Hasil untuk "iot"

O. Elijah, T. A. Rahman, I. Orikumhi et al.

The surge in global population is compelling a shift toward smart agriculture practices. This coupled with the diminishing natural resources, limited availability of arable land, increase in unpredictable weather conditions makes food security a major concern for most countries. As a result, the use of Internet of Things (IoT) and data analytics (DA) are employed to enhance the operational efficiency and productivity in the agriculture sector. There is a paradigm shift from use of wireless sensor network (WSN) as a major driver of smart agriculture to the use of IoT and DA. The IoT integrates several existing technologies, such as WSN, radio frequency identification, cloud computing, middleware systems, and end-user applications. In this paper, several benefits and challenges of IoT have been identified. We present the IoT ecosystem and how the combination of IoT and DA is enabling smart agriculture. Furthermore, we provide future trends and opportunities which are categorized into technological innovations, application scenarios, business, and marketability.

M. Al-garadi, Amr M. Mohamed, A. Al-Ali et al.

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

C. Stergiou, K. Psannis, Byung-Gyu Kim et al.

R. Sinha, Yiqiao Wei, Seung-Hoon Hwang

Abstract By 2020, more than twenty five billion devices would be connected through wireless communications. In accordance with the rapid growth of the internet of things (IoT) market, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, narrowband (NB)-IoT and long range (LoRa) are two leading technologies. In this paper, we provide a comprehensive survey on NB-IoT and LoRa as efficient solutions connecting the devices. It is shown that unlicensed LoRa has advantages in terms of battery lifetime, capacity, and cost. Meanwhile, licensed NB-IoT offers benefits in terms of QoS, latency, reliability, and range.

Mardiana binti Mohamad Noor, W. H. Hassan

Abstract The results of IoT failures can be severe, therefore, the study and research in security issues in the IoT is of extreme significance. The main objective of IoT security is to preserve privacy, confidentiality, ensure the security of the users, infrastructures, data, and devices of the IoT, and guarantee the availability of the services offered by an IoT ecosystem. Thus, research in IoT security has recently been gaining much momentum with the help of the available simulation tools, modellers, and computational and analysis platforms. This paper presents an analysis of recent research in IoT security from 2016 to 2018, its trends and open issues. The main contribution of this paper is to provide an overview of the current state of IoT security research, the relevant tools,IoT modellers and simulators.

Shu Tang, Dennis R. Shelden, C. Eastman et al.

Abstract The integration of Building Information Modeling (BIM) with real-time data from the Internet of Things (IoT) devices presents a powerful paradigm for applications to improve construction and operational efficiencies. Connecting real-time data streams from the rapidly expanding set of IoT sensor networks to the high-fidelity BIM models provides numerous applications. However, BIM and IoT integration research are still in nascent stages, there is a need to understand the current situation of BIM and IoT device integration. This paper conducts a comprehensive review with the intent to identify common emerging areas of application and common design patterns in the approach to tackling BIM-IoT device integration along with an examination of current limitations and predictions of future research directions. Altogether, 97 papers from 14 AEC related journals and databases in other industry over the last decade were reviewed. Several prevalent domains of application namely Construction Operation and Monitoring, Health & Safety Management, Construction Logistic & Management, and Facility Management were identified. The authors summarized 5 integration methods with description, examples, and discussion. These integration methods are utilizing BIM tools' APIs and relational database, transform BIM data into a relational database using new data schema, create new query language, using semantic web technologies and hybrid approach. Based on the observed limitations, prominent future research directions are suggested, focusing on service-oriented architecture (SOA) patterns and web services-based strategies for BIM and IoT integration, establishing information integration & management standards, solving interoperability issue, and cloud computing.

Fatima Hussain, Rasheed Hussain, Syed Ali Hassan et al.

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

A. Khanna, Sanmeet Kaur

Abstract During recent years, one of the most familiar name scaling new heights and creating a benchmark is Internet of Things (IoT). It is indeed the future of communication that has transformed Things (Objects) of the real world into smarter devices. The functional aspect of IoT is to unite every object of the world in such a manner that humans have the ability to control them via Internet. Furthermore, these objects also provide regular as well as timely updates on their current status to its end user. Although IoT concepts were proposed a couple of years ago, it may not be incorrect to quote that this term has become a benchmark for establishing communication among objects. In context to the present standings of IoT, identification of the most prominent applications in the field of IoT have been highlighted and a comprehensive review has been done specifically in the field of Precision Agriculture. This article evaluates contributions made by various researchers and academicians over the past few years. Furthermore, existing challenges faced while performing agricultural activities have been highlighted along with future research directions to equip novel researchers of this domain to assess the current standings of IoT and to further improve upon them with more inspiring and innovative ideas.

Fotios Zantalis, G. Koulouras, S. Karabetsos et al.

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

Yang Lu, Li D. Xu

As an emerging technology, the Internet of Things (IoT) revolutionized the global network comprising of people, smart devices, intelligent objects, data, and information. The development of IoT is still in its infancy and many related issues need to be solved. IoT is a unified concept of embedding everything. IoT has a great chance to make the world a higher level of accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, how to protect IoT is a challenging task. System security is the foundation for the development of IoT. This article systematically reviews IoT cybersecurity. The key considerations are the protection and integration of heterogeneous smart devices and information communication technologies (ICT). This review provides useful information and insights to researchers and practitioners who are interested in cybersecurity of IoT, including the current research of IoT cybersecurity, IoT cybersecurity architecture and taxonomy, key enabling countermeasures and strategies, major applications in industries, research trends and challenges.

Sebastián García, Agustin Parmisano, María José Erquiaga

Eirini Anthi, Lowri Williams, M. Slowinska et al.

The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the “weakest” link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer intrusion detection system (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behavior of each IoT device connected to the network; 2) identifies malicious packets on the network when an attack is occurring; and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of eight popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories, such as denial of service (DoS), man-in-the-middle (MITM)/spoofing, reconnaissance, and replay. Additionally, the system is also evaluated against four scenarios of multistage attacks with complex chains of events. The performance of the system’s three core functions result in an ${F}$ -measure of: 1) 96.2%; 2) 90.0%; and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.

Junqin Huang, L. Kong, Guihai Chen et al.

Industrial Internet of Things (IIoT) plays an indispensable role for Industry 4.0, where people are committed to implement a general, scalable, and secure IIoT system to be adopted across various industries. However, existing IIoT systems are vulnerable to single point of failure and malicious attacks, which cannot provide stable services. Due to the resilience and security promise of blockchain, the idea of combining blockchain and Internet of Things (IoT) gains considerable interest. However, blockchains are power-intensive and low-throughput, which are not suitable for power-constrained IoT devices. To tackle these challenges, we present a blockchain system with credit-based consensus mechanism for IIoT. We propose a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously. In order to protect sensitive data confidentiality, we design a data authority management method to regulate the access to sensor data. In addition, our system is built based on directed acyclic graph -structured blockchains, which is more efficient than the Satoshi-style blockchain in performance. We implement the system on Raspberry Pi, and conduct a case study for the smart factory. Extensive evaluation and analysis results demonstrate that credit-based PoW mechanism and data access control are secure and efficient in IIoT.

Imran Makhdoom, M. Abolhasan, Haider Abbas et al.

Abstract The underlying technology of Bitcoin is blockchain, which was initially designed for financial value transfer only. Nonetheless, due to its decentralized architecture, fault tolerance and cryptographic security benefits such as pseudonymous identities, data integrity and authentication, researchers and security analysts around the world are focusing on the blockchain to resolve security and privacy issues of IoT. However, presently, not much work has been done to assess blockchain's viability for IoT and the associated challenges. Hence, to arrive at intelligible conclusions, this paper carries out a systematic study of the peculiarities of the IoT environment including its security and performance requirements and progression in blockchain technologies. We have identified the gaps by mapping the security and performance benefits inferred by the blockchain technologies and some of the blockchain-based IoT applications against the IoT requirements. We also discovered some practical issues involved in the integration of IoT devices with the blockchain. In the end, we propose a way forward to resolve some of the significant challenges to the blockchain's adoption in IoT.

S. M. Tahsien, H. Karimipour, P. Spachos

Abstract Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for smart devices and network, IoT is now facing more security challenges than ever before. There are existing security measures that can be applied to protect IoT. However, traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness. Thus, a strong-dynamically enhanced and up to date security system is required for next-generation IoT system. A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT. In order to detect attacks and identify abnormal behaviors of smart devices and networks, ML is being utilized as a powerful technology to fulfill this purpose. In this survey paper, the architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks. Moreover, ML-based potential solutions for IoT security has been presented and future challenges are discussed.

Vishal A. Thakor, M. Razzaque, Muhammad R. A. Khandaker

IoT is becoming more common and popular due to its wide range of applications in various domains. They collect data from the real environment and transfer it over the networks. There are many challenges while deploying IoT in a real-world, varying from tiny sensors to servers. Security is considered as the number one challenge in IoT deployments, as most of the IoT devices are physically accessible in the real world and many of them are limited in resources (such as energy, memory, processing power and even physical space). In this paper, we are focusing on these resource-constrained IoT devices (such as RFID tags, sensors, smart cards, etc.) as securing them in such circumstances is a challenging task. The communication from such devices can be secured by a mean of lightweight cryptography, a lighter version of cryptography. More than fifty lightweight cryptography (plain encryption) algorithms are available in the market with a focus on a specific application(s), and another 57 algorithms have been submitted by the researchers to the NIST competition recently. To provide a holistic view of the area, in this paper, we have compared the existing algorithms in terms of implementation cost, hardware and software performances and attack resistance properties. Also, we have discussed the demand and a direction for new research in the area of lightweight cryptography to optimize balance amongst cost, performance and security.

Xiaokang Zhou, Wei Liang, Weimin Li et al.

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

P.M.D. Raj Vincent, K. Srinivasan, G. Reina et al.

The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems.

Sajjad Dadkhah, Hassan Mahdikhani, Priscilla Kyei Danso et al.

The Internet of Things (IoT) is an emerging technology that enables the development of low-cost and energy-efficient IoT devices across various solutions from smart cities to healthcare domains. With such a complex and heterogeneous instance of IoT devices and their applications, numerous challenges arise in both device management and security concerns. Thus, it is essential to develop intelligent IoT identification/profiling and intrusion detection components that are tailored to IoT applications. Such systems require a realistic and multidimensional reference IoT dataset for training and evaluation. Device identification/profiling ensures the authenticity of the devices attached to the IoT network and environment which can be achieved by fingerprinting a device. Since fingerprinting is mostly examined by device network flows and device local attributes, we have proposed this study to intelligently recognize machine-to-machine communication and identify each device properly. In this paper, we analyzed the behaviour of 60 IoT devices during experiments conducted in our lab setup at the Canadian Institute for Cybersecurity (CIC). Our IoT devices include WiFi, ZigBee, and Z-Wave devices. We collected data from each device in four stages: powered on, idle, active, and interactions. Besides these stages, different scenario experiments were conducted using a microcosm of devices to simulate the network activity of a smart home. Additionally, we have generated two attack datasets, namely flood denial-of-service attack and RTSP brute-force attack. Lastly, we implement an extensive case study on the transferability of the RF classifier and train our model with the dataset from our lab, transfer the model to the dataset from a different lab and test the trained model on their dataset. This paper’s dataset materials are available on the CIC dataset page under the CIC IoT dataset 20221.

Quy Vu Khanh, Nam Vi Hoai, Linh Dao Manh et al.

Communication technologies are developing very rapidly and achieving many breakthrough results. The advent of 5th generation mobile communication networks, the so-called 5G, has become one of the most exciting and challenging topics in the wireless study area. The power of 5G enables it to connect to hundreds of billions of devices with extreme-high throughput and extreme-low latency. The 5G realizing a true digital society where everything can be connected via the Internet, well known as the Internet of Things (IoT). IoT is a technology of technologies where humans, devices, software, solutions, and platforms can connect based on the Internet. The formation of IoT technology leads to the birth of a series of applications and solutions serving humanity, such as smart cities, smart agriculture, smart retail, intelligent transportation systems, and IoT ecosystems. Although IoT is considered a revolution in the evolution of the Internet, it still faces a series of challenges such as saving energy, security, performance, and QoS support. In this study, we provide a vision of the Internet of Things that will be the main force driving the comprehensive digital revolution in the future. The communication technologies in the IoT system are discussed comprehensively and in detail. Furthermore, we also indicated indepth challenges of existing common communication technologies in IoT systems and future research directions of IoT. We hope the results of this work can provide a vital guide for future studies on communication technologies for IoT in 5G.