Hasil untuk "iot"

R. Ratasuk, B. Vejlgaard, N. Mangalvedhe et al.

E. B. Farquhar, E. B. Goldstein, P. J. Bresnahan et al.

Abstract Chronic flooding is an issue for low‐lying coastal communities globally, and it is expected to worsen with rising sea levels. Predicting when and where these floods occur can be difficult as they can be hyper‐local and ephemeral, depending on the flood drivers (e.g., tides, rain). These factors make it difficult to measure the full spatial and temporal extent of chronic floods with in situ sensors. Here, we introduce a low‐cost (<$400 USD), privacy‐preserving camera system that identifies flooding over block‐by‐block spatial extents at high frequencies (20 s–6 min). Our device—a Tiny Camera with machine learning (ML) (TinyCamML)—is a small, solar‐powered, microcontroller‐based camera that uses on‐device ML to classify images of roadways as containing a “flood” or “no flood.” TinyCamMLs transmit only the classifications (a 1 or 0) to a website in real time, providing situation awareness during flood events over the entire image area while keeping data‐transmission costs low and preserving privacy. We demonstrate the TinyCamML's utility during both tidal and compound flood events in North Carolina, USA, which showed differences in flood spatial extents. During this deployment, the TinyCamML detected floods with an 81% accuracy, a 72% precision, and a 90% recall. The utility of the device extends beyond roadway flooding, as the onboard ML model can be easily retrained to capture other rare or ephemeral phenomena.

R. M. Magdaong, Ma. R. C. O. Ang, Ma. R. C. O. Ang et al.

Air pollution poses significant environmental and public health risks, particularly in urban areas of low and middle-income countries like the Philippines. Regulatory air quality monitoring stations, while accurate, are expensive and limited in spatial coverage, highlighting the need for low-cost IoT-based sensor networks to provide broader and real-time air quality data. This study establishes a methodology using Geographic Information Systems (GIS) and a heuristic algorithm to determine locations for deploying low-cost IoT-based air quality sensors in urban environments, focusing on near-road areas in Quezon City. Using multi-criteria analysis, Street Aspect Ratio (SAR), traffic emissions, Global Horizontal Irradiance (GHI), and road proximity were combined to produce a suitability map; scores ranged from 0 to 6. The algorithm then selected sensor locations by combining suitability and population rasters while enforcing a minimum spacing between nodes. In a 40‑sensor test, the resulting networks covered approximately 1.27 - 1.35 million residents (23.0%&ndash;24.4% of the city&rsquo;s population) across weighting schemes while maintaining balanced spatial dispersion. These results indicate that the method achieves substantial population coverage in high‑exposure corridors and aligns with public‑health priorities. The framework is reproducible for other cities to enhance near‑road air quality monitoring and management.

Subhadeep Sarkar, S. Misra

David Khoury, Samir Haddad, Patrick Sondi et al.

The Internet of Things (IoT) is expanding rapidly, but the security of IoT devices remains a noteworthy concern due to resource limitations and existing security conventions. This research investigates and proposes the use of a Light certificate with the Constrained Application Protocol (CoAP) instead of the X509 certificate based on traditional PKI/CA. We start by analyzing the impediments of current CoAP security over DTLS with the certificate mode based on CA root in the constrained IoT device and suggest the implementation of LightCert4IoT for CoAP over DTLS. The paper also describes a new modified handshake protocol in DTLS applied for IoT devices and Application server certificate authentication verification by relying on a blockchain without the complication of the signed certificate and certificate chain. This approach streamlines the DTLS handshake process and reduces cryptographic overhead, making it particularly suitable for resource-constrained environments. Our proposed solution leverages blockchain to reinforce IoT gadget security through immutable device characters, secure device registration, and data integrity. The LightCert4IoT is smaller in size and requires less power consumption. Continuous research and advancement are pivotal to balancing security and effectiveness. This paper examines security challenges and demonstrates the effectiveness of giving potential solutions, guaranteeing the security of IoT networks by applying LightCert4IoT and using the CoAP over DTLS with a new security mode based on blockchain.

Iain Baird, Isam Wadhaj, Baraq Ghaleb et al.

The rapid expansion of Internet of Things (IoT) devices has heightened the demand for lightweight and secure cryptographic mechanisms suitable for resource-constrained environments. While SHA-256 remains a widely used standard, the emergence of SHA-3 particularly the KangarooTwelve variant offers potential benefits in flexibility and post-quantum resilience for lightweight resource-constrained devices. This paper presents a comparative evaluation of the energy costs associated with SHA-256 and SHA-3 hashing in Contiki 3.0, using three generationally distinct IoT platforms: Sky Mote, Z1 Mote, and Wismote. Unlike previous studies that rely on hardware acceleration or limited scope, our work conducts a uniform, software-only analysis across all motes, employing consistent radio duty cycling, ContikiMAC (a low-power Medium Access Control protocol) and isolating the cryptographic workload from network overhead. The empirical results from the Cooja simulator reveal that while SHA-3 provides advanced security features, it incurs significantly higher CPU and, in some cases, radio energy costs particularly on legacy hardware. However, modern platforms like Wismote demonstrate a more balanced trade-off, making SHA-3 viable in higher-capability deployments. These findings offer actionable guidance for designers of secure IoT systems, highlighting the practical implications of cryptographic selection in energy-sensitive environments.

Ellie Zontou, Antonia Kyprioti

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making. By integrating IoT seamlessly into nursing workflows and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights, leading to better treatment outcomes and resource allocation. This paper outlines the protocol's objectives, key components, and expected benefits, while emphasizing the importance of ethical considerations and ongoing evaluation to ensure successful implementation.

Hassan Wasswa, Timothy Lynar

Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift. Existing solutions typically rely on periodic classifier retraining, resulting in high computational overhead and the risk of catastrophic forgetting. To address these challenges, this paper proposes a scalable framework for adaptive IoT threat detection that eliminates the need for continuous classifier retraining. The proposed approach trains a classifier once on latent-space representations of historical traffic, while an alignment model maps incoming traffic to the learned historical latent space prior to classification, thereby preserving knowledge of previously observed attacks. To capture inter-instance relationships among attack samples, the low-dimensional latent representations are further transformed into a graph-structured format and classified using a graph neural network. Experimental evaluations on real-world heterogeneous IoT traffic datasets demonstrate that the proposed framework maintains robust detection performance under concept drift. These results highlight the framework's potential for practical deployment in dynamic and large-scale IoT environments.

Youssef Charkaoui, Dounia Tebr, Zeineb El Hammoumi et al.

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving them the opportunity to expand their customer base. The Internet of Things refers to an inter-connected system of smart devices that communicate and exchange data and big data analytics over the internet. As this involves the process of the treating data to unlock hidden information, patterns, and insights, the combination of both tools creates a revolution in customer relations and gives us the opportunity to understand our customers’ needs before they do themselves. This article presents an exploratory literature review of studies analyzing the relationship between IOT and big data in marketing. It provides a deep analysis of various scholars’ works that examine the methodology used by these tools to reinforce customer relations and acquire new ones. This review provides an overview of the most interesting research on this topic and the methods and techniques employed as well as an analysis of the obstacles and challenges involved. The results of this research show that IOT and big data analytics are key factors for an efficient analysis of clients’ needs.

Very Kurnia Bakti, Abdul Basit, Rais et al.

Penyedia Air Minum dan Sanitasi Berbasis Masyarakat atau biasa disebut Pamsimas merupakan program pemerintah, Pamsimas Tirta Abadi di Desa Rangimulya Kecamatan Warureja Kabupaten Tegal dengan jumlah penduduk sekitar 3104 Jiwa, Pamsimas Tirta Abadi memiliki 120 pelanggan dan keberadaan Pamsimas sangat penting bagi masyarakat. Dalam menghitung penggunaan air bersih pengelola Pamsimas masih menggunakan meteran air analog, banyak potensi kesalahan dalam pencatatan penggunaan air.  Penerapan  IoT  bisa menjadi solusi, sensor yang memungkinkan untuk menghitung penggunaan air adalah sensor waterflow dipadukan dengan ESP32 dan modul LoRa. dari beberapa teknologi tersebut dibangun sebuah sistem meteran air cerdas atau smart water meter dengan tujuan meningkatkan pelayanan dan pendapatan.  Metode pada smart water meter ini membaca kondisi air dengan penggunaan sensor, sensor waterflow membaca nilai kubikasi dengan konstanta 9,5 dengan nilai 5,9 dari uji pembacaan air 1 Liter, nilai baca 4.00 pada sensor PH  memiliki nilai pH mulai dari 0 hingga 7, sensor turbidity mendeteksi tingkat kejernihan / kekeruhan yang diperoleh Sensor Turbidity Output (v): 4.42 dengan NTU: 0.52. GPS sensor mengirimkan latitude dan longitude pada gateway untuk melihat titik pelanggan, Data semua sensor yang terbaca dikirim dari node kepaa gateway dengan modul lora pada frekuensi 915E6 dan ditampilkan pada interface berbasis website menggunakan modul internet ESP32. Kata Kunci: PAMSIMAS, IoT, Lora, waterflow, PH, Turbidity, GPS

Imane Taleb, Jean-Loup Guillaume

The growth of IoT and connected devices has increased demand for low-latency, energy-efficient processing across the Cloud-Fog-Edge continuum. While microservices enable scalable distributed computing, their placement remains challenging due to dynamic resource needs and interdependencies. This work proposes a graph-based microservice placement approach using user-centered local community detection. By integrating user nodes, our method adapts to shifting demands and resource availability, reducing energy consumption and communication overhead. Additionally, strategic mutualization and controlled duplication further enhance efficiency while preserving response time and resource constraints. Our results highlight the effectiveness of user-centric strategies in achieving scalable and sustainable deployments, reducing energy consumption by approximately 50% compared to state-of-the-art global methods while slightly improving deployment time.

Yosia Adi Susetyo, Hanna Arini Parhusip, Suryasatriya Trihandaru et al.

Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer dengan arsitektur terpilih menunjukkan kinerja unggul dengan nilai Mean Square Error (MSE) sebesar 5,844 x10⁻⁵, Mean Absolute Error (MAE) sebesar 0,005971, dan Root Mean Square Error (RMSE) sebesar 0, 007645. Arsitektur Environment Intelligence versi (a) dengan Nadam Optimizer terbukti paling efektif dalam memproses data sensor, sehingga dipilih untuk integrasi dengan perangkat LSTM-IOT. Implementasi LSTM-IOT dalam skenario dunia nyata dilakukan pada wadah web lokal yang memungkinkan akses real-time ke data suhu dan kelembaban di berbagai lokasi. Halaman web berbasis Streamlit ini menampilkan visualisasi data, performa LSTM, dan hasil prediksi. Uji fungsional menunjukkan bahwa LSTM-IOT memenuhi kebutuhan perusahaan, termasuk penyimpanan data dalam database internal serta prediksi kondisi lingkungan hingga 150 menit ke depan. Dengan fitur prediksi dan pemantauan yang canggih, perangkat ini memberikan solusi efisien dan bernilai tinggi bagi perusahaan dalam memantau kondisi lingkungan secara akurat dan proaktif.   Abstract Problems in the industry related to temperature and humidity data loss are often caused by device interference or loss of connection. This data is important to determine the feasibility of the product to be distributed. To overcome these problems, an LSTM-IOT innovation was developed, namely an IoT device that is integrated with the Long Short-Term Memory (LSTM) model in the Environment Intelligence architecture. This architecture has been optimized through experiments using different types of optimizers, such as Adam, RMSprop, AdaGrad, SGD, Nadam, and Adadelta. From the optimization results, the combination of Nadam Optimizer with the selected architecture shows superior performance with a mean square error (MSE) value of 5.844 x 10⁻⁵, a mean absolute error (MAE) of 0.005971, and a root mean square error (RMSE) of 0.007645. The Environment Intelligence architecture version (a) with Nadam Optimizer proved to be the most effective in processing sensor data, so it was chosen for integration with LSTM-IOT devices. The implementation of LSTM-IOT in real-world scenarios is carried out on a local web container that allows real-time access to temperature and humidity data in various locations. This Streamlit-based webpage displays data visualizations, LSTM performance, and prediction results. Functional tests show that LSTM-IOT meets the needs of the company, including data storage in an internal database and prediction of environmental conditions for up to the next 150 minutes. With advanced prediction and monitoring features, these devices provide efficient and high-value solutions for companies to monitor environmental conditions accurately and proactively.

Mira Maisura, Cut Putroe Yuliana, Ridwan Ridwan et al.

Revolusi Industri 4.0 menuntut integrasi literasi teknologi seperti Internet of Things (IoT) ke dalam kurikulum pendidikan. Namun, kendala utama di tingkat SMA/Madrasah  adalah minimnya media pelatihan IoT yang terjangkau, praktis, dan sesuai dengan konteks,  infrastruktur sekolah yang terbatas, dan sumber daya yang kompeten. Penelitian ini bertujuan untuk mengembangkan dummy boom IoT yang dapat digunakan sebagai media pelatihan IoT yang layak digunakan dalam pembelajaran. Metode pengembangan yang diterapkan adalah model 4D dengan tahapan define, design, develop dan dissemination. Penggunaan model 4D memastikan quality control dari media,.  Hasil uji kelayakan media didapatkan nilai rata-rata 4,414 (dalam skala likert), yang menunjukkan bahwa 92,45 % dari total responden setuju bahwa media sangat sesuai dan layak digunakan.

Helder Holanda Prezotto, Natássya Barlate Floro da Silva, Lucas Dias Hiera Sampaio et al.

The electric shower is the main form of heating water for bathing in Brazilian homes and one of the significant appliances related to electricity and water consumption. Internet of Things (IoT) projects make it possible to connect objects to the Internet and collect data from machines remotely. In this work, we developed an Internet of Things system for monitoring an electric shower, called Shower–IoT, whose sensor data are water temperature, electric tension, electric current, and water flow. To implement the software infrastructure, we used the services present in cloud computing, such as a broker, processing, and storage, in which the information about the electric shower was made available through an Android application. The results demonstrate that our system can monitor an electric shower integrated with cloud services, allowing the users to visualize its behavior in real time and detect possible failures by comparing sensor data from previous evaluations.

Yosia Adi Susetyo, Hanna Arini Parhusip, Suryasatriya Trihandaru et al.

Masalah dalam industri terkait kehilangan data suhu dan kelembaban sering terjadi akibat gangguan perangkat atau hilangnya koneksi. Data ini penting untuk menentukan kelayakan produk yang akan didistribusikan. Untuk mengatasi permasalahan tersebut, dikembangkan inovasi LSTM-IOT, yaitu perangkat IoT yang terintegrasi dengan model Long Short-Term Memory (LSTM) dalam arsitektur Environment Intelligence. Arsitektur ini telah dioptimalkan melalui eksperimen menggunakan berbagai jenis optimizer, seperti Adam, RMSprop, AdaGrad, SGD, Nadam, dan Adadelta. Dari hasil optimasi, kombinasi Nadam Optimizer dengan arsitektur terpilih menunjukkan kinerja unggul dengan nilai Mean Square Error (MSE) sebesar 5,844 x10⁻⁵, Mean Absolute Error (MAE) sebesar 0,005971, dan Root Mean Square Error (RMSE) sebesar 0, 007645. Arsitektur Environment Intelligence versi (a) dengan Nadam Optimizer terbukti paling efektif dalam memproses data sensor, sehingga dipilih untuk integrasi dengan perangkat LSTM-IOT. Implementasi LSTM-IOT dalam skenario dunia nyata dilakukan pada wadah web lokal yang memungkinkan akses real-time ke data suhu dan kelembaban di berbagai lokasi. Halaman web berbasis Streamlit ini menampilkan visualisasi data, performa LSTM, dan hasil prediksi. Uji fungsional menunjukkan bahwa LSTM-IOT memenuhi kebutuhan perusahaan, termasuk penyimpanan data dalam database internal serta prediksi kondisi lingkungan hingga 150 menit ke depan. Dengan fitur prediksi dan pemantauan yang canggih, perangkat ini memberikan solusi efisien dan bernilai tinggi bagi perusahaan dalam memantau kondisi lingkungan secara akurat dan proaktif.   Abstract Problems in the industry related to temperature and humidity data loss are often caused by device interference or loss of connection. This data is important to determine the feasibility of the product to be distributed. To overcome these problems, an LSTM-IOT innovation was developed, namely an IoT device that is integrated with the Long Short-Term Memory (LSTM) model in the Environment Intelligence architecture. This architecture has been optimized through experiments using different types of optimizers, such as Adam, RMSprop, AdaGrad, SGD, Nadam, and Adadelta. From the optimization results, the combination of Nadam Optimizer with the selected architecture shows superior performance with a mean square error (MSE) value of 5.844 x 10⁻⁵, a mean absolute error (MAE) of 0.005971, and a root mean square error (RMSE) of 0.007645. The Environment Intelligence architecture version (a) with Nadam Optimizer proved to be the most effective in processing sensor data, so it was chosen for integration with LSTM-IOT devices. The implementation of LSTM-IOT in real-world scenarios is carried out on a local web container that allows real-time access to temperature and humidity data in various locations. This Streamlit-based webpage displays data visualizations, LSTM performance, and prediction results. Functional tests show that LSTM-IOT meets the needs of the company, including data storage in an internal database and prediction of environmental conditions for up to the next 150 minutes. With advanced prediction and monitoring features, these devices provide efficient and high-value solutions for companies to monitor environmental conditions accurately and proactively.

Poornima Mahadevappa, Redhwan Al-amri, Gamal Alkawsi et al.

Edge data analytics refers to processing near data sources at the edge of the network to reduce delays in data transmission and, consequently, enable real-time interactions. However, data analytics at the edge introduces numerous security risks that can impact the data being processed. Thus, safeguarding sensitive data from being exposed to illegitimate users is crucial to avoiding uncertainties and maintaining the overall quality of the service offered. Most existing edge security models have considered attacks during data analysis as an afterthought. In this paper, an overview of edge data analytics in healthcare, traffic management, and smart city use cases is provided, including the possible attacks and their impacts on edge data analytics. Further, existing models are investigated to understand how these attacks are handled and research gaps are identified. Finally, research directions to enhance data analytics at the edge are presented.

Alaa M. Odeh, Isam Ishaq

In the face of our rapidly expanding global population, the necessity of meeting the fundamental needs of every individual is more pressing than ever. Human survival depends upon access to water, making it a vital resource that demands novel solutions to ensure universal availability. Although our planet is abundant in water, 97.5% of it is saltwater, compelling nations to investigate ways to make it suitable for consumption. Seawater desalination is becoming increasingly vital for water sustainability. While seawater desalination offers a solution, existing methods often grapple with high energy consumption and maintaining consistent water quality. This paper proposes a novel hybrid water desalination system that addresses these limitations. Our system leverages solar energy, a readily available renewable resource, to power the desalination process, significantly improving its environmental footprint and operational efficiency. Additionally, we integrated a network of sensors and the Internet of Things (IoT) to enable the real-time monitoring of system performance and water quality. This allows for the immediate detection and improvement in any potential issues, ensuring the consistent production of clean drinking water. By combining solar energy with robust quality control via IoT, our hybrid desalination system offers a sustainable and reliable approach to meet the growing demand for freshwater.

Mohammad Goudarzi, Arash Shaghaghi, Simon Finn et al.

The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on context-sharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployments, making their security essential to ensure the resilience and reliability of these 'systems of systems'. In this paper, we take the first steps toward systematically and comprehensively addressing the security of IoT context-sharing platforms. We propose a framework for threat modelling and security analysis of a generic IoT context-sharing solution, employing the MITRE ATT&CK framework. Through an evaluation of various industry-funded projects and academic research, we identify significant security challenges in the design of IoT context-sharing platforms. Our threat modelling provides an in-depth analysis of the techniques and sub-techniques adversaries may use to exploit these systems, offering valuable insights for future research aimed at developing resilient solutions. Additionally, we have developed an open-source threat analysis tool that incorporates our detailed threat modelling, which can be used to evaluate and enhance the security of existing context-sharing platforms.

Kyohei Ono, Tomoko K. Matsushima, Shoichiro Yamasaki et al.

Recently, visible light communications (VLCs) have attracted attention as a complement to wireless communications by radio frequency. In addition, the demand for Internet of Things (IoT) communications has increased in the last decade, and VLC-based IoT systems are being considered. Because IoT devices operate at various data rates, multirate transmission is required. Herein, we propose a scheme that supports multirate data transmission for synchronous optical code division multiple access systems. This scheme employs a generalized modified prime sequence code and multiple extended bi-orthogonal codes. The proposed scheme realizes multirate transmission with an arbitrary number of data rate levels by superimposing spreading codes of different codelengths. In addition, the proposed scheme eliminates not only interference between users with the same data rate, but also interference between users with different data rates. Furthermore, we investigated the bit error rate performance and energy efficiency of the proposed scheme, considering the effects of background light, dark current, and PIN photodiode noise including shot noise, and thermal noise.