Multifactor task allocation problem in wireless sensor and actuator networks

Task allocation is an essential part of many military applications of WSAN such as intelligent minefield.The key problem of task allocation decision in these systems for the optimal scheme on task allocation is how to obtain the node-target assignment.Combing with the factors of target’s parameters and node’s own status,the various influence factors of task efficiency were analyzed and the positive and negative indicators were processed by range normalization method separately.The linear-weighted task efficiency function was proposed in the applications of WSAN such as intelligent minefield.It can be utilized as the evaluation index of assignment schemes since it reflected the integrated impact of target’s threat and node’s value on the system more comprehensively.Task efficiency function can be constructed flexibly based on the demand of different multifactor task allocation application.The essence of the problem was how to make node-target assignment to achieve the maximum task efficiency of the whole system.This problem turned into assignment problem and can be solved.Finally,an application was implemented to demonstrate this scheme.The results show that the model is suitable for small-scale multifactor task allocation problem in intelligent minefield system.     

无线传感网规则拓扑结构的部署与设计综述

在无线传感网应用中部署规则拓扑结构能实现感知区域的完全覆盖和采集点的精确定位,以较小的部署代价提供更好的网络性能,为无线传感网的优化设计提供参照依据,对规则拓扑结构的相关研究进行综述。基于节点有效面积这个指标,探讨了完全覆盖、k-覆盖、l-连通情况下的拓扑部署方式与效能,并从连通性、能耗、传输性能、路由方案和MAC协议等方面总结了典型规则拓扑结构的性能、变化规律和适用范围。指出了各结构的最佳适用条件,并提出了若干有待进一步研究的方向。     

Deterministic deployment based on information coverage in wireless sensor networks

In this study, a deterministic deployment problem in wireless sensor networks is examined. On the basis of information coverage, we study equilateral triangle and square deployment strategies, and we provide the maximum distance between sensors in order to reach the required detection probability for any point in the monitoring field. First, we provide a model of the signal attenuation. On the basis of the detected signal from the K sensors, the best linear and unbiased estimation is used to estimate the signal parameter with the corresponding error. For the equilateral triangle deployment, the maximum distance between sensors is computed and provided when the received signal data from two or three sensors is used. Similarly, we have computed and supplied the maximum distance between sensors in the square deployment. Simulations are performed to show the relationship between the number of sensors and the detection probability. The simulation results show that it is not a good choice to improve the detection probability with a larger number of sensors.Copyright © 2012 John Wiley & Sons, Ltd.     

无线传感器网络的神经网络模型

无线传感器网络是复杂的无线网络。无线传感器网络拥有大量的网络节点。网络节点是无线传感器网络的基础。为了研究复杂的无线传感器网络，采用了神经元描述了WSN的网络节点，用神经元模型表示了无线传感器网络。给出了无线待感器网络节点的神经元模型和无线传感器网络的神经网络模型，并将神经网络应用于无线传感器网络的数据融合应用。结果表明，基于神经网络的无线传感器网络研究可以使得复杂研究变得简单，利于开展WSN的深入研究。     

UEEDA: Uniform and energy‐efficient deployment algorithm for wireless sensor networks

Efficient and accurate sensor deployment is a critical requirement for the development of wireless sensor networks. Recently, distributed energy‐efficient self‐deployment algorithms, such as the intelligent deployment and clustering algorithm (IDCA) and the distributed self‐spreading algorithm (DSSA), have been proposed to offer almost uniform distribution for sensor deployment by employing a synergistic combination of cluster structuring and a peer‐to‐peer deployment scheme. However, both DSSA and IDCA suffer from unnecessary movements that have arisen from an inappropriate design in partial force. To improve the performance of self‐deployment algorithms, a uniform and energy‐efficient deployment algorithm (UEEDA) is proposed in this paper. Simulation results demonstrate that the proposed UEEDA outperforms both DSSA and IDCA in terms of uniformity and algorithm convergence speed. Copyright © 2007 John Wiley & Sons, Ltd.     

Fault analysis and research of wireless sensor network based on kernel partial least squares

With the development of intelligent and networked sensor technology,wireless sensor networks were widely used in human life and commercial fields,because wireless sensor network nodes usually only carry limited resources,it is prone to failures due to insufficient resources,the accurate and timely fault diagnosis of WSN nodes can ensure the reliability of information,thus improving the maintainability of WSN and prolonging the service life of WSN.A method of using kernel partial least squares has been proposed to predict the fault reasons,the method overcomes the defects of traditional linear regression method and the nonlinear high dimensional space for data analysis.Through many experiments,the method can absorb the characteristics of canonical correlation analysis and principal component analysis method,provide a more thorough and rich content analysi,that the reason of the fault can be predicted effectively.     

基于网络分割的无线传感器网络多信道MAC协议

无线传感器网络中,可用正交信道数目较少和噪声干扰问题制约着多信道MAC协议性能的提升,结合数据采集应用的特点,提出一种基于网络分割的多信道MAC协议。在最小化网络总干扰值的基础上,网络分割引入碰撞因子进一步优化子树结构、降低树内干扰。并利用基于图着色理论的分配策略为每棵子树分配一条高质量信道。仿真实验结果表明,该协议显著提高了网络吞吐量,并且大幅降低了传输延迟和分组丢失率。     

基于分簇的无线传感器网络协议

本文针对目前无线传感器网络中传统MAC协议在动态性、低时延方面的不足,在前人研究的基础上,提出一种基于分簇的自适应AMAC协议.该协议将簇分为簇首节点和簇内成员节点,簇内成员节点可以根据自身的状态向簇首节点提出时隙申请,簇首节点对这些申请信息进行仲裁,从而及时调整时间帧的长度,使其能更符合当前网络的负载情况和拓扑结构....     

基于RSSI的传感器网络节点安全定位机制

针对RSSI测距存在的脆弱性问题,提出了一种基于完整性编码和不间断占用信道的安全RSSI测距协议,该协议不仅可以抵抗伪造插入、重放/虫洞等常规攻击,而且可以防止信标信号被恶意干扰而削弱,即可抵抗虚增测距的外部攻击。在此基础上,设计了一种基于RSSI的传感器网络节点安全定位机制,该机制采用可校验的多边测量法来过滤虚减测距的外部攻击,实现安全定位,并对测距协议和定位机制的安全性进行了理论分析。     

利用移动内点来修复传感器网络空洞的算法

针对目标区域中存在感知空洞问题,提出了一种空洞修复准则。在此基础上,设计了基于移动节点无需地理信息的修复算法SOI,该算法通过计算空洞边缘可移动的最佳内点,使该边缘节点到达新的位置,实现减少目标区域中空洞面积的目的。分析与仿真实验表明,与VHR算法相比,SOI算法在部署密集的传感网络中需要移动的总距离更少。     

Modulated relay based stable election protocol for large-scale wireless sensor networks

Internet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy-efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R-SEP (MR-SEP) for large-scale WSN-based IoT applications. MR-SEP is based on the well-known stable election protocol (SEP). MR-SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi-hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR-SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR-SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.     

动态无线传感器网络自组织协议的研究

目前多数移动自组织路由协议选择路径都是基于跳数最短的单径路由.从能量角度看,多数路由协议并没有充分利用网络资源.文章提出一种多径节能路由(Muhipath Saving Dynamic Source Routing,MSDR)协议.实验结果表明,该协议不仅提高了网络可靠性,而且还有效地延长了节点的工作时间和网络的整体寿命.     

基于Beta分布的无线传感器网络分簇路由信任管理机制研究

信任管理机制为保障无线传感器网络安全提供了一种有效方案,通过对节点的行为进行评价,建立整个网络的信任管理机制,对判断的恶意节点采用相应的限制措施来保障安全。文章以Beta概率密度分布函数的期望值作为信任值,优化网络分簇路由协议,可在保障网络能量高效利用的同时,有效提升整个网络的安全性。     

Joint optimization of energy allocation and routing problems in wireless sensor networks

Energy allocation problems and routing problems are both important research issues in the wireless sensor network (WSN) field. The former usually aims at considering how to allocate a certain number of sensor devices in a sensing region to form a WSN so that the objective function value (e.g., the network connectivity or the network lifetime) of the constructed network is optimized. For the message routing problem in WSNs, researchers tend to consider how to find an energy conservable message transmission routing scheme for notifying the supervisor of the WSN when an event occurs. Till now, many solutions have been proposed for the above two categories of optimization problems. However, unifying the above two network optimization problems to maximize the network lifetime, to the best of our knowledge, still lacks related research. This paper considers a joint optimization problem for energy allocation and energy‐aware routing called the joint optimization of energy allocation and routing problem (JOEARP) for a hierarchical cluster‐based WSN. We propose an exact algorithm to provide the optimum solution for the JOEARP. The simulation results show that this solution performed better in prolonging the network lifetime of a WSN in a real situation, compared to other compositions of conventional energy allocation schemes with some known routing algorithms. Copyright © 2009 John Wiley & Sons, Ltd.     

移动Sink传感网中基于IEEE 802.15.4的拓扑控制与路由

针对查询驱动的移动Sink无线传感器网络,基于IEEE 802.15.4标准,提出了一个联合簇树组网和移动路由的跨层协议设计方案。以能耗、网络连通度、负载均衡和子树深度等指标为评价因子,在多目标优化模型下定义了一个拓扑综合评价函数,构建稳定的最优簇树网络结构;改进了IEEE 802.15.4 MAC中基于信标帧调度的分布式睡眠同步算法,维护较低占空比下的网络同步与拓扑修正;利用拓扑形成过程中依"最早信标帧"、"最好链路"、"最小层次"3种准则确立的父子关系,建立移动Sink与N-Hop范围内节点的双向路由路径。最终将上述MAC层的拓扑控制方案与网络层的查询路由方法完整实现于TinyOS协议栈中。实验结果表明,评价函数能够有效地反映组网质量,N-Hop后验式路由结合睡眠同步机制,在"最好链路"拓扑下表现出较高的能量效率。     

Dynamic and adaptive detection method for flooding in wireless sensor networks

In recent years, wireless sensor networks (WSNs) have attracted an increasing attention in several fields. However, WSNs must be treated with significant challenges in their design due to their special characteristics such as limited energy, processing power, and data storage that make the energy consumption saving a real challenge. Also, regarding their distributed deployment in open radio frequency and lack of physical security, these networks are vulnerable and exposed to several attacks: passive eavesdropping, active attacks, and identity theft. In this paper, we propose a new method called accordion method to detect and apprehend denial of service attacks in WSNs. This approach is a dynamic and an adaptive method based on using clustering method which allows electing control nodes that analyze the traffic inside a cluster and send warnings to the cluster head whenever an abnormal behavior is suspected or detected. The proposed method relies on the analysis of the evolution of the threshold messages (alerts) sent in the cluster. The proposed method has been evaluated, and the obtained numerical results show its benefit compared with other detection methods.     

A novel sine‐curve mobility model for intrusion detection in wireless sensor networks

Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight‐line intrusion path and a Boolean sensing model. However, a straight‐line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real‐world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel sine‐curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.     

基于拓扑重构的水下移动无线传感器网络拓扑优化

针对水下移动无线传感器网络（MUWSN, mobile underwater wireless sensor networks）拓扑随洋流动态演化对其网络性能会产生很大影响,提出了一种基于拓扑重构的水下移动无线传感器网络拓扑优化方法,首先通过模拟鱼群行为对传感器节点位置进行调整,优化网络覆盖度;其次,利用冗余节点修复网络中不连通位置,消除关键节点,优化网络连通性,最后,通过仿真对比实验验证了该方法的合理性和有效性。实验结果表明,所提算法能在较低能耗下,保证网络覆盖度长期维持在97%左右,连通率达到89%以上。     

无线传感器网络的簇头功率控制技术

在军事侦察与环境监测中,无线传感器网络一般部署在无人区域或危险区域,不能依靠人对系统进行配置与管理.网络节点通过撒播造成分簇后密度不均,影响了网络性能.由于同簇节点通信使用同一信道,簇的大小直接关系到每个节点的通信能力.当簇内节点个数处于一个合适的范围时,网络才能发挥最好的性能.因此需要对簇头进行功率控制来优化网络结构.针对这一缺乏准确数学模型的过程,提出一种基于PID的模糊自适应的变步长簇头功率控制方案,把簇内节点数目控制在一个合理的范围内.其特点是概念简单、易于理解和提高系统的鲁棒性,仿真结果从理论上证明了通过控制分簇大小以后,网络的寿命和通信能力都有所增加.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.