Machine learning for internet of things data analysis: a survey

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.     

Smart integrated IoT healthcare system for cancer care

The emergence of the internet of things (IoT) has drastically influenced and shaped the world of technology in the contexts of connectivity, interconnectivity, and interoperability with smart connected sensors, objects, devices, data, and applications. In fact, IoT has brought notable impacts on the global economy and human experience that span from industry to industry in a variety of application domains, including healthcare. With IoT, it is expected to facilitate a seamless interaction and communication of objects (devices) with humans in the environment. Therefore, it is imperative to embrace the potentials and benefits of IoT technology in healthcare delivery to ensure saving lives and to improve the quality of life using smart connected devices. In this paper, we focus on the IoT based healthcare system for cancer care services and business analytics/cloud services and also propose the adoption and implementation of IoT/WSN technology to augment the existing treatment options to deliver healthcare solution. Here, the business analytics/cloud services constitute the enablers for actionable insights, decision making, data transmission and reporting for enhancing cancer treatments. Furthermore, we propose a variety of frameworks and architectures to illustrate and support the functional IoT-based solution that is being considered or utilized in our proposed smart healthcare solution for cancer care services. Finally, it will be important to understand and discuss some security issues and operational challenges that have characterized the IoT-enabled healthcare system.

     

A review on evolving domains of Internet of Things: Architecture,applications, and technical challenges

The Internet of Things (IoT) is a system that includes smart items with different sensors, advanced technologies, analytics, cloud servers, and other wireless devices that integrate and work together to create an intelligent environment that benefits end users. With its wide spectrum of applications, IoT is revolutionizing both the current and future generations of the Internet. IoT systems can be employed for broad-ranging real applications, such as agriculture, the environment, cities, healthcare, and the industrial sector. In this paper, we briefly discuss the three-tier architectural view of IoT, its different communication technologies, and the smart sensors. Moreover, we study various application areas of IoT such as the environmental domain, healthcare, agriculture, smart cities, and industrial, commercial, and general aspects. A critical analysis is shown for the existing schemes and techniques related to this work. Further, this paper addresses the basic context, tools and evaluation approaches, future scope, and the advantages and disadvantages of the aforestated IoT applications. A comprehensive analysis is provided for each domain along with its fundamental parameters like the quality of service (QoS), network longevity, scalability, energy efficiency, accuracy, and cost. Finally, this study highlights the technical challenges and open research problems existing in different IoT applications.     

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.     

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.

     

Machine learning-based clustering protocols for Internet of Things networks: An overview

The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML-based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology-centered ML-based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML-based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.     

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.     

Deep learning for the internet of things: Potential benefits and use-cases

The massive number of sensors deployed in the Internet of Things (IoT) produce gigantic amounts of data for facilitating a wide range of applications. Deep Learning (DL) would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter IoT. In this regard, exploring the potential of DL for IoT data analytics becomes highly crucial. This paper begins with a concise discussion on the Deep Neural Network (DNN) and its different architectures. The potential benefits that DL will bring to the IoT are also discussed. Then, a detailed review of DL-driven IoT use-cases is presented. Moreover, this paper formulates a DL-based model for Human Activity Recognition (HAR). It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other techniques. Apart from enlightening the potential of DL in IoT applications, this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.     

IoT-based systemic lupus erythematosus prediction model using hybrid genetic algorithm integrated with ANN

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.     

Quantitative Estimate of Infrastructure Interdependence

The expanded deployment of smart objects in IoT applications is pushing existing IoT platform architectures and their security functionalities to their limits. Indeed, smart objects exhibit semi-autonomous behaviours, are not centrally controlled all the time and therefore need more dynamic approaches in protecting them against vulnerabilities and security incidents. In this paper, we introduce a novel framework for securing the latest generation of IoT applications that involve smart objects, while illustrating its application in securing an Ambient Assisted Living (AAL) system that comprises socially assistive robots. The framework’s innovative aspects lie in the use of predictive analytics for anticipating the behaviour of smart objects, including abnormalities in their security behaviour. The importance of anticipating such abnormalities is validated, demonstrated and discussed in the context of the AAL application.

     

CIRUS: an elastic cloud-based framework for Ubilytics

The Internet of Things (IoT) has become a reality with the availability of chatty embedded devices. The huge amount of data generated by things must be analysed with models and technologies of the “Big Data Analytics”, deployed on cloud platforms. The CIRUS project aims to deliver a generic and elastic cloud-based framework for Ubilytics (ubiquitous big data analytics). The CIRUS framework collects and analyses IoT data for Machine to Machine services using Component-off-the-Shelves (COTS) such as IoT gateways, Message brokers or Message-as-a-Service providers and big data analytics platforms deployed and reconfigured dynamically with Roboconf. In this paper, we demonstrate and evaluate the genericity and elasticity of CIRUS with the deployment of a Ubilytics use case using a real dataset based on records originating from a practical source.     

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.     

基于智慧医疗模式的网络安全防护研究

随着技术的发展,智慧医疗已逐渐成为医疗健康领域的一个重要分支,其通过集成IoT、云计算、大数据、人工智能等技术,为医疗机构和患者提供了更加高效、个性化的医疗服务。然而,这种技术集成也带来了诸多网络安全挑战。文中深入探讨了智慧医疗模式下的网络安全威胁,并提出了一系列综合的安全防护策略。此外,还强调了与供应商、制造商及其他相关方的合作与信息共享在确保网络安全中的关键作用。     

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.     

Cognitive IoT-Cloud Integration for Smart Healthcare: Case Study for Epileptic Seizure Detection and Monitoring

We propose a cognitive Internet of Things (IoT)–cloud-based smart healthcare framework, which communicates with smart devices, sensors, and other stakeholders in the healthcare environment; makes an intelligent decision based on a patient’s state; and provides timely, low-cost, and accessible healthcare services. As a case study, an EEG seizure detection method using deep learning is also proposed to access the feasibility of the cognitive IoT–cloud smart healthcare framework. In the proposed method, we use smart EEG sensors (apart from general healthcare smart sensors) to record and transmit EEG signals from epileptic patients. Thereafter, the cognitive framework makes a real-time decision on future activities and whether to send the data to the deep learning module. The proposed system uses the patient’s movements, gestures, and facial expressions to determine the patient’s state. Signal processing and seizure detection take place in the cloud, while signals are classified as seizure or non-seizure with a probability score. The results are transmitted to medical practitioners or other stakeholders who can monitor the patients and, in critical cases, make the appropriate decisions to help the patient. Experimental results show that the proposed model achieves an accuracy and sensitivity of 99.2 and 93.5%, respectively.     

BDN-GWMNN: Internet of Things (IoT) Enabled Secure Smart City Applications

Nowadays, next-generation networks such as the Internet of Things (IoT) and 6G are played a vital role in providing an intelligent environment. The development of technologies helps to create smart city applications like the healthcare system, smart industry, and smart water plan, etc. Any user accesses the developed applications; at the time, security, privacy, and confidentiality arechallenging to manage. So, this paper introduces the blockchain-defined networks with a grey wolf optimized modular neural network approach for managing the smart environment security. During this process, construction, translation, and application layers are created, in which user authenticated based blocks are designed to handle the security and privacy property. Then the optimized neural network is applied to maintain the latency and computational resource utilization in IoT enabled smart applications. Then the efficiency of the system is evaluated using simulation results, in which system ensures low latency, high security (99.12%) compared to the multi-layer perceptron, and deep learning networks.

     

基于物联网大数据的智慧城市可持续建设研究

文中针对智慧城市建设可持续发展与规划的重要意义和重大影响,利用物联网和大数据分析技术,提出了一种以物联网和大数据分析为基础的智慧城市可持续发展规划与建设框架。首先,设计了一种基于物联网的异构聚合智慧城市架构;然后,对框架进行了大数据分析与探讨;最后,利用Hadoop Eco系统,对数据吞吐率曲线与数据量大小进行了评估,验证了所提框架的实用性和先进性。     

物联网的技术路线及属性形成

区别于传统强调人与人连接的互联网,物联网是互联网的技术扩展和应用延伸。本文阐述了物联网融合传感器网络、EPC系统和泛在网络的泛在多元信息获取方式,利用电信网、互联网以及广电网等通信网络和各类接入网络实现泛在数据传送,为社会不同行业的应用需求提供泛在服务能力。指出物联网的技术路线是在现有互联网的基础上,通过借鉴和吸收相关学科的研究和应用成果,形成的一个物理空间与虚拟空间、人与物交叉融合的信息服务基础平台;物联网的根本属性是泛在化,即泛在网络,具体体现在泛在互联、泛在技术支持和泛在应用整合等方面。     

Real Time Scheduling in TSCH for IoT Using Neurogenetic Algorithm

Internet of Things (IoT) is a widely adoptable technology in industrial, smart home, smart grid, smart city and smart healthcare applications. The real world objects are remotely connected through internet and it provides services with the help of friendly devices. Currently IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) standard is gaining a part of consideration among the IoT research community because of its effectiveness to improvise the reliability of communication which is orchestrated by the scheduling. As TSCH is an emerging Medium Access Control (MAC) protocol, it is used in the proposed work to enhance the network scheduling by throughput maximization and delay minimization. The paper focuses on proper utilization of the channel through node scheduling. NeuroGenetic Algorithm (NGA) has been proposed for TSCH scheduling and its performance is evaluated with respect to time delay and throughput. The system is implemented in real time IoT devices and results are perceived and analyzed. The proposed algorithm is compared with existing TSCH scheduling algorithms.

     

Time to forge ahead: The Internet of Things for healthcare

Situated at the intersection of technology and medicine, the Internet of Things (IoT) holds the promise of addressing some of healthcare's most pressing challenges, from medical error, to chronic drug shortages, to overburdened hospital systems, to dealing with the COVID-19 pandemic. However, despite considerable recent technological advances, the pace of successful implementation of promising IoT healthcare initiatives has been slow. To inspire more productive collaboration, we present here a simple—but surprisingly underrated—problem-oriented approach to developing healthcare technologies. To further assist in this effort, we reviewed the various commercial, regulatory, social/cultural, and technological factors in the development of the IoT. We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem. To this end, we explore the key enabling technologies that underpin the fog architecture, from the sensing layer all the way up to the cloud. It is our hope that ongoing advances in sensing, communications, cryptography, storage, machine learning, and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.