Shini Girija, Pranav M. Pawar, Raja Muthalagu
et al.
Privacy has always been a critical issue in the digital era, particularly with the increasing use of Internet of Things (IoT) devices. As the IoT continues to transform industries such as healthcare, smart cities, and home automation, it has also introduced serious challenges regarding the security of sensitive and private data. This paper examines the complex landscape of digital privacy in IoT ecosystems, highlighting the need to protect personally identifiable information (PII) of individuals and uphold their rights to digital independence. Global events, such as the COVID-19 pandemic, have accelerated the adoption of IoT, raising concerns about privacy and data protection. This paper provides an in-depth examination of digital privacy risks in the IoT domain and introduces a clear taxonomy for evaluating them using the IEEE Digital Privacy Model. The proposed framework categorizes privacy risks into five types: identity-oriented, behavioral, inference, data manipulation, and regulatory risks. We review existing digital privacy solutions, including encryption technologies, blockchain, federated learning, differential privacy, reinforcement learning, AI, and dynamic consent mechanisms, to mitigate these risks. We also highlight how these privacy-enhancing technologies (PETs) help with data confidentiality, access control, and trust management. Additionally, this study presents AURA-IoT, a futuristic framework that tackles AI-driven privacy risks through a multi-layered structure. AURA-IoT integrates adversarial robustness, explainability, transparency, fairness, compliance, dynamic consent, and policy enforcement mechanisms to ensure digital privacy, security, and accountable IoT operations. Finally, we discuss ongoing challenges and potential research directions for integrating AI and encryption-based privacy solutions to achieve comprehensive digital privacy in future IoT systems.
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.
This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation.
The Fourth Industrial Revolution (4IR) has significantly transformed the cyber logistic chain through advancements in automation, fueled by the Internet of Things (IoT) and artificial intelligence (AI). Despite these innovations, increased connectivity has brought new vulnerabilities, such as issues with trust, open networks, and insufficient security measures, which have led to a surge in cybercrime, including ransomware attacks. This study has systematically reviewed 61 studies published between 2015 and 2024, sourced from six significant databases, to examine cybersecurity challenges and solutions in the cyber supply chain (CSC). This review has highlighted critical confidentiality, integrity, and availability (CIA) issues while evaluating preventive strategies and access control mechanisms. To address these challenges, this study introduces the CyberChainGuard framework that incorporates access control mechanisms, blockchain technology, and the Clark-Wilson model to combat ransomware and strengthen CSC security. The findings aimed to provide a benchmark for researchers and practitioners by offering actionable insights that could enhance cybersecurity in the digital logistics chain.
Divija Swetha Gadiraju, Ryan McMaster, Saeed Eftekhar Azam
et al.
Bridge health monitoring is critical for infrastructure safety, especially with the growing deployment of IoT sensors. This work addresses the challenge of securely storing large volumes of sensor data and extracting actionable insights for timely damage detection. We propose BIONIB, a novel framework that combines an unsupervised machine learning approach called the Novelty Index (NI) with a scalable blockchain platform (EOSIO) for secure, real-time monitoring of bridges. BIONIB leverages EOSIO’s smart contracts for efficient, programmable, and secure data management across distributed sensor nodes. Experiments on real-world bridge sensor data under varying loads, climatic conditions, and health states demonstrate BIONIB’s practical effectiveness. Key findings include CPU utilization below 40% across scenarios, a twofold increase in storage efficiency, and acceptable latency degradation, which is not critical in this domain. Our comparative analysis suggests that BIONIB fills a unique niche by coupling NI-based detection with a decentralized architecture, offering real-time alerts and transparent, verifiable records across sensor nodes.
Natchanai Roongmuanpha, Jetwara Tangjit, Mohammad Faseehuddin
et al.
This paper introduces a compact analog configuration that concurrently realizes a mixed-mode biquadratic filter and a dual-mode quadrature oscillator (QO) by employing a single differential differencing gain amplifier (DDGA) and all-grounded passive components. The proposed design supports four fundamental operation modes—voltage-mode (VM), current-mode (CM), trans-impedance-mode (TIM), and trans-admittance-mode (TAM)—utilizing the same circuit topology without structural modifications. In filter operation, it offers low-pass, high-pass, band-pass, band-stop, and all-pass responses with orthogonal and electronic pole frequency and quality factor. In oscillator operation, it delivers simultaneous voltage and current quadrature outputs with independent tuning of oscillator frequency and condition. The grounded-component configuration simplifies layout and enhances its suitability for monolithic integration. Numerical simulations in a 0.18-μm CMOS process with ±0.9 V supply confirm theoretical predictions, demonstrating precise gain-phase characteristics, low total harmonic distortion (<7%), modest sensitivity to 5% component variations, and stable operation from −40 °C to 120 °C. These results, combined with the circuit’s low component count and integration suitability, suggest strong potential for future development in low-power IoT devices, adaptive communication front-ends, and integrated biomedical systems.
Anshuman Sharma, Maryam Khokhar, Yongrui Duan
et al.
In recent years, the increasing prominence of artificial intelligence (AI) and sustainable business models (SBM) has had a significant impact on various industries. Despite rapid development, the field remains fragmented, and the evolving concepts and analytical frameworks have led to a lack of unified understanding. Previous studies have mainly emphasized the technical aspects of AI and business models, often relegating sustainability to a secondary position. In addition, there is still uncertainty about the extent to which AI affects key sustainability indicators. To bridge these gaps, this study systematically reviews 170 articles and proposes two key contributions: First, it identifies and examines emerging trends in AI-driven sustainable business models (SBMs) and provides a structured overview of current research. Second, it proposes an integrative framework that integrates multiple perspectives on AI and sustainable business models, providing insights for addressing managerial challenges. By integrating existing knowledge, this study not only clarifies research gaps but also outlines a forward-looking agenda to deepen the understanding of the role of AI in shaping sustainable business practices.
Md Ridoy Ad Sumon, Mushran Siddiqui, Gazi M. Ehsan ur Rahman
et al.
Internet of Things (IoT) and wireless sensor networks (WSN) have significant potential for a wide range of applications. However, these methods are hindered by resource constraints, long-range connectivity and energy challenges. Existing clustering protocols attempt to address some of these problems but fall short when handling the demands of real-time mobility in large-scale WSNs. Many of these algorithms rely on high processing power and offline optimization and fail to account for the dynamic mobility of nodes within the network. This article introduces the Multi-hop Mobility-based Real-Time Clustering (MHM-RTC) algorithm, which utilizes long-range (LoRa) communication between mobile sensor nodes (SNs) and a mobile data sink (DS). Unlike traditional approaches, this algorithm enables dynamic, on-the-fly clustering through the active participation of both SNs and DS. It adapts to continuous topology changes in highly mobile environments, supporting real-time responsiveness and efficient wide-area WSN coverage. The algorithm includes a mathematical model to calculate the clustering time across various network sizes, focusing on energy optimization and extended coverage in mobile networks. The simulation results demonstrate significant improvements over existing mobility-supporting algorithms, such as the Energy Efficient Routing Algorithm (EERA), Energy-Efficient Mobility-Based Cluster Head Selection (EEMCS) and Lightweight Dynamic Clustering Algorithm (LDCA). Specifically, MHM-RTC reduced the clustering time by up to 20%, increased the sensor node coverage by 37-53%, and reduced the average residual energy consumption by 3.77–8.69% when the total number of sensor nodes varied from 100 to 250. These results highlight that MHM-RTC is an effective solution for real-time energy-efficient clustering in large-scale mobile sensor networks.
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.
The Multi-access Edge Computing (MEC) network enhances the processing capability of Internet of Things (IoT) terminals by deploying 3C resources (computing resources, cache resources, and communication resources) at the IoT edge side, effectively improving the quality of service (QoS) of IoT applications. The majority of current MEC network research focuses on task offloading and energy management, with few studies looking at resource allocation from a service provisioning perspective. In this paper, we focus on the resource allocation problem when processing multiple tasks requested by IoT terminal devices in the MEC network. Firstly, we transform various tasks requested by IoT end devices into an SFC-based resource allocation problem. Then, we model the problem as a graph theory-based virtualized network function (VNF) node deployment cost optimization problem. Furthermore, we propose two resource allocation and deployment strategies by jointly considering the computing cost and security cost in VNF node deployment. Finally, extensive simulation experiments show that the proposed method can significantly improve the success rate of SFC task processing by more than 30% and network resource utilization by more than 20% when compared to other deployment methods for processing multiple SFC tasks with dynamic requests.
Context: The increase in Internet of Things (IoT) devices gives rise to an increase in deceptive manipulations by malicious actors. These actors should be prevented from targeting the IoT networks. Cybersecurity threats have evolved and become dynamically sophisticated, such that they could exploit any vulnerability found in IoT networks. However, with the introduction of the Software Defined Network (SDN) in the IoT networks as the central monitoring unit, IoT networks are less vulnerable and less prone to threats. %Although, the SDN itself is vulnerable to several threats. Objective: To present a comprehensive and unbiased overview of the state-of-the-art on IoT networks security enhancement using SDN controllers. Method: We review the current body of knowledge on enhancing the security of IoT networks using SDN with a Systematic Mapping Study (SMS) following the established guidelines. Results: The SMS result comprises 33 primary studies analyzed against four major research questions. The SMS highlights current research trends and identifies gaps in the SDN-IoT network security. Conclusion: We conclude that the SDN controller architecture commonly used for securing IoT networks is the centralized controller architecture. However, this architecture is not without its limitations. Additionally, the predominant technique utilized for risk mitigation is machine learning.
Renny Eka Putri, Widi Darmadi, Dinah Cherie
et al.
Machine learning and internet of thing (IoT) would be the best option for monitoring the appropriate soil pH condition. This research aimed on the design an automatic soil pH control system based on IoT for monitoring the cultivation of Aloe vera plants. The Telegram application was occupied as an IoT platform and was connected to a free and easy access application, Node MCU 8266. Furthermore, relay, Arduino Uno and smartphone were occupied in the system. According to the system testing, soil pH sensor readings are close to the actual value as evidenced by the linear regression value or R2 on sensors 1 and 2 which are close to 1. Meanwhile, the total percentage of system performance testing was 93% while the error value for the pH sensors were 0.96 and 1.6% for sensor 1 and sensor 2, respectively. Furthermore, the plant observations showed that the average leaf length of plants with a control system was 24.78 cm while with the manual system was 23.11 cm. From the results of the T test obtained, it was found that the control system applied to Aloe vera cultivation had a significant effect on the growth and development of Aloe vera compared to Aloe vera plants with a manual system.
The primary objective of the current fire protection system in high-rise buildings is to extinguish fires in close proximity to the detectors. However, in the event of rapidly spreading fires, it is more effective to limit the transmission of fire and smoke. This study aims to develop an IoT-based real-time pre-response system for high-rise building fires that is capable of limiting the spread of fire and smoke. The proposed system collects fire data from sensors and transmits them to a cloud computer for real-time analysis. Based on the analysis results, the cloud computer controls the actions of alarm devices, ventilation equipment, and fine water mist nozzles. The system can dynamically adjust the entire system’s behavior in real time by adopting pre-response measures to extinguish fires and limit the spread of fires and smoke. The system was tested on a simulation platform similar to actual high-rise buildings to evaluate its impact on fires and smoke. The results demonstrate the system’s effectiveness in extinguishing fires and suppressing the spread of fires and smoke.
Luca Sciullo, Alberto De Marchi, Angelo Trotta
et al.
Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario.
In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.
Internet of Things (IoT) and its applications are the most popular research areas at present. The characteristics of IoT on one side make it easily applicable to real-life applications, whereas on the other side expose it to cyber threats. Denial of Service (DoS) is one of the most catastrophic attacks against IoT. In this paper, we investigate the prospects of using machine learning classification algorithms for securing IoT against DoS attacks. A comprehensive study is carried on the classifiers which can advance the development of anomaly-based intrusion detection systems (IDSs). Performance assessment of classifiers is done in terms of prominent metrics and validation methods. Popular datasets CIDDS-001, UNSW-NB15, and NSL-KDD are used for benchmarking classifiers. Friedman and Nemenyi tests are employed to analyze the significant differences among classifiers statistically. In addition, Raspberry Pi is used to evaluate the response time of classifiers on IoT specific hardware. We also discuss a methodology for selecting the best classifier as per application requirements. The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier's performance.
The possibility of block chain innovation revolutionizing business operations and interpersonal interactions in Industry 4.0 is becoming more widely acknowledged. Industry 4.0 and the Industrial Internet of Things (IoT) are among the new application fields. As a result, the purpose of this article is to investigate the block chain applications that are already being used in IoT and Industry 4.0. In particular, it looks at current research trends in various IoT applications, addressing problems, concerns, and potential future uses of integrating block chain technology. This article also includes a thorough discussion of the key elements of block chain databases, including Merkle trees, transaction management, sharding, long-term memory, and short-term memory. In order to do this, more than 46 pertinent primary research that have been published in reputable journals have been chosen for additional examination. The workflow of a block chain network utilizing IoT is also demonstrated, demonstrating how IoT devices communicate with one another and how they contribute to the network's overall operation. The taxonomy diagram below serves to illustrate the contribution.
IoT deployments grow in numbers and size and questions of long time support and maintainability become increasingly important. To prevent vendor lock-in, standard compliant capabilities to transfer control of IoT devices between service providers must be offered. We propose a lightweight protocol for transfer of control, and we show that the overhead for the involved IoT devices is small and the overall required manual overhead is minimal. We analyse the fulfilment of the security requirements to verify that the stipulated requirements are satisfied.