EAI Endorsed Transactions on Internet of Things
type: channel
description: EAI Endorsed Transactions on Internet of Things is open access, a peer-reviewed scholarly journal focused on all areas related to the technologies and application fields related to the Internet of Things. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications on a quarterly frequency. INDEXING: Scopus (CiteScore: 4.5), DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions eISSN: 2414-1399
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Internet of Things
issn: 2414-1399
copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
items: Janus: Joint Prefill/Decode Disaggregation with KV-Cache-Aware Multi-Cloud Routing for Edge-Adjacent LLM Serving
items: A Computationally Efficient Ensemble Boosting Framework for Optimized DoS Attack Detection in Wireless Sensor Networks
items: Real-Time Mosquito Breeding Site Detection Using a Quantized YOLOv8-Tiny Model on an FPGA-Based Drone Platform
items: A Low-Cost Multi-Node Architecture with ESP32 Microcontrollers for Assisted Home Automation of Elderly and Disabled Residents
items: A Novel Gated Fusion CNN-LSTM Model for Multi-Horizon Intraday Gold Price Forecasting
items: AI-Enabled Workflow Redesign for EECP Microcirculation Follow-Up: A Design Science Approach
items: Multi-View Vehicle Detection and Tracking for Smart City Traffic Monitoring
items: Optimizing Crop Yield and Monitoring Leaves with an Intelligent Internet of Things
items: Hybrid Genetic Algorithm and Water Wave Optimization Approach for QoS Aware Multi Objective Task Scheduling and Load Balancing in Cloud Environments
items: Flood Mapping employing U-Net: A Case Study of Teesta Dam
items: CNN and Blockchain Integrated E-Governance Framework System for Tamper-Proof Documents
items: An Investigative Analysis of Adaptive Consensus Mechanisms for Distributed Blockchain Systems
items: Quantum-Classical Adaptive Navigation: QAOA-PPO Framework for GNSS-Denied Urban Environments
items: Comparative Analysis of Quantum and Classical Computing: Performance, Error Rates, and Hybrid Architectures
items: Quantum-Enhanced Optimization of AV-Drone Fleets for Urban Last-Mile Logistics
items: Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review
items: System of digitizing and paying of civil status documents via an RFID card
items: Risk-Aware Reinforcement Learning for Cooperative Autonomous Vehicle Coordination with Adaptive Risk Sensitivity and Multi-Agent Optimization
items: LPWAN Localization via RSSI/SNR Fingerprinting and Lightweight Machine Learning
items: Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation
items: Adaptive Spatio-Temporal Deep Learning for Robust Cyber Threat Detection Across IoT Environments
items: Trust-Aware Federated Learning with Differential Privacy for Secure AIoT in Critical Infrastructures
items: Culturally-Aware Artificial Intelligence: Personal Values and Technology Acceptance among AI Researchers in China and Germany
items: Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology
items: The Statistical Analytical Review of IoT Threat Detection Mechanisms: Quantitative Evaluation and Multidimensional Performance Assessment Analysis
items: Multi-Agent Soft Actor-Critic with Graph Attention Networks for Adaptive Traffic Signal Optimization (MASAC-GAT)
items: A Q-Learning and Blockchain Framework for Secure Dynamic Bandwidth Allocation in Heterogeneous IoT
items: Design and Analysis of Hybrid GDI-PTL Based Wallace Tree Multipliers for Low-Power DCT Architecture in Image Compression
items: Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions
items: An Adaptive Intrusion Detection System for Securing the Internet of Medical Things Using Deep Learning

Janus: Joint Prefill/Decode Disaggregation with KV-Cache-Aware Multi-Cloud Routing for Edge-Adjacent LLM Serving
type: item
description: INTRODUCTION: Disaggregated large language model (LLM) serving separates the compute-bound prefill phase from the memory-bound decode phase and is increasingly deployed across heterogeneous multi-cloud and edge-adjacent fleets serving geographically distributed (including IoT and edge) clients. The key-value (KV) cache that couples the two phases raises a stateful routing problem—migrate, recompute, or partially ship the cache across inter-cloud links of varying bandwidth, on hardware of varying capability, under spot prices that change every few minutes—that, to our knowledge, no published framework fully addresses. OBJECTIVES: To jointly optimize prefill placement, decode placement, KV-cache transport policy, and slow-timescale pool sizing across clouds with heterogeneous link bandwidths, GPU capabilities, and volatile spot prices, with explicit provable guarantees. METHODS: We present Janus, an online scheduler that formulates per-request scheduling as a constrained graph-routing problem with stateful edges and decomposes it into a monotone-submodular prefix-aware placement subproblem and a Lyapunov drift-plus-penalty control subproblem, with four KV-transport policies including a hybrid layer-pipelined policy admitting a closed-form layer-split optimum. A 17.1K-line prototype implements the scheduling logic; evaluation uses a trace-driven, discrete-event simulator whose timing and cost models are calibrated against measured single-pod microbenchmarks, configured to model a 96-pod (512-GPU) three-cloud, six-region fleet. RESULTS: We prove a (1 1/e) approximation for prefix reuse under continuous greedy (with a 1/2 guarantee for the deployed combinatorial greedy under slack capacity, degrading to 1/3 when heterogeneous KV capacity binds), an O(1/V ) gap to the best policy in the decomposed class with O(V ) queue bound stated with its explicit additive constants, a sample-path robustness guarantee under adversarially time-varying prices and bandwidth, and a hybrid-…
subject: Research article
  topic: section
subject: LLM inference
  topic: keywords
subject: disaggregated serving
  topic: keywords
subject: multi-cloud
  topic: keywords
subject: edge serving
  topic: keywords
subject: KV-cache
  topic: keywords
subject: Lyapunov optimization
  topic: keywords
subject: scheduling
  topic: keywords
subject: submodular optimization
  topic: keywords
creator: K. B. Aruna
creator: V. Kaliraj
creator: I. Sudha
creator: V. Sureka
rights: Copyright (c) 2026 K. B. Aruna, V. Kaliraj, I. Sudha, V. Sureka https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-12
publication date: 2026-08-12
doi: 10.4108/eetiot.13349

A Computationally Efficient Ensemble Boosting Framework for Optimized DoS Attack Detection in Wireless Sensor Networks
type: item
description: As Wireless Sensor Networks (WSNs) become more prevalent in modern IoT deployments, Denial of Service (DoS) attacks, which are capable of exhausting node resources and disrupting entire network segments, have emerged as their most significant security threat. In this research, we propose a computationally efficient intrusion detection framework based on the eXtreme Gradient Boosting (XGBoost) model, specifically tailored for energy-constrained environments, and designed for deployment at the cluster-head or gateway levels of WSN architectures. The framework integrates a systematic feature selection process that reduces the initial 19-column curated dataset to an optimal 16-feature subset, addressing correlation and redundancy while maintaining high-fidelity detection. Unlike many existing studies that optimize for accuracy alone, this work uniquely combines four complementary evaluation dimensions: classification accuracy, per-fold stability (standard deviation), ultra-low prediction latency, and adaptive threshold analysis for deployment flexibility. We validated our results through five-fold cross-validation on the WSN-DS dataset. The proposed framework achieved a mean accuracy of 99.74% with an exceptionally low standard deviation of ±0.000174, a prediction latency of 0.0017 ms per sample, and demonstrated a 20× computational efficiency advantage over state-of-the-art ensemble learners like CatBoost, with a compressed model size of 235 KB and an estimated inference cost of 2,550 FLOPs per prediction. The overall outcomes of the study support that the proposed framework provides a resilient, adaptable solution for real-world WSN deployment
subject: Research article
  topic: section
subject: IoT/WSN Security
  topic: keywords
subject: Wireless Sensor Networks
  topic: keywords
subject: Intrusion Detection System (IDS)
  topic: keywords
subject: Denial of Service (DoS) Attacks
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Computationally Efficient IDS
  topic: keywords
subject: Cybersecutiy
  topic: subjects
subject: NIDS
  topic: subjects
subject: Machine Learning
  topic: subjects
creator: Mohamed Loughmari
creator: Anass El Affar
rights: Copyright (c) 2026 Mohamed Loughmari, Anass El Affar https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-11
publication date: 2026-08-11
doi: 10.4108/eetiot.13137

Real-Time Mosquito Breeding Site Detection Using a Quantized YOLOv8-Tiny Model on an FPGA-Based Drone Platform
type: item
description: The increasing prevalence of mosquito-borne diseases demands scalable, accurate, and energy-efficient surveillance systems capable of real-time operation in resource-constrained environments. This extended study presents an FPGA-optimized, drone-based mosquito breeding site detection framework leveraging quantized deep learning models for edge deployment. Building upon prior work, this paper introduces an enhanced implementation of YOLOv8-Tiny and YOLOv9-Small architectures, optimized through INT8 quantization, batch normalization folding, and FPGA-aware architectural refinements for execution on the PYNQ-Z2 platform. High-resolution aerial imagery acquired from unmanned aerial vehicles (UAVs) is processed in real time using the proposed system to identify and classify potential mosquito breeding sites such as stagnant water bodies and container habitats. Experimental evaluations carried out on six state-of-the-art object detection architectures showed that the proposed quantized YOLOv8-Tiny variant offers the optimal compromise among accuracy, speed, and energy efficiency by achieving 90.2% accuracy in the field, 20 FPS performance, and consuming 7.8 W of power. It is also proved that the proposed system reduces DSP and BRAM resources by more than 20% over the floating-point processor. The results obtained in the real-world deployment scenario proved the robustness of the system under different environmental situations. It is also found that the system can cover 10 km² of area per hour while providing a 44% reduction in operational costs over traditional methods. The proposed framework using FPGA acceleration and quantization awareness is useful for developing efficient vector surveillance systems.
subject: Research article
  topic: section
subject: Mosquito Breeding Detection
  topic: keywords
subject: FPGA Acceleration
  topic: keywords
subject: YOlOv8-Tiny
  topic: keywords
subject: Quantization
  topic: keywords
subject: UAV Surveillance
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Real-Time Processing
  topic: keywords
creator: A. Rosi
creator: N. Suresh
creator: J. B. Veeramalini
creator: P Ramesh Reddy
creator: M. Ramesh
creator: M. Prabhu
rights: Copyright (c) 2026 A. Rosi, N. Suresh, J. B. Veeramalini, P Ramesh Reddy, M. Ramesh, M. Prabhu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-05
publication date: 2026-05-05
doi: 10.4108/eetiot.12911

A Low-Cost Multi-Node Architecture with ESP32 Microcontrollers for Assisted Home Automation of Elderly and Disabled Residents
type: item
description: INTRODUCTION: Elderly and disabled populations worldwide are growing faster than care systems can absorb them, creating strong demand for affordable home-automation tools that let residents remain safe at home without continuous caregiver presence. Most low-cost IoT prototypes reported in the literature cover only one or two functional subsystems and send alerts to a single recipient, leaving a practical gap for multi-subsystem, multi-recipient solutions. OBJECTIVES: To design, build, and test a modular home-automation system that brings together four specialised sensor nodes—radio-frequency access control, ultrasonic proximity detection, combustible-gas monitoring, and rain-triggered physical protection—plus a fifth node combining multi-recipient alert coordination with remote lighting control across five areas of the dwelling, all over a coordinated wireless network with real-time alerts sent to multiple users simultaneously. METHODS: A waterfall lifecycle combined with test-driven development guided component selection and firmware coding. Five ESP32 microcontrollers exchange data through a lightweight publish-subscribe protocol; each node handles its own control logic and actuator independently, while a companion mobile application allows remote supervision and manual override. RESULTS: All four subsystems were functionally validated through virtual simulation followed by physical demonstration on a scale dwelling model: the RFID module granted or denied access correctly for authorised and unauthorised cards, the ultrasonic module opened and closed the back door in response to detected presence, the gas sensor triggered visual, audible, and remote alerts above the configured threshold, and the rain sensor activated the laundry-protection servo on detected precipitation. The Telegram notification channel delivered alerts to the user with an observed average response time of under two seconds. Total component cost came to approximately 73 United States dollars, fa…
subject: Research article
  topic: section
subject: home automation
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: ESP32
  topic: keywords
subject: embedded systems
  topic: keywords
subject: publish-subscribe protocol
  topic: keywords
subject: assistive technology
  topic: keywords
subject: elderly care
  topic: keywords
subject: low-cost prototype
  topic: keywords
creator: Paulo Cesar Pérez-Ayala
creator: Lendy Delims Salvador-Boza
creator: Michael Anderson Lopez-Lopez
creator: Carlos Alejandro Soto-Oroya
creator: Maritza Raquel Cabana-Cáceres
creator: Cristian Castro-Vargas
rights: Copyright (c) 2026 Paulo Cesar Pérez-Ayala, Lendy Delims Salvador-Boza, Michael Anderson Lopez-Lopez, Carlos Alejandro Soto-Oroya, Maritza Raquel Cabana-Cáceres, Cristian Castro-Vargas https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-29
publication date: 2026-06-29
doi: 10.4108/eetiot.12871

A Novel Gated Fusion CNN-LSTM Model for Multi-Horizon Intraday Gold Price Forecasting
type: item
description: Gold serves as a key inflation hedge and portfolio stabilizer, making accurate price forecasting essential for investors. Hourly gold prices exhibit pronounced non-linearity and microstructure noise that limit traditional econometric models, motivating a shift toward deep learning. Existing CNN-LSTM architectures cascade convolutional and recurrent layers sequentially, without a mechanism to reconcile their complementary representations. We propose a Dual-Branch CNN-LSTM architecture with Gated Fusion, combining a convolutional-recurrent deep branch with a parallel raw-input skip branch, adaptively merged by a learned gate inspired by the Gated Multimodal Unit. Input sequences are restructured via sliding windows across three forecast horizons (24→1, 48→2, and 72→3 hours). The model is validated on 37,140 hourly XAUUSDm observations (Exness, 2020-2026) against 1D-CNN, LSTM, and Sequential CNN-LSTM baselines, achieving the best or tied-best accuracy across all configurations. For the 24-hour horizon, MAE = 9.00 USD, R² = 0.9994, and DA = 52.6%, on the original USD scale. Diebold-Mariano tests confirm significant gains over the 1D-CNN and Sequential CNN-LSTM baselines (p < 0.001), while McNemar tests confirm directional-accuracy gains in 8 of 9 comparisons (p < 0.05). These results show a modest but robust improvement over prior CNN-LSTM designs, offering a reliable foundation for risk management pending economic backtesting.
subject: Research article
  topic: section
subject: gold price forecasting
  topic: keywords
subject: CNN-LSTM
  topic: keywords
subject: gated fusion
  topic: keywords
subject: deep learning
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: directional accuracy
  topic: keywords
subject: Deep Sequence Model, CNN, LSTM, Gold Price Forcasting, Intraday Prediction
  topic: subjects
creator: Nguyen Hoang Ha
creator: Cuong Hoa Nguyen-Dinh
creator: Truong An Binh
rights: Copyright (c) 2026 Nguyen Hoang Ha, Cuong Hoa Nguyen-Dinh, Truong An Binh https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-08-13
publication date: 2026-08-13
doi: 10.4108/eetiot.12806

AI-Enabled Workflow Redesign for EECP Microcirculation Follow-Up: A Design Science Approach
type: item
description: Enhanced external counterpulsation (EECP) is a non-invasive cardiovascular rehabilitation therapy, but its follow-up in routine outpatient practice is often difficult to standardize, interpret efficiently, and communicate consistently. Although nailfold videocapillaroscopy (NVC) offers a direct view of peripheral microcirculation, its practical use remains constrained by image-quality variation, manual reading burden, and inconsistent reporting. To address these issues, this study adopts a Design Science Research perspective to develop a generative-AI–assisted medical information system for NVC-based EECP follow-up. The proposed artifact integrates image quality control, structured AI-assisted interpretation, report generation, human review, and audit logging into a governable outpatient workflow. A pilot implementation in a single clinic involving three physicians suggests that the system can reduce turnaround time and labor burden, support more scalable service delivery, and provide a structured basis for improving report consistency, though systematic consistency evaluation remains for future work. This study contributes an MIS artifact that demonstrates how generative AI can be operationally embedded into EECP follow-up.
subject: Research article
  topic: section
subject: Enhanced External Counterpulsation (EECP)
  topic: keywords
subject: nailfold Videocapillaroscopy (NVC)
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Medical Information Systems
  topic: keywords
subject: Design Science Research
  topic: keywords
subject: Generative AI
  topic: subjects
subject: Medical Information Systems
  topic: subjects
creator: Chun-Yi Wu
creator: Chao-Chien Chen
creator: Hui-Hsin Chin
rights: Copyright (c) 2026 Chun-Yi Wu, Chao-Chien Chen, Hui-Hsin Chin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.4108/eetiot.12481

Multi-View Vehicle Detection and Tracking for Smart City Traffic Monitoring
type: item
description: Urban traffic monitoring plays a crucial role in intelligent transportation systems. The development of surveillance camera networks has generated a large amount of image and video data that can be exploited for traffic flow detection, tracking, and analysis tasks. However, detecting and tracking vehicles from fixed traffic surveillance cameras still faces many challenges. The main challenges include obscured objects, small target size, and high traffic density. This study presents a deep learning-based traffic monitoring framework for detecting and tracking multiple objects in urban traffic monitoring systems. The proposed framework integrates YOLOv11 for vehicle detection and DeepSORT with a Kalman filter-based state estimation method for tracking multiple objects. In addition, the SAHI technique is integrated to investigate its ability to support the detection of small objects in traffic data. The research framework was evaluated using a dataset collected from traffic cameras in Thai Nguyen, Vietnam. Numerous test scenarios were conducted with varying traffic densities, observation distances, and camera viewing angles. Experimental results showed that the YOLOv11 configuration combined with DeepSORT achieved a processing speed of approximately 10.1 FPS; for object detection tasks, the model achieved an mAP@0.5 of 0.66. Simultaneously, experimental results show that the proposed framework can maintain vehicle detection and tracking across consecutive frames under varying observation conditions. In addition, the integration of SAHI techniques recorded an improvement in detecting small objects, with mAP@0.5 increasing from 0.66 to 0.70 and AP_S increasing from 0.29 to 0.40. The results obtained demonstrate the potential applicability of the proposed framework to traffic detection, tracking, and monitoring problems in urban environments.
subject: Research article
  topic: section
subject: Deep Learning
  topic: keywords
subject: Intelligent transportation systems
  topic: keywords
subject: YOLOv11
  topic: keywords
subject: Vehicle Detection
  topic: keywords
subject: Multi-Object Tracking
  topic: keywords
subject: SAHI
  topic: keywords
creator: Thang C. Vu
creator: Dung T. Nguyen
creator: Long Q. Dinh
creator: Mui D. Nguyen
creator: Minh T. Nguyen
rights: Copyright (c) 2026 Thang C. Vu, Dung T. Nguyen, Long Q. Dinh, Mui D. Nguyen, Minh T. Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
doi: 10.4108/eetiot.12412

Optimizing Crop Yield and Monitoring Leaves with an Intelligent Internet of Things
type: item
description: INTRODUCTION: Agriculture is very important in facilitating global food security but the high rate of population growth, global warming, unpredictable climatic conditions and scarcity of natural resources are subjecting the traditional farming techniques to a lot of pressure. OBJECTIVES: , An IoT-based system is developed to monitor the structure of leaf, which is one of the main indicators of plant health and growth. METHODS: A multi-stage research procedure to develop an IoT-enabled leaf structure analysis system to boost agricultural output. RESULTS: IoT-based leaf structure monitoring system has proven very well in a variety of datasets and even better than the conventional ML models yet remains computationally efficient in the IoT setting. CONCLUSION: An IoT-enabled gadget in the form of a leaf structure that has the potential to revolutionize precision agriculture, increasing the yield of harvests and reducing the overall impact on the environment, is examined.
subject: Research article
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Agriculture
  topic: keywords
subject: Leaf Structure Monitoring
  topic: keywords
subject: Crop Yield
  topic: keywords
subject: Precision Farming
  topic: keywords
subject: Real-time Data Sensors
  topic: keywords
subject: Data Analytics
  topic: keywords
creator: Ankit Khare
creator: Bramah Hazela
creator: Awanish Mishra
creator: Brijesh Kumar Chaurasia
rights: Copyright (c) 2026 Ankit Khare, Bramah Hazela, Awanish Mishra, Brijesh Kumar Chaurasia https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetiot.12194

Hybrid Genetic Algorithm and Water Wave Optimization Approach for QoS Aware Multi Objective Task Scheduling and Load Balancing in Cloud Environments
type: item
description: Efficient task scheduling and load balancing in cloud computing continue to present significant challenges as a result of heterogeneous, dynamic workloads, particularly for AI/ML applications in edge-to-cloud and multi-cloud deployments. Classic and single-metaheuristic techniques sometimes converge prematurely, lack adaptability, and fail to manage multi-objective trade-offs. This paper introduces the Adaptive Evolutionary Wave Optimisation Scheduler (AEWOS), a novel hybrid metaheuristic that combines genetic algorithm evolutionary operators for global diversity generation with adaptive water wave optimisation for propagation, refraction towards elite solutions, and breaking-based localised refinement. A weighted composite fitness function with thirteen QoS metrics, diversity-variance-driven self-adaptive parameter optimisation, and constraint-aware repair techniques maintains virtual machine capacity and exclusive task allocation limits in AEWOS. CloudSim Plus's ability to conduct extensive simulations of Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Salp Swarm Algorithm (SSA), Gaussian Blackwinged Kite Algorithm (GBKA), Enhanced Osprey Optimisation Algorithm (EOOA), and Gazelle Coati Optimisation Algorithm (GCOA) represents the six advanced benchmarks that AEWOS consistently exceeds. Additionally, the use of actual portions of the ATLAS (Alibaba Trace for Learning-Augmented Scheduling) workload traces within machine computation across various scaling scenarios (500-2500 tasks on 20-100 heterogeneous virtual machines) demonstrates that AEWOS consistently outperforms these six advanced benchmarks. Significant improvements include 3-13 percentage point increases in resource utilisation, 8-22% reductions in makespan, 10-35% decreases in transmission and waiting durations, 40-70% relative decreases in imbalance degree, nearly constant throughput (0.272 tasks/s), 10-25 percentage point reductions in SLA violation rates, improved energy efficiency an…
subject: Research article
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Task Scheduling
  topic: keywords
subject: Load Balancing
  topic: keywords
subject: Hybrid metaheuristic
  topic: keywords
subject: Genetic Algorithm
  topic: keywords
subject: Water Wave Optimization
  topic: keywords
subject: Multi Objective Optimization
  topic: keywords
subject: Virtual Machines
  topic: keywords
creator: Nihar Ranjan Sabat
creator: Rashmi Ranjan Sahoo
creator: Biswaranjan Acharya
creator: Raghvendra Kumar
rights: Copyright (c) 2026 Nihar Ranjan Sabat, Rashmi Ranjan Sahoo, Biswaranjan Acharya, Raghvendra Kumar https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-12
publication date: 2026-03-12
doi: 10.4108/eetiot.12172

Flood Mapping employing U-Net: A Case Study of Teesta Dam
type: item
description: River Teesta is prone to weather fluctuations that leads to cloud burst or heavy rainfall resulting in overflowing of Teesta dam (27.0018057°N, 88.4404352°E) situated across the Teesta Basin region. Dam overflow is one of the major causes of sudden and uncontrolled release of water, which often leads to flooding in nearby downstream areas. Accurate flood mapping is important to reduce the damage caused by floods in low-lying areas. This study uses a U-Net deep learning model to identify flooded regions near Kalimpong in the Teesta River area using satellite images. The proposed method identifies flood-affected areas accurately and provides useful support for early warning and water management systems. The model was compared with existing methods such as Otsu thresholding, Random Forest, SegNet, and PSPNet using IoU, F1-score, precision, and recall values. The proposed U-Net model achieved 98% IoU, 98% F1-score, 99% precision, and 97% recall, showing better results than the other methods. The IoU value was 26% higher than Otsu thresholding, 18% higher than Random Forest, 10% higher than SegNet, and 8% higher than PSPNet. The training and validation graphs show stable learning with very little overfitting and steady performance during all 100 epochs. In addition, NDVI time-series analysis for Lachung city and the Lachung–Teesta meeting point showed important changes in vegetation that are useful for water resource management. These results show that the proposed U-Net model can identify flood areas effectively and can help improve early warning systems and safe dam management. However, the study is limited to Sentinel-2 image-based flood segmentation for the Teesta basin and does not include real-time hydrological inputs.
subject: Research article
  topic: section
subject: Flood mapping
  topic: keywords
subject: Teesta dam
  topic: keywords
subject: NCC natural colour composite
  topic: keywords
subject: U-Net
  topic: keywords
subject: Sentinel 2-A
  topic: keywords
creator: Akanksha Singh
creator: Syed Wajahat Abbas Rizvi
creator: Prabhat K. Srivastava
rights: Copyright (c) 2026 Akanksha Singh, Syed Wajahat Abbas Rizvi, Prabhat K. Srivastava https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-07-21
publication date: 2026-07-21
doi: 10.4108/eetiot.11486

CNN and Blockchain Integrated E-Governance Framework System for Tamper-Proof Documents
type: item
description: Land registration and record management systems worldwide continue to face significant challenges, including document fraud, long processing times, and inefficient maintenance procedures. Traditional methods involve several technical limitations that reduce reliability and transparency. To address these issues, the proposed system leverages blockchain technology to improve process efficiency, data integrity, and security in land registration workflows. In the proposed framework, users upload property details and supporting land documents while initiating a sale. However, fraudulent document uploads remain a common issue, enabling sellers to receive payments using forged records without the buyer’s knowledge. To mitigate such risks, a Convolutional Neural Network (CNN) is integrated to authenticate and validate uploaded land documents before further processing. Only documents verified as authentic are stored on the blockchain. The government authority converts these validated documents into Non-Fungible Tokens (NFTs) and mints them on the Ethereum blockchain. A unique hash is generated for each document, enabling secure verification and traceability through platforms such as Etherscan. Once the documents are confirmed to be valid, the property is approved for sale, and the ownership transfer between the seller and buyer is securely executed through the blockchain enabled system. We evaluated the performance of proposed framework by considering both blockchain performance metrics and CNN evaluation metrics.
subject: Research article
  topic: section
subject: Blockchain
  topic: keywords
subject: Land Registration
  topic: keywords
subject: CNN
  topic: keywords
subject: Non-Fungible Token
  topic: keywords
subject: Verification System
  topic: keywords
subject: Cybersecurity
  topic: subjects
subject: CNN
  topic: subjects
subject: Artificial Intelligence
  topic: subjects
subject: Blockchain Technologies
  topic: subjects
creator: C. Rupa
creator: K. Venkata Subba Reddy
creator: Srinivas Jagirdar
creator: Srinivas Rao Pulluri
creator: N.Md. Jubair Basha
creator: Md. Sirajuddin
rights: Copyright (c) 2026 C. Rupa, K. Venkata Subba Reddy, Srinivas Jagirdar, Srinivas Rao Pulluri, N.Md. Jubair Basha, Md. Sirajuddin Sirajuddin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-03
publication date: 2026-06-03
doi: 10.4108/eetiot.11425

An Investigative Analysis of Adaptive Consensus Mechanisms for Distributed Blockchain Systems
type: item
description: INTRODUCTION: Blockchain technology has achieved widespread adoption across diverse application domains, yet its foundational consensus mechanisms remain largely static and may not suited to the dynamic, heterogeneous conditions of modern decentralized networks. While adaptive consensus has emerged as a promising solution, a comprehensive and systematic framework for classifying, evaluating, and selecting adaptive mechanisms remains notably absent. OBJECTIVES: This survey addresses this critical gap by introducing a structured taxonomy of adaptive consensus mechanisms for distributed blockchain systems, underpinned by a systematic review of performance evidence and real- world deployment experiences. METHODS: Following PRISMA 2020 guidelines, we conducted a systematic search of 1,675 records from Scopus (2020– 2025), ultimately including 68 peer-reviewed studies. We analyze four principal categories of adaptive consensus namely dynamic parameter adjustment, consensus algorithm switching, hybrid mechanisms, and AI/ML-enhanced protocols across five key performance dimensions: throughput, scalability, energy efficiency, security level, and implementation complexity. Case studies of SABEC for UAV coordination, 6G cognitive radio spectrum management, and supply chain networks illustrate real-world deployment trade-offs. RESULTS: Our comparative analysis reveals fundamental performance–security trade-offs inherent to each adaptive category. Dynamic parameter adjustment mechanisms offer low implementation complexity but limited scalability gains; algorithm-switching approaches achieve high throughput (100–1,000 TPS) at the cost of very high implementation complexity; hybrid schemes such as HyFlexChain demonstrate 112.5+ TPS under BFT mode with high security but remain at the prototype stage; and AI/ML-enhanced protocols show theoretical promise yet face critical security challenges including adversarial attacks and model poisoning that undermine system trustworthiness. The…
subject: Review article
  topic: section
subject: Adaptive Consensus
  topic: keywords
subject: Blockchain Technology
  topic: keywords
subject: Hybrid Consensus
  topic: keywords
subject: Dynamic Consensus
  topic: keywords
subject: Consensus Mechanisms
  topic: keywords
subject: Proof of Work
  topic: keywords
subject: Proof of Stake
  topic: keywords
subject: Byzantine Fault Tolerance
  topic: keywords
subject: Scalability
  topic: keywords
subject: Security
  topic: keywords
creator: Smita Sachin Bhore
creator: Natraj N.A.
rights: Copyright (c) 2026 Smita Sachin Bhore, Natraj N.A. https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-23
publication date: 2026-04-23
doi: 10.4108/eetiot.11144

Quantum-Classical Adaptive Navigation: QAOA-PPO Framework for GNSS-Denied Urban Environments
type: item
description: This study shows a mixed quantum-classical guidance system that combines an eight-layer Quantum Approximate Optimization Algorithm (QAOA) with Proximal Policy Optimization (PPO) reinforcement learning for cities that do not have GNSS. The design changes the settings of the quantum circuit on the fly based on real-time data from the environment. This makes noise in cities less of a static mistake source and more of an optimization constraint. After testing on 47.3 kilometers of GNSS-denied paths in three cities using a three-tier DARPA QuANET-aligned protocol and NIST-traceable equipment, the results show that the average positional accuracy is 0.15 ± 0.03 meters, which is 70% better than high-grade GPS/INS systems. With adaptive correction, the framework keeps 98.2% of its coherence under ISO 16750-3 shaking and 95.3% of its operational reliability during 24-hour signal rejection. It also reduces drift by 82% compared to LiDAR-SLAM over a kilometer. It is statistically significant (F (3,1196) =87.3, p<0.001, ·²=0.38), and the effect sizes are big. The system passes SAE Level 4 power limits (45.2W), goes beyond DARPA QuANET 2025 goals, and gets Hybrid Readiness Level 7 approval. Each unit is expected to cost $210,000, and it creates the first policy-ready quantum navigation platform for smart city application.
subject: Research article
  topic: section
subject: Autonomous vehicles
  topic: keywords
subject: GNSS-denied navigation
  topic: keywords
subject: NIST validation
  topic: keywords
subject: quantum approximate optimization algorithm
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: urban positioning
  topic: keywords
creator: Gabriel Silva Atencio
rights: Copyright (c) 2026 Gabriel Silva Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetiot.11077

Comparative Analysis of Quantum and Classical Computing: Performance, Error Rates, and Hybrid Architectures
type: item
description: Classical computing is approaching its physical limits, so quantum computing as an alternative model requires careful evaluation. This study compares various aspects of two systems by looking at formal algorithmic complexity, simulation-based comparisons with realistic noise models, quantitative meta-analysis, figuring out the difference between physical and logical error rates, and clearly defining contributions. A thorough study examined 24,100 papers published between 2015 and 2024 from leading scientific databases. A study of algorithmic complexity using Big-O notation confirmed that Shor's algorithm is faster than standard O(exp((64/9)²/³ (log N)²/³ (log log N)²/³)) by a super polynomial factor, Grover's algorithm is faster by a quadratic factor (O(√N) versus O(N)), and quantum simulation is faster by an exponential factor. There were Five quantum machines exhibited physical error rates ranging from 10⁻² to 10⁻⁵. Simulation-based testing revealed that the quantum advantage emerges above certain complexity thresholds: at extremely high complexity, quantum processing reached 10.3 μs versus 40.2 μs for classical computing, with better error resistance (2.3% for quantum vs. 5.9% for classical). Quantum computing has real benefits in terms of speed, mistake tolerance, and energy economy for problems that are too complicated to solve with traditional methods. However, significant challenges remain in scalability and workforce development. Hybrid quantum-classical models offer the most promising path to near-term practical benefits. On the other hand, the shift to postquantum security needs instant attention, regardless of when quantum hardware will be available.
subject: Research article
  topic: section
subject: Algorithmic complexity analysis
  topic: keywords
subject: Classical computing
  topic: keywords
subject: Hybrid quantum-classical models
  topic: keywords
subject: postquantum cryptography
  topic: keywords
subject: Quantum computing
  topic: keywords
subject: Quantum error correction
  topic: keywords
creator: Gabriel Silva Atencio
rights: Copyright (c) 2026 Gabriel Silva Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetiot.11029

Quantum-Enhanced Optimization of AV-Drone Fleets for Urban Last-Mile Logistics
type: item
description: The rise of e-commerce and calls for sustainability make things hard for businesses in cities, showing where standard shipping methods fail. The study shows a new hybrid quantum-classical optimization scheme that bridges three major gaps: AV-drone coordination that does not work well in the real world; environmental claims that have not been proven; and real-world limits that are not considered enough. In a modern design, the Quantum Approximate Optimization Algorithm (QAOA) and Deep Q-Networks (DQN) are put together. There are two main variables used for optimization: regulatory factors (β =0.32) and energy limits (β =0.56). The framework cuts costs by 28% and speeds up delivery times by 32%, while keeping 89% of solution accuracy on noisy quantum simulations. Run 1,000 Monte Carlo models and compare them to real data to do this. Checking the methods with three steps—computer simulation, stakeholder analysis (n=25, ±=0.81), and policy paper review—is what makes sure they work. The results make it possible to measure the difference in success between models and real life. They show that in the real world, savings of 40% in efficiency drop to 22%. It turns out that regulatory division is the main problem, causing 92% of practical errors. The study produces the idea of energy-constrained quantum advantage and shows that it can only be useful with solid-state batteries (≥400 Wh/kg) and regulatory unification. These efforts set new standards for urban mobility research that involves multiple fields and show how driverless operations can be used in a way that is viable.
subject: Research article
  topic: section
subject: Autonomous vehicles
  topic: keywords
subject: Drone delivery
  topic: keywords
subject: Quantum optimization
  topic: keywords
subject: Reinforcement learning
  topic: keywords
subject: Urban logistics
  topic: keywords
subject: XAI
  topic: keywords
creator: Gabriel Silva Atencio
rights: Copyright (c) 2026 Gabriel Silva Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-02
publication date: 2026-03-02
doi: 10.4108/eetiot.11026

Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review
type: item
description: INTRODUCTION: The proliferation of the amalgamation of IoT and edge computing has increased the demand for decentralised trust and security mechanisms capable of operating across heterogeneous and resource-limited devices. Approaches such as federated learning, Zero Trust architectures, lightweight blockchain and distributed neural models offer alternatives to centralised control. OBJECTIVES: This review examines various state-of-the-art decentralised mechanisms and evaluates their effectiveness in terms of securing IoT networks at the edge. METHODS: Thirty recent studies were analysed to compare how decentralised architectures establish trust, support secure communication and enable intrusion and anomaly detection. Frameworks, such as DFGL-LZTA, SecFedDNN and COSIER were assessed. RESULTS: Decentralised designs enhance privacy, reduce single points of failure and improve adaptive threat response, though challenges remain in scalability, efficiency and interoperability. CONCLUSION: The study identifies key considerations and future research needs for building secure and resilient trust-aware IoT edge ecosystems.
subject: Review article
  topic: section
subject: Decentralised Security
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Zero Trust Architecture
  topic: keywords
subject: Trust Management
  topic: keywords
subject: IoT
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: Decentralised security and trust mechanisms for IoT edge computing
  topic: subjects
creator: Khandoker Ashik Uz Zaman
creator: Mahdi H. Miraz
creator: Mohammed N. M. Ali
rights: Copyright (c) 2026 Khandoker Ashik Uz Zaman, Mahdi H. Miraz, Mohammed N. M. Ali https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/eetiot.10996

System of digitizing and paying of civil status documents via an RFID card
type: item
description: Nowadays, with the remarkable advancement of technology in almost all fields of activity, we notice a considerable decrease in data search time. Thus we are witnessing the birth of new technologies and applications that continue to make our lives easier and in the most beautiful ways. However, the computerization of systems remains a valuable aid, particularly in the field of administrations such as civil status. At the time, before the idea of computerizing civil status in Senegal, all citizens' files were managed on paper. Which continued to create problems within these services (loss of transcription, loss of time for research), queues and jostling. We had to make an observation that all civil status documents, in any case a good number in the world are computerized so that obtaining papers has become simpler than usual while in Senegal obtaining civil status documents is generally done on paper, but there are areas where the process is digitalized. First of all, the user presents the badge or RFID card which is scanned by the RFID module and the system checks the existence of the card at the database level. Then, if the card is registered, it connects to the network in order to access the web page to enter its password. And finally the person obtains his request if the password is correct. The account is recharged manually by the platform administrator or automatically by the user via mobile money operators such as Orange money, Wave and others.
subject: Technical article
  topic: section
subject: RFID card
  topic: keywords
subject: civil status documents
  topic: keywords
subject: paying
  topic: keywords
subject: IoT (Internet of Things)
  topic: keywords
creator: Mohamed E.A. Ouesse
creator: Diatta Baboucar
creator: Basse Adrien
creator: Bouallegue Ridha
rights: Copyright (c) 2025 Mohamed E.A. Ouesse, Diatta Baboucar, Basse Adrien, Bouallegue Ridha https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-18
publication date: 2025-11-18
doi: 10.4108/eetiot.10970

Risk-Aware Reinforcement Learning for Cooperative Autonomous Vehicle Coordination with Adaptive Risk Sensitivity and Multi-Agent Optimization
type: item
description: Ensuring safe and efficient coordination of autonomous vehicles (AVs) in intelligent transportation systems is particularly difficult under dense, uncertain, and rapidly changing traffic conditions. Many existing reinforcement learning (RL) methods show good performance in simplified environments but fail to fully account for heterogeneous risk exposure and non-stationary, multi-agent interactions. To address this gap, this paper introduces a Risk-Aware Reinforcement Learning (RARL) framework that couples adaptive risk sensitivity with Bayesian risk estimation in a cooperative multi-agent setting. Within RARL, reward signals are dynamically reshaped using real-time probabilistic risk measures, allowing AV agents to jointly balance safety and traffic efficiency across signalised intersections, multi-lane highways, and roundabout scenarios. The proposed approach is evaluated using SUMO, CARLA, NGSIM, and INTERACTION benchmarks. Compared with strong multi-agent RL baselines such as Bi-AC, MACPO, and MAPPO-L, RARL achieves up to 30% fewer collisions, about 25% higher throughput, roughly 30% improvement in scenario-recognition accuracy, and around 20% faster training convergence. These empirical results show that explicit and adaptive risk modelling significantly enhances policy robustness, scalability, and cooperative behaviour in heterogeneous traffic. By tightly integrating risk-aware decision making with multi-agent coordination, RARL provides a scalable and practically deployable paradigm for next-generation autonomous driving, improving safety, reliability, and real-time adaptability.
subject: Research article
  topic: section
subject: Risk-aware reinforcement
  topic: keywords
subject: cooperative autonomous vehicles
  topic: keywords
subject: adaptive risk sensitivity
  topic: keywords
subject: multi-agent reinforcement learning
  topic: keywords
subject: Bayesian risk modelling
  topic: keywords
subject: intelligent transportation systems
  topic: keywords
creator: Malikireddy Ramesh Reddy
creator: Annalakshmi Govindaraj
rights: Copyright (c) 2026 Malikireddy Ramesh Reddy, Annalakshmi Govindaraj https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-05
publication date: 2026-03-05
doi: 10.4108/eetiot.10944

LPWAN Localization via RSSI/SNR Fingerprinting and Lightweight Machine Learning
type: item
description: Accurate geolocation for low-power wide-area (LPWAN) devices is desirable when GNSS is unavailable or too energy-expensive, yet RSSI-/TDoA-based approaches are often fragile under channel variability, collisions and cross-device heterogeneity. We address this gap with a reproducible, tabular pipeline that maps LoRa RSSI/SNR/ToA and PHY metadata to 2D positions, compares strong tabular baselines (k-NN, Random Forest, LightGBM, XGBoost), and crucially evaluates them under group-aware (device-wise) splits to avoid identity leakage. On an ns-3-generated LoRa dataset of about 3.3 × 104 labeled receptions, Random Forest attains the tightest distribution with p50 ≈ 0 m and p95 < 1 m, whereas k-NN, despite a low median, exhibits a much heavier tail (p95 ≈ 187 m), underscoring the need to report both central and tail metrics. These results indicate that simple, edge-feasible models can perform gateway-side inference with robust accuracy when fed cleaned features and evaluated with realistic splits, making the approach attractive for practical LPWAN/IoT deployments.
subject: Research article
  topic: section
subject: LPWAN
  topic: keywords
subject: LoRA
  topic: keywords
subject: Localization
  topic: keywords
subject: Fingerprinting
  topic: keywords
subject: RSSI
  topic: keywords
subject: SNR
  topic: keywords
subject: IoT
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: LPWAN
  topic: subjects
subject: LoRa
  topic: subjects
subject: Localization
  topic: subjects
subject: Deep Learning
  topic: subjects
subject: Fingerprinting
  topic: subjects
creator: Désiré Guel
creator: Flavien Hervé Somda
creator: P. Justin Kouraogo
creator: Boureima Zerbo
creator: Oumarou Sié
rights: Copyright (c) 2026 Désiré Guel, Flavien Hervé Somda, P. Justin Kouraogo, Boureima Zerbo, Oumarou Sié https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-01
publication date: 2026-06-01
doi: 10.4108/eetiot.10888

Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation
type: item
description: We often plan and build applications that carry out various processes requiring precise time management. Time management is attributed to the microcontroller, which can use various techniques such as instruction execution time, which requires delicate control of loops, or the use of functions such as delay, which cause your program to lose time in order to create delays. The problem with this kind of time management is that the time lost is of unknown duration. To alleviate this problem, new microcontrollers almost always offer specialized time-counting and time-management circuits, known as timers. A timer is a kind of clock built into the microcontroller, enabling the duration of an event to be measured. Their use is not complicated, but requires a good understanding of the structure of the various timers on a microcontroller. In this paper, we’ve chosen to demonstrate the improved and management of timers on Microchip’s PIC 16F877 microcontroller using assembly language.
subject: Research article
  topic: section
subject: PIC16f877 microcontroller
  topic: keywords
subject: timer register
  topic: keywords
creator: Souleymane Mbathie
creator: Mohamed Ouesse
creator: Baboucar Diatta
creator: Adrien Basse
creator: Ridha Bouallegue
rights: Copyright (c) 2025 Souleymane Mbathie, Mohamed Ouesse, Baboucar Diatta, Adrien Basse, Ridha Bouallegue https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-14
publication date: 2025-11-14
doi: 10.4108/eetiot.10862

Adaptive Spatio-Temporal Deep Learning for Robust Cyber Threat Detection Across IoT Environments
type: item
description: In interconnected systems such as the Internet of Things (IoT), industrial control systems, and smart cities, real-time intrusion detection is crucial in smart network environments. It analyses network traffic in real time, enabling rapid detection and mitigation of cyber threats. With the help of artificial intelligence, including deep learning, real-time intrusion detection systems (IDS) can spot suspicious patterns, adapt to new-fangled attack vectors, and keep latency low, all without sacrificing system performance or reliability. This paper addresses intrusion detection in smart network environments by introducing Net Sentry DL, a deep learning framework. To extract spatio-temporal features and improve interpretability, the model uses a combination of CNNs, TCNs, and attention-guided fusion. Data imbalance and noise are addressed using an entropy-based pre-processing method and a class-preserving algorithm called Synthetic Minority Oversampling Technique (SMOTE). The UNSW-NB15, BoT-IoT, and TON_IoT benchmark datasets are used to assess Net Sentry DL. It surpasses models such as SVM, Random Forest, LSTM, GRU, and CNN in binary classification, reaching up to 0.99 accuracy and 0.98 F1-score on BoT-IoT. With TON_IoT, it achieves an accuracy of 0.95, and with BoT-IoT, up to 0.97, in multi-class configurations. Using SHAP, attention heatmaps, and gated fusion visualisations, the model exhibits strong explainability and robust generalisation, as demonstrated by cross-dataset testing. Through ONNX conversion and low quantisation loss (0.7%), it efficiently deploys and achieves low inference time (37ms/sample). The significance of each module, particularly CP-SMOTE and the TCN-attention combination, has been confirmed by ablation studies. For ever-changing IoT-based infrastructures, Net Sentry DL demonstrates competitive accuracy, interpretability, and deployment efficiency for intrusion detection.
subject: Research article
  topic: section
subject: Real-time Intrusion detection
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Temporal Convolutional Networks
  topic: keywords
subject: Synthetic Minority Oversampling Technique
  topic: keywords
subject: Industrial control systems
  topic: keywords
subject: Internet of Things
  topic: keywords
creator: Narendra Kumar
creator: Kalai Vani YS
creator: Muneshwara M S
creator: Chittibabulu Sape
creator: V. Subba Reddy
creator: Idimadakala Madhavilatha
rights: Copyright (c) 2026 Narendra Kumar, Kalai Vani YS, Muneshwara M S, Chittibabulu Sape, V. Subba Reddy, Idimadakala Madhavilatha https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-30
publication date: 2026-06-30
doi: 10.4108/eetiot.10791

Trust-Aware Federated Learning with Differential Privacy for Secure AIoT in Critical Infrastructures
type: item
description: Federated learning offers a scalable solution for distributed intelligence in Artificial Intelligence of Things (AIoT) systems, yet privacy leakage, adversarial attacks, and system heterogeneity remain persistent challenges in critical infrastructures such as smart cities, agriculture, and forestry. This paper proposes PriSec- FedGuardNet, a trust-aware federated learning framework that integrates differential privacy, homomorphic secure aggregation, and graph neural network–based trust evaluation to safeguard both data and model updates. The framework preserves sensitive information by perturbing gradients with calibrated noise, encrypts local updates for aggregation without decryption, and assigns trust scores to filter unreliable participants. Experimental validation on ToN-IoT, Bot-IoT, and real-world sensor datasets demonstrates that PriSec-FedGuardNet maintains above 97.3% relative utility under strict privacy budgets, improves anomaly detection F1-scores by up to 18% under poisoning attacks, and reduces device-level energy overheads to less than 12%. Domain-specific evaluations across Indian smart city, agricultural, and forestry deployments further highlight the adaptability and efficiency of the framework. By balancing privacy, security, and utility, PriSec-FedGuardNet establishes a robust paradigm for secure federated learning in AIoT-driven critical infrastructures.
subject: Research article
  topic: section
subject: federated learning
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Homomorphic Encryption
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Trust-Aware Aggregation
  topic: keywords
subject: Critical Infrastructures
  topic: keywords
subject: AIoT
  topic: keywords
creator: Ramana Kadiyala
creator: C. V. Lakshmi Narayana
creator: S. China Ramu
creator: Narsaiah Putta
creator: Shyam Sunder Pabboju
creator: B. Ramana Reddy
rights: Copyright (c) 2025 Ramana Kadiyala, C. V. Lakshmi Narayana, S. China Ramu, Narsaiah Putta, Shyam Sunder Pabboju, B. Ramana Reddy https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.4108/eetiot.10656

Culturally-Aware Artificial Intelligence: Personal Values and Technology Acceptance among AI Researchers in China and Germany
type: item
description: INTRODUCTION: AI development is driven by innovation and cultural contexts of collaboration. As AI and IoT systems shape global interaction, understanding cultural influences on technology perception is key for adaptive design and governance. This study compares personal values and AI acceptance among researchers in China and Germany – two leading yet culturally distinct ecosystems. OBJECTIVES: To identify value patterns supporting trustworthy AI and effective cross-cultural collaboration in research and IoT contexts. METHODS: A cross-national survey (n = 200) using the Portraits Value Questionnaire (PVQ) and the Digital Technology Acceptance Scale (DTAS) examined factors shaping AI perception. RESULTS: Chinese participants show higher AI acceptance and stress self-enhancement and conservation; Germans emphasize self-transcendence and greater caution. CONCLUSION: Findings inform culture-aware AI design, value-aligned governance, and intercultural collaboration.
subject: Research article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cross-Cultural Collaboration
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
subject: Responsible AI
  topic: keywords
subject: Personal Values
  topic: keywords
subject: Technology Acceptance
  topic: keywords
subject: Artificial Intelligence
  topic: subjects
subject: Cross-Cultural Studies
  topic: subjects
subject: Human–Computer Interaction
  topic: subjects
subject: AI Ethics and Governance
  topic: subjects
subject: Socio-Technical Systems
  topic: subjects
subject: Collaborative Computing
  topic: subjects
creator: Dominic Lammert
creator: Ming Liu
creator: Stefanie Betz
creator: Jacqueline Lammert
creator: Jürgen Pfeffer
rights: Copyright (c) 2026 Dominic Lammert, Ming Liu, Stefanie Betz, Jacqueline Lammert, Jürgen Pfeffer https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-05
publication date: 2026-01-05
doi: 10.4108/eetiot.10618

Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology
type: item
description: Electronic Shelf Labels (ESL) are increasingly replacing traditional paper-based pricing systems due to their flexible update capabilities, ease of management, and environmental benefits. This paper presents the research and development of an optimized ESL device that incorporates LoRa technology and a 2.13-inch E-paper display capable of using low power consumption. By analyzing and considering design factors, the ESL device is engineered to achieve compact dimension, low energy consumption, long lifespan, and improved communication range. The paper also details an efficient operational workflow for integrating these devices into a system. The measurement results indicate that the ESL device can have a lifespan exceeding 5 years, highlighting its efficiency in energy consumption within an ESL system. The device in this paper can operate reliably at distances of up to 23 meters in an obstacle-rich environment, achieving a packet delivery rate of over 96%.
subject: Research article
  topic: section
subject: Internet of things
  topic: keywords
subject: LoRaWAN
  topic: keywords
subject: Miniature Antenna
  topic: keywords
subject: Low Power Wide Area Network
  topic: keywords
subject: Electronic Shelf Label
  topic: keywords
subject: E-paper
  topic: keywords
creator: Le Huy Trinh
creator: Thanh-Tuan Phan
creator: Fabien Ferrero
creator: Nguyen Vu Truong
rights: Copyright (c) 2025 Le Huy Trinh, Thanh-Tuan Phan, Fabien Ferrero, Nguyen Vu Truong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-10
publication date: 2025-10-10
doi: 10.4108/eetiot.9488

The Statistical Analytical Review of IoT Threat Detection Mechanisms: Quantitative Evaluation and Multidimensional Performance Assessment Analysis
type: item
description: Increased deployment of IoT systems in industrial, healthcare, smart city, and home environments has expanded the attack surface and complexity of cyber threats. Though a plethora of detection techniques have emerged in literature in the last decade, an unforgivable absence of statistical rigors and compare-and-contrast analysis on the operational characteristics is apparent in the literature sets. This paper presents a statistical analytical review of various contemporary IoT threat detection methods across a wide array of architectures: classical machine learning, deep learning, federated learning, blockchain-based systems, quantum-enhanced frameworks, and hybrid models. The review employs a multidimensional evaluation strategy, extracting both qualitative and quantitative metrics from each study; these enable an objective comparison across heterogeneous systems, Standard performance parameters—Scalability, Delay, Time Complexity, Memory Complexity, Make span, and Analysis Efficiency—are tabulated; thus, an unfolding of a universal analytical framework with almost 300 data points exposes trade-offs, bottlenecks to efficiency, and constraints to deployment. Furthermore, evaluation of the application-specific techniques for healthcare, agriculture, and smart grids were conducted in relation to adaptability and domain specifications. The work identifies that hybrid deep networks (e.g., CNN-LSTM) provide better accuracy at higher computation cost, while TinyML and ensemble models present a trade-off factor for both detection accuracy versus hardware efficiency. In addition, whereas quantum and blockchain Integrated systems have shown to be solid in theory, they face practical impairments. Research gaps identified here lead the discussion on future directions, toward explainability, energy-aware design, and adversarial resilience, thus providing a tangible roadmap toward the next generation of secure IoT frameworks.
subject: Review article
  topic: section
subject: IoT Security
  topic: keywords
subject: Threat Detection
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Performance Evaluation
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Process
  topic: keywords
creator: Lanka Kavitha
creator: Kareemulla Shaik
rights: Copyright (c) 2026 Lanka Kavitha, Kareemulla Shaik https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-12
publication date: 2026-01-12
doi: 10.4108/eetiot.10529

Multi-Agent Soft Actor-Critic with Graph Attention Networks for Adaptive Traffic Signal Optimization (MASAC-GAT)
type: item
description: INTRODUCTION: Adaptive Traffic Signal Optimisation (ATSO) is a challenging problem for urban traffic networks, having important implications for congestion reduction, traffic efficiency, and environmental conservation. Conventional traffic signal control techniques, i.e., fixed-time and rule-based control, fail to respond to dynamic traffic behaviour efficiently. OBJECTIVES: Recent developments in Reinforcement Learning (RL) have been promising for ATSO but are plagued by poor scalability, lack of coordination in multi-intersection networks, and inefficiency in dealing with continuous action spaces. METHODS: Furthermore, most RL-based solutions are based on simplistic state representation and fail to incorporate complex interdependencies between traffic signals. Considering these limitations, this paper introduces a new framework, Multi-Agent Soft Actor-Critic with Graph Attention Networks (MASAC-GAT), which unites the sample efficiency and stability of Soft Actor-Critic (SAC) with the relational modelling ability of Graph Attention Networks (GATs). RESULTS: The proposed method exhibited significant performance gains on three important traffic metrics: Signal Adjustment Efficiency (92%), Average Waiting Time (20–35 seconds), and Congestion Prediction Accuracy (93%), outperforming DQL, PPO, A2C, GNN-based variants, and knowledge sharing DDPG (KS-DDPG). Through minimised redundant signal changes and reduced vehicle delays, the method ushers in the next generation of smart transportation systems. CONCLUSION: The proposed method facilitates decentralised yet coordinated control of traffic signals by utilising local observations and global context. The proposed method unites real-time traffic observations, e.g., traffic volume, vehicle speeds, weather, accident reports, and signal status, into a customised OpenAI Gym environment for training and evaluation.
subject: Research article
  topic: section
subject: Traffic Signal Opmisation
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Intelligent Transportation Systems
  topic: keywords
subject: Sustainable Urban Mobility
  topic: keywords
subject: Graph neural Network
  topic: keywords
subject: Attention
  topic: keywords
creator: R. M. Bommi
creator: E. Bhuvaneswari
creator: M. Rohini
creator: G. Uganya
rights: Copyright (c) 2026 R. M. Bommi, E. Bhuvaneswari, M. Rohini, G. Uganya https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-05
publication date: 2026-01-05
doi: 10.4108/eetiot.10486

A Q-Learning and Blockchain Framework for Secure Dynamic Bandwidth Allocation in Heterogeneous IoT
type: item
description: The rapid expansion of the Internet of Things (IoT) has intensified the challenge of achieving dynamic bandwidth allocation while maintaining security across heterogeneous devices and communication protocols. Conventional static allocation schemes lack adaptability, while existing learning-based or blockchain-based approaches typically optimize performance or trust in isolation. To address this gap, this paper proposes a hybrid framework that integrates Q-learning–based adaptive bandwidth allocation with a lightweight, permissioned blockchain-based trust mechanism. The framework is evaluated through MATLAB-based simulations involving 100 heterogeneous IoT devices under dynamic traffic conditions and adversarial behavior. Performance is compared against multiple baselines, including static allocation, learning-only and blockchain-only schemes, classical scheduling algorithms (WFQ and DRR), and a deep reinforcement learning approach (DQN). The results reveal clear trade-offs among bandwidth utilization, fairness, energy consumption, and security. Static and classical schedulers provide predictable fairness but remain vulnerable to malicious activity. Learning-only and deep reinforcement learning approaches improve adaptability but lack intrinsic trust awareness, while blockchain-only enforcement enhances security at the expense of responsiveness. By coupling adaptive decision-making with trust validation, the proposed hybrid framework achieves a balanced operating point, offering stable bandwidth utilization, improved energy efficiency, and robust attack resilience under noisy and uncertain conditions. These findings highlight the importance of aligning learning mechanisms with trust-aware constraints for secure and scalable bandwidth management in heterogeneous IoT networks.
subject: Research article
  topic: section
subject: Heterogeneous IoT
  topic: keywords
subject: Dynamic Bandwidth Allocation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Network Security
  topic: keywords
creator: Ahmed Saeed Obied
creator: Ahmad Shahidan Abdullah
creator: Hind Mowafaq Taha
creator: Sanaa Abd Tarish
rights: Copyright (c) 2026 Ahmed Saeed Obied, Ahmad Shahidan Abdullah, Hind Mowafaq Taha, Sanaa Abd Tarish https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-03
publication date: 2026-02-03
doi: 10.4108/eetiot.10464

Design and Analysis of Hybrid GDI-PTL Based Wallace Tree Multipliers for Low-Power DCT Architecture in Image Compression
type: item
description: This paper presents a hybrid low-power Wallace Tree multiplier architecture employing Gate Diffusion Input (GDI) and Pass Transistor Logic (PTL) techniques, specifically designed for efficient integration into Discrete Cosine Transform (DCT)-based image compression systems. The proposed design incorporates a novel hybrid full adder that leverages the low-power advantages of GDI and the high-speed characteristics of PTL, resulting in a compact, power-optimized solution. GDI logic is utilized for less-transistor-count realization of sum and carry logic, while PTL enhances XOR computation and signal propagation with minimal delay and area overhead. This hybrid adder is embedded into a Wallace Tree multiplier, which serves as a critical computational block within the 2D-DCT transform engine, commonly used in JPEG image compression. The multiplier's efficient structure significantly reduces the number of logic stages needed for partial product reduction, ensuring high throughput and reduced switching activity. Implemented in 90 nm and 45 nm CMOS technologies, the design achieves notable improvements in power-delay product (PDP), area, and energy efficiency when compared to conventional CMOS or single-style logic designs. Simulation of the design is performed using Cadence EDA spectre simulator, results up to a 10× reduction in power consumption and substantial area savings. These results establish the hybrid GDI-PTL-based Wallace Tree multiplier as a highly suitable solution for real-time and portable image processing applications, including mobile devices, low-power high efficiency video encoders, and energy-constrained embedded systems.
subject: Research article
  topic: section
subject: Hybrid full adder
  topic: keywords
subject: GDI logic
  topic: keywords
subject: PTL
  topic: keywords
subject: Wallace Tree multiplier
  topic: keywords
subject: 2D-DCT
  topic: keywords
subject: image compression
  topic: keywords
subject: Low power
  topic: keywords
subject: area optimization
  topic: keywords
subject: delay reduction
  topic: keywords
creator: K.B. Bhavya
creator: A.M. Vijaya Prakash
rights: Copyright (c) 2026 K.B. Bhavya, A.M. Vijaya Prakash https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-03
publication date: 2026-03-03
doi: 10.4108/eetiot.10362

Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions
type: item
description: Wireless Sensor Networks (WSNs) have become a foundational technology across diverse domains, ranging from critical healthcare monitoring to large-scale environmental management. However, the severe energy constraints of sensor nodes remain a persistent bottleneck, threatening both operational efficiency and network longevity. While metaheuristic algorithms offer promising solutions, existing reviews often focus on isolated network layers or rely on outdated datasets. Addressing this gap, this Systematic Literature Review (SLR) analyzes 48 primary studies published between 2019 and 2024, offering a holistic taxonomy that integrates routing and clustering optimizations. The findings reveal that Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) continue to dominate the field, each appearing in 23.5% of studies. However, a decisive shift is observed toward hybrid techniques such as Firefly–PSO and Grey Wolf Optimization variants which demonstrate enhanced adaptability in avoiding local optima, albeit at higher computational costs. Performance evaluations remain heavily simulation-driven, primarily focusing on energy consumption (31.2%), network lifetime (29.8%), and throughput (19.9%), while real-world validations in domains like Industrial IoT remain scarce. Furthermore, the review identifies emerging trends integrating Machine Learning, Edge Computing, and UAV-assisted routing into metaheuristic frameworks, signaling a transition toward more secure and multi-objective optimization strategies. This study concludes by highlighting critical open issues in fault tolerance, heterogeneous node management, and security-aware routing, providing a strategic roadmap for developing resilient, deployment-ready WSN solutions.
subject: Review article
  topic: section
subject: Wireless Sensor Networks
  topic: keywords
subject: Metaheuristics
  topic: keywords
subject: Energy Optimization
  topic: keywords
subject: Routing
  topic: keywords
subject: Clustering
  topic: keywords
subject: Hybrid Algorithms
  topic: keywords
creator: Rudi Hartono
creator: I Wayan Mustika
creator: Selo Sulistyo
rights: Copyright (c) 2026 Rudi Hartono, I Wayan Mustika, Selo Sulistyo https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-12
publication date: 2026-01-12
doi: 10.4108/eetiot.10328

An Adaptive Intrusion Detection System for Securing the Internet of Medical Things Using Deep Learning
type: item
description: INTRODUCTION: The Internet of Medical Things (IoMT) has expanded rapidly, with a growing number of medical devices becoming interconnected and increasingly integral to healthcare delivery. However, this expansion has also introduced significant cybersecurity risks, making IoMT networks vulnerable to sophisticated cyber-attacks that threaten patient data confidentiality and system reliability. OBJECTIVES: This study aims to develop a robust intrusion detection framework capable of accurately identifying both known and unknown cyber-attacks in IoMT environments while minimizing false positives and false negatives. METHODS: The proposed framework employs a Capsule Neural Network (CapsNet) to effectively capture spatial hierarchies and part–whole relationships in network traffic data. Additionally, the Theory of Association (TOA) is utilized for batch-size hyperparameter tuning to enhance learning efficiency and detection performance. The model is evaluated using standard performance metrics to assess its effectiveness in detecting malicious traffic. RESULTS: Experimental results demonstrate that the proposed Intrusion Detection System (IDS) achieves an accuracy of 98.37%, precision of 98.57%, recall of 98.17%, and an F1 score of 98.37%. These results indicate strong real-time detection capability with minimal false positives and false negatives. CONCLUSION: The findings highlight the effectiveness of integrating deep learning techniques, particularly CapsNet with TOA-based optimization, in strengthening cybersecurity for IoMT networks. The proposed IDS provides a secure and efficient solution for protecting healthcare data and ensuring patient confidentiality, offering a promising approach to enhancing the security and performance of healthcare IoMT systems.
subject: Research article
  topic: section
subject: Intrusion Detection System
  topic: keywords
subject: Internet of Medical Things
  topic: keywords
subject: Capsule Networks
  topic: keywords
subject: Teamwork Optimization Algorithm
  topic: keywords
creator: Abdullah M. Albarrak
rights: Copyright (c) 2026 Abdullah M. Albarrak https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-19
publication date: 2026-02-19
doi: 10.4108/eetiot.10326