Application Research of Cata Automatic Collection and Aggregation Middleware for Ecological Monitoring IoT based on Python

According to the application requirements in the process of data automatic acquisition and collection of the ecological monitoring Internet of Things. This paper refers to the related systems and developed a multi-source heterogeneous data automatic collection and aggregation middleware. The middleware had flexible scalability, it could cooperate with data sensing and collecting devices of ecological monitoring Internet of things from different manufacturers, obtain multi-source heterogeneous real-time data, then automatically stored the data into database after normalization. Based on modular design concept, the middleware was composed of three modules: data automatic collecting module, data automatic parsing processing module and data automatic storage module. These modules had high cohesion and low coupling, and closely cooperated to complete the full automatic processing of the monitoring data flow. The middleware was implemented with Python, which were fully used the object-oriented programming. The design of the class was followed by a single responsibility principle and interfaces-oriented programming, which ensured the top-down inheritance and extensibility of the program. The middleware has been fully tested for several months, and it could accomplish the business requirements of monitoring data collecting, parsing processing and automatic warehousing of the ecological monitoring Internet of Things. For the ecological monitoring IoT system, the data automatic collection and aggregation middleware has considerable reference significance and application value.     

基于Python的生态监测物联网数据自动采汇中间件应用研究

针对生态监测物联网数据自动获取和采集过程中的应用需求,参考此前研发的相关子系统,分析了其原生的不足之处,用面向对象理念进行了全新设计,使其以多源异构数据自动采汇中间件的形式呈现,被后端的观测数据自动综汇平台调用。该中间件具有灵活的可扩展性,能够与不同厂商、不同来源的物联网数据采集设备协作,获取实时的多源异构监测数据,然后对数据进行归一化处理后,自动汇总存入监测数据库。该中间件运用模块化的软件工程理念设计,主要由数据自动获取模块、数据自动解析处理模块、数据自动入库模块这3部分组成,模块之间高内聚、低耦合,以数据流为纽带,紧密配合,完成整个监测数据流采集入库的全自动化处理流程。与原有子系统相比较,其具有更明确的模块划分、更高的灵活性和更好的可维护性。该中间件采用简洁高效的Python语言实现,完全采用面向对象编程思想,所有类的设计遵循单一职责原则,面向接口编程,保证了程序具备稳定的功能和灵活的扩展性。该中间件经过充分测试及几个月的试运行,能够满足现有生态监测物联网监测数据的自动获取、解析处理和入库的业务需求。对于野外地理环境和生态环境的监测,只要涉及类似的数据采集处理流程,本文设计和实现的数据自动采汇中间件都具有一定的参考意义和应用价值。     

基于区块链技术的电力物联网终端安全认证系统应用研究

电网状态全感知的目标意味着物联网技术在电网的广泛应用,大量物联网终端通过传感技术、通信技术和计算机技术接入网络。电力物联网终端分布呈现数量大、地域广、采集数据复杂的特点,容易被攻击者突破和入侵,而且传统的中心化认证存在单点失败、性能瓶颈等问题。本文基于区块链技术,研发并应用了基于终端标识符的DID数字身份、区块链多源适配的DID解析器、基于终端凭证信息的零知识证明、基于机器学习算法的设备管理等技术,设计出了一种基于区块链的电力物联网终端安全认证系统。该系统在电网内成功应用,实现了物联网设备接入认证的去中心化,降低了设备接入的网络安全风险,减少了中心化基础设施建设和维护的成本,提高了运维人员的工作效率。     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Human emotion recognition and analysis in response to audio music using brain signals

Human emotion recognition using brain signals is an active research topic in the field of affective computing. Music is considered as a powerful tool for arousing emotions in human beings. This study recognized happy, sad, love and anger emotions in response to audio music tracks from electronic, rap, metal, rock and hiphop genres. Participants were asked to listen to audio music tracks of 1 min for each genre in a noise free environment. The main objectives of this study were to determine the effect of different genres of music on human emotions and indicating age group that is more responsive to music. Thirty men and women of three different age groups (15–25 years, 26–35 years and 36–50 years) underwent through the experiment that also included self reported emotional state after listening to each type of music. Features from three different domains i.e., time, frequency and wavelet were extracted from recorded EEG signals, which were further used by the classifier to recognize human emotions. It has been evident from results that MLP gives best accuracy to recognize human emotion in response to audio music tracks using hybrid features of brain signals. It is also observed that rock and rap genres generated happy and sad emotions respectively in subjects under study. The brain signals of age group (26–35 years) gave best emotion recognition accuracy in accordance to the self reported emotions.     

面向数据保护的区块链物联网边缘卸载策略

物联网（IoT）设备的广泛应用带来了数据安全性和完整性的挑战。针对这一问题,研究提出了一种区块链物联网边缘卸载策略,专注于数据保护。该策略通过将IoT设备数据上传至区块链,利用其不可窜改性和可追溯性来保障数据安全。鉴于区块链的工作量证明（PoW）共识算法在数据验证和区块添加方面的高计算资源需求,该策略采用边缘计算技术,将PoW共识过程卸载至边缘服务器执行。进一步地,设计并实现了一个多目标边缘卸载算法（multi-object edge offloading algorithm,MEOA）,以寻找最优卸载策略,动态调整PoW共识难度,实现系统安全性与运行效率的平衡。仿真实验结果显示,该策略相比其他卸载策略,在提高IoT设备数据上链效率、降低时间和能耗成本方面表现优异,同时确保了数据安全性和完整性。     

Music genre recognition using convolutional recurrent neural network architecture

The genre is an abstract feature, but still, it is considered to be one of the important characteristics of music. Genre recognition forms an essential component for a large number of commercial music applications. Most of the existing music genre recognition algorithms are based on manual feature extraction techniques. These extracted features are used to develop a classifier model to identify the genre. However, in many cases, it has been observed that a set of features giving excellent accuracy fails to explain the underlying typical characteristics of music genres. It has also been observed that some of the features provide a satisfactory level of performance on a particular dataset but fail to provide similar performance on other datasets. Hence, each dataset mostly requires manual selection of appropriate acoustic features to achieve an adequate level of performance on it. In this paper, we propose a genre recognition algorithm that uses almost no handcrafted features. The convolutional recurrent neural network‐based model proposed in this study is trained on melspectrogram extracted from 3‐s duration audio clips taken from GTZAN dataset. The proposed model provides an accuracy of 85.36% on 10‐class genre classification. The same model has been trained and tested on 10 genres of MagnaTagATune dataset having 18,476 clips of 29‐s duration. The model has yielded an accuracy of 86.06%. The experimental results suggest that the proposed architecture with melspectrogram as input feature is capable of providing consistent performances across the different datasets     

Mobile edge-enabled trust evaluation for the Internet of Things

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. However, due to the relatively long distance between remote cloud and end nodes, cloud computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this paper, we propose a novel trust evaluation model based on the trust transitivity on a chain assisted by mobile edge nodes, which is used to ensure the reliability of nodes in the Internet of Things and prevent malicious attacks. The mobile edge nodes offer a new solution to solve the above problems with relatively strong computing and storage abilities. Firstly, we design calculation approaches to different trust chains to measure their trust degrees. Secondly, we propose an improved Dijkstra’s algorithm for collecting trust information of sensor nodes by mobile edge nodes. Finally, the experimental results show that our trust model based on mobile edge nodes can evaluate sensor nodes more precisely and enhance the security on the Internet of Things.     

Internet of Things in Healthcare: Architecture,Applications, Challenges,and Solutions

Healthcare, the largest global industry, is undergoing significant transformations with the genesis of a new technology known as the Internet of Things (IoT). Many healthcare leaders are investing more money for transforming their services to harness the benefits provided by IoT, thereby paving the way for the Internet of Medical Things (IoMT), an extensive collection of medical sensors and associated infrastructure. IoMT has many benefits like providing remote healthcare by monitoring health vitals of patients at a distant place, providing healthcare services to elderly people, and monitoring a large group of people in a region or country for detection and prevention of epidemics. This paper provides a review of IoT in the healthcare domain by first describing the enabling technologies for delivering smart healthcare, followed by some of the key applications of IoT in healthcare. Next, a fog-based architecture consisting of three layers for IoT-based healthcare applications is proposed. Finally, we focus on some of the open challenges of IoT in healthcare, like fault tolerance, interoperability, latency, energy efficiency, and availability. Existing solutions for these challenges are also discussed.     

Exploring the Internet of Things sequence-structure detection and supertask network generation of temporal-spatial-based graph convolutional neural network

The Journal of Supercomputing - The study is designed to improve the efficiency of Internet of Things (IoT) structure detection and achieve the smooth operation of IoT networks. First, the...     

A sequential pattern mining approach to design taxonomies for hierarchical music genre recognition

In this paper, music genre taxonomies are used to design hierarchical classifiers that perform better than flat classifiers. More precisely, a novel method based on sequential pattern mining techniques is proposed for the extraction of relevant characteristics that enable to propose a vector representation of music genres. From this representation, the agglomerative hierarchical clustering algorithm is used to produce music genre taxonomies. Experiments are realized on the GTZAN dataset for performances evaluation. A second evaluation on GTZAN augmented by Afro genres has been made. The results show that the hierarchical classifiers obtained with the proposed taxonomies reach accuracies of 91.6 % (more than 7 % higher than the performances of the existing hierarchical classifiers).     

基于频谱空间域特征注意的音乐流派分类算法

为了提升深度卷积神经网络对音乐频谱流派特征的提取效果,提出一种基于频谱空间域特征注意的音乐流派分类算法模型DCNN-SSA。DCNN-SSA模型通过对不同音乐梅尔谱图的流派特征在空间域上进行有效标注,并且改变网络结构,从而在提升特征提取效果的同时确保模型的有效性,进而提升音乐流派分类的准确率。首先,将原始音频信号进行梅尔滤波,以模拟人耳的滤波操作对音乐的音强及节奏变化进行有效过滤,所生成的梅尔谱图进行切割后输入网络;然后,通过深化网络层数、改变卷积结构及增加空间注意力机制对模型在流派特征提取上进行增强;最后,通过在数据集上进行多批次的训练与验证来有效提取并学习音乐流派特征,从而得到可以对音乐流派进行有效分类的模型。在GTZAN数据集上的实验结果表明,基于空间注意的音乐流派分类算法与其他深度学习模型相比,在音乐流派分类准确率和模型收敛效果上有所提高,准确率提升了5.36个百分点～10.44个百分点。     

ISHSN：一种异构传感网融合系统

针对异构传感网导致的系统融合问题,提出了一种异构传感网融合系统(ISHSN)。ISHSN由物联网关和接入代理构成,物联网关对于上行数据进行同一化转换,对于下行控制命令按照目的传感网控制协议进行转换;接入代理进行数据汇集、链路合并以及命令转发,并且运用基于历史增量信息预测的接入代理调度算法,有效分散传感网接入负载。实验表明ISHSN在异构传感网数据汇集以及传感网控制方面具有良好的可扩展性和可用性。     

Improved Key Agreement Based Kerberos Protocol for M-Health Security

The development of wireless sensor network with Internet of Things (IoT) predicts various applications in the field of healthcare and cloud computing. This can give promising results on mobile health care (M-health) and Telecare medicine information systems. M-health system on cloud Internet of Things (IoT) through wireless sensor network (WSN) becomes the rising research for the need of modern society. Sensor devices attached to the patients’ body which is connected to the mobile device can ease the medical services. Security is the key connect for optimal performance of the m-health system that share the data of patients in wireless networks in order to maintain the anonymity of the patients. This paper proposed a secure transmission of M-health data in wireless networks using proposed key agreement based Kerberos protocol. The patients processed data are stored in cloud server and accessed by doctors and caregivers. The data transfer between the patients, server and the doctors are accessed with proposed protocol in order to maintain the confidentiality and integrity of authentication. The efficiency of the proposed algorithm is compared with the existing protocols. For computing 100 devices it consumes only 91milllisecond for computation.     

物联网无线协议安全综述

近些年来,随着物联网的快速发展,其应用场景涵盖智慧家庭、智慧城市、智慧医疗、智慧工业以及智慧农业。相比于传统的以太网,物联网能够将各种传感设备与网络结合起来,实现人、电脑和物体的互联互通。形式多样的物联网协议是实现物联网设备互联互通的关键,物联网协议拥有不同的协议栈,这使得物联网协议往往能表现出不同的特性。目前应用较广的物联网协议有ZigBee、BLE、Wi-Fi、LoRa、RFID等,这些协议能根据自身特性的不同应用在不同领域,比如说LoRa被广泛应用于低功耗广域网、RFID被用于设备识别。然而,由于物联网端设备只拥有受限的计算和存储资源,无法在其上实施完备的安全算法,许多物联网协议会在功耗和安全性之间进行取舍,使得物联网协议的安全性得不到保障。物联网协议的安全性直接关系到物联网系统的安全性,所以有必要对物联网协议的安全性进行分析。本文阐述常见的几种物联网协议所具备的安全能力,包括物联网协议在保护机密性、完整性以及身份认证上所制定的规则。然后从常见的无线协议攻击出发,包括窃听攻击、重放攻击、电池耗尽以及射频干扰,分析了这几种协议在面对这些攻击时的表现。除此之外,我们比较了常见的几种物...     

Data Security Storage Model of the Internet of Things Based on Blockchain

With the development of information technology, the Internet of Things (IoT) has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet. The application of the IoT has brought great convenience to people’s production and life. However, the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them. The traditional centralized data storage and management model of the IoT is easy to cause transmission delay, single point of failure, privacy disclosure and other problems, and eventually leads to unpredictable behavior of the system. Blockchain technology can effectively improve the operation and data security status of the IoT. Referring to the storage model of the Fabric blockchain project, this paper designs a data security storage model suitable for the IoT system. The simulation results show that the model is not only effective and extensible, but also can better protect the data security of the Internet of Things.     

The Neural Knowledge DNA Based Smart Internet of Things

Abstract

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet of Things that enables IoT to extract knowledge from past experiences, as well as to store, evolve, share, and reuse such knowledge aiming for smart functions. By catching decision events, this approach helps IoT gather its own daily operation experiences, and it uses such experiences for knowledge discovery with the support of machine learning technologies. An initial case study is presented at the end of this paper to demonstrate how this approach can help IoT applications become smart: the proposed approach is applied to fitness wristbands to enable human action recognition.     

物联网安全架构与技术路线研究

文章回顾了物联网的概念和发展历程,讨论了如何理解物联网概念。介绍了物联网的体系结构,并指出物联网架构中有特色的网络技术是：6LoWPAN、EPCglobal和M2M。提出了物联网的安全架构,及一些思考,包括：物联网安全的总体概貌、物联网安全架构的层次模型、物联网安全设计的参考流程图。最后分析了物联网安全学科与信息安全学科以及物联网工程学科的关联。     

物联网动态带宽资源分配算法及其应用

在物联网环境中,网络需要传输的数据和信息量急剧增加,从而造成带宽不足。为此,提出一种采用动态带宽资源分配算法的物联网远程机械控制方案,通过改变控制信号的采样速率,达到优化分配带宽资源的目的。仿真结果表明,在相同网络带宽条件下,该方案可降低重构信号的误差,并有效提高物联网系统智能分配带宽资源的能力。     

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.