Numerical simulation of forest fire propagation based on modified two-dimensional model

A modified two-dimensional two-phase mathematical model of forest wildfires propagation is considered. The model is based on the averaging of three-dimensional equations of two-phase medium over the height of the forest fuel (FF) layer and it includes the (k?ε)-turbulence model with additional turbulence production and dissipation terms in the forest layer and the Eddy Break-up Model for the combustion rate in the gas phase. The developed model can be used to carry out numerical simulation of the forest fire-front propagation under the conditions of a heterogeneous FF distribution, the presence of obstacles to the fire propagation, and the wind effects. This model can be used for real-time computation of the fire propagation, for expert assessments of emergency situations, and for assessments of the damage caused by forest fires.     

A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

This work presents the design of an Internet of Things (IoT) edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places. A wireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design. A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design. A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries, expressed using Neo4j Cypher, to provide insights from the stored data for decision support. As proof of concept, a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture. Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected. The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.     

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis accelerometer in a smartwatch. It includes 92781 training samples and 91025 testing samples with two labeled classes, namely non-fall and fall. Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0% compared to three machine learning models, i.e., K-nearest neighbors, decision trees and traditional random forest, and two deep learning models, which are dense neural networks and convolutional neural networks. By considering security and privacy aspects in the future work, our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.     

基于深度卷积长短期记忆网络的森林火灾烟雾检测模型

针对采样的每帧烟雾特征具有极大的相似性，以及森林火灾烟雾数据集相对较小且单调等问题，为充分利用烟雾的静态与动态信息来达到预防森林火灾的目的，提出一种深度卷积集成式长短期记忆网络（DC-ILSTM）模型。首先，使用在ImageNet数据集上预训练好的VGG-16网络进行基于同构数据的特征迁移，以有效提取出烟雾特征；其次，基于池化层与长短期记忆网络（LSTM）提出一种集成式长短期记忆网络（ILSTM），并利用ILSTM分段融合烟雾特征；最后，搭建一种可训练的深度神经网络模型用于森林火灾烟雾检测。烟雾检测实验中，与深卷积长递归网络（DCLRN）相比，DC-ILSTM在最佳效率下以10帧的优势检测到烟雾，而且在测试准确率上提高了1.23个百分点。实验结果表明，DC-ILSTM在森林火灾烟雾检测中有很好的适用性。     

基于文件分时索引的大规模流量实时IoT终端识别算法

随着5G时代的来临,诸如工业区,校园网等开放性园区网络中存在大量的物联网(Internet of Things,IoT)终端,IoT终端由于其数据流量巨大,伪造IoT终端进行网络攻击的问题日益严重.现有IoT终端识别技术在面对海量数据时计算资源的成本逐渐提高.针对以上问题,提出了基于文件分时索引的大规模流量实时IoT终端识别算法.首先,建立内存分时索引元数据;其次,使用文件的分时索引来存储构建会话的中间数据;最后,控制内存分时索引元数据触发从少量文件中提取特征并进行IoT终端识别.实验中,在不损失IoT终端识别算法精度条件下,仅消耗少量磁盘,可将内存消耗降低92%.实验结果表明,该技术能够用于实时IoT终端识别框架中.     

实时工业物联网的功率域非正交多址接入基站选址算法

针对工业物联网（IoT）中接入延迟较大的问题,提出一种实时工业IoT的功率域非正交多址接入（PDNOMA）基站选址算法。该算法在PD-NOMA技术的基础上,以数据收集基站的位置为优化手段,通过最大限度地实现用户功率分复用来实现接入延迟的最小化。首先,证明对任意两用户若实现它们的并行传输,则合格的基站可解码区域必为圆,因此,所有的两用户组合可得到基站可解码区域的集合,且集合中区域间的每个最小相交区域必为凸区域,从而可知这些最小相交区域必定包含数据收集基站的最优位置。然后,对于每一个最小相交区域,以基站放置在该区域的网络生成图的最小链划分数作为接入延迟的度量标准。最后,通过最小链划分数的比较得出基站的最优位置。实验结果表明,解码阈值为2用户数为30时,所提算法的平均接入延迟降低为经典时分多路方式的36.7%,并且随着解码阈值的降低和信道衰减因子的增加,接入延迟可获得近似线性的降低。所提算法对海量超可靠低延迟通信从接入层角度提供了参考。     

Analysis of forest fires in Northeast China from 2003 to 2011

Wildfires, a common disturbance in ecosystems, can be an immediate and dominant source of interannual carbon variability. In this study, we used an instantaneous Moderate Resolution Imaging Spectroradiometer (MODIS) global disturbance index algorithm to explore continuous spatiotemporal patterns of forest fires in Northeast China. The forest fires that were sensed remotely were then validated by field records. The findings suggest that the disturbance index is effective in locating forest fires in Northeast China, as evidenced by a close match with field fire records. We found that the incidence of forest fires was closely linked to extreme conditions of climate warming and drought, and more fires occurred in dry years than in wet years. Among different forest types, shrublands, mixed forest, and deciduous needleleaf forests were more prone to wildfires because of their fire regime characteristics. The study demonstrates that the algorithm was effective in detecting forest fires from 2003 to 2011 in Northeast China, providing fundamental data for forest inventory and large-scale ecological applications.     

基于物联网区块链的轻量级共识算法研究

面对规模庞大的物联网数据,高效的共识算法是区块链技术与物联网应用相结合的关键。为解决大规模物联网区块链系统中传统共识算法通信开销大、扩展性低、共识机制复杂度高的问题,基于Hyperledger Fabric搭建一个物联网区块链框架,并设计基于投票和交易证明的轻量级共识算法PoVT。在链码验证交易后,根据节点之间发起和收到的交易,选择交易的源节点和目标节点作为代表参与共识。在共识阶段通过设计新的投票方式简化共识流程,仅需一次全节点广播即可生成新的区块。以优先收集到一定投票数的节点作为主节点进行投票广播,在所有节点收到足够多投票消息的同时进行上一轮交易区块确认。对安全性、出块时间和带宽需求进行分析,结果表明,PoVT算法在网络中存在拜占庭节点的情况下能够以较短的时间验证交易和区块,在每秒交易数量相同时,该算法生成区块的时间为PBFT算法的1/3,网络带宽占用也能减少30%,证明所提物联网区块链框架在不同应用场景中具有较高的可扩展性。     

A Fog-Cloud based cyber physical system for Ulcerative Colitis diagnosis and stage classification and management

Ulcerative Colitis is a fairly common, chronic or long-term disease that causes inflammation of the large intestine. It can be debilitating and can sometimes lead to life threatening complications. Therefore, its diagnosis in nascent stages is important. Healthcare services based on Fog-Cloud assisted Cyber-Physical Systems are emerging as a proactive and efficacious solution to provide remote monitoring of individuals for early detection and consequent management of several diseases. This paper presents a novel IoT-Fog-Cloud assisted Cyber Physical System for diagnosis and stage classification of Ulcerative Colitis using Naïve Bayes classifier and Deep Neural Network respectively. A vital point of this paper is real-time alert generation from Fog Layer in case the user need emergency treatment if he/she is already diagnosed with UC. Finally, analysis results and compiled medical information of each user is stored on cloud. Implementation results of the proposed framework proves its efficiency in diagnosis and subsequent stage classification of Ulcerative Colitis with real-time classification mechanism at fog layer. Furthermore, alert generation improves the efficacy of the proposed system.     

物联网环境下基于云边协同的数据审计方案

针对数据完整性审计过程中,半可信的云服务提供商存在不诚实地选取数据的行为,提出一种物联网环境下基于云边协同的高效数据审计方案。首先,为了适应物联网环境,采用端—边—云三层审计框架,将标签生成算法外包到边缘节点进行,提高响应速度,降低物联网设备端的计算成本。其次,设计了一种链表结构存储文件信息,以支持数据监测和实时更新。最后,根据用户对文件的访问频率将数据分为非风险数据和风险数据,并设计了基于用户行为的抽样方法,通过优先审计风险数据实现有效的针对性审计。安全性分析和实验表明,该方案能实现更安全、更高效的审计。     

Satellite-based forest fire detection for fire control in boreal forests

Forest fires in large sparsely populated areas in the boreal forest zone are difficult to detect by ground based means. Satellites can be a viable source of information to augment air-borne reconnaissance. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) satellites has been used to detect and map fires in the past mainly in the tropics and mainly for environmental monitoring purposes. This article describes real-time forest fire detection where the aim is to inform local fire authorities on the fire. The fire detection is based on the 3.7 mu m channel of the NOAA AVHRR sensor. In the fire detection algorithm, imaging geometry is taken into account in addition to the data from the near-infrared and thermal infrared channels. In an experiment in summer 1995, 16 fires were detected in Finland. One was a forest fire, 11 were prescribed burnings and 4 false alarms. Three of the false alarms were due to steel factories. We conclude that satellite-based fire detection for fire control is feasible in the boreal forest zone if the continuous supply of frequent middle-infrared data can be guaranteed in the future.     

物联网设备识别及异常检测研究综述

随着物联网技术的发展,物联网设备广泛应用于生产和生活的各个领域,但也为设备资产管理和安全管理带来了严峻的挑战.首先,由于物联网设备类型和接入方式的多样性,网络管理员通常难以得知网络中的物联网设备类型及运行状态.其次,物联网设备由于其计算、存储资源有限,难以部署传统防御措施,正逐渐成为网络攻击的焦点.因此,通过设备识别了解网络中的物联网设备并基于设备识别结果进行异常检测,以保证其正常运行尤为重要.近几年来,学术界围绕上述问题开展了大量的研究.系统地梳理物联网设备识别和异常检测方面的相关工作.在设备识别方面,根据是否向网络中发送数据包,现有研究可分为被动识别方法和主动识别方法.针对被动识别方法按照识别方法、识别粒度和应用场景进行进一步的调研,针对主动识别方法按照识别方法、识别粒度和探测粒度进行进一步的调研.在异常检测方面,按照基于机器学习算法的检测方法和基于行为规范的规则匹配方法进行梳理.在此基础上,总结物联网设备识别和异常检测领域的研究挑战并展望其未来发展方向.     

一种高效隐私的区块链认知物联网框架

针对动态物联网隐私安全问题及低效推荐系统问题,提出一种高效隐私的区块链认知物联网框架。该框架分为区块链物联网管理层、认知过程层和需求层三层,区块链物联网管理层为认知层提供所需信息,然后对系统的可管理元素进行安全隐私的操作;在认知层中,认知引擎观察有关系统的信息,然后执行适当的算法来管理系统;在需求层中,通过认知规范语言（cognitive specification language,CSL）来描述网络的目标和行为。所提区块链物联网框架的认知推荐系统从过去发生的经验中学习,改进关于物联网推荐的决策,与其他物联网框架比较,所提框架和推荐系统具有隐私安全和高性能的推荐能力。     

林火巡护与扑救车载定位系统设计

针对林火发生的特点,结合我国林区现状,研究并设计了用于林火地面巡护与早期扑救的车载卫星定位系统和远程管理软件系统,结合卫星定位系统和移动互联网技术,实时精确地采集和回传灾害现场的关键信息,生成地图地形标记,为防火指挥部门提供详细准确的火灾现场数据。实验表明,该系统可以实时可靠地回传现场坐标、海拔、温度等信息,地图位置标注准确。     

UML4IoT—A UML-based approach to exploit IoT in cyber-physical manufacturing systems

Internet of Things (IoT) is changing the world. The manufacturing industry has already identified that the IoT brings great opportunities to retain its leading position in economy and society. However, the adoption of the IoT changes the development process of the manufacturing system and raises many challenges. In this paper, the modern manufacturing system is considered as a composition of cyber-physical, cyber and human components, and IoT is used as a glue for their integration as far as their cyber interfaces are concerned. An approach based on a UML profile for the IoT is presented to fully automate the generation process of the IoT-compliant layer that is required for the cyber-physical component to be effectively integrated into the modern IoT manufacturing environment. The approach can also be applied at the source code level specification of the component in case that a UML design specification is not available. A prototype implementation of the myLiqueur production laboratory system is used to demonstrate the applicability and effectiveness of the UML4IoT approach.     

A comprehensive health assessment framework to facilitate IoT-assisted smart workouts: A predictive healthcare perspective

Enormous potential of Internet of Things (IoT) Technology has made it feasible to perceive and analyze real time health conditions in ubiquitous manner. Moreover, incorporation of IoT in healthcare industry has led researchers around the world to develop smart applications like mobile healthcare, health-aware recommendations, and intelligent healthcare systems. Inspired from these aspects, this research presents an intelligent healthcare framework based on IoT Technology to provide ubiquitous healthcare to person during his/her workout sessions. The intelligence of the presented framework lies with its ability to analyze real time health conditions during workouts and predict probabilistic health state vulnerability. For predictive purpose, the proposed framework indulges the utilization of Artificial Neural Network (ANN) model, which is comprised of three phases namely, monitor, learn, and predict. In addition to this, the presented framework is supported by a mathematical foundation to predict probabilistic vulnerability, in terms of Probabilistic State of Vulnerability (PSoV). In order to determine the validity and applicability of the proposed framework, experiments were performed where 5 people with different attributes are monitored for 14 days using numerous smart sensors. Results, upon comparison with various state-of-the-art techniques, depict that the proposed system is superior in performance and is highly effective in delivering healthcare services during workouts.     

A framework for use of wireless sensor networks in forest fire detection and monitoring

Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance systems for forest fires lack in supporting real-time monitoring of every point of a region at all times and early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather sensory data values, such as temperature and humidity, from all points of a field continuously, day and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor networks face serious obstacles like limited energy resources and high vulnerability to harsh environmental conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and communication protocols. The aim of the framework is to detect a fire threat as early as possible and yet consider the energy consumption of the sensor nodes and the environmental conditions that may affect the required activity level of the network. We implemented a simulator to validate and evaluate our proposed framework. Through extensive simulation experiments, we show that our framework can provide fast reaction to forest fires while also consuming energy efficiently.     

Optimized traffic engineering in Software Defined Wireless Network based IoT (SDWN-IoT): State-of-the-art,research opportunities and challenges

Wireless networks have been in focus since the last few decades due to their indispensable role in the future generation networks like the Internet of Things (IoT). However, the associated challenges in wireless network implementation such as distance, line-of-sight, interference, weather, power issues, etc., affect the performance adversely. Software Defined Networking (SDN) is a future generation networking technology and has been proven to alleviate the performance challenges in the existing wireless IoT networks. It helps to evolve the wireless IoT domain in the form of Software Defined Wireless Network based IoT (SDWN-IoT). Traffic Engineering (TE) has been part of traditional network designs since long back, to improve the performance of the communication networks. However, its more optimized forms and their usefulness in SDWN-IoT networks have been under active investigation. This work explores the existing literature related to the major types of SDWN-IoT networks namely, Software Defined Wireless Sensor Network based IoT (SDWSN-IoT) and Software Defined Wireless Mesh Network based IoT (SDWMN-IoT). Additionally, the article also draws some useful inferences, and compares respective contributions and shortcomings. Finally, various research opportunities and challenges have been discussed with respect to the SDWSN-IoT and SDWMN-IoT networks.     

基于实体-数据的物联网服务建模

物联网服务作为信息世界软件服务通过物联网向现实世界的延伸,其在物联网系统具有重要的作用.然而,不同于传统Web服务,物联网服务具有现实感知、数据驱动、异构分布、时空相关等新特点,使得现有的服务模型不足以对物联网服务有效刻画,进而也不能满足物联网应用中的后续服务发现、服务卸载、服务组合等需求.在凝练分析物联网服务建模需求和已有物联网服务模型的基础上,提出了一种基于实体-数据的物联网服务建模框架,该框架提出了服务、实体、数据三元信息融合的物联网服务模型概念及概念关系,重点定义了服务、实体、数据的时空属性及时空依赖关系,以支持基于时空相关性的物联网服务关联表示与分析,并通过扩展OWL-S(ontology Web language for services)给出了基于实体-数据的物联网服务描述方式.最后,结合一个高速公路物联网应用案例对模型的使用方式和效果进行了讨论.     

非特定人手语数据生成及其有效性检测

根据手势手语的特点,提出了手语语言学和人体运动学相结合的非特定人手语数据的生成和检测方法. 首先,Mean-Shift 算法有控制生成强度的优点,将改进的Mean-Shift 算法应用于手形数据通道的生成,以保持手势手 语的语言学特性,并应用关键手形的音韵标记进行数据有效性的检测;其次,为了丰富手语手势动作的运动特性,将 改进的遗传算法应用于与运动相关的数据通道进行数据生成,并应用拉班舞谱对其进行数据有效性检测;最后,提出 了基于原始样本的检测实验框架,使得所提出的检测方法适用于语言类的多类别数据检测问题.实验结果表明,所提 出的非特定人手语数据的生成和检测方法是有效的.