Efficient Pr-Skyline Query Processing and Optimization in Wireless Sensor Networks

As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the Pr-Skyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and energy-efficient way. We formulate the problem and prove that it is NP-Complete and cannot be approximated in a given expression. However, the proposed algorithm SKY-SEARCH with pruning techniques can guarantee the computational efficiency given relatively large input size, while the filter-based distributed optimization strategy significantly reduces the transmission cost and the required storage space of the sensor nodes. Extensive experiments verify the efficiency and scalability of SKY-SEARCH and the distributed optimizing strategy.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

Stream Segmentation-A Data Fusion Approach for Sensor Networks

Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

Hash-area-based data dissemination protocol in wireless sensor networks

HashQuery,a Hash-area-based data dissemination protocol,was designed in wireless sensor networks. Using a Hash function which uses time as the key,both mobile sinks and sensors can determine the same Hash area. The sensors can send the information about the events that they monitor to the Hash area and the mobile sinks need only to query that area instead of flooding among the whole network,and thus much energy can be saved. In addition,the location of the Hash area changes over time so as to balance the energy consumption in the whole network. Theoretical analysis shows that the proposed protocol can be energy-efficient and simulation studies further show that when there are 5 sources and 5 sinks in the network,it can save at least 50% energy compared with the existing two-tier data dissemination(TTDD) protocol,especially in large-scale wireless sensor networks.

Investigating Approaches of Data Integrity Preservation for Secure Data Aggregation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the sanctity of the aggregated data needs to be ensured. Especially, the data integrity of the aggregated result is critical as any malicious update to it can jeopardize not one, but many sensor readings. In this paper, we analyse three different approaches to providing integrity support for SDA in WSNs. The first one is traditional MAC, in which each leaf node and intermediate node share a key with parent (symmetric key). The second is aggregate MAC (AMAC), in which a base station shares a unique key with all the other sensor nodes. The third is homomorphic MAC (Homo MAC) that is purely symmetric key-based approach. These approaches exhibit diverse trade-off in resource consumption and security assumptions. Adding together to that, we also propose a probabilistic and improved variant of homomorphic MAC that improves the security strength for secure data aggregation in WSNs. We carry out simulations in TinyOS environment to experimentally evaluate the impact of each of these on the resource consumption in WSNs.

How does the Behaviour of Dairy Cows during Recording Affect an Image Processing Based Calculation of the Udder Depth?

Precision Livestock Farming studies are based on data that was measured from animals via technical devices. In the means of automation, it is usually not accounted for the animals’ reaction towards the devices or individual animal behaviour during the gathering of sensor data. In this study, 14 Holstein-Friesian cows were recorded with a 2D video camera while walking through a scanning passage comprising six Microsoft Kinect 3D cameras. Elementary behavioural traits like how long the cows avoided the passage, the time they needed to walk through or the number of times they stopped walking were assessed from the video footage and analysed with respect to the target variable “udder depth” that was calculated from the recorded 3D data using an automated procedure. Ten repeated passages were recorded of each cow. During the repetitions, the cows adjusted individually (p < 0.001) to the recording situations. The averaged total time to complete a passage (p = 0.05) and the averaged number of stops (p = 0.07) depended on the lactation numbers of the cows. The measurement precision of target variable “udder depth” was affected by the time the cows avoided the recording (p = 0.06) and by the time it took them to walk through the scanning passage (p = 0.03). Effects of animal behaviour during the collection of sensor data can alter the results and should, thus, be considered in the development of sensor based devices.

Symmetric-Key Based Homomorphic Primitives for End-to-End Secure Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.

工程领域裂纹检测实验方法的进展

裂纹故障是影响生产生活和设备安全运行的一大因素,裂纹检测实验对于检测技术应用和裂纹故障信息获取具有重要意义,但领域内缺乏对检测实验的系统性归纳与剖析。该文对当前代表性的裂纹检测实验方法进行了分类比较,针对裂纹信息介绍了定位、形貌和深度的检测实验,从先进传感器、数据处理与机器学习应用等3个方面探究了裂纹检测实验方法进展。最后根据国内外裂纹检测实验的研究现状,提出了在裂纹扩展方向检测、误报漏报及实验与实际工况贴近程度方面所面临的挑战,并从实时动态、远程无线和多故障耦合检测等方面进行了展望,为今后裂纹检测实验方法的研究与发展提供了参考。

力-热耦合下的MEMS传感器位置偏移自校准方法

在解决力-热耦合作用引发的微机电系统(Micro-Electro-Mechanical System,MEMS)传感器位置偏移问题时,通常运用传统遗传算法实现位置偏移自校准,受到固定参数的影响,该算法容易得到局部最优解,使得自校准后的MEMS传感器测量误差依旧较大。因此,提出一种力-热耦合下的MEMS传感器位置偏移自校准新方法。采用数据融合思想,将多批次采集的MEMS传感器测量数据汇总起来,对比与真实数据之间的差异,判断传感器是否在力-热耦合作用下偏移出工作区。引入误差相位偏移法进一步处理传感器测量数据,获取等间隔变换误差序列,解算出传感器误差模型,以此来体现传感器位置偏移变化。以传感器位置偏差最小化为目标,设定偏移自校准目标函数,并运用结合自适应调整思想的改进型遗传算法进行寻优求解,获取最优自校准参数取值。实验结果表明:以嵌入式角位移传感器为例,运用该方法完成自校准处理后,传感器的测量误差峰值保持在6″以下,有效解决了力-热耦合作用引起的传感器精度损失问题。

基于物联网技术的农业机械智能监测与故障诊断

智能监测与故障诊断系统可以实时监控农业机械的运行状态,及时发现潜在问题并进行预警,减少机械故障导致的停机时间,进而提高农业生产的整体效率。该文基于物联网技术,对智能监测系统数据采集模块、通信模块以及数据处理与分析模块进行设计,并结合多传感器融合技术和先进的算法,实现了对机械状态的全面监测和故障诊断。该系统能够显著提高农业机械的工作效率和可靠性,降低故障率和维护成本,并提出了进一步优化和改进系统性能的建议。研究结果旨在为推动农业机械智能化发展提供理论基础和技术支持。

智能可穿戴柔性数据手套传感技术研究进展

智能可穿戴领域是一个集多学科多门类的交叉研究领域,智能可穿戴数据手套在监测人体健康、信息传递、通信、虚拟交互等领域具有广阔的应用前景。运用Citespace软件以Wed of Science核心数据库为数据来源,对近5年的相关研究文献进行检索,最终纳入126篇文献进行全文系统分析,从应用于数据手套基于不同工作原理的传感器类型、用于可穿戴传感显示转化的数据处理方法、多功能可穿戴传感手套集成的方法3个方面,对最新研究进行全面梳理总结。讨论了方法和技术的局限性,指出了存在的挑战和机遇。

基于无线传感器网络的鱼塘水质管理研究

随着水产养殖业的快速发展,鱼塘水质管理成为保障养殖效率与产品品质的关键因素。文章探讨了一种基于无线传感器网络的鱼塘水质监测系统的设计与应用。该系统集成了多种水质传感器,如溶解氧、pH值、温度和氨氮浓度传感器,以实时监测鱼塘的水质参数。传感器节点通过无线方式将采集到的数据传输至汇聚节点,再由汇聚节点将数据发送至远程监控中心,实现对水质的远程监控与管理。该系统采用自组织网络技术,能够适应鱼塘复杂多变的环境,降低布线成本,提高监测灵活性。此外,该研究还涉及数据处理算法与预警机制,以便及时发现水质异常并采取相应措施,确保水质维持在适宜养殖的范围内。通过试验验证,该系统有效提升了鱼塘水质管理的效率与精确度,对促进水产养殖业的智能化发展具有重要意义。

基于改进储备池计算的高精度扭秤动力学状态预测方法

在空间引力波探测任务中,超高精度惯性传感器在入轨之前必须经过地面测试与评价,悬丝扭秤是地面测试的首选装置。为了获得悬丝扭秤的精确动力学模型,以减小其固有系统误差,提出了一种基于改进储备池计算(RC)的扭秤动力学预测模型。所提模型利用注意力机制强化时间序列数据的长期依赖特征,并通过贝叶斯优化算法定位模型最优超参数空间,提高了动力学模型的预测精度。在实验室收集的扭秤转角时序数据上验证了所提方法的有效性,相较于传统RC的模型预测误差平均降低40%以上,可以为惯性传感器的地面测试提供可靠的动力学参考。

基于多源数据融合与卷积长短期记忆神经网络的聚合物挤出过程熔体密度监测方法

聚合物挤出过程中熔体密度是影响产品质量的关键因素。由于挤出加工过程的高温、高压复杂工况,寻求能准确、在线监测聚合物挤出过程中熔体密度的方法是一个具有挑战性的问题。尽管基于机器学习的质量监测方法提供了一种解决方案,但在聚合物挤出加工过程中,由于数据类型、工艺参数、操作环境等多变性因素的影响,传统的机器学习方法可能难以捕捉聚合物加工中不同输入参数和输出质量参数之间的复杂关系,使得监测任务难以获得理想的准确性。本文提出了一种基于多源数据融合与卷积长短期记忆神经网络(CNN–LSTM)的熔体密度监测方法,用于在线监测聚碳酸酯–丙烯腈–丁二烯–苯乙烯共聚物(PC/ABS)共混体系的熔体密度。首先,通过实时采集安装在挤出机模头处的近红外、拉曼及超声3种传感器数据,对3种传感数据进行预处理并融合后作为输入;然后,通过合理设计的网络结构,构建CNN–LSTM监测模型,利用CNN的特征提取能力与LSTM的预测能力,最终实现对聚合物共混过程中的熔体密度的实时监测。基于独立开发的多源传感数据实时采集装置获取的数据,利用所提方法对PC/ABS共混挤出过程的熔体密度进行实时监测,结果表明:本文方法能够准确监测聚合物熔体密度,其在测试集上的均方根误差和决定系数分别为0.975 5、0.006 3 g/cm3,比传统的卷积神经网络方法、长短期记忆网络方法、岭回归方法、偏最小二乘回归方法、多层感知机方法和支持向量机回归方法具有更高的预测精度;本文方法的10次输入平均预测时间为1.523 5 s,能够满足实际生产过程的实时监测。综上所述,所提出的基于多源数据融合与CNN–LSTM的熔体密度监测方法显著提高了聚合物挤出过程中熔体密度的实时监测精度,为挤出过程中聚合物的质量提供了可靠的技术支持。

数据手套用柔性传感系统及信号特征分析技术

对数据手套采集信息的各组成部件进行归纳,总结数据手套中的传感系统,重点探讨柔性传感器的制备和传感阵列在手套中的集成。综述数据手套在不同应用场景下对采集信号的处理模式,特别介绍深度学习及神经网络计算方法在手势识别中的研究进展。最后,总结数据手套需突破的问题及未来发展趋势。

IoT感知的业务微流程建模

基于物联大数据赋能的业务流程能够更快更准地感知物理世界并及时做出响应的需求突现,提出一种物联网(Internet of Things,IoT)感知的业务微流程建模方法。首先,以单个IoT对象为中心建模,融合MAPE-K(monitor,analysis,plan,execution and knowledge base,MAPE-K)模型思想,将IoT对象实例生命周期的行为状态与微流程实例状态一一映射,实现对单个IoT对象的环形自动监控和调节;其次,基于从IoT传感设备获取的数据,定义基于SASE+语言的业务规则,提取对业务流程有意义的业务事件,避免了无关事件对宏流程的干扰;最后,通过设计一个微流程建模工具原型系统,结合真实案例分析,验证了提出建模方法的有效性,实现了业务流程与IoT实时流式感知数据的结合,并显著减少了宏流程需要处理的业务事件数量。