基于移动Sink的WSN安全数据收集方法

由于目前基于移动Sink的WSN数据收集方法存在网络攻击检测率不高、内存开销大等问题,导致网络较易受到网络攻击且难以被实际应用。针对该问题,提出一种基于移动Sink的WSN安全数据收集方法,利用能量感知的凸壳算法,识别数据收集点,使用椭圆加密算法（ECC）为网络内的所有节点生成密钥,通过ElGamal算法实现节点身份和消息的认证,使用支持向量机（SVM）识别网络攻击类型。仿真实验结果表明,所提出的安全数据收集方法在攻击检测率、内存开销以及数据包投递率方面都表现出较好的性能。     

An energy-efficient clustered distributed coding for large-scale wireless sensor networks

A wireless sensor network (WSN) usually consists of a large number of battery-powered low-cost sensors with limited data collection and processing capacity. In order to prolong the lifetime of the WSN with a target error performance, a novel clustered distributed coding framework, referred to as distributed multiple-sensor cooperative turbo coding (DMSCTC), is developed for a large-scale WSN with sensor grouped in cooperative cluster. In the proposed DMSCTC scheme, a simple forward error correction is employed at each sensor and a simple multi-sensor joint coding is adopted at the cluster head, while complicated joint iterative decoding is implemented only at the data collector. The proposed DMSCTC scheme achieves extra distributed coding gain and cooperative spatial diversity without introducing extra complexity burden on the sensors by transferring the complicated joint decoding process to the data collector. With the proposed scheme, the WSN can achieve the target error performance with less power consumption, thus prolonging its lifetime. The error performance and energy efficiency of the proposed DMSCTC scheme are analyzed, and followed by Monte Carlo simulations. Both analytical and simulation results show that the DMSCTC can substantially improve the energy efficiency of the clustered WSN.     

压缩感知在无线传感器网络数据采集中的应用

提出了一种无线传感器网络中基于压缩感知的数据采集方法。通过分析信号压缩观测过程,提出了适合在硬件资源有限的传感器节点中实现的循环稀疏伯努利观测矩阵CSBM(Cyclic-Sparse-Bernoulli Measurement),该矩阵使用循环稀疏矩阵与伪随机伯努利序列,采用结构化的方法构造,具有非零元素少、良好的伪随机性、硬件易于实现等优点。仿真实验表明,与其他类型的观测矩阵相比,CSBM矩阵在一定信号重构精度前提下具有更低的压缩采样比CSR(Compress Sampling Rate)。在无线传感器网络数据采集应用中,感知节点可以通过压缩观测得到更少的观测数据,能够大大减少网络通信数据量。     

WSN-UAV基于FPGA的异步带优先级数据转发轮询控制系统

在林、农作物监测作业中,可通过散布无线传感器节点形成的自组织WSN收集作物环境信息。将UAV应用于分簇式WSN作为移动的数据收集节点sink时,UAV不仅需要转发各簇首节点的信息,还需转发自带传感器检测到的重要信息。为了保证UAV传感器检测信息及时转发,并进一步降低簇首节点能耗,延长WSN网络寿命,提出异步带优先级的数据转发轮询控制系统方案,应用马尔可夫链和概率母函数进行建模以分析系统性能。用自带异步读写FIFO功能的FPGA作为主控器件,基于FPGA设计该轮询系统硬件电路结构,验证了该轮询控制系统的可行性和高效性。实验结果表明,该方案能保证UAV及时转发重要数据,缩短服务延迟,提高WSN能耗效率,优化系统性能。     

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.     

移动基站无线传感器网络性能优化研究

针对一种移动基站协助数据收集模式,依据无线传感器网络的能量消耗模式,改进了石高涛等人提出的r<<R情况下的缓冲区设计,得到的结论是：缓冲区的中心位于2^-2/2R-l时,网络的寿命最长,其中l为与r和R相关的一个较小的数,并分析给出了能量消耗不均衡的原因,提供了简洁的优化缓冲区设计的计算方法。对传感器网络的优化具有较好的指导作用。     

基于时间相关性的无线传感器网络数据压缩与优化算法

针对无线传感器网络(WSN)数据采集存在数据冗余度大、累积误差大和数据精度低等问题,根据采集数据之间的时间相关性,提出一种无线传感器网络数据压缩与优化算法。该算法通过分析时间序列中采集数据的线性关系,建立分段一元线性回归模型;根据采集数据与回归模型预测值之间的误差,自适应地调整下一个采集时间,并动态地优化回归模型。仿真结果表明该算法在不同的数据变化情况下,均能降低数据冗余度和网络通信量,提高采集数据的重构精度。最后在真实的无线传感器网络应用环境中验证了算法的可行性。     

Energy and delay optimized contention for wireless sensor networks

In wireless sensor network (WSN) studies, the main objective is minimizing the energy consumption so that the lifetime is maximized under the limited battery capacity constraints. Additionally, in most event-driven WSN applications, the end-to-end delay, and hence, the medium access delay should be minimized. Majority of the WSN MAC protocols are contention-based wherein contention window size setting involves an important trade-off between the collision probability and idle listening durations in contentions where both are aimed to be lowered for efficient network operation. In this paper, the energy optimizing and the delay optimizing contention window sizes are derived as a function of the number of contending nodes. For this purpose, we present separate analyses for the contention delay and for the energy consumed which are verified with detailed simulations. In order to obtain close to optimal performance values in a distributed manner, we propose a method for estimating the number of contending nodes since the individual wireless sensor nodes do not have this information readily. Simulations of an event-driven WSN application verify that the proposed method successfully improve both delay and energy efficiency of the contention-based medium access. The end-to-end network performance is also investigated by employing a geographical routing protocol. Results show that using the heuristic method proposed that use the optimum contention window size analyses presented, the overall network performance can be improved without incurring any overhead to the system.     

无线传感器网络分布式调度方法研究

无线传感器网络(Wireless sensor network, WSN)是一个资源受限的网络系统.已提出了多种调度方法来提高网络性能. 本文归纳了WSN分布式调度方法的设计原则和分类方法,并按调度对象对调度方法进行了分类讨论. 详细论述了一些典型调度方法的内在机理,分析了每一类调度方法的特点. 对这些调度方法的设计目标和性能特点进行了对比.最后总结了WSN分布式调度方法的研究现状, 提出了该领域今后发展需要关注的重要因素.     

Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol (DEAOA-MHRP) for WSN. The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN. To accomplish this, DEAOA-MHRP model initially integrates the concepts of Different Evolution (DE) and Arithmetic Optimization Algorithms (AOA) to improve convergence rate and solution quality. Besides, the inclusion of DE in traditional AOA helps in overcoming local optima problems. In addition, the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance. In order to ensure the energy efficient performance of DEAOA-MHRP model, a detailed comparative study was conducted and the results established its superior performance over recent approaches.     

WSN中基于改进蚁群算法的移动Agent路径规划

在无线传感器网络(Wireless Sensor Networks,简写为WSN)系统中增加了移动Agent,能提高系统的整体性能及工作效率.参考Sink节点的功能及特点,移动Agent具有收集和简单处理信息数据并将其传输到上一级单位的功能,它的移动功能提高了网络的连通性、传感覆盖率及使用寿命等性能.采用传感覆盖率高,...     

基于WSN的数据挖掘技术在海洋预测中的应用

论文旨在提出一种理想的海洋趋势预测方法.为此,提出了一种基于无线传感器网络和计算机技术的海洋水文精确监测方案.然后,利用支持向量回归算法对传感器网络采集的数据进行处理.为了获得最优的算法参数,引入粒子群算法,通过粒子间的相互竞争来寻找全局最优解.在此基础上,根据纽约港的水文情况,建立了海洋水文数据采集与观测系统.然后,...     

WSN中利用蚁群路径优化的时隙选择重排算法

针对无线传感器网络(WSN)汇聚传输中的数据传输时间和功耗问题,提出了考虑时间同步和唤醒延迟的汇聚传输时隙选择重排算法。将时分多址接入(TDMA)用作介质访问协议,并允许每个节点在传输时隙期间可以发送或接收数据;设计新的WSN数据收集树模型,将传感器节点生成的数据通过无线链路形成的多跳网络发送到汇聚节点,在数据收集树的每条链路上分析时隙顺序,优化时隙选择,并基于蚁群算法优化路径选择,减少传输能量消耗和均衡簇头能量。实验结果表明,提出的算法可以实现显著的数据传输性能提高和功耗节约。     

Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks

A wireless sensor network (WSN) is a large collection of sensor nodes with limited power supply, constrained memory capacity, processing capability, and available bandwidth. The main problem in event gathering in wireless sensor networks is the formation of energy-holes or hot spots near the sink. Due to the restricted communication range and high network density, events forwarding in sensor networks is very challenging, and require multi-hop data forwarding. Improving network lifetime and network reliability are the main factors to consider in the research associated with WSN. In static wireless sensor networks, sensors nodes close to the sink node run out of energy much faster than nodes in other parts of the monitored area. The nodes near the sink are more likely to use up their energy because they have to forward all the traffic generated by the nodes farther away to the sink. The uneven energy consumption results in network partitioning and limit the network lifetime. To this end, we propose an on-demand and multipath routing algorithm that utilizes the behavior of real termites on hill building termed Termite-hill which support sink mobility. The main objective of our proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The performance of our proposed algorithm was tested on static, dynamic and mobile sink scenarios with varying speed, and compared with other state-of-the-art routing algorithms in WSN. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that our proposed routing algorithm was able to balance the network traffic load, and prolong the network lifetime.     

基于WSN的声表面波微压力传感器的研究

无线传感器网络是一种新型综合智能信息系统,它在实现数据采集、信息感知、智能控制方面展现了较高的应用价值.无线传感器网络技术在灵敏性、容错性、可扩展性和连通性等方面的要求决定了它面临着许多制约因素和挑战性问题,亟待解决和完善.在无线传感器网络原理的基础上,对传感器节点中的微压力传感器进行研究.通过分析不同压电材料的性能,选取基片的材料,并使用有限元分析法对基片结构进行受力分析,确定边界条件,基于最小二乘法建立线性回归数学模型并进行曲线拟合,得出频率与压力的关系.该研究能够模拟无线传感网络节点中数据的采集,对无线传感网络的数据优化、算法优化有重要的意义.     

WSN中利用XGBoost和加权自适应HFLMS的数据约减组合预测方法

针对无线传感器网络(WSN)中能量、带宽和内存等各种资源的限制问题,提出了一种XGBoost结合加权自适应分层分数最小均方误差(hierarchical fractional least-mean-square,HFLMS)的数据约减组合预测方法。首先,利用XGBoost方法对损失函数进行了二阶的泰勒展开,权衡模型的复杂度和损失函数的下降速度,实现了资源限制的稳定预测;然后提出自适应HFLMS滤波器实现WSN数据约简的传输,并基于误差估计来预测所感测的数据,有效降低了WSN中的能量约束;最后,利用两个评估参数(能量和预测误差)来验证所提组合预测方法的性能。实验结果表明,相比没有预测、近似最速下降算法和分层最小均方滤波技术,提出的预测方法获得的预测结果更好。     

eHIP: An energy-efficient hybrid intrusion prohibition system for cluster-based wireless sensor networks

Verifying authenticity and integrity of delivered data is indispensable for security-sensitive wireless sensor networks (WSN). Unfortunately, conventional security approaches are unsuitable for WSN because energy efficiency is really not an important issue. However, energy conservation is truly a critical issue in WSN. In this paper, a proposed hybrid security system, called energy-efficient hybrid intrusion prohibition (eHIP) system, combines intrusion prevention with intrusion detection to provide an energy-efficient and secure cluster-based WSN (CWSN). The eHIP system consists of authentication-based intrusion prevention (AIP) subsystem and collaboration-based intrusion detection (CID) subsystem. Both subsystems provide heterogeneous mechanisms for different demands of security levels in CWSN to improve energy efficiency. In AIP, two distinct authentication mechanisms are introduced to verify control messages and sensed data to prevent external attacks. These two authentication mechanisms are customized according to the relative importance of information contained in control messages and sensed data. However, because the security threat from compromised sensor nodes cannot be fully avoided by AIP, CID is therefore proposed. In CID, the concept of collaborative monitoring is proposed to balance the tradeoff between network security and energy efficiency. In order to evaluate the performance of eHIP, theoretical analyses and simulations of AIP and CID are also presented in this paper. Simulation results fully support the theoretical analysis of eHIP.     

一种及时恢复故障路由的无线传感器路由算法

无线传感器网络以其布网灵活,连接方便,功耗小,成本低,逐渐被应用于煤矿、消防、化工安全等民用领域.根据煤矿安全应用的特殊情况,提出一种无线传感器路由算法.该算法在定向扩散算法的基础上进行了改进,采用簇的方式,由簇首协调簇内节点的数据采集和整合,并且在每一煤层设置层首作为协调节点,通过层首之间建立的主干路由,有效地减少冗余数据的传播.该算法的特点就是能够及时恢复出现故障的路由.实验模拟结果也表明算法有效地减少了冗余数据和传输延时.     

基于Q学习和规划的传感器节点任务调度算法*

为了改善节点的学习策略,提高节点的应用性能,以数据收集为应用建立任务模型,提出基于Q学习和规划的传感器节点任务调度算法,包括定义状态空间、延迟回报、探索和利用策略等基本元素.根据无线传感器网络(WSN)特性,建立基于优先级机制和过期机制的规划过程,使节点可以有效利用经验知识,改善学习策略.实验表明,文中算法具备根据当前WSN环境进行动态任务调度的能力.相比其它任务调度算法,文中算法能量消耗合理且获得较好的应用性能.     

TinyOS环境下音频数据采集原型系统的设计与实现

无线传感器网络中音频数据的采集具有采样频率快、数据量大的特点,对无线传感器网络数据的存储、压缩与传输提出了挑战。结合音频数据采集的特点,针对其应用环境,提出了一种具有存储、压缩与可靠传输功能的音频数据采集协议,并在MicaZ节点硬件平台上实现了该协议的原型系统。实验表明,该原型系统可以保证音频数据的可靠传输,并能够有效减少节点的发包量。