Problem of Standardization of Configuration Data in Wireless Sensor Networks: The Case of Routing Protocols

The exploitation of Wireless Sensor Networks (WSN) is constrained by limited power, low computing power and storage and short-range radio transmission. Many routing protocols respecting these constraints were developed but, it still lacks formal and standardized solutions being able to help in their configuration. The configuration management that responds to this concern is very important in this type of network. It consists of the definition of data models to configure and is very necessary for the good network performance. Tangible results were obtained in traditional networks with the emergence of NETCONF and YANG standards, but on the best of our humble knowledge there are none yet in WSNs. We propose in this paper wsn-routing-protocol, a YANG data model for routing protocols configuration in WSNs. Following our model, we propose two YANG configuration data models based on the latter: they are respectively aodv for AODV and rpl for RPL.

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements.The randomly missing measurements are introduced in order to better reflect the reality in the sensor network.To improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is used.We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square sense.By constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are obtained.Finally,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.

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.

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.

A Distributed Dynamic Clustering Algorithm for Wireless Sensor Networks

This paper proposes a distributed dynamic k-medoid clustering algorithm for wireless sensor networks （WSNs）, DDKCAWSN. Different from node-clustering algorithms and protocols for WSNs, the algorithm focuses on clustering data in the network. By sending the sink clustered data instead of practical ones, the algorithm can greatly reduce the size and the time of data communication, and further save the energy of the nodes in the network and prolong the system lifetime. Moreover, the algorithm improves the accuracy of the clustered data dynamically by updating the clusters periodically such as each day. Simulation results demonstrate the effectiveness of our approach for different metrics.

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.

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.

A Survey on Underwater Acoustic Sensor Networks: Perspectives on Protocol Design for Signaling, MAC and Routing

Underwater acoustic sensor networks (UASNs) are often used for environmental and industrial sensing in undersea/ocean space, therefore, these networks are also named underwater wireless sensor networks (UWSNs). Underwater sensor networks are different from other sensor networks due to the acoustic channel used in their physical layer, thus we should discuss about the specific features of these underwater networks such as acoustic channel modeling and protocol design for different layers of open system interconnection (OSI) model. Each node of these networks as a sensor needs to exchange data with other nodes;however, complexity of the acoustic channel makes some challenges in practice, especially when we are designing the network protocols. Therefore based on the mentioned cases, we are going to review general issues of the design of an UASN in this paper. In this regard, we firstly describe the network architecture for a typical 3D UASN, then we review the characteristics of the acoustic channel and the corresponding challenges of it and finally, we discuss about the different layers e.g. MAC protocols, routing protocols, and signal processing for the application layer of UASNs.

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.

Cooperative Sensing and Distributed Control of a Diffusion Process Using Centroidal Voronoi Tessellations

This paper considers how to use a group of robots to sense and control a diffusion process.The diffusion process is modeled by a partial differential equation (PDE),which is a both spatially and temporally variant system.The robots can serve as mobile sensors,actuators,or both.Centroidal Voronoi Tessellations based coverage control algorithm is proposed for the cooperative sensing task.For the diffusion control problem,this paper considers spraying control via a group of networked mobile robots equipped with chemical neutralizers,known as smart mobile sprayers or actuators,in a domain of interest having static mesh sensor network for concentration sensing.This paper also introduces the information sharing and consensus strategy when using centroidal Voronoi tessellations algorithm to control a diffusion process.The information is shared not only on where to spray but also on how much to spray among the mobile actuators.Benefits from using CVT and information consensus seeking for sensing and control of a diffusion process are demonstrated in simulation results.

分布式无线传感网中低延迟数据调度算法研究

分布式无线传感网中节点数据量增大,会导致节点能耗不均匀,数据传输过程出现低延迟现象,影响数据传输效率。为解决这一问题,提出分布式无线传感网中低延迟数据调度算法研究。基于分布式无线传感网络结构分析了低延迟数据调度延迟的主要因素,依据分析结果采用MDAS-ST算法,建立低延迟初始信息数据,对低延迟数据节点构建父节点候选集,建立低延迟数据候选调度模型,以低延迟数据父节点候选集为主,从中选取一个父节点,并分别分配到分布式无线传感网络时隙内;建立低延迟数据调度序列,利用该序列实现低延迟数据的调度研究。仿真结果表明,该方法的平均发送次数约为1.2次、调度延迟为322μs、网络吞吐率为99%,能耗为500 nJ/bit。

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。

基于随机LSTM块映射特征提取的旋转机械故障诊断方法

针对旋转机械失效机理复杂,特征信息差异大,导致的传统诊断模型依赖先验知识,准确率低,适应性差的难题.提出一种基于随机量化数据增强-随机LSTM(Long Short Term Memory)块映射特征提取-随机配置网络(Randomized Quantization-Randomized LSTM Block Mapping Method-Stochastic Configuration Network,简称RQ-RLBM-SCN)的旋转机械故障诊断方法.首先,为了解决失效机械特征信息小子样,训练样本不足的难题,使用随机量化数据增强将多传感器原始数据样本进行扩充,从而提高模型的适应性、准确率和缓解过拟合问题.其次用随机LSTM块映射方法来提取特征,解决SCN不擅长提取时序数据特征难的问题;然后使用随机配置网络(SCN)进行分类,SCN可以动态配置参数,无需反向传播来更新参数,在保证学习率的同时,还有效的避免梯度爆炸或梯度消失等问题.采用RQ-RLBM-SCN方法能准确识别出轴承和齿轮故障,在10次重复实验中,轴承和齿轮的多传感器数据集上的平均准确率分别达到99.80%、98.75%均高于原始SCN、TSC-SCN、VMD-SCN、SVM和KNN故障诊断方法;该方法可以为建立旋转机械的健康监测模型提供动态方法和诊断思路.

低碳园区智能感知系统优化配置设计方法

针对目前低碳园区具有规模较大、信息分散、数据采集成本较高的问题,本文提出了一种智能感知系统的多目标优化配置方法。基于提出的智能感知系统结构,以传感网络节点覆盖率最高为目标进行节点选址;同时以投资成本、故障损失成本和运维成本最小为目标,建立数据采集设备优化配置模型。采用量子遗传算法求解多目标优化模型,结合工程实际,以获得传感网络覆盖率和经济效益最大化的配置方案。最后通过算例分析验证所提出的基于量子遗传算法的智能感知系统多目标优化配置方法的有效性。

用于配网线缆识别和定位的多传感器引导系统

不停电配网作业视场条件复杂,传统机器视觉方法存在配网线缆识别定位精度与速度无法满足要求等问题。为实现智能化配网作业中配网线缆的快速准确识别与定位,该文设计了多传感器视觉引导系统以及搭载的配网线缆分割算法。首先,面向配网作业需求进行了针对性的传感器选型,提出了一套使用可见光相机+激光雷达(RGB+Lidar)对末端执行机构进行引导的系统,以解决传统配网检修人工作业方式劳动强度大、工作效率低等问题。其次,针对传统机器视觉方法参数量大、推理速度慢的问题,将深度可分离卷积引入轻量化的图像分割模型的设计,输入部分增加了雷达点云提供稀疏的深度信息,并引入了直线注意力模块以进一步提高精度。最后,经过配网作业中采集的数据集进行图像分割模型的测试,验证了其速度和精度能够满足不停电配网环境下的识别定位需求。

无线传感器网络中的信号处理与数据融合

为了探讨无线传感器网络(WSN)中信号处理与数据融合的应用效果及其对网络性能的影响,采用理论分析与实验验证相结合的方法分析了信号处理技术在数据预处理、信号检测与估计、信号编码与压缩方等方面的应用及数据融合技术在环境监测、目标跟踪、健康监测等场景下的实际效用,构建模型并开展一系列实验。结果表明,有效的信号处理与数据融合技术能显著提高无线传感器网络的数据准确性和系统效率,信号处理与数据融合的集成应用进一步增强了网络处理能力及应用范围,此结果可为无线传感器网络的优化配置和应用提供理论依据和实践指导。

基于云计算的物联网传感网络技术分析

随着信息技术的飞速发展,基于云计算的物联网传感网络技术成为连接现实世界与数字世界的桥梁。这项技术不仅为数据处理和分析提供了新的可能性,也为各行各业的智能化转型打开了新大门。全面分析基于云计算的物联网传感网络的原理、构成、应用及其面临的挑战和未来发展趋势,特别关注数据处理与存储、系统的可扩展性和灵活性、高效的数据集成与管理以及安全和隐私保护等问题,为未来的技术发展和应用提供指导。

基于智能算法的无线传感器数据处理系统设计

基于无线传感器网络中数据采集和处理的复杂性及其对实时性、准确性的需求,文中提出了一种基于深度学习的数据处理系统。该系统的结构设计充分考虑了无线传感器网络的特点,通过数据采集、处理和分析三大模块的协同工作,有效提升了数据的处理效率和分析精度。在数据采集模块,系统基于多源传感器实现了高效的数据采集和预处理。数据处理模块进一步优化了数据的清洗和特征提取过程,以确保数据质量。数据分析模块则应用深度学习算法来深入挖掘数据的潜在价值,为决策提供依据。该系统不仅提高了数据处理的效率和准确性,还利用深度学习技术加强了对数据的深层次分析,显著提升了无线传感器网络的整体性能和应用价值。

面向区域能源互联网的数据采集技术分析

阐述面向区域能源互联网的数据采集技术方案,探讨互联网架构、分布式网络、先进传感器技术、数据安全、大数据及云计算技术。通过数据计算,分析这一技术方案的应用。