Hasil untuk "iot"

Somayya Madakam, R. Ramaswamy, Siddharth Tripathi

One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently.

Alem Čolaković, M. Hadzialic

Abstract IoT (Internet of Things) is a new paradigm which provides a set of new services for the next wave of technological innovations. IoT applications are nearly limitless while enabling seamless integration of the cyber-world with the physical world. However, despite the enormous efforts of standardization bodies, alliances, industries, researchers and others, there are still numerous problems to deal with in order to reach the full potential of IoT. These issues should be considered from various aspects such as enabling technologies, applications, business models, social and environmental impacts. In focus of this paper are open issues and challenges considered from the technological perspective. Just for clarification, we put in light different visions that stand behind this paradigm in order to facilitate a better understanding of the IoT's features. Furthermore, this exhaustive survey provides insights into the state-of-the-art of IoT enabling and emerging technologies. The most relevant among them are addressed with some details. The main scope is to deliver a comprehensive overview of open issues and challenges to be tackled by future research. We provide some insights into specific emerging ideas in order to facilitate future research. Also, this paper brings order in the existing literature by classifying contributions according to different research topics.

Alfonso Panarello, Nachiket Tapas, Giovanni Merlino et al.

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.

Liang Xiao, Xiaoyue Wan, Xiaozhen Lu et al.

The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.

S. Bibri

Mario Frustaci, P. Pace, G. Aloi et al.

Social Internet of Things (SIoT) is a new paradigm where Internet of Things (IoT) merges with social networks, allowing people and devices to interact, and facilitating information sharing. However, security and privacy issues are a great challenge for IoT but they are also enabling factors to create a “trust ecosystem.” In fact, the intrinsic vulnerabilities of IoT devices, with limited resources and heterogeneous technologies, together with the lack of specifically designed IoT standards, represent a fertile ground for the expansion of specific cyber threats. In this paper, we try to bring order on the IoT security panorama providing a taxonomic analysis from the perspective of the three main key layers of the IoT system model: 1) perception; 2) transportation; and 3) application levels. As a result of the analysis, we will highlight the most critical issues with the aim of guiding future research directions.

M. T. Hammi, Badis Hammi, P. Bellot et al.

Abstract There is no doubt that Internet of Things (IoT) occupy a very important role in our daily lives. Indeed, numerous objects that we use every time, are being equipped with electronic devices and protocol suites in order to make them interconnected and connected to the Internet. In IoT, things process and exchange data without human intervention. Therefore, because of this full autonomy, these entities need to recognize and authenticate each other as well as to ensure the integrity of their exchanged data. Otherwise, they will be the target of malicious users and malicious use. Due to the size and other features of IoT, it is almost impossible to create an efficient centralized authentication system. To remedy this limit, in this paper, we propose an original decentralized system called bubbles of trust, which ensures a robust identification and authentication of devices. Furthermore, it protects the data integrity and availability. To achieve such a goal, our approach relies on the security advantages provided by blockchains, and serves to create secure virtual zones (bubbles) where things can identify and trust each other. We also provided a real implementation of our mechanism using the C++ language and Ethereum blockchain. The obtained results prove its ability to satisfy IoT security requirements, its efficiency, and its low cost.

P. Sharma, Mu-Yen Chen, J. Park

The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.

N. Naik

The standard and real-time communication technology is an unalloyed inevitability for the development of Internet of Things (IoT) applications. However, the selection of a standard and effective messaging protocol is a challenging and daunting task for any organisation because it depends on the nature of the IoT system and its messaging requirements. Copious messaging protocols have been developed and employed by various organisations based on their requirements in the last two decades. Though, none of them is able to support all messaging requirements of all types of IoT systems. Messaging protocol is an ongoing dilemma for the IoT industry; consequently, it is important to understand the pros and cons of the widely accepted and emerging messaging protocols for IoT systems to determine their best-fit scenarios. Therefore, this paper presents an evaluation of the four established messaging protocols MQTT, CoAP, AMQP and HTTP for IoT systems. Firstly, it presents the broad comparison among these messaging protocols to introduce their characteristics comparatively. Afterwards, it performs a further in-depth and relative analysis based on some interrelated criteria to gain insight into their strengths and limitations. Thus, based on this detailed evaluation, the user can decide their appropriate usage in various IoT systems according to their requirements and suitability.

Omar Alrawi, Chaz Lever, M. Antonakakis et al.

Home-based IoT devices have a bleak reputation regarding their security practices. On the surface, the insecurities of IoT devices seem to be caused by integration problems that may be addressed by simple measures, but this work finds that to be a naive assumption. The truth is, IoT deployments, at their core, utilize traditional compute systems, such as embedded, mobile, and network. These components have many unexplored challenges such as the effect of over-privileged mobile applications on embedded devices. Our work proposes a methodology that researchers and practitioners could employ to analyze security properties for home-based IoT devices. We systematize the literature for home-based IoT using this methodology in order to understand attack techniques, mitigations, and stakeholders. Further, we evaluate \numDevices devices to augment the systematized literature in order to identify neglected research areas. To make this analysis transparent and easier to adapt by the community, we provide a public portal to share our evaluation data and invite the community to contribute their independent findings.

P. Kumar, Randhir Kumar, Gautam Srivastava et al.

With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: a two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.

P. Ray, D. Dash, K. Salah et al.

Internet of Things (IoT) and blockchain technologies are being heavily exploited and used in may domains, especially for e-healthcare. In healthcare, IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed. Collected IoT data are subjected to centralized computation, processing, and storage. Such centralization can be problematic, as it can be a single point of failure, mistrust, data manipulation and tampering, and privacy evasion. Blockchain can solve such serious problems by providing decentralized computation and storage for IoT data. Therefore, the integration IoT and blockchain technologies can become a reasonable choice for the design of a decentralized IoT-based e-healthcare systems. In this article, first, we give a brief background on blockchain. Second, popular consensus algorithms used in blockchain are discussed in the context of e-health. Third, blockchain platforms are reviewed for their appropriateness in IoT-based e-healthcare. Finally, few use cases are methodologically given to show how key features of the IoT and blockchain can be leveraged to support healthcare services and ecosystems. We also propose a data-flow architecture that combines the IoT with blockchain, called IoBHealth, that can be used for storing, accessing, and managing of e-healthcare data.

Jianxin Wang, M. Lim, Chao Wang et al.

Abstract To reveal the origin of the IoT, evaluate its mainstream research topics, and discuss the challenges facing the IoT in the future, this paper conducts a bibliometric study of the IoT from 2000 to 2019. First, this paper presents a basic bibliometric overview of the IoT. Second, co-citation, coupling and cluster analysis methods are used to analyse collaboration networks, and VOSviewer is used to visualize the networks. Third, biblioshiny is used to analyse the thematic trends of IoT. Finally, this paper discusses IoT challenges and provides some suggestions to address them. The limitations of this paper are also summarized. Research results show that, the mainstream studies in this field mainly focus on IoT security, wireless sensor networks, IoT management, IoT challenges and privacy. In addition, the thematic evolution of keywords shows that security and algorithm issues have become basic themes in the field of IoT research in recent years.

Binghong Liu, Chenxi Liu, M. Peng

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.

A. M. Seid, Gordon Owusu Boateng, B. Mareri et al.

The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networks. Besides, the edge base station (BS) with the computation server is allowed massive traffic and is vulnerable to disasters. The UAV is a promising technology that provides aerial base stations (ABSs) to assist the edge network in enhancing the ground network performance, extending network coverage, and offloading computationally intensive tasks from UEs or IoT devices. In this paper, we deploy a clustered multi-UAV to provide computing task offloading and resource allocation services to IoT devices. We propose a multi-agent deep reinforcement learning (MADRL)-based approach to minimize the overall network computation cost while ensuring the quality of service (QoS) requirements of IoT devices or UEs in the IoT network. We formulate our problem as a natural extension of the Markov decision process (MDP) concerning stochastic game, to minimize the long-term computation cost in terms of energy and delay. We consider the stochastic time-varying UAVs’ channel strength and dynamic resource requests to obtain optimal resource allocation policies and computation offloading in aerial to ground (A2G) network infrastructure. Simulation results show that our proposed MADRL method reduces the average costs by 38.643%, and 55.621% and increases the reward by 58.289% and 85.289% compared with the different single agent DRL and heuristic schemes, respectively.

Mehedi Masud, G. S. Gaba, Karanjeet Choudhary et al.

Internet of Things (IoT) produces massive heterogeneous data from various applications, including digital health, smart hospitals, automated pathology labs, and so forth. IoT sensor nodes are integrated with the medical equipment to enable the health workers to monitor the patients’ health condition and appliances in real time. However, due to security vulnerabilities, an unauthorized user can access health-related information or control the IoT nodes attached to the patient’s body resulting in unprecedented outcomes. Due to wireless channels as a medium of communication, IoT poses several threats such as a denial of service attack, man-in-the-middle attack, and modification attack to the IoT networks’ security and privacy. The proposed research presents a lightweight and anonymity-preserving user authentication protocol to counter these security threats. The given scheme establishes a secure session for the legitimate user and prohibits unauthorized users from gaining access to the IoT sensor nodes. The proposed protocol uses only lightweight cryptography primitives (hash) to alleviate the node’s tiny processor burden. The proposed protocol is efficient and superior because it has low computational and communication costs than conventional protocols. The proposed scheme uses password protection to let only the legitimate user access the IoT sensor nodes to obtain the patient’s real-time health report.

I. Cvitić, Dragan Peraković, Marko Periša et al.

The emergence of the Internet of Things (IoT) concept as a new direction of technological development raises new problems such as valid and timely identification of such devices, security vulnerabilities that can be exploited for malicious activities, and management of such devices. The communication of IoT devices generates traffic that has specific features and differences with respect to conventional devices. This research seeks to analyze the possibilities of applying such features for classifying devices, regardless of their functionality or purpose. This kind of classification is necessary for a dynamic and heterogeneous environment, such as a smart home where the number and types of devices grow daily. This research uses a total of 41 IoT devices. The logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model. Multiclass classification model was developed using 13 network traffic features generated by IoT devices. Research has shown that it is possible to classify devices into four previously defined classes with high performances and accuracy (99.79%) based on the traffic flow features of such devices. Model performance measures such as precision, F-measure, True Positive Ratio, False Positive Ratio and Kappa coefficient all show high results (0.997–0.999, 0.997–0.999, 0.997–0.999, 0–0.001 and 0.9973, respectively). Such a developed model can have its application as a foundation for monitoring and managing solutions of large and heterogeneous IoT environments such as Industrial IoT, smart home, and similar.

Leonardo Babun, K. Denney, Z. Berkay Celik et al.

Abstract The Internet of Things (IoT) redefines the way how commodity and industrial tasks are performed every day. The integration of sensors, lightweight computation, and the proliferation of different wireless technologies on IoT platforms enable human beings to easily interact with their surrounding physical world thoroughly. With the recent rise of IoT, several different IoT platforms have been introduced for researchers and developers to ease the management and control of various IoT devices. In general, the IoT platforms act as a bridge between core IoT functionalities and users by providing APIs. Due to their wide variety of applications, IoT platforms are mostly unique in their architectures and designs. Thus, IoT administrators, developers, and researchers (i.e., IoT users) are challenged with substantial configuration differences in the proper configuration, implementation, and protection of the IoT solutions. In this survey, we conduct an in-depth analysis of popular IoT platforms from different application domains. More specifically, we define a comprehensive evaluation framework that considers seven different technical comparison criteria: (1) topology design, (2) programming languages, (3) third-party support, (4) extended protocol support, (5) event handling, (6) security, and (7) privacy. Then, we use the framework to evaluate the different IoT platforms highlighting their distinguishing attributes on communications, security, and privacy. First, we describe the communication protocols supported by the different IoT platforms surveyed. Then, rather than uncovering novel threats affecting IoT, we aim to analyze how the different IoT platforms handle security and privacy vulnerabilities affecting the most common security services of confidentiality, integrity, availability, and access control. Further, we present possible solutions that these platforms could implement to strengthen security and privacy within the IoT solution. Finally, we discuss the advantages and disadvantages of every IoT platform, so IoT administrators, developers, and researchers (i.e., IoT users) can make an informed decision on the use of specific platforms to implement their IoT solutions. To the best of our knowledge, this is the first comprehensive survey to evaluate different IoT platforms using the criteria defined in this work.

S. Abu, Adnan Abir, A. Anwar et al.

The Internet of Things (IoT) is a rapidly emerging field of technologies that delivers numerous cutting-edge solutions in various domains including the critical infrastructures. Thanks to the IoT, the conventional power system network can be transformed into an effective and smarter energy grid. In this article, we review the architecture and functionalities of IoT-enabled smart energy grid systems. Specifically, we focus on different IoT technologies including sensing, communication, computing technologies, and their standards in relation to smart energy grid. This article also presents a comprehensive overview of existing studies on IoT applications to the smart grid system. Based on recent surveys and literature, we observe that the security vulnerabilities related to IoT technologies have been attributed as one of the major concerns of IoT-enabled energy systems. Therefore, we review the existing threat and attack models for IoT-enabled energy systems and summarize mitigation techniques for those security vulnerabilities. Finally, we highlight how advanced technologies (e.g., blockchain, machine learning, and artificial intelligence) can complement IoT-enabled energy systems to be more resilient and secure and overcome the existing difficulties so that they become more effective, robust, and reliable in operation. Precisely, this article will help understand the framework for IoT-enabled smart energy system, associated security vulnerabilities, and prospects of advanced technologies to improve the effectiveness of smart energy systems.

Usman Inayat, M. F. Zia, Sajid Mahmood et al.

Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.