Hasil untuk "iot"

Faezeh Dehghan Tarzjani, Mostafa Salehi

The Internet of Things (IoT) is applied in various fields, and the number of physical devices connected to the IoT is increasingly growing. There are significant challenges to the IoT's growth and development, mainly due to the centralized nature and large-scale IoT networks. The emphasis on the decentralization of IoT's architecture can overcome challenges to IoT's capabilities. A promising decentralized platform for IoT is blockchain. Owing to IoT devices' limited resources, traditional consensus algorithms such as PoW and PoS in the blockchain are computationally expensive. Therefore, the PoA consensus algorithm is proposed in the blockchain consensus network for IoT. The PoA selects the validator as Turn-based selection (TBS) that needs optimization and faces system reliability, energy consumption, latency, and low scalability. We propose an efficient, lightweight blockchain for decentralizing IoT architecture by using virtualization and clustering to increase productivity and scalability to address these issues. We also introduce a novel PoA based on the Weight-Based-Selection (WBS) method for validators to validate transactions and add them to the blockchain. By simulation, we evaluated the performance of our proposed WBS method as opposed to TBS. The results show reduced energy consumption, and response time, and increased throughput.

Chinthamu Narender, Ashish, P Mathiyalagan et al.

Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining methodologies. However existing works mainly focus on conventional ML models without provide systems design real world applications systems and do not include any dimension related to network security dimension, cost and benefit analyzing dimension utility dimension and light weight A.I model for edge computing. In this paper, we contribute with a systematic literature review of state-of-the-art data-mining techniques for predictive maintenance with emphasis on hybrid AI frameworks, deep learning and online data processing approaches, as well as, privacy-aware methods. We contribute by providing a number of real-world industrial use case which differentiate us from previous researched; we discuss details of cybersecurity issues in IoT-enabled PdM; and we discuss use of XAI (Explainable AI) to build interpretable models. Moreover, this survey introduces marginal AI applications in edge computing, predictive maintenance frameworks with scalability, and AI-powered anomaly identification for enhancing predictions in industrial-scale production. It also covers a review of predictive maintenance methodologies in addition to a future research agenda, highlighting emerging patterns such as digital twins, Industry 5.0, and reinforcement learning in predictive maintenance. The current study aims to bridge critical gaps in the literature and support valuable direction for researchers, industry practitioners and policymakers for effective predictive maintenance strategies and task performance.

Jian Liu, Yuxin Chen, Hao Ding

The rapid proliferation of Internet of Things (IoT) devices has dramatically increased the demand for efficient data processing, making caching a critical solution for achieving high-performance and cost-effective storage in edge environments. However, small-scale edge devices often suffer from severe resource constraints. Furthermore, there is a scarcity of academic analyses addressing how various caching algorithms perform in such environments. To bridge this knowledge gap, we have proposed a cache simulation framework, CacheSim, as an open-source software solution for caching evaluation. CacheSim provides comprehensive metrics, including hit rate, performance, CPU usage, and power consumption, offering researchers valuable insights into the efficiency of different caching strategies. Through this platform, we aim to stimulate innovation in caching algorithms, encouraging the development of techniques optimized for the unique challenges posed by edge devices.

Abdul Manan Sheikh, Md. Rafiqul Islam, Mohamed Hadi Habaebi et al.

Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security threats include encryption, secure authentication, regular updates, tamper-resistant hardware, and lightweight security protocols. Physical Unclonable Functions (PUFs) are digital fingerprints for device authentication that enhance interconnected devices’ security due to their cryptographic characteristics. PUFs produce output responses against challenge inputs based on the physical structure and intrinsic manufacturing variations of an integrated circuit (IC). These challenge-response pairs (CRPs) enable secure and reliable device authentication. Our work implements the Arbiter PUF (APUF) on Altera Cyclone IV FPGAs installed on the ALINX AX4010 board. The proposed APUF has achieved performance metrics of 49.28% uniqueness, 38.6% uniformity, and 89.19% reliability. The robustness of the proposed APUF against machine learning (ML)-based modeling attacks is tested using supervised Support Vector Machines (SVMs), logistic regression (LR), and an ensemble of gradient boosting (GB) models. These ML models were trained over more than 19K CRPs, achieving prediction accuracies of 61.1%, 63.5%, and 63%, respectively, thus cementing the resiliency of the device against modeling attacks. However, the proposed APUF exhibited its vulnerability to Multi-Layer Perceptron (MLP) and random forest (RF) modeling attacks, with 95.4% and 95.9% prediction accuracies, gaining successful authentication. APUFs are well-suited for device authentication due to their lightweight design and can produce a vast number of challenge-response pairs (CRPs), even in environments with limited resources. Our findings confirm that our approach effectively resists widely recognized attack methods to model PUFs.

Govardhan Rao Kambhampati Venkata, Anuradha Devi Tellapati, Anusha Kunduru et al.

Electrical energy is the most dependable form of energy. The advancement of technology demands substantial energy use. Conventional energy sources are producing pollution, and fossil fuels are diminishing daily, so paving the way for renewable energy sources. Wind energy is the most reliant renewable energy source. The maintenance of wind turbines is intricate, continuous monitoring is challenging due to their elevated positions, and they are situated in rural locations. A dependable condition monitoring system is crucial for turbines working on wind. to reduce downtime and enhance output. The objective of this project is to monitor the parameters of turbine working on wind and enhance early defect identification. Sensors are employed to assess the state of the wind turbine. The utilized sensors are a temperature sensor, a vibration sensor, and a voltage sensor. Should any sensor provide an anomalous value, the data is transmitted to the IoT cloud within a matter of seconds. This project utilizes an Arduino UNO and a Wi-Fi module. The Arduino UNO gathers sensor data from several wind turbine sensors, and the Wi-Fi module transmits this information to an IoT cloud application, such as Telegram, already loaded on our mobile devices. The operation of the kit and the performance evaluation have been conducted on the suggested system.

Tianhui Ma, Yongle Duan, Wenshuo Duan et al.

Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes an intelligent real-time monitoring and early warning framework that integrates deep learning, MS monitoring, and Internet of Things (IoT) technologies. The methodology includes db4 wavelet-based signal denoising for preprocessing, an improved Gaussian Mixture Model for automated waveform recognition, a U-Net-based neural network for P-wave arrival picking, and a particle swarm optimization algorithm with Lagrange multipliers for event localization. Furthermore, a cloud-based platform is developed to support automated data processing, three-dimensional visualization, real-time warning dissemination, and multi-user access. Field application in a deep-buried railway tunnel in Southwest China demonstrates the system’s effectiveness, achieving an early warning accuracy of 87.56% during 767 days of continuous monitoring. Comparative verification further indicates that the fine-tuned neural network outperforms manual approaches in waveform picking and event identification. Overall, the proposed system provides a robust, scalable, and intelligent solution for rockburst hazard mitigation in deep underground construction.

Vitou That, Kimchheang Chhea, Jung-Ryun Lee

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the Whale Optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian Optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

Lyberius Ennio F. Taruc, Arvin R. De La Cruz

The main objective of this research paper is to analyze the available use cases of Narrowband-IoT and IoT in universities, campuses, and educational institutions. A literature review was conducted using multiple databases such as IEEE Xplore, ACM Digital Library, and Scopus. The study explores the benefits of IoT adoption in higher education. Various use cases of NB-IoT in educational institutions were analyzed, including smart campus management, asset tracking, monitoring, and safety and security systems. Of the six use cases assessed, three focused on the deployment of IoT Things, while three focused on NB-IoT Connectivity. The research paper concludes that NB-IoT technology has significant potential to enhance various aspects of educational institutions, from smart campus management to improving safety and security systems. The study recommends further exploration and implementation of NB-IoT technology in educational settings to improve efficiency, security, and overall campus management. The research highlights the potential applications of NB-IoT in universities and educational institutions, paving the way for future studies in this area. The social implications of this research could involve enhancing the overall learning experience for students, improving campus safety, and promoting technological advancements in educational settings. Keywords: narrowband-IoT, Internet-of-Things, smart campus, smart institutions

Fatemeh Arvaneh, Faraneh Zarafshan, Abbas Karimi

This study investigates the application of heuristic and meta-heuristic algorithms to address resource allocation challenges in Internet of Things (IoT) applications within fog computing environments. The primary advantage of these algorithms lies in their ability to optimize functions without the need for stringent restrictions, allowing adaptability to various linear, nonlinear, continuous, or discrete problems. Through the implementation and comparison of the Cheetah algorithm, Gray Wolf algorithm, Particle Swarm-Gravitational Search algorithm, and Gray Wolf-Cuckoo Search algorithm using MATLAB software in a simulation environment, the study aims to minimize criterion function and total time and energy consumption for IoT applications. Preliminary results indicate that the statistical average performance of the Cheetah algorithm surpasses that of the Gray Wolf algorithm, the combined Particle Swarm-Gravitational Search algorithm, and the Gray Wolf-Cuckoo Search algorithm. This suggests the efficacy of the Cheetah algorithm in IoT resource allocation optimization within fog computing environments. The study provides insights into the comparative performance of these algorithms, laying the foundation for further exploration into enhancing resource allocation strategies in the dynamic and resource-constrained IoT and fog computing landscapes.

Геннадій Осокін

В статті досліджується, як цифровізація впливає на структуру та функціонування ланцюгів постачання, а також на трансформацію традиційних бізнес-моделей. Розглянуто вплив цифрових технологій на сучасні бізнес-процеси в системі управління ланцюгами постачання. Досліджено, як цифровізація, зокрема впровадження інтернету речей (IoT), великих даних та штучного інтелекту, кардинально змінює спосіб взаємодії компаній зі своїми постачальниками та клієнтами. Доведено, що цифрові інструменти забезпечують підвищення ефективності, прозорості та гнучкості ланцюгів постачання, сприяючи адаптації бізнес-моделей до швидко змінюваних ринкових умов; проаналізовано, які саме зміни торкаються елементів діючих бізнес-моделей. Окрему увагу приділено викликам та ризикам, пов'язаним з інтеграцією цифрових технологій в ланцюги постачання та запропоновано рекомендації для подолання цих перешкод.

Naijing Su, Shifeng Huang, Chuanjun Su

The transition to smart manufacturing introduces heightened complexity in regard to the machinery and equipment used within modern collaborative manufacturing landscapes, presenting significant risks associated with equipment failures. The core ambition of smart manufacturing is to elevate automation through the integration of state-of-the-art technologies, including artificial intelligence (AI), the Internet of Things (IoT), machine-to-machine (M2M) communication, cloud technology, and expansive big data analytics. This technological evolution underscores the necessity for advanced predictive maintenance strategies that proactively detect equipment anomalies before they escalate into costly downtime. Addressing this need, our research presents an end-to-end platform that merges the organizational capabilities of data warehousing with the computational efficiency of Apache Spark. This system adeptly manages voluminous time-series sensor data, leverages big data analytics for the seamless creation of machine learning models, and utilizes an Apache Spark-powered engine for the instantaneous processing of streaming data for fault detection. This comprehensive platform exemplifies a significant leap forward in smart manufacturing, offering a proactive maintenance model that enhances operational reliability and sustainability in the digital manufacturing era.

Khaled Alhassoon, Yaaqoub Malallah, Fahad N. Alsunaydih et al.

The design of the aperture-fed annular ring (AFAR) microstrip antenna is presented. This proposed design will ease the fabrication and usability of the 3D-printed and solderless 2D materials. This antenna consists of three layers: the patch, the slot within the ground plane as the power transfer medium, and the microstrip line as the feeding. The parameters of the proposed design are investigated using the finite element method FEM to achieve the 50 Ω impedance with the maximum front-to-back ratio of the radiation pattern. This study was performed based on four steps, each investigating one parameter at a time. These parameters were evaluated based on an initial design and prototype. The optimized design of 3D AFAR attained S₁₁ around 17 dB with a front-to-back ratio of more than 30 dB and a gain of around 3.3 dBi. This design eases the process of using a manufacturing process that involves 3D-printed and 2D metallic materials for antenna applications.

Mangirdas Morkūnas, Yufei Wang, Jinzhao Wei

This paper discusses how integrating renewable energy, AI, and IoT becomes important in promoting climate-smart agriculture. Due to the changing climate, rise in energy costs, and ensuring food security, agriculture faces unprecedented challenges; therefore, development toward innovative technologies is emerging for its sustainability and efficiency. This review synthesizes existing literature systematically to identify how AI and IoT could optimize resource management, increase productivity, and reduce greenhouse gas emissions within an agricultural context. Key findings pointed to the importance of managing resources sustainably, the scalability of technologies, and, finally, policy interventions to ensure technology adoption. The paper further outlines trends in the global adoption of renewable energy and smart agriculture solutions, indicating areas of commonality and difference and emphasizing the need for focused policies and capacity-building initiatives that will help, particularly in the developing world, the benefits of such innovations. Eventually, this research covers some gaps in understanding how AI, IoT, and renewable energy could jointly contribute to driving towards a greener and more resilient agriculture sector.

Kun Wang, Jianwei Shi, Wenxuan Lai et al.

Abstract Integrated circuit anti-counterfeiting based on optical physical unclonable functions (PUFs) plays a crucial role in guaranteeing secure identification and authentication for Internet of Things (IoT) devices. While considerable efforts have been devoted to exploring optical PUFs, two critical challenges remain: incompatibility with the complementary metal-oxide-semiconductor (CMOS) technology and limited information entropy. Here, we demonstrate all-silicon multidimensionally-encoded optical PUFs fabricated by integrating silicon (Si) metasurface and erbium-doped Si quantum dots (Er-Si QDs) with a CMOS-compatible procedure. Five in-situ optical responses have been manifested within a single pixel, rendering an ultrahigh information entropy of 2.32 bits/pixel. The position-dependent optical responses originate from the position-dependent radiation field and Purcell effect. Our evaluation highlights their potential in IoT security through advanced metrics like bit uniformity, similarity, intra- and inter-Hamming distance, false-acceptance and rejection rates, and encoding capacity. We finally demonstrate the implementation of efficient lightweight mutual authentication protocols for IoT applications by using the all-Si multidimensionally-encoded optical PUFs.

Sashidhar Jakkamsetti, Youngil Kim, Gene Tsudik

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation. This prompts, respectively, privacy and security/safety issues. The dangers of hidden IoT devices have been recognized and prior research suggested some means of mitigation, mostly based on traffic analysis or using specialized hardware to uncover devices. While such approaches are partially effective, there is currently no comprehensive approach to IoT device transparency. Prompted in part by recent privacy regulations (GDPR and CCPA), this paper motivates and constructs a privacy-agile Root-of-Trust architecture for IoT devices, called PAISA: Privacy-Agile IoT Sensing and Actuation. It guarantees timely and secure announcements about IoT devices' presence and their capabilities. PAISA has two components: one on the IoT device that guarantees periodic announcements of its presence even if all device software is compromised, and the other that runs on the user device, which captures and processes announcements. Notably, PAISA requires no hardware modifications; it uses a popular off-the-shelf Trusted Execution Environment (TEE) -- ARM TrustZone. This work also comprises a fully functional (open-sourced) prototype implementation of PAISA, which includes: an IoT device that makes announcements via IEEE 802.11 WiFi beacons and an Android smartphone-based app that captures and processes announcements. Both security and performance of PAISA design and prototype are discussed.

Babakhouya Ayoub, Naji Abdelwahab, Daaif Abdelaziz et al.

Agriculture plays a crucial role in our existence by supplying food, raw materials, and employment opportunities. In Morocco, it serves as the backbone of the economy, employing 40% of the workforce and contributing approximately 13% to the country's GDP [1]. IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in the agri-food industry. The primary focus of this paper is to assess the diverse utilization of Artificial Intelligence in agriculture, specifically in tasks like irrigation, weeding, and spraying. These applications employ sensors and integrated systems in robots and drones, effectively reducing water and chemical usage, preserving soil fertility, optimizing labor, and enhancing productivity and quality. The research identifies the most common AI strategies used in the industry. Furthermore, we conducted an analysis of significant trends and provided researchers and practitioners with valuable insights for future research endeavors in addition to challenges hindering AgriTech applications in Moroccan farms.

Mehdi Rouissat, Mohammed Belkheir, Ibrahim S. Alsukayti et al.

Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels.

Phisca Aditya Rosyady, Muhammad Andika Agustian

Ikan mas koki sangat populer dan diminati untuk dipajang di dalam kolam kaca atau akuarium karena ikan tersebut memiliki warna yang cantik dan menarik. Tetapi banyak pembudidaya ikan hias yang kesulitan dalam memaksimalkan hasil budidaya bahkan sampai menimbulkan kerugian yang cukup besar.  Pada saat ini para pembudidaya masih melakukan pemantauan dan mengontrol kolam secara langsung. Penelitian ini melakukan monitoring serta kontrol otomatis kadar keasaman (pH) dan suhu untuk perangkat yang dapat diakses secara online dengan menggunakan jaringan wifi supaya pengguna dapat dengan mudah untuk melakukan monitoring serta kontrol dari jarak jauh. Dengan monitoring air kolam ikan menggunakan sensor suhu DS18B20 dan Module 4502C dengan sensor pH electrode, untuk kontrol menggunakan 2 solenoid valve (asam dan basa), water heater dan  fan cooler. Monitoring dan kontrol pH dan suhu dilakukan menggunakan aplikasi Blynk dengan program ada pada ESP32 serta koneksi jaringan Wi-Fi sebagai penghubung. Hasil penelitian dengan Alat Sensor DS18B20 adalah 0,631% hampir tidak ada kesalahan karena penempatan sensor yang tepat dan berdampingan dengan suhu digital dan pada sensor diberikan resistor 4,7 kOhm sehingga teganggan yang masuk pada sensor DS18B20 menjadi stabil. Pengamatan pH Module 4502C 4,128% hal ini didaptkan suatu kesalahan karena memang teganggan yang masuk pada Module  4502C kurang stabil dikarenakan jalur jumper yang dilewati arus dari Module 4502C  mempengaruhi tegangan yang masuk ke sensor pH electrode. Pengamatan Water Heater dengan hasil suhu DS18B20 selama 5 menit 1,939°C dan suhu digital 1,950°C. pengamatan Fan Cooler dengan hasil suhu selama 10 menit 3,039°C dan suhu digital 3,097°C. Solenoid Valve asam dan basa hasil rata-rata dari keluaran Solenoid Valve selama 6 detik sama dengan 1mil air asam dan basa yang keluar dengan pengukuran pH Electrode yaitu  2,907 dan PH Digital yaitu 2,840.

Ashu Taneja, Adi Alhudhaif, Shtwai Alsubai et al.

The diverse Internet-of-things (IoT) applications involve massive machine type communication (mMTC) with large number of communicating nodes. The energy and resource overhead owing to shorter battery lives and limited network resources are the main challenges of mMTC in IoT. To support this massive random access and to overcome these challenges, future wireless networks are envisioned with collision resolution capabilities, reduced latency and ultra-high reliability. This paper presents a novel scheme for 6G assisted massive machine type communication (mMTC) with collision resolution capabilities and reduced latency. A cell-free network model is proposed in which the communication of mMTC devices is assisted through access points (APs) cooperation. The performance of proposed network is evaluated for achieved signal-to-noise ratio (SNR) and accuracy of node detection for different node locations, fading parameters and cell-areas. With increase in cell area and shadow fading, the SNR achieved by active nodes decreases. Further, an algorithm is proposed in the paper that makes AP clusters for serving the communicating nodes. The tendency of network for successful node detection is determined for different cluster sizes with different activation probabilities. In the end, the proposed algorithm is compared with two other schemes, namely, random clustering scheme and nearest-neighbour clustering scheme. It is found that the proposed approach achieves best performance in the detection of active communicating nodes in the system model with 9.09% improvement as compared to random scheme and 1.1% as compared to nearest-neighbour scheme.

Jesús-Eduardo Consuegra-Fontalvo, Jair Calderón-Velaides, Gabriel-Elías Chanchí-Golondrino

IoT has had a wide diffusion in monitoring variables of interest in applications such as health, agriculture, environment, and industry, among others. In the context of sport, although wearable devices can monitor physiological variables, they are limited by the fact that they are linked to proprietary applications, have limited storage and perform analyses based on descriptive statistics without including the application of data analytics models. In this paper, we present the construction of an IoT system for monitoring and analysing physiological variables in athletes based on the use of unsupervised learning models. This system is articulated in the IoT four-layer architecture (capture, storage, analysis and visualization). It has the advantage of benefiting from the data provided by commercial devices, storing them in a non-relational database and applying clustering algorithms to the historical data. The proposed system is intended to serve as a reference to be replicated in sports training contexts in order to take advantage of the data provided by commercial wearable devices for decision-making based on the use of machine learning models.