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.     

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.     

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.     

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.     

Energy aware cloud-edge service placement approaches in the Internet of Things communications

In recent years, applying Internet of Things (IoT) applications has significantly increased to facilitate and improve quality of human life activities in various fields such as healthcare, education, industry, economics, etc. The energy aware cloud-edge computing paradigm has developed as a hybrid computing solution to provide IoT applications using available cloud service providers and fog nodes for the smart devices and mobile applications. Since the IoT applications are developed in the form of several IoT services with various quality of service (QoS) metrics which can deploy on the cloud-edge providers with different resource capabilities, finding an efficient placement solution as one of challenging topics to be measured for IoT applications. The service placement issue arranges IoT applications on the cloud-edge providers with various capabilities of atomic services though sufficient different QoS factors to support service level agreement (SLA) contracts. This paper presents a technical analysis on the cloud-edge service placement approaches in IoT systems. The key point of this technical analysis is to identify substantial studies in the service placement approaches which need additional consideration to progress more efficient and effective placement strategies in IoT environments. In addition, a side-by-side taxonomy is proposed to categorize the relevant studies on cloud-edge service placement approaches and algorithms. A statistical and technical analysis of reviewed existing approaches is provided, and evaluation factors and attributes are discussed. Finally, open issues and forthcoming challenges of service placement approaches are presented.     

Data gathering and aggregation with selective transmission technique to optimize the lifetime of Internet of Things networks

The periodic sensor networks (PSNs) represent the bigger provider of data to the Internet of Things (IoT) due to their use in a wide range of IoT applications. Examples of IoT applications using PSNs are disaster recovery, connected vehicles, smart healthcare, smart cities, smart grids, and networks of robots. In PSNs, the large volume of data gathering and aggregation represent significant challenges that must be handled in the IoT applications. Therefore, it is necessary to find a dynamic way to gather data and get rid of the redundancy in the gathered data prior to transferring it to the sink (base station) for the sake of extending the PSN lifetime and preserving its energy. This article proposes data gathering and aggregation with selective transmission (DGAST) technique for optimizing lifetime in PSNs of IoT applications. DGAST gathers periodically the sensor data to extend the sensor's battery lifetime. DGAST protocol divides the lifetime of PSN into rounds. There are four phases in each round: data gathering, data aggregation, selective transmission, and adjusting the frequency of samples taken for each node in the context of dynamic climate change of the sensed environment. OMNeT++ simulator and real sensory data gathered at Intel Lab are used in the simulation experiments. The results of the simulation demonstrate DGAST efficiency in comparison with prefix frequency filtering (PFF) and Harb protocols, that is, overhead reduction up to 67% in gathered data, 73% in transmitted data, and 78% in consumed energy while maintaining the accuracy of sent data as high as 94.6%.     

A socio-technical framework for Internet-of-Things design: A human-centered design for the Internet of Things

This study presents a case application of a socio-technical framework to assess and predict the development of the Internet of Things (IoT) in Korea. Applying a socio-technical system approach to the IoT, this paper seeks a clear understanding of how the IoT will evolve and stabilize in a smart environment. It investigates the complex interaction between social and technical aspects of the IoT, by highlighting the co-evolution, interaction, and interface, which constitute the next generation network environment. It describes the challenges in designing, deploying, and sustaining the diverse components of the IoT, and provides a snapshot of Korea’s current approach to meeting this challenge. Finally, the findings of this study provide insights into these challenges and opportunities, by offering a socio-technical analysis of IoT development. The insights help to conceptualize how the IoT can be designed and situated within human-centered contexts.     

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.

     

物联网核心技术与应用场景

首先对物联网的概念、组成部分以及服务体系进行简要介绍,使读者首先对物联网在整体上有较为清晰的认识,其次,依托对传感器无线射频识别技术（RFID）两种主要数据采集手段的坚实论述,融合ZigBee、WIA-PA两种常用标准,着重阐述物联网的核心-传感器网络,为读者详细论述了传感器网络中的数据采集和处理以及无线通信这两个方面。最后,展望物联网的发展前景,剖析过程中的机遇与挑战。     

A low power high speed MTJ based non-volatile SRAM cell for energy harvesting based IoT applications

Powering billions of devices is one of the most challenging barrier in achieving the future vision of IoT. Most of the sensor nodes for IoT based systems depend on battery as their power source and therefore fail to meet the design goals of lifetime power supply, cost, reliable sensing and transmission. Energy harvesting has the potential to supplant batteries and thus prevents frequent battery replacement. However, energy autonomous systems suffer from sudden power variations due to change in external natural sources and results in loss of data. The memory system is a main component which can improve or decrease performance dramatically. The latest versions of many computing system use chip multiprocessor (CMP) with on-chip cache memory organized as array of SRAM cell. In this paper, we outline the challenges involved with the efficient power supply causing power outage in energy autonomous/self-powered systems. Also, various techniques both at circuit level and system level are discussed which ensures reliable operation of IoT device during power failure. We review the emerging non-volatile memories and explore the possibility of integrating STT-MTJ as prospective candidate for low power solution to energy harvesting based IoT applications. An ultra-low power hybrid NV-SRAM cell is designed by integrating MTJ in the conventional 6T SRAM cell. The proposed LP8T2MTJ NV-SRAM cell is then analyzed using multiple key performance parameters including read/write energies, backup/restore energies, access times and noise margins. The proposed LP8T2MTJ cell is compared to conventional 6T SRAM counterpart indicating similar read and write performance. Also, comparison with the existing MTJ based NV-SRAM cells show 51–78% reduction in backup energy and 42–70% reduction in restore energy.     

电信运营商物联网客户运营分析系统的构建及应用

简要分析了中国联通在物联网客户运营能力上的挑战与诉求,结合中国联通研究院的大数据分析能力,提出运用能力构建完善标准体系,推动物联网规模应用,实现物联网客户运营大数据化的目标。基于电信行业物联网运营模式的实践经验,描述了如何以大数据能力为基础进行面向运营商的更为精细化的企业客户运营,从技术创新的角度实现客户运营服务的精准化、高效化。以提升客户数据运营的准确性和前瞻性为目的,探索如何利用数据分析能力,构建精准的客户运营支撑体系。最后,讲述了大数据应用于客户运营场景的重要价值和对客户运营未来的展望。     

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.     

Security and privacy issues of physical objects in the IoT: Challenges and opportunities

In the Internet of Things (IoT), security and privacy issues of physical objects are crucial to the related applications. In order to clarify the complicated security and privacy issues, the life cycle of a physical object is divided into three stages of pre-working, in-working, and post-working. On this basis, a physical object-based security architecture for the IoT is put forward. According to the security architecture, security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail. Considering the development of IoT technologies, potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized. At the same time, possible directions for dealing with these challenges are also pointed out.     

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.     

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.

     

Middleware technologies for cloud of things: a survey

The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things (CoT), which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable “middleware” which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.     

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.

     

Service community construction method of internet of things based on semantic similarity

Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.     

A review on smart home present state and challenges: linked to context-awareness internet of things (IoT)

The smart home is considered as an essential domain in Internet of Things (IoT) applications, it is an interconnected home where all types of things interact with each other via the Internet. This helps to automate the home by making it smart and interconnected. However, at the same time, it raises a great concern of the privacy and security for the users due to its capability to be controlled remotely. Hence, the rapid technologically growth of IoT raises abundant challenges such as how to provide the home users with safe and secure services keeping privacy in the account and how to manage the smart home successfully under the controlled condition to avoid any further secrecy or theft of personal data. A number of the research papers are available to address these critical issues, researchers presented different approaches to overcome these stated issues. This research review will analyze smart home approaches, challenges and will suggest possible solutions for them and illustrate open issues that still need to be addressed.

     

Health care service delivery based on the Internet of things: A systematic and comprehensive study

Today, the Internet of Things (IoT) becomes a heterogeneous and highly distributed structure which can respond to the daily needs of people and different organizations. With the fast development of IT‐based technologies such as IoT and cloud computing, low‐cost health services and their support, efficient supervision of the centralized management, and monitoring of public health can be realized. Therefore, there has been increasing attention in the integration of IoT and health care both in academic and the business world. However, while the health care service industry fully holds the welfares of information systems for its personnel and patients, there is a need for an improved understanding of the issues and opportunities related to IoT‐based health care systems. But, as far as we know, the detailed review and deep discussion in this field are very rare. Hence, in this paper, we presented a literature review on the IoT‐based health care services from papers published until 2018. Moreover, the drawbacks and benefits of the reviewed mechanisms have been discussed, and the main challenges of these mechanisms are highlighted for developing more efficient IoT techniques over health care services in the future. The results of this paper will be valuable for both practitioners and academicians, and it can provide visions into future research areas in this domain. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and working professionals for better knowing the progress in IoT mechanisms. As a general result, we found that IoT could help the governments to improve health services in society and commercial interactions.