A roadmap for security challenges in the Internet of Things

Unquestionably, communicating entities (object, or things) in the Internet of Things (IoT) context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA (Defense Advanced Research Projects Agency) as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the IoT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the IoT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current standardization activities are surveyed and discussed to the ensure the security of IoT components and applications.     

A Review on the Security of the Internet of Things: Challenges and Solutions

The Internet of Things (IoT) has emerged as a modern wave of Internet technologies that promises great transformation of life in areas such as smart health, smart cities, smart homes, intelligent transport, amongst others. However, security often serves as a critical reason for the widespread adoption of any innovation. While the IoT has increased business productivity and enriched diverse areas of life over the years, the world is yet to see a methodical revolution of its humongous application and transformation given its ubiquity and highly interconnected global network structure. The main culprit for such lapses is principally attributed to security and privacy issues which have been widely discussed in research articles and reviews but remain largely unaddressed in the literature. Hence, this paper provides a state-of-the-art review of IoT security and its challenges. It overviews technical and legal solutions that are useful to private, organizational, and governmental enterprises. The study encompasses the review and security analysis of IoT’s evolution and revolution, IoT security assessments, requirements, current research challenges in security and much more. Consequently, it offers potential solutions to address the security challenges discussed and further present open research issues, research gaps, opportunities, future development, and recommendations. This overview is intended to serve as a knowledgebase that will proffer novel foresight to guide users and administrators in positioning themselves and their organizations in a manner that is consistent with their overall objectives, mission, and vision for remarkable outcomes. Likewise, interested scholars and researchers can explore topics and directions from the study in providing better solutions to the numerous problems in IoT security.

     

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.     

基于关注人员动态感知技术的警务物联网构架研究

在当前全球范围内不断增加的恐怖袭击威胁下,公安机关和相关执法机构试图找到更有效的方法来实现对重点关注人员的监测和预警。物联网技术手段的出现为实现这个目标提供了可能,但采用什么体系架构更加有利于实现这个目标是一个挑战。本文通过现有架构和实际需求的具体分析,基于分层模式提出了一个分布式的、可互操作的、适应于警务工作的物联网体系结构,来解决我们传统物联网架构中遇到的问题,为解决实际工作需求提供了一种全新思路。     

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.     

A survey on indexing techniques for mobility in Internet of Things': Challenges,performances, and perspectives

Mobile nodes arouse new challenges that expand the performance in those environments. The nodes of Internet of Things (IoT) generate a large amount of data, which have to be stored and processed in a seamless and interpretable form by indexing. Therefore, indexing is a challenge in IoT, particularly when the nodes are mobile. The current indexing techniques dedicated to mobile environments are unsatisfying because the data are transmitted from different locations within different time periods and randomness of sensor movement. Although huge research efforts have been dedicated to this subject amid the last decades, little consideration has been paid for the research summarization and guidance. The objective of this survey is to identify the relationship between activities of mobile sensors in the context of IoT, that concerned on transferring and collecting data as well as the effectiveness of indexing techniques. The contribution of this review is to investigate the techniques of mobile IoT nodes, to find the source of challenges that adversely affect the index effectiveness. In‐depth investigation and analyses of approaches to apply IoT mobile nodes will enable the researcher to understand the behavior of mobile environments to extract the deformities that adversely affect the indexing effectiveness. The analyzed approaches are meta‐heuristic, spatial‐temporal indexing, and prediction model approach. Each approach is analyzed by discussing the features and limitations from the mobility perspectives. Furthermore, the indexing techniques are analyzed according to their features and limitations and mobile IoT indexing requirements. In conclusion, recognize and contemplate different open issues that got little focus or still unknown at this point.     

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.

     

物联网海量异构数据存储与共享策略研究

随着物联网向各行业的深入发展,各行业的信息化进程也进入了快车道.信息服务作为物联网在各行业应用中重要的公共服务之一,一直受到广泛关注.然而,当前物联网信息服务系统面对物联网海量异构数据存在性能低下、共享困难等问题.因此,本文提出了一种基于NoSQL、REST以及国家物联网标识管理公共服务平台(NIOT)的存储与共享策略,并着重对该系统的构成、逻辑设计进行了详尽阐述.针对性能改进的策略设计了适当的量化评测,实验结果表明提出策略具有较好的效果,基于实验结果对进一步的优化进行了讨论.     

Classification and evaluation of IoT brokers: A methodology

Since the term Internet of Things (IoT) was coined by Kevin Ashton in 1999, a number of middleware platforms have been developed to cope with important challenges such as the integration of different technologies. In this context of heterogeneous technologies, IoT message brokers become critical elements for the proper function of smart systems and wireless sensor networks (WSN) infrastructures. There are several evaluations made on IoT messaging middleware performance. Nevertheless, most of them ignore crucial aspects of the IoT context that also need to be included, such as reliability and other qualitative aspects. Thus, in this article, we propose a methodology for classification and evaluation of IoT brokers to help the scientific community and technology industry on evaluating them according to their interests, without leaving out important aspects for the context of smart environments. Our methodology bases its qualitative evaluations on the ISO/IEC 25000 (SQuaRE) set of standards and its quantitative evaluations on Jain's process for performance evaluation. We developed a case study to illustrate our proposal with 12 different open-source brokers, validating the feasibility of our methodological approach.     

A middleware approach for reliable resource selection on Internet‐of‐Things

Decision making plays a vital role in the selection of resources so that they actively participate for communication and computation on the Internet‐of‐Things platform. For the same, they require the elimination of the challenges related to knowledge representation, discovery, trust, and security due to continuously changing mobility patterns, heterogeneity, interoperability, and scalability on the network. To address the challenges, a novel three‐layered approach, namely, middleware approach for reliable resource selection on Internet‐of‐Things (MARRS‐IoT), is proposed. It performs a search through neighbor discovery algorithm and evaluates trust score of the discovered resources, both locally and globally using fuzzy‐decision algorithm and performs efficient communication among resources via hybrid M‐gear protocol. The approach is simulated and compared against algorithms, namely, particle swarm optimization, ants colony optimization, and binary genetic to evaluate its performance. The obtained results support the efficacy of the MARRS‐IoT with respect to throughput and execution time.     

Enhancing IoT anomaly detection performance for federated learning

Federated Learning (FL) with mobile computing and the Internet of Things (IoT) is an effective cooperative learning approach. However, several technical challenges still need to be addressed. For instance, dividing the training process among several devices may impact the performance of Machine Learning (ML) algorithms, often significantly degrading prediction accuracy compared to centralized learning. One of the primary reasons for such performance degradation is that each device can access only a small fraction of data (that it generates), which limits the efficacy of the local ML model constructed on that device. The performance degradation could be exacerbated when the participating devices produce different classes of events, which is known as the class balance problem. Moreover, if the participating devices are of different types, each device may never observe the same types of events, which leads to the device heterogeneity problem. In this study, we investigate how data augmentation can be applied to address these challenges and improving detection performance in an anomaly detection task using IoT datasets. Our extensive experimental results with three publicly accessible IoT datasets show the performance improvement of up to 22.9% with the approach of data augmentation, compared to the baseline (without relying on data augmentation). In particular, stratified random sampling and uniform random sampling show the best improvement in detection performance with only a modest increase in computation time, whereas the data augmentation scheme using Generative Adversarial Networks is the most time-consuming with limited performance benefits.     

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.     

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.     

物联网搜索中主客观融合的动态信誉评估

与传统的互联网搜索相比,物联网搜索更加强调数据质量。为了提高数据质量,物联网搜索中心需要依数据质量为提供者支付相应报酬以激励其持续提供符合质量需求的数据。这就使如何评估数据提供者的数据质量及其信誉成为物联网搜索中的一个基础问题。为此,引入动态信誉机制来综合评估数据提供者的可信性,为数据选择、收集提供依据。首先,提出了主观意愿及客观质量评价计算方法,并设计了交互行为贴现及信誉衰减机制;然后,提出了融合主客观要素的动态信誉计算模型,并基于信号传递机制进行博弈分析,以获取交互次数、贴现率、支付价格及数据成本之间约束关系。实验证明该模型能够较好地反映数据质量变化情况,为数据选择提供依据,并满足物联网搜索的实时性及动态性需求。     

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.     

Interoperability in semantic Web of Things: Design issues and solutions

The significant improvement in processing power, communication, energy consumption, and the size of computational devices has led to the emergence of the Internet of Things (IoT). IoT projects raise many challenges, such as the interoperability between IoT applications because of the high number of sensors, actuators, services, protocols, and data associated with these systems. Semantics solves this problem by using annotations that define the role of each IoT element and reduces the ambiguity of information exchanged between the devices. This work presents SWoTPAD, a semantic framework that helps in the development of IoT projects. The framework is designer oriented and provides a semantic language that is more user‐friendly than OWL‐S and WSML and allows the IoT designer to specify devices, services, environment, and requests. Following this, it makes use of these specifications and maps them for RESTful services. Additionally, it generates an automatic service composition engine that is able to combine services needed to handle complex user requests. We validated this approach with two case studies. The former concerns a residential security system and the latter, the cloud application deployment. The average time required for service discovery and automatic service composition corresponds to 72.9% of the service execution time in the case study 1 and 64.4% in the case study 2.     

电信运营商物联网发展模式比较分析

近年来，物联网发展如火如荼。对于国内运营商来说，它对目前的转型发展有着十分重要的意义。在此背景上，运营商如何实现在物联网产业“弯道超车”，商业模式至关重要。基于对国内运营商物联网运营现状的梳理，比较国内外物联网行业的发展策略，总结为 3 种不同的发展策略：单一模式、多环节覆盖模式和全产业模式。电信运营商在客户、渠道等方面具有优势，但受限于多方面因素。建议在构建发展模式时，遵循“循序渐进”的原则，充分根据自己的企业定位、结合国家的政策，注重自身在物联网产业链的定位，关注风险。最终实现较好的发展模式，改善电信行业目前“增量不增收”的现状。     

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.