Decision Based Model for Real-Time IoT Analysis Using Big Data and Machine Learning

Use of internet of things (IoT) in different fields including smart cities, health care, manufacturing, and surveillance is growing rapidly, which results in massive amount of data generated by IoT devices. Real-time processing of large-scale data streams is one of the main challenges of IoT systems. Analyzing IoT data can help in providing better services, predicting trends and timely decision making for industries. The systematic structure of IoT data follows the pattern of big data. In this paper, a novel approach is proposed in which big data tools are used to perform real-time stream processing and analysis on IoT data. We have also applied Spark’s built-in support of the machine learning library in order to make real-time predictions. The efficiency of the proposed system is evaluated by conducting experiments and reporting results on the case scenario of IoT based weather station.

     

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

     

物联网智能终端技术研究

物联网智能终端从炒作阶段进入产品化阶段。微型化、集成化、智能化和多样化的传感器帮助物联网智能终端将现实世界数字化。而具备应用处理能力的处理器和先进的操作系统实现物联网终端的智能,通过云计算和大数据加强其智能化。和智能手机一样,物联网智能终端需要建立活跃的生态系统,激发开发者创新。旨在探讨、分析和展示上述几个关键技术的新进展和演进趋势。     

Challenges and research directions for Internet of Things

The emergence of Internet of Things (IoT) is empowered by the availability of the high volume of smart sensors, Radio Frequency Identification, a suitable communication technologies and protocols. In the near future, the Internet will be full of heterogeneous connected devices. In recent years, the IoT has drawn significant attention as it can solve difficult problems. However, the heterogeneity of devices and the large scale networks expose the IoT to many challenges that must be addressed; otherwise, the systems performance will deteriorate. As an attempt to identify these challenges, this paper comprehensibly cites the main IoT concepts, the serious IoT challenges and the quality of services presented in the recent literature. It also investigates the corresponding main research directions and the proposed solutions. This paper can increase the knowledge of the reader since it is the first IoT survey that presents load balancing algorithms utilized in solving the extreme data storage challenge.     

Securing Internet of Things (IoT) with machine learning

Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet‐connected devices are part of the IoT ecosystem. IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host‐based and network‐based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices.     

Leveraging AI-enabled 6G-driven IoT for sustainable smart cities

Many scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low-latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost-efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain-enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in-depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.     

A systematic literature review of machine learning applications in IoT

The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.     

A Survey of IoT Key Enabling and Future Technologies: 5G,Mobile IoT,Sematic Web and Applications

The Internet of Things (IoT) is the communications paradigm that can provide the potential of ultimate communication. The IoT paradigm describes communication not only human to human (H2H) but also machine to machine (M2M) without the need of human interference. In this paper, we examine, review and present the current IoT technologies starting from the physical layer to the application and data layer. Additionally, we focus on future IoT key enabling technologies like the new fifth generation (5G) networks and Semantic Web. Finally, we present main IoT application domains like smart cities, transportation, logistics, and healthcare.     

Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring

Traditional wearable devices have various shortcomings, such as uncomfortableness for long-term wearing, and insufficient accuracy, etc. Thus, health monitoring through traditional wearable devices is hard to be sustainable. In order to obtain healthcare big data by sustainable health monitoring, we design “Smart Clothing”, facilitating unobtrusive collection of various physiological indicators of human body. To provide pervasive intelligence for smart clothing system, mobile healthcare cloud platform is constructed by the use of mobile internet, cloud computing and big data analytics. This paper introduces design details, key technologies and practical implementation methods of smart clothing system. Typical applications powered by smart clothing and big data clouds are presented, such as medical emergency response, emotion care, disease diagnosis, and real-time tactile interaction. Especially, electrocardiograph signals collected by smart clothing are used for mood monitoring and emotion detection. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make smart clothing ubiquitous for a wide range of applications.     

Research of authentication techniques for the Internet of things

Identity authentication technology is a key technology in the Internet of things (IoT)security field which ensures the authenticity of the identity information of users and device nodes connected to the IoT.Due to the low cost,low power consumption,small storage of IoT devices and heterogeneity of IoT network,the identity authentication mechanisms in traditional computer networks are often not applicable.Firstly,the development process of IoT was introduced,the security risks of IoT and the challenges faced by the authentication work were analyzed.Then the emphasis was put on comparison of the advantages and disadvantages among five typical authentication protocols.Moreover,the authentication technologies in several practical scenarios of RFID,smart grid,Internet of vehicles,and smart home were summarized and analyzed.Finally,the future research direction was discussed.     

Security and Privacy in IoT: A Survey

The Internet of Things (IoT) is a network of globally connected physical objects, which are associated with each other via Internet. The IoT foresees the interconnection of few trillions of intelligent objects around us, uniquely and addressable every day, these objects have the ability to accumulate process and communicate data about themselves and their surrounding environment. The best examples of IoT systems are health care, building smart city with advance construction management system, public and defense surveillance and data acquisition. Recent advancement in the technology has developed smart and intelligent sensor nodes and RFIDs lead to a large number of wireless networks with smart and intelligent devices (object, or things) connected to the Internet continuously transmit the data. So to provide security and privacy to this data in IoT is a very challenging task, which is to be concerned at highest priority for several current and future applications of IoT. Devices such as smart phone, WSNs and RFIDs etc., are the major components of IoT network which are basically resource constrained devices. Design and development of security and privacy management schemes for these devices is guided by factors like good performance, low power consumption, robustness to attacks, tampering of the data and end to end security. Security schemes in IoT provide unauthorized access to information or other objects by protecting against alterations or destruction. Privacy schemes maintain the right to control about the collected information for its usage and purpose. In this paper, we have surveyed major challenges such as Confidentiality, Integrity, Authentication, and Availability for IoT in a brief manner.

     

A comprehensive layered approach for implementing internet of things-enabled smart grid: A survey

The current power grid confronts severe challenges in satisfying customers' demands. Fast transition to the much more flexible power grid enriched with renewable energies, micro-grid, and distributed energy resources has been considered as a straightforward solution to the customers’ high demand. Using smart equipment and renewable energies, electric power generation and storage through the power grid domains will be facilitated, which enables bi-directional energy and information flows. The power grid with such enhanced features is called Smart Grid (SG). Controlling and managing the diverse sets of variables in the SG requires precise measuring, monitoring, communicating, and analytic systems which increase the complexity of the grid. This complexity is the main barrier to the realization of the SG up to now. The emergence of the Internet of Things (IoT) simplifies monitoring, communications, and data processing among smart things to connect to anything in the world. This motivates the SG stakeholders and researchers to proceed with the best way to exploit the IoT technologies in the SG. In this survey paper, we summarize various efforts in this regard to highlight the advantages of the IoT-enabled SG and its probable gaps. To this end, a comprehensive layered approach has been proposed in this paper to classify various applications of the IoT technologies in the SG. Investigating IoT opportunities in each architecture layer facilitates the role of each technology and its relationship with other technologies. Also, open issues and future measures for the realization of IoT-enabled SG have been discussed in the paper.     

认知网络管理在物联网中的应用

The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new opportunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the“Internet of Things” (IoT). Since a huge number of heterogeneous resources are brought into IoT, one of the main challenges is how to efficiently manage the increasing complexity of IoT in a scalable, flexible, and autonomic way. Furthermore, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Internet. In this paper, we propose a new IoT network management architecture based on cognitive network management technology and Service-Oriented Architecture to provide effective and efficient network management of IoT.     

离散选择模型在智慧社区中的应用

随着信息科学基础的大力发展,“智慧城市”已成为我国各个城市探索与发展的趋势,作为“智慧城市”重要内容的“智慧社区”探索与建设也成为一种必然。大数据挖掘技术为智慧社区的实现提供了关键性技术支撑,让社区管理与服务智能化、信息化成为了可能。本文通过离散选择模型技术在社区智慧旅游套餐产品的应用为案例,详细阐述了大数据技术在智慧社区中的实际应用,有效证实了大数据技术应用的可行性。     

现阶段智慧城市推进中面临的不确定性研究与对策

现阶段,各地积极贯彻国家大数据战略,加快建设数字中国,借力万物互联、大数据、AI等信息技术提高城市管理与治理能力,推动我国智慧城市建设迅猛发展.智慧城市作为城市发展的新形态,本质上就蕴藏着不确定基因,无论对于智慧城市的理解,还是智慧城市建设本身,都面临着诸多挑战.文章基于智慧城市顶层设计与实践,着重研究如何厘清现阶段智...     

System for recognizing lecture quality based on analysis of physical parameters

In this paper we have presented a smart classroom system that is able to classify students’ satisfaction with the lecture quality by examining parameters of the physical environment obtained using different smart devices. The system is based on the Random forest classifier, which showed the best accuracy among all machine learning algorithms available in Weka tool, with dataset collected during 28 lectures and evaluated using 10-fold cross validation. The system is implemented using different set of tools (such as Matlab and Weka) and can extract features from the ambient sound and analyze values obtained from different smart devices deployed in the classroom. Based on the extracted and captured data the system provides in real time information about the students’ satisfaction with the lecture quality. For the validation purposes, we recorded 13 more lectures attended by four different student groups where the number of students varied from 5 to 18. The system accuracy was evaluated by comparing system outputs with the students’ feedback and ranged from 70.7% to 83.9%.     

面向智能电网的物联网信息聚合技术

物联网应用于智能电网是信息通信技术发展到一定阶段的必然结果,利用物联网技术将能有效整合电力系统基础设施资源,提高电力系统信息化水平,改善现有电力系统基础设施的利用效率。本文针对物联网技术和我国智能电网建设规划,研究面向智能电网应用的物联网网络架构及关键技术,总结了技术特点。在阐明网络架构的基础上,进一步针对智能电网应用中海量设备终端和海量采集信息的特点,详细论述物联网信息聚合技术,分析信息聚合技术带来的网络收益,提出信息聚合技术基本功能框架及实现方式。物联网信息聚合技术在采集原始数据的同时进行大量的信息处理和计算,从海量的、杂乱无章、难以理解的原始数据中抽取并推导出对于智能电网一体化管理平台具有特定意义和判决参考价值的数据,并且能够降低网络数据传输总量、减少网络拥塞发生、提高网络性能,是物联网发展的重要技术方向之一。本文针对智能电网目前相对薄弱的配用电环节提出配变电设备监测物联网的主要功能与信息聚合方案。     

基于5G的智慧社区应用探讨

随着新一轮科技革命和产业变革的深入,围绕社区全生活链服务需求,以人本化、生态化、数字化为价值导向的智慧社区将成为人类更加向往的居住地。智慧社区采用5G网络技术,以大数据、物联网、人工智能等主要技术手段为支撑,统筹各类服务资源,搭建智能运营管理平台,构建各种智慧应用场景,创建高品质社区生活。以长三角地区某城市智慧社区建设为例,分析了智慧社区建设需求,阐述了设计思路、建设内容与应用场景设计,并对智慧社区的发展提出了针对性建议。     

Reliable and secure data transfer in IoT networks

With the rapid technological improvements in mobile devices and their inclusion in Internet of Things (IoT), secure key management becomes mandatory to ensure security of information exchange. For instance, IoT applications, such as smart health-care and smart homes, provide automated services to the users with less or no user intervention. As these application use user-sensitive data, ensuring their security and privacy should be paramount, especially during the key management process. However, traditional approaches for key management will not suit well in IoT environment because of the inherent resource constraint property of IoT devices. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The proposed scheme efficiently provides security to IoT devices by delegating most of the resource consuming cryptographic processing to a local entity. This entity coordinates with other peer entities to provide a distributed key as well as an authentication mechanism to network devices. In particular, the proposed scheme exploits the advantages of mobile agents by deploying them in different subnetworks as and when required: (1) to process the cryptography work for the IoT devices, and (2) to act as an local authenticated entity to perform fast authentication process. To verify the effectiveness and correctness of our proposed scheme, we have simulated it in a large IoT scenario and evaluated against relevant metrics that includes user mobility, certification generation time, and communication overhead.

     

大数据安全研究综述

随着人工智能、云计算、移动互联网和物联网等技术的融合发展,传统的基于边界安全域和基于已知特征库的网络安全防护方式已经无法有效应对大数据环境下新的安全威胁。通过对大数据环境下面临的安全问题和挑战进行分析,提出基于大数据分析和威胁情报共享为基础的大数据协同安全防护体系,将大数据安全技术框架、数据安全治理、安全测评和运维管理相结合,在数据分类分级和全生命周期安全的基础上,体系性的解决大数据不同层次的安全问题。基于该安全防护体系,分析了数据安全的关键技术及其目前的发展现状,并展望和分析了大数据安全领域面临的挑战。全面的分析和研究了大数据安全的威胁、政策、标准、方案、关键技术和挑战,对开展大数据安全建设和工程应用有重要参考意义。