Hasil untuk "iot"

Seyoung Huh, Sangrae Cho, Soohyung Kim

Naser Hossein Motlagh, Miloud Bagaa, T. Taleb

Unmanned aerial vehicles are gaining a lot of popularity among an ever growing community of amateurs as well as service providers. Emerging technologies, such as LTE 4G/5G networks and mobile edge computing, will widen the use case scenarios of UAVs. In this article, we discuss the potential of UAVs, equipped with IoT devices, in delivering IoT services from great heights. A high-level view of a UAV-based integrative IoT platform for the delivery of IoT services from large height, along with the overall system orchestrator, is presented in this article. As an envisioned use case of the platform, the article demonstrates how UAVs can be used for crowd surveillance based on face recognition. To evaluate the use case, we study the offloading of video data processing to a MEC node compared to the local processing of video data onboard UAVs. For this, we developed a testbed consisting of a local processing node and one MEC node. To perform face recognition, the Local Binary Pattern Histogram method from the Open Source Computer Vision is used. The obtained results demonstrate the efficiency of the MEC-based offloading approach in saving the scarce energy of UAVs, reducing the processing time of recognition, and promptly detecting suspicious persons.

N. Ahmed, D. De, I. Hussain

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

R. Krishnamurthi, Adarsh Kumar, Dhanalekshmi Gopinathan et al.

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

Ankit Thakkar, Ritika Lohiya

F. Zhu, Yisheng Lv, Yuan-yuan Chen et al.

IoT-driven intelligent transportation systems (ITS) have great potential and capacity to make transportation systems efficient, safe, smart, reliable, and sustainable. The IoT provides the access and driving forces of seamlessly integrating transportation systems from the physical world to the virtual counterparts in the cyber world. In this paper, we present visions and works on integrating the artificial intelligent transportation systems and the real intelligent transportation systems to create and enhance “intelligence” of IoT-enabled ITS. With the increasing ubiquitous and deep sensing capacity of IoT-enabled ITS, we can quickly create artificial transportation systems equivalent to physical transportation systems in computers, and thus have parallel intelligent transportation systems, i.e. the real intelligent transportation systems and artificial intelligent transportation systems. The evolution process of transportation system is studied in the view of the parallel world. We can use a large number of long-term iterative simulation to predict and analyze the expected results of operations. Thus, truly effective and smart ITS can be planned, designed, built, operated and used. The foundation of the parallel intelligent transportation systems is based on the ACP theory, which is composed of artificial societies, computational experiments, and parallel execution. We also present some case studies to demonstrate the effectiveness of parallel transportation systems.

Yash Sharma, Priyankar Datta

In old Days Farmers was very interested to figure out the fertility of soil and impact on feeling to grow which to quite yield. They brought some thoughts which leads to detect humidity level water level climatic condition with the help of internet of things (IOT) which is redesigning the farming sector through the wide range of strategies, as an example accuracy furthermore as practical farming to house challenging within farming sector. The application of IOT helps in gathering of information which is quietly helpful in farming sector like changing in climatic condition fertility of soil , amount of water needed for crops , bug location interruption of creature to the sphere, horticulture, .IOT helps farmers to proper utilize the technology together with the information with his residence from wherever and at whatever point. Different types of sensors are used for the inspection and control of the crop which are very significant under their precise output and use. cameras are used for remotely monitoring the field. IOT technology helps in best crop management, increase in productivity and reduce the trouble of farmer as compared to normal farming.

Safa Ben Atitallah, Maha Driss, Wadii Boulila et al.

Abstract The rapid growth of urban populations worldwide imposes new challenges on citizens’ daily lives, including environmental pollution, public security, road congestion, etc. New technologies have been developed to manage this rapid growth by developing smarter cities. Integrating the Internet of Things (IoT) in citizens’ lives enables the innovation of new intelligent services and applications that serve sectors around the city, including healthcare, surveillance, agriculture, etc. IoT devices and sensors generate large amounts of data that can be analyzed to gain valuable information and insights that help to enhance citizens’ quality of life. Deep Learning (DL), a new area of Artificial Intelligence (AI), has recently demonstrated the potential for increasing the efficiency and performance of IoT big data analytics. In this survey, we provide a review of the literature regarding the use of IoT and DL to develop smart cities. We begin by defining the IoT and listing the characteristics of IoT-generated big data. Then, we present the different computing infrastructures used for IoT big data analytics, which include cloud, fog, and edge computing. After that, we survey popular DL models and review the recent research that employs both IoT and DL to develop smart applications and services for smart cities. Finally, we outline the current challenges and issues faced during the development of smart city services.

Tejasvi Alladi, V. Chamola, B. Sikdar et al.

As consumer Internet of Things (IoT) devices become increasingly pervasive in our society, there is a need to understand the underpinning security risks. Therefore, in this article, we describe the common attacks faced by consumer IoT devices and suggest potential mitigation strategies. We hope that the findings presented in this article will inform the future design of IoT devices.

Wajid Rafique, Lianyong Qi, Ibrar Yaqoob et al.

Millions of sensors continuously produce and transmit data to control real-world infrastructures using complex networks in the Internet of Things (IoT). However, IoT devices are limited in computational power, including storage, processing, and communication resources, to effectively perform compute-intensive tasks locally. Edge computing resolves the resource limitation problems by bringing computation closer to the edge of IoT devices. Providing distributed edge nodes across the network reduces the stress of centralized computation and overcomes latency challenges in the IoT. Therefore, edge computing presents low-cost solutions for compute-intensive tasks. Software-Defined Networking (SDN) enables effective network management by presenting a global perspective of the network. While SDN was not explicitly developed for IoT challenges, it can, however, provide impetus to solve the complexity issues and help in efficient IoT service orchestration. The current IoT paradigm of massive data generation, complex infrastructures, security vulnerabilities, and requirements from the newly developed technologies make IoT realization a challenging issue. In this research, we provide an extensive survey on SDN and the edge computing ecosystem to solve the challenge of complex IoT management. We present the latest research on Software-Defined Internet of Things orchestration using Edge (SDIoT-Edge) and highlight key requirements and standardization efforts in integrating these diverse architectures. An extensive discussion on different case studies using SDIoT-Edge computing is presented to envision the underlying concept. Furthermore, we classify state-of-the-art research in the SDIoT-Edge ecosystem based on multiple performance parameters. We comprehensively present security and privacy vulnerabilities in the SDIoT-Edge computing and provide detailed taxonomies of multiple attack possibilities in this paradigm. We highlight the lessons learned based on our findings at the end of each section. Finally, we discuss critical insights toward current research issues, challenges, and further research directions to efficiently provide IoT services in the SDIoT-Edge paradigm.

Mohamed Abdel-Basset, Gunasekaran Manogaran, Abduallah Gamal et al.

Internet of Things (IoT) has gain the importance with the growing applications in the fields of ubiquitous and context-aware computing. In IoT, anything can be a portion of it, whether it is unintelligent objects or sensor nodes; thus extremely different kinds of services can be developed. In this regard, data storage, resource management, service creation and discovery, and resource and power management would facilitate advanced mechanism and much better infrastructure. Cloud computing and fog computing play an important role when the quantity of data and information IoT are critical. Thus, it would not be potential for standalone strength forced IoT to handle. Cloud of things is an integration of IoT with cloud computing or fog computing which can aid to realize the objectives of evolving IoT and future Internet. Fog computing is an expansion to the notion of cloud computing to the network brim, making it suitable for IoT and other implementations that need real-time and fundamental interactions. Regardless of many virtually and services unlimited resources presented by cloud-like intelligent building monitoring and others, it yet countenances various difficulties when interfering many smart things in human’s life. Mobility, response time, and location consciousness are the most prominent problems. Fog and mobile edge computing have been established, to get rid of these difficulties of cloud computing. In this article, we suggest a novel framework based on computer propped diagnosis and IoT to detect and observe type-2 diabetes patients. The recommended healthcare system aims to obtain a better accuracy of diagnosis with mysterious data. The overall experimental results indicate the validity and robustness of our proposed algorithms.

Hamid Tahaei, Firdaus Afifi, A. Asemi et al.

Abstract With the proliferation of the Internet of Things (IoT), the integration and communication of various objects have become a prevalent practice. The huge growth of IoT devices and different characteristics in the IoT traffic patterns have brought attention to traffic classification methods to address various raised issues in IoT applications. While network traffic classification has been well discussed in a number of surveys and review papers, it is still immature in IoT due to the differences in traffic characteristics in IoT and Non-IoT devices. This survey looks at the emerging trends of network traffic classification in IoT and the utilization of traffic classification in its applications. It also compares the legacy of traffic classification methods and presents an overview of traditional models. This paper extends the discussion with a taxonomy of the current network traffic classification within the IoT context. We then expose commercial and real-world use cases of the IoT traffic classification and finally outline open research issues and challenges in this domain.

Kewei Sha, T. Yang, Wei Wei et al.

Abstract Pervasive IoT applications enable us to perceive, analyze, control, and optimize the traditional physical systems. Recently, security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk. Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications. As an extension of the cloud, the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications. Although there are some research efforts in this area, edge-based security designs for IoT applications are still in its infancy. This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs. We first present an edge-centric IoT architecture. Then, we extensively review the edge-based IoT security research efforts in the context of security architecture designs, firewalls, intrusion detection systems, authentication and authorization protocols, and privacy-preserving mechanisms. Finally, we propose our insight into future research directions and open research issues.

Dr. S. Smys, Dr. Abul Basar, Dr. Haoxiang Wang

Internet of things (IoT) is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. Since IoT drive the world and changes people lives with its wide range of services and applications. However, IoT provides numerous services through applications, it faces severe security issues and vulnerable to attacks such as sinkhole attack, eaves dropping, denial of service attacks, etc., Intrusion detection system is used to detect such attacks when the network security is breached. This research work proposed an intrusion detection system for IoT network and detect different types of attacks based on hybrid convolutional neural network model. Proposed model is suitable for wide range of IoT applications. Proposed research work is validated and compared with conventional machine learning and deep learning model. Experimental result demonstrate that proposed hybrid model is more sensitive to attacks in the IoT network.

C. Iwendi, Praveen Kumar Reddy Maddikunta, T. Gadekallu et al.

Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state‐of‐the‐art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

T. Fernández-Caramés

Although quantum computing is still in its nascent age, its evolution threatens the most popular public-key encryption systems. Such systems are essential for today’s Internet security due to their ability for solving the key distribution problem and for providing high security in insecure communications channels that allow for accessing websites or for exchanging e-mails, financial transactions, digitally signed documents, military communications or medical data. Cryptosystems like Rivest–Shamir–Adleman (RSA), elliptic curve cryptography (ECC) or Diffie–Hellman have spread worldwide and are part of diverse key Internet standards like Transport Layer Security (TLS), which are used both by traditional computers and Internet of Things (IoT) devices. It is especially difficult to provide high security to IoT devices, mainly because many of them rely on batteries and are resource constrained in terms of computational power and memory, which implies that specific energy-efficient and lightweight algorithms need to be designed and implemented for them. These restrictions become relevant challenges when implementing cryptosystems that involve intensive mathematical operations and demand substantial computational resources, which are often required in applications where data privacy has to be preserved for the long term, like IoT applications for defense, mission-critical scenarios or smart healthcare. Quantum computing threatens such a long-term IoT device security and researchers are currently developing solutions to mitigate such a threat. This article provides a survey on what can be called post-quantum IoT systems (IoT systems protected from the currently known quantum computing attacks): the main post-quantum cryptosystems and initiatives are reviewed, the most relevant IoT architectures and challenges are analyzed, and the expected future trends are indicated. Thus, this article is aimed at providing a wide view of post-quantum IoT security and give useful guidelines to the future post-quantum IoT developers.

Han Liu, Dezhi Han, Dun Li

IoT devices have some special characteristics, such as mobility, limited performance, and distributed deployment, which makes it difficult for traditional centralized access control methods to support access control in current large-scale IoT environment. To address these challenges, this paper proposes an access control system in IoT named fabric-iot, which is based on Hyperledger Fabric blockchain framework and attributed based access control (ABAC). The system contains three kinds of smart contracts, which are Device Contract (DC), Policy Contract (PC), and Access Contract (AC). DC provides a method to store the URL of resource data produced by devices, and a method to query it. PC provides functions to manage ABAC policies for admin users. AC is the core program to implement an access control method for normal users. Combined with ABAC and blockchain technology, fabric-iot can provide decentralized, fine-grained and dynamic access control management in IoT. To verify the performance of this system, two groups of simulation experiments are designed. The results show that fabric-iot can maintain high throughput in large-scale request environment and reach consensus efficiently in a distributed system to ensure data consistency.

M. Shafiq, Zhihong Tian, A. Bashir et al.

© 2020 Elsevier Ltd Machine Learning (ML) plays very significant role in the Internet of Things (IoT) cybersecurity for malicious and intrusion traffic identification. In other words, ML algorithms are widely applied for IoT traffic identification in IoT risk management. However, due to inaccurate feature selection, ML techniques misclassify a number of malicious traffic in smart IoT network for secured smart applications. To address the problem, it is very important to select features set that carry enough information for accurate smart IoT anomaly and intrusion traffic identification. In this paper, we firstly applied bijective soft set for effective feature selection to select effective features, and then we proposed a novel CorrACC feature selection metric approach. Afterward, we designed and developed a new feature selection algorithm named Corracc based on CorrACC, which is based on wrapper technique to filter the features and select effective feature for a particular ML classifier by using ACC metric. For the evaluation our proposed approaches, we used four different ML classifiers on the BoT-IoT dataset. Experimental results obtained by our algorithms are promising and can achieve more than 95% accuracy.

Faisal Bashir Hussain, Syed Ghazanfar Abbas, G. Shah et al.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.

Yaozhi Chen, Yan Guo, Yun Gao et al.

Abstract The extensive use of Internet of Things (IoT) technology produces unprecedented connectivity and cyberattack exposure. Recent attack detection tools have poor accuracy, efficiency, and adaptability in the case of IoT systems with scarce resources. To counter these challenges, the current study proposes a hybrid model incorporating an efficient convolutional neural network (CNN) and an enhanced pelican optimization algorithm (EPOA) to detect IoT network attacks. Inspired by how pelicans hunt, EPOA maximizes CNN’s hyperparameters and feature selection for higher accuracy and efficiency in computation. Experimentation with the Bot-IoT, CICIDS2018, and NSL-KDD datasets validates the performance of the proposed EPOA-based deep learning method for cyberattack detection. The model achieves 98.1% accuracy on Bot-IoT, 97.4% on NSL-KDD, and 97.9% on CICIDS2018, outperforming conventional approaches like long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and recurrent neural network (RNN). The model also produces a minimum loss value of 0.17, outperforming other approaches with the shortest execution duration. With its efficient design and high detection performance, the proposed approach is highly suitable for continuous IoT cyberattack detection in practical deployment scenarios.