, respectively. Furthermore, the active detection with sensor networks is presented by a classical measurement model based on metric manifold, and the information resolution is presented for the range and angle measurements sensor networks. The preliminary analysis results introduced in this paper indicate that our approach is able to offer consistent and more comprehensive means to understand and solve sensor network problems containing sensors management and target detection, which are not easy to be handled by conventional analysis methods.     

The N-policy of a discrete time Geo/G/1 queue with disasters and its application to wireless sensor networks

We analyze an N-policy of a discrete time Geo/G/1 queue with disasters. We obtain the probability generating functions of the queue length, the sojourn time, and regeneration cycles such as the idle period and the busy period. We apply the queue to a power saving scheme in wireless sensor networks under unreliable network connections where data packets are lost by external attacks or shocks. We present various numerical experiments for application to power consumption control in wireless sensor networks. We investigate the characteristics of the optimal N-policy that minimizes power consumption and derive practical insights on the operation of the N-policy in wireless sensor networks.     

Modelling and solving optimal placement problems in wireless sensor networks

In this work, the optimal sensor displacement problem in wireless sensor networks is addressed. It is assumed that a network, consisting of independent, collaborative and mobile nodes, is available. Starting from an initial configuration, the aim is to define a specific sensors displacement, which allows the network to achieve high performance, in terms of energy consumption and travelled distance. To mathematically represent the problem under study, different innovative optimization models are proposed and defined, by taking into account different performance objectives. An extensive computational phase is carried out in order to assess the behaviour of the developed models in terms of solution quality and computational effort. A comparison with distributed approaches is also given, by considering different scenarios.     

Global dynamics and control of malicious signal transmission in wireless sensor networks

This paper studies the global dynamics of a discontinuous delayed model of malicious signal transmission in wireless sensor networks under the framework of differential inclusion. The local stability of two types of steady states are investigated for the discontinuous system by studying the corresponding characteristic equation. The sufficient conditions for the existence of two types of globally asymptotically stable steady states are obtained for the discontinuous system by using the comparison arguments method. Furthermore, the optimal control of the discontinuous system is investigated by using Pontryagin’s maximum principle. Numerical simulations of two examples are carried out to illustrate the main theoretical results. The obtained results can help us to better control and predict the spread of malicious signal transmission in wireless sensor networks.     

Routing tree maintenance based on trajectory prediction in mobile sensor networks

With the wireless sensor networks (WSNs) becoming extremely widely used, mobile sensor networks (MSNs) have recently attracted more and more researchers’ attention. Existing routing tree maintenance methods used for query processing are based on static WSNs, most of which are not directly applicable to MSNs due to the unique characteristic of mobility. In particular, sensor nodes are always moving in real world, which seriously affects the stability of the routing tree. Therefore, in this paper, we propose a novel method, named routing tree maintenance based on trajectory prediction in mobile sensor networks (RTTP), to guarantee a long term stability of routing tree. At first, we establish a trajectory prediction model based on extreme learning machine (ELM), by which we can predict sensor node’s trajectory to choose an appropriate parent node for each non-effective node. Then, an Improved version of RTTP method (I-RTTP) that using probabilistic method to minimize the error and improve the accuracy is proposed, to improve the performance of RTTP. Therefore, the state of the routing tree in MSNs can be made more stable. Finally, extensive experimental results show that RTTP and I-RTTP can effectively improve the stability of routing tree and greatly reduce energy consumption of mobile sensor nodes.     

Exact and heuristic methods to maximize network lifetime in wireless sensor networks with adjustable sensing ranges

Wireless sensor networks involve many different real-world contexts, such as monitoring and control tasks for traffic, surveillance, military and environmental applications, among others. Usually, these applications consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. We want to individuate a set of covers (that is, subsets of sensors that can cover the whole set of targets) and appropriate activation times for each of them in order to maximize the total amount of time in which the monitoring activity can be performed (network lifetime), under the constraint given by the limited power of the battery contained in each sensor. A variant of this problem considers that each sensor can be activated in a certain number of alternative power levels, which determine different sensing ranges and power consumptions. We present some heuristic approaches and an exact approach based on the column generation technique. An extensive experimental phase proves the advantage in terms of solution quality of using adjustable sensing ranges with respect to the classical single range scheme.     

Modeling and optimization of energy efficient routing in wireless sensor networks

Wireless sensor networks (WSNs) have important applications in remote environmental monitoring and target tracking. The development of WSNs in recent years has been facilitated by the availability of sensors that are smaller, less expensive, and more intelligent. The design of a WSN depends significantly on its desired applications and must take into account factors such as the environment, the design objectives of the application, the associated costs, the necessary hardware, and any applicable system constraints. In this study, we propose mathematical models for a routing protocol (network design) under particular resource restrictions within a wireless sensor network. We consider two types of constraints: the distance between the linking sensors and the energy used by the sensors. The proposed models aim to identify energy-efficient paths that minimize the energy consumption of the network from the source sensor to the base station. The computational results show that the presented models can be used efficiently and applied to other network design contexts with resource restrictions (e.g., to multi-level supply chain networks).     

衰减信道下的决策融合问题

本文针对国际上近几年兴起的研究热点——无线传感器网络在信道衰减下的决策融合问题进行探讨。分析了已有传感器决策融合问题算法的结构,在已知信道传输错误概率的条件下,将信道无传输错误的分析方法推广到信道衰减的情况,对传输错误和融合律的关系进行了深入分析。对给定融合律的网络决策融合问题,获得了最优容错传感器观测量化器的必要条件并设计了迭代算法。在没有增加问题的计算复杂度的情况下,得到了最优观测量化器。与国际上现有结果不同,新结果不要求传感器决策条件独立,也不要求各传感器与融合中心之间的信道相互独立,具有更加广泛的使用范围。     

Real-time data gathering in sensor networks

Wireless sensor networks represent a new generation of real-time traffic communications and high data rate sensor applications, such as structural health monitoring and control. We study some problems related to data gathering in sensor networks when the sensors collect the sensed data about their environment and this information should be delivered to a collecting central Base Station. We prove that scheduling messages through the network to minimize the maximal delivery time with restrictions on the total idle time allowed is NP-hard. We also refer to a special case of linear network topology for which we present two polynomial time optimization algorithms: One is for minimizing the maximal lateness and maximal delay, while the other is for minimizing the number of tardy messages.     

Cognitive and self-selective routing for sensor networks

New approaches to Quality-of-Service (QoS) routing in wireless sensor networks which use different forms of learning are the subject of this paper. The Cognitive Packet Network (CPN) algorithm uses smart packets for path discovery, together with reinforcement learning and neural networks, while Self-Selective Routing (SSR) is based on the “Ant Colony” paradigm which emulates the pheromone-based technique which ants use to mark paths and communicate information about paths between different insects of the same colony (Koenig et al. in Ann Math Artif Intell 31(1–4): 41–76, 2001). In this paper, we present first experimental results on a network test-bed to evaluate CPN’s ability to discover paths having the shortest delay, or shortest length. Then, we present small test-bed experiments and large-scale network simulations to evaluate the effectiveness of the SSR algorithm. Finally, the two approaches are compared with respect to their ability to adapt as network conditions change over time.     

Collision-free path coloring with application to minimum-delay gathering in sensor networks

Efficient data gathering is an important challenge in sensor networks. In this paper we address the problem of gathering sensed data to the sink of a sensor network minimizing the time to complete the process. We present optimal time data gathering algorithms for any sensor network topology, in the half-duplex with directional antennas model, when each sensor has one data packet to be gathered and merging of packets is not allowed at intermediate nodes. Our results improve on existing approximation algorithms. We approach the gathering problem by obtaining optimal solutions to a path coloring problem in graphs.     

Limit laws for coverage in backbone-sensor networks

We study coverage in sensor networks having two types of nodes, namely, sensor nodes and backbone nodes. Each sensor is capable of transmitting information over relatively small distances. The backbone nodes collect information from the sensors. This information is processed and communicated over an ad hoc network formed by the backbone nodes, which are capable of transmitting over much larger distances. We consider two models of deployment for the sensor and backbone nodes. One is a Poisson–Poisson cluster model and the other a dependently thinned Poisson point process. We deduce limit laws for functionals of vacancy in both models using properties of association for random measures.     

An Iterative Exact Solution for the Dual Power Management Problem in Wireless Sensor Network

We study the dual power management problem in wireless sensor networks. Given a wireless sensor network with two possible power levels (heigh and low) for each sensor, the problem consists in minimizing the number of sensors assigned heigh power while ensuring the connectivity of the network. We formulate the problem by a binary integer programming model to minimize the total power consumption. Since the problem is NP-complete, we provide an iterative approximation based on iterative methods in combinatorial optimization. We solve the separation subproblem as a minimum spanning tree.     

Cooperative target localization by multiple unmanned aircraft systems using sensor fusion quality

Over the past several years, researchers at the U.S. Air Force Academy developed cooperative, distributed aerial sensor networks (Pack et?al. in IEEE Trans Syst Man Cybern Part B Cybern 39(4):959?C970, 2009) using multiple small unmanned aerial vehicles (SUAVs) to search, detect, and locate ground targets. The use of distributed SUAVs, however, introduced a set of problems, including difficulties in reliable air-to-air communication and clock synchronization among onboard systems of multiple SUAVs. The communication problems further aggravate the synchronization problem contributing to a large target localization error. Conventional methods use multiple sensor outputs of the same target seen from different perspectives to increase the target localization accuracy. These methods are effective only when the pose errors of sensor platforms based on GPS data are modeled accurately, which is not a reasonable assumption for SUAVs, especially when SUAVs operate in an environment with wind gusts. In this paper, we propose a robust, novel technique that analyzes what we call ??sensor fusion quality?? to assign an appropriate sensor reliability value to each set of updated sensor data. In the proposed approach, we characterize the quality of a set of newly acquired sensor data, containing a target, by examining the joint target location probability density function. The validity of the proposed method is demonstrated using flight test data.     

Efficient band monitoring with sensors outer positioning

The article considers models for monitoring a band with a preset width using sensor networks shaped as disc covers. Every cover disc is a centred sensor operation area. The researchers determine a min-density band cover with the discs of one, two and three radii. The specific requirement for the cover is that the disc centres shall not be inside the band (external monitoring). Various efficient cover models are proposed and their characteristics are determined.     

Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks

In this paper, we consider the duty scheduling of sensor activities in wireless sensor networks to maximize the lifetime. We address full target coverage problems contemplating sensors used for sensing data and transmit it to the base station through multi-hop communication as well as sensors used only for communication purposes. Subsets of sensors (also called covers) are generated. Those covers are able to satisfy the coverage requirements as well as the connection to the base station. Thus, maximum lifetime can be obtained by identifying the optimal covers and allocate them an operation time. The problem is solved through a column generation approach decomposed in a master problem used to allocate the optimal time interval during which covers are used and in a pricing subproblem used to identify the covers leading to maximum lifetime. Additionally, Branch-and-Cut based on Benders’ decomposition and constraint programming approaches are used to solve the pricing subproblem. The approach is tested on randomly generated instances. The computational results demonstrate the efficiency of the proposed approach to solve the maximum network lifetime problem in wireless sensor networks with up to 500 sensors.     

Design of optimal utility of wireless rechargeable sensor networks via joint spatiotemporal scheduling

In wireless rechargeable sensor networks, how to optimize energy resources for maximizing the sensor data is a challenging problem. In this paper, mobile charging vehicle scheduling, sensor charging time splitting and rate control with battery capacity constraints are considered together to maximize network utility. However, they are considered independently in exist works even though these problems are interdependent. In order to improve network performance through collaborative optimization of three problems, a joint optimization problem is formulated firstly. Then, a multistage approach is developed to jointly optimize the three subproblems iteratively. Furthermore, an accelerated distributed algorithm is integrated to improve the convergence speed of rate control. The results of extended experiments demonstrate that proposed approach can obtain higher network utility and charging efficiency compared to other charging scheduling methods.