Fire monitoring in coal mines using wireless underground sensor network and interval type-2 fuzzy logic controller

From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.

Energy optimal routing for long chain-type wireless sensor networks in underground mines

Wireless sensor networks are useful complements to existing monitoring systems in underground mines. They play an important role of enhancing and improving coverage and flexibility of safety monitoring systems.Regions prone to danger and environments after disasters in underground mines require saving and balancing energy consumption of nodes to prolong the lifespan of networks.Based on the structure of a tunnel,we present a Long Chain-type Wireless Sensor Network（LC-WSN）to monitor the safety of underground mine tunnels.We define the optimal transmission distance and the range of the key region and present an Energy Optimal Routing（EOR）algorithm for LC-WSN to balance the energy consumption of nodes and maximize the lifespan of networks.EOR constructs routing paths based on an optimal transmission distance and uses an energy balancing strategy in the key region.Simulation results show that the EOR algorithm extends the lifespan of a network,balances the energy consumption of nodes in the key region and effectively limits the length of routing paths,compared with similar algorithms.

Sensor deployment strategy for chain-type wireless underground mine sensor network

Wireless sensor networks (WSNs) are very important for monitoring underground mine safety. Sensor node deployment affects the performances of WSNs. In our study, a chain-type wireless underground mine sensor network (CWUMSN) is first pre- sented. A CWUMSN can monitor the environment and locate miners in underground mines. The lowest density deployment strate- gies of cluster head nodes are discussed theoretically. We prove that the lifetime of CWUMSN with a non-uniform deployment strategy is longer than with a uniform deployment strategy. Secondly, we present the algorithm of non-uniform lowest density de- ployment of cluster head nodes. Next, we propose a dynamic choice algorithm of cluster head nodes for CWUMSN which can im- prove the adaptability of networks. Our experiments of CWUMSN with both non-uniform lowest density and uniform lowest den- sity deployments are simulated. The results show that the lifetime of CWUMSN with non-uniform lowest density deployment is almost 2.5 times as long as that of the uniform lowest density deployment. This work provides a new deployment strategy for wire- less underground mine sensor networks and then effectively promotes the application of wireless sensor networks to underground mines.

Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network lifetime.An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization.This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource efficiency.The proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of salps.Through experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency optimization.Hence,the network’s lifetime is prolonged.Indeed,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,respectively.Furthermore,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

Anomaly Detection Based on Data-Mining for Routing Attacks in Wireless Sensor Networks

With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks.

Improved Data Discrimination in Wireless Sensor Networks

In Wireless Sensors Networks, the computational power and storage capacity is limited. Wireless Sensor Networks are operated in low power batteries, mostly not rechargeable. The amount of data processed is incremental in nature, due to deployment of various applications in Wireless Sensor Networks, thereby leading to high power consumption in the network. For effectively processing the data and reducing the power consumption the discrimination of noisy, redundant and outlier data has to be performed. In this paper we focus on data discrimination done at node and cluster level employing Data Mining Techniques. We propose an algorithm to collect data values both at node and cluster level and finding the principal component using PCA techniques and removing outliers resulting in error free data. Finally a comparison is made with the Statistical and Bucket-width outlier detection algorithm where the efficiency is improved to an extent.

Development of underground mine monitoring and communication system integrated ZigBee and GIS

An automated underground mine monitoring and communication system based on the integration of new technologies is introduced to promote safety and health,operational management and cost-effectiveness.The proposed system integration considering Wireless Sensor Network(WSN) assisted Geographic Information System(GIS) enables to monitor and control underground mining applications from surface office.Based on the capabilities of WSNs,ZigBee network is adapted for near real-time monitoring,ventilation system control and emergency communication in underground mine.ZigBee nodes were developed to sense environmental attributes such as temperature,humidity and gases concentration;switching ON and OFF ventilation fans;and texting emergency messages.A trigger action plan for monitored attributes above normal and threshold value limits is programmed in the surface GIS management server.It is designed to turn the auxiliary fans on remotely or automatically in orange condition and sending evacuation messages for underground miners in unsafe(red) condition.Multi-users operation and 3D visualisations are other successful achievements of the proposed system for the underground monitoring and communication.

Optimal Deployment Patterns for k-Coverage and k-Connectivity 3D WSNs in Underground Tunnel

In this paper,we first model the mining structure as a arched shape in terms of the tunnel geometry.Based on the regularly placement of nodes in that model,a set of deployment patterns for seamless coverage and k-connectivity is proposed,i.e.1-and 2-connectivity.Further,we figure out the maximum horizontal distance of tangent plane to determine the placement of nodes for k-coverage（k=1,2）.Besides,the mathematical relations between the communication radius and the sensing radius are derived,when the network keeps k-coverage（k=1,2） and k-connectivity（k=2,3） under certain regularity constrains for various patterns.Finally,we demonstrate the optimality of several approaches that we have proposed in the aspect of energy consumption and network lifetime.

基于Python的无线传感器网络数据采集系统

无线传感器因节点结构复杂度高,数据量巨大,无法在海量数据中快速采集用户需求数据.为解决该问题,研究设计了基于Python的无线传感器网络数据采集系统,系统硬件包括数据采集芯片选型单元、无线收发芯片选型单元与串口通讯电路设计单元;软件模块包括网络数据挖掘采集框架设计模块、采集数据压缩模块和采集数据缓存模块.通过对上述硬件单元与软件模块的设计与开发,实现无线传感器网络数据采集系统的运行与应用.实验数据显示:相较于基于Android安全容器的网络数据采集系统,应用设计系统采集数据耗时更短、压缩率数值更大、能耗更小,充分证实设计系统的数据采集性能更佳.

铀矿井下无线网络技术适用性分析

针对铀矿山井下有线网络铺设费用高、重复利用率低,以及无线网络覆盖范围小、干扰多等问题,对铀矿现有网络系统进行深入调研;并结合实际情况进行需求分析,根据对井下人员、设备的信号无线传输、定位、远程监控等要求,以无线局域网全覆盖为目标,在统一网络传输协议的基础上,开展了5G、Wi-Fi等无线网络传输技术井下适用性分析,遴选出了合适的设备和工作频段,并搭建网络测试平台进行了无线信号传输速率、延时实验,验证了井下无线网络技术的适用性。研究结果可指导铀矿井下网络建设,为井下生产设备监控、环境监测、定位、通信等重要数据传输提供可靠网络支撑。

无线地下传感网信道传播实验虚拟仿真与可视化教学

为帮助学生更好地理解和掌握无线地下传感网信道传播特性,推导并建立一个融合土壤特性参数的改进地下无线信道传播模型,在该模型基础上开发无线地下传感网实验虚拟仿真系统。该仿真系统不仅解决实验环境受限、难以开展实验教学的问题,且具有易于理解和操作的特点,仿真结果以图形化和可视化的形式呈现。通过此系统,学生可直观地观察和比较不同参数影响下地下无线信道传播变化情况,深入理解地下无线信道的复杂传播特性,提高学生的学习效率,激发学生的学习兴趣,促进学生学习的主动性和积极性。

多用户源头无线传感网络不完整数据挖掘算法

针对无线传感网络多用户源头数据受噪声影响,导致数据缺失的问题,为了提高数据的完整性,提出多用户源头无线传感网络不完整数据挖掘算法。采用组合广义形态滤波方法对多用户源头无线传感网络数据展开去噪处理,避免噪声数据影响数据填补结果;采用集成学习方法对数据进行深度挖掘,将挖掘出的数据展开分类处理;利用低秩矩阵填充理论对分类后的数据展开首次填补,在此基础上引入曲线相似分类对缺失数据进行二次填补,完成多用户源头无线传感网络数据的完整挖掘。仿真结果表明,所提方法在不同数据集中获得的均方根误差低于0.164%,信噪比高于41.8dB,补全后的数据平均绝对误差为0.023%、平均百分比误差为3.5%、均方根误差为0.021%。因此,所提方法具有较好的去噪效果和较高的数据填补性能。

基于LoRaWAN自适应数据速率的地下无线传感网络性能评估

随着城镇化进程的不断加快和物联网技术的迅速进步,地下复杂土壤介质中的无线传感网络正成为新的研究热点。在此背景下,首先概述了基于LoRaWAN的地下无线传感网络基本架构和研究现状,接着搭建了网络仿真器并实现了自适应数据速率(ADR)调节机制在传感器节点端和网络服务端的2个优化算法,最后依据网络整体数据接收率和网络能耗等性能指标,分别从传感器节点的地下部署环境(土壤含水量和传感器埋藏深度)和LoRa物理层参数两方面进行了网络性能定量评估。结果表明,针对土壤这一复杂介质,ADR调节机制对LoRaWAN中的物理层参数具有较好的调节能力,该机制可为地下无线网络性能优化提供有利手段,有望大幅降低网络能耗。

基于Linux socket的煤矿无线监控系统设计与实现

为改变当前国内落后的煤矿安全监控技术现状,解决煤矿安全监控中遇到的困难,提出利用无线传感器网络在煤矿安全监控中的优势,并结合Linux socket便捷、安全、高效的特点设计煤矿无线监控系统;选用ZIGBEE传输系统作为硬件平台,完成面向煤矿安全监控的多功能便携式无线传感器节点设计和基站传感器节点设计;上位机采用Linux操作系统的Socket技术,把数据送入主控站点计算机,并生成图表完成系统数据统计和监控;彻底解决了传统RFID监控技术通信距离受限瓶颈、数据离散不直观、无法为分析员提供分析依据等一系列技术难题,具有一定的创新性和实用性,对研究煤矿安全生产数据收集新方案具有重要意义。

基于TinyOS的煤矿环境温度检测系统

传统的煤矿环境温度检测通常通过有线传感器来获得信号,但很多情况下,煤矿现场不便于进行有线测试。为了解决这一问题,运用无线传感网络技术构建小型的无线传感网络,并运用基于嵌入式系统TinyOS的结构化编程语言nesC编制了煤矿环境温度检测系统的软件,实现了对煤矿巷道有关点的温度等参数的无线实时检测。通过仿真工具Tossin进行仿真和试验证明该系统运行有效可靠。

基于多Sink节点的煤矿巷道无线传感器网络路由协议

针对传统的单Sink节点无线传感器网络应用于煤矿安全监控系统中时,远离巷道口的传感器节点无法及时、准确地将巷道深处的监测数据传输到巷道口Sink节点的问题,提出一种适用于长带状结构煤矿巷道的基于多Sink节点的无线传感器网络路由协议。该协议引入多Sink节点的无线传感器网络结构和非均匀成簇的思想,采用基于多Sink节点的功率控制算法和非均匀成簇算法,对各个Sink节点的最优通信半径、发射功率、簇首的选择和非均匀竞争半径的计算进行优化。仿真结果表明,该路由协议在连通度、延时和网络生存期上具有优势,可有效降低无线传感器网络整体能耗,延长网络生存期。

基于无线传感网络技术的智能照明系统在高校地下车库的应用实践

天津某高校地下车库采用传统照明系统,存在耗能大、寿命短和照度不够等问题。为了有效改善车库灯光环境,节省车库的运营和管理费用,借助无线传感网络技术,对灯光参数进行精确管理和智能化控制。通过介绍项目的改造概况,阐述智能照明系统在高校地下车库节能改造中的应用原则、模式设计,总结节能改造的方法。基于校园能源监测系统的用能数据,列出智能照明系统改造前后同期的用电量对比并计算回收期,以衡量智能照明系统的经济性。结果显示,在照明智能化改造后,该校地下车库每年可节约电量31.63万kWh,2年多即可收回成本,节能率达91.01%。

基于WinCE技术矿山机电设备动态信息闭环点检系统研究与设计

结合我国现代化矿井建设中自动化、信息化迫切需要,在分析大量煤矿企业设备检修工作、设备维修制度和现在已有的设备点检仪器的工作基础上,利用WinCE的嵌入式手持网络终端技术实现点检信息采集以及点检人员管理。结合无线局域网络Zigbee无线传感器网络设计出一种全新的煤矿机电设备闭环点检解决方案。

基于无线传感网络与UWB技术的煤矿井下采掘人员定位方法

由于传统方法在煤矿井下人员定位中应用效果不佳,不仅定位结果相对误差比较大,而且定位失败人员数量较多,无法达到预期的定位效果,为此提出基于无线传感网络与UWB技术的煤矿井下采掘人员定位方法。搭建无线传感网络场景,利用无线传感网络采集井下人员位置信号,利用自适应算法对信号自适应滤波处理,利用离散快速傅里叶变换对无线传感网络信号矩阵的各个维度做波束形成,对矩阵的各个维度传感信号进行加权合并,增强各个维度上目标信号的强度,利用UWB技术对信号传输,根据增强后的目标信号传输信息确定井下人员位置,以此实现基于无线传感网络与UWB技术的煤矿井下采掘人员定位。经实验证明,设计方法定位相对误差低于10 cm,定位失败人员数量与总人数比例为3%,设计方法在煤矿井下采掘人员定位方面具有良好的应用前景。

基于特征补全的无线传感器网络异常数据流检测

由于节点所感知数据有缺失或者错误的情况,使异常数据流检测受困,导致检测准确率、漏报率和能耗等方面存在问题,因此,提出基于特征补全的无线传感器网络异常数据流检测方法。根据传感网络内数据流间的相关性,在特定环境内对缺失和错误数据进行估计与补全;从补全后的无线传感器网络数据流中抽取数据,并完成数据特征挖掘,为之后的异常数据流检测做好准备;使用支持向量机将正常数据和异常数据分隔,从而实现对无线传感器网络异常数据流检测。结果表明:特征补全后的无线传感器网络异常数据检测,其检测的准确率维持在99%以上,漏报率在0.3%以下,能耗下降率最高可达到35.87%,检测用时在0.8 s以下,具有准确率高、漏报率低、能耗少且用时短的优势。