EAI Endorsed Transactions on Internet of Things
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: 4.2), DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions eISSN: 2414-1399
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Real-Time Mosquito Breeding Site Detection Using a Quantized YOLOv8-Tiny Model on an FPGA-Based Drone Platform
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.
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
Optimizing Crop Yield and Monitoring Leaves with an Intelligent Internet of Things
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.
Copyright (c) 2026 Ankit Khare, Bramah Hazela, Awanish Mishra, Brijesh Kumar Chaurasia https://creativecommons.org/licenses/by-nc-sa/4.0
Hybrid Genetic Algorithm and Water Wave Optimization Approach for QoS Aware Multi Objective Task Scheduling and Load Balancing in Cloud Environments
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 and scalability, and up to 65% reductions in task migration overhead. It has been demonstrated through an ablation study that the Adaptive Mechanism and Wave Propagation Model are the key factors that have an effect on system performance. AEWOS provides a self-adjusting, cohesive framework that improves the sustainability and resilience of multi-objective cloud resource management.
Copyright (c) 2026 Nihar Ranjan Sabat, Rashmi Ranjan Sahoo, Biswaranjan Acharya, Raghvendra Kumar https://creativecommons.org/licenses/by-nc-sa/4.0
An Investigative Analysis of Adaptive Consensus Mechanisms for Distributed Blockchain Systems
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 maturity assessment confirms that higher implementation complexity consistently correlates with limited real-world deployment. CONCLUSION: Our findings demonstrate that no universal adaptive consensus mechanism exists for blockchain applications. This structured, evidence-grounded survey provides an effective methodology for designing, evaluating, and selecting adaptive consensus solutions for diverse decentralized system deployments.
Copyright (c) 2026 Smita Sachin Bhore, Natraj N.A. https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum-Classical Adaptive Navigation: QAOA-PPO Framework for GNSS-Denied Urban Environments
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.
quantum approximate optimization algorithm
Copyright (c) 2026 Gabriel Silva Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
Comparative Analysis of Quantum and Classical Computing: Performance, Error Rates, and Hybrid Architectures
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.
Quantum-Enhanced Optimization of AV-Drone Fleets for Urban Last-Mile Logistics
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.
Decentralised Trust and Security Mechanisms for IoT Networks at the Edge: A Comprehensive Review
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.
Decentralised security and trust mechanisms for IoT edge computing
Copyright (c) 2026 Khandoker Ashik Uz Zaman, Mahdi H. Miraz, Mohammed N. M. Ali https://creativecommons.org/licenses/by-nc-sa/4.0
System of digitizing and paying of civil status documents via an RFID card
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.
Copyright (c) 2025 Mohamed E.A. Ouesse, Diatta Baboucar, Basse Adrien, Bouallegue Ridha https://creativecommons.org/licenses/by-nc-sa/4.0
Risk-Aware Reinforcement Learning for Cooperative Autonomous Vehicle Coordination with Adaptive Risk Sensitivity and Multi-Agent Optimization
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.
Copyright (c) 2026 Malikireddy Ramesh Reddy, Annalakshmi Govindaraj https://creativecommons.org/licenses/by-nc-sa/4.0
Improved PIC16f877 microcontroller timer register to delimit the execution time of an operation
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.
Copyright (c) 2025 Souleymane Mbathie, Mohamed Ouesse, Baboucar Diatta, Adrien Basse, Ridha Bouallegue https://creativecommons.org/licenses/by-nc-sa/4.0
Trust-Aware Federated Learning with Differential Privacy for Secure AIoT in Critical Infrastructures
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.
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
Culturally-Aware Artificial Intelligence: Personal Values and Technology Acceptance among AI Researchers in China and Germany
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.
Copyright (c) 2026 Dominic Lammert, Ming Liu, Stefanie Betz, Jacqueline Lammert, Jürgen Pfeffer https://creativecommons.org/licenses/by-nc-sa/4.0
Research and Design a Low Power Electronic Shelf Label Based on E-paper Display and LoRa Technology
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%.
Copyright (c) 2025 Le Huy Trinh, Thanh-Tuan Phan, Fabien Ferrero, Nguyen Vu Truong https://creativecommons.org/licenses/by-nc-sa/4.0
The Statistical Analytical Review of IoT Threat Detection Mechanisms: Quantitative Evaluation and Multidimensional Performance Assessment Analysis
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.
Copyright (c) 2026 Lanka Kavitha, Kareemulla Shaik https://creativecommons.org/licenses/by-nc-sa/4.0
Multi-Agent Soft Actor-Critic with Graph Attention Networks for Adaptive Traffic Signal Optimization (MASAC-GAT)
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.
Copyright (c) 2026 R. M. Bommi, E. Bhuvaneswari, M. Rohini, G. Uganya https://creativecommons.org/licenses/by-nc-sa/4.0
A Q-Learning and Blockchain Framework for Secure Dynamic Bandwidth Allocation in Heterogeneous IoT
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.
Copyright (c) 2026 Ahmed Saeed Obied, Ahmad Shahidan Abdullah, Hind Mowafaq Taha, Sanaa Abd Tarish https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Analysis of Hybrid GDI-PTL Based Wallace Tree Multipliers for Low-Power DCT Architecture in Image Compression
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.
Copyright (c) 2026 K.B. Bhavya, A.M. Vijaya Prakash https://creativecommons.org/licenses/by-nc-sa/4.0
Metaheuristic Approaches for Energy Optimization in Wireless Sensor Networks: A Systematic Review of Trends, Challenges, and Future Directions
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.
Copyright (c) 2026 Rudi Hartono, I Wayan Mustika, Selo Sulistyo https://creativecommons.org/licenses/by-nc-sa/4.0
An Adaptive Intrusion Detection System for Securing the Internet of Medical Things Using Deep Learning
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.
Copyright (c) 2026 Abdullah M. Albarrak https://creativecommons.org/licenses/by-nc-sa/4.0
A Comprehensive Survey on Objective Functions in RPL Routing with Various Networking and Application Scenarios
Technological breakthroughs in the Internet of Things (IoT) have positioned the Routing Protocol for Low-Power and Lossy Networks (RPL) as a cornerstone for enabling connectivity in resource-constrained and highly dynamic environments. The Objective Function (OF) lies at the heart of RPL, which guides parent selection and route optimization. However, conventional OFs are mostly limited to basic metrics, frequently overlook critical factors such as link heterogeneity, dynamic traffic patterns, energy fairness, reliability, and application-specific Quality of Service (QoS) demands. This survey presents a systematic and technically rigorous review of 108 influential studies published between 2015 and 2024, aimed at focusing on the multidimensional challenges of OF design across varied network environments and application scenarios. Rather than offering a broader overview like existing surveys, it critically assesses the adaptability of different OFs, the trade-offs they introduce, and evaluates their impact on routing performance, while identifying unresolved research gaps that limit scalability, interoperability, and practical deployment. It further highlights emerging solutions such as multi-metric optimization, context-aware routing, and machine learning–based OFs as more promising strategies to enhance the resilience and efficiency of RPL. By integrating fragmented knowledge into a cohesive framework, this survey not only strengthens theoretical understanding but also outlines a research agenda for developing next-generation OFs that are adaptive, cross-domain, and ready for practical implementation, thereby creating lasting impact for future IoT deployments.
Copyright (c) 2025 Ibrahim Ali Alnajjar https://creativecommons.org/licenses/by-nc-sa/4.0
A Trust Based Energy Efficient Routing Design Based on Hybrid Particle Swarm Optimization (HPSO) for Wireless Sensor Networks
Congestion in wireless sensor networks (WSNs) reduces resource availability, often leading to sensor node failures and misbehavior. Additionally, high energy consumption decreases the overall lifespan and performance of the network. To address these limitations, this paper presents a trust-based and congestion-aware optimization method for WSNs. The proposed strategy consists of two main stages. In the first stage, congestion levels and node trust values are evaluated to derive an optimal congestion metric. In the second stage, a Hybrid Particle Swarm Optimization (HPSO) algorithm is applied to determine optimal data routing paths from Sensor Nodes (SNs) to the Base Station (BS), considering both distance and trust-congestion parameters. The proposed HPSO combines the immigration and emigration processes of the Biogeography-Based Optimization (BBO) algorithm with the mutation process of Particle Swarm Optimization (PSO) to achieve efficient data distribution. Experimental comparisons with existing methods demonstrate that the proposed approach significantly improves performance in terms of energy consumption, latency, packet delivery ratio (PDR), and network lifetime.
Copyright (c) 2025 S. Gnana Selvan, G. Gifta Jerith, C. Mahesh, S. Ravikumar, S. Samsudeen Shaffi , S. Jagadeesh https://creativecommons.org/licenses/by-nc-sa/4.0
A Scalable Hybrid RF-BiLSTM Framework for Reliable IoT Traffic Threat Detection via Feature Selection and Temporal Pattern Recognition
In this research, we addressed the recurring challenges of securing IoT networks against emerging cyber security threats. Taking advantage of the complementary strengths of Random Forest (RF) for feature selection and Bidirectional Long Short-Term Memory (BiLSTM) networks for sequential learning; we developed a novel Hybrid RF-BiLSTM model that combines feature level insights with temporal pattern recognition to provide a reliable solution for IoT traffic threats. We conducted extensive experiments with Aposemat IoT-23 dataset, where we used equal volumes of benign and malicious traffic samples leading to balanced evaluation. Furthermore, the Hybrid RF-BiLSTM model achieved a performance of 99.87%, while the Random Forest and BiLSTM performance were 99.37% and 93.32%, respectively, demonstrating the power of the hybrid approach over individual ones. The analysis gave more details about the model's performance, showing the confusion matrix and calculating the performance metrics that substantiates the model's reliability to minimize false positive and false negatives while also achieving high precision and recall. It shows that how well the integration of feature selection and sequential learning works for IoT cyber security. This Hybrid RF-BiLSTM approach lays a scalable and practical framework for real-world IoT security problems and a stepping stone for future studies in hybrid ML models for anomaly detection and threat analysis.
Copyright (c) 2025 Nadia Ansar, Suraiya Parveen, Ihtiram Raza Khan, Bhavya Alankar https://creativecommons.org/licenses/by-nc-sa/4.0
A Quantitative Framework for the Selection of Hybrid Consensus Mechanisms in Blockchain-IoT Systems
INTRODUCTION: The application of blockchain technology to Internet of Things (IoT) systems offers substantial potential for enhancing security, but traditional consensus mechanisms are ill-suited for resource-constrained environments. While hybrid consensus solutions have emerged as a promising alternative, a systematic framework for their classification and evaluation is notably absent. OBJECTIVES: This study addresses this critical gap by introducing a novel, application-driven framework for analyzing hybrid consensus mechanisms, underpinned by a quantitative synthesis of performance benchmarks. METHODS: We analyze diverse architectures—including combinations of Proof of Work (PoW) and Proof of Stake (PoS), PBFT-enhanced systems, and hierarchical models—through the lens of specific IoT application priorities, such as latency, energy efficiency, and scalability. Case studies of IOTA's Tangle, IoTeX's Roll-DPoS, and Hyperledger Fabric illustrate these practical trade-offs. RESULTS: Our framework reveals not only primary performance trade-offs but also critical "second-order" complexities, such as emergent vulnerabilities at the intersection of different consensus layers. CONCLUSION: Our findings demonstrate that this structured, quantitatively-grounded approach provides an effective methodology for designing and selecting regulatory-compliant hybrid consensus solutions for specific IoT applications.
Practical Byzantine Fault Tolerance (PBFT)
Copyright (c) 2025 N. A. Natraj, J. J. Midhunchakkaravarthy, Brojo Kishore Mishra https://creativecommons.org/licenses/by-nc-sa/4.0
Enhanced Beamspace Channel Recovery in mmWave MIMO Using Deep Neural Networks
Millimeter-wave (mmWave) massive MIMO systems use many antennas. These systems offer high data rates. But using many radio frequency (RF) chains increases cost and power use. To solve this, lens antenna arrays are used. Energy is focused, allowing the use of fewer RF chains. However, this creates a new challenge. With fewer RF chains, it is hard to estimate the wireless channel. Accurate channel estimation is needed for good system performance. In beamspace, the channel is sparse. This shows that only a few values are large. The rest are close to zero. Because of this, the problem is seen as sparse signal recovery. AMP (Approximate Message Passing) is one popular algorithm used for this. A better version named LAMP (Learned AMP) uses deep learning. But it still does not give the best results. This paper proposes a new method GM-LAMP. It improves the channel estimation accuracy. It uses prior knowledge about the channel. It assumes that the beamspace channel follows a Gaussian mixture distribution. First, a new shrinkage function is created based on this distribution. Then, the original function in the LAMP network is replaced with the new one. As a result, a better deep learning model is developed. The final GM-LAMP network estimates the beamspace channel more precisely. It works well with both theoretical models and real-world data. Simulations show that GM-LAMP performs better than earlier methods. This approach combines math knowledge and deep learning. It shows that using prior information helps deep networks make smarter predictions. The proposed method offers better accuracy and is useful for future mmWave systems.
Copyright (c) 2025 V. Saraswathi, Vatsala Anand, Yaddanapudi Venkata Bhaskara Lakshmi, Samana Vinaya Kumar, Vijaya Babu Burra, U.S.B.K. Mahalaxmi, Suneetha Jalli, V. Vijayasri Bolisetty, Sarala Patchala https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing 5G Traffic Management with Programmable Intelligence and Open RAN Integration
INTRODUCTION: 5G networks are complex. They must handle different types of connections. These networks support industries, cities and mobile users. Managing traffic is difficult. Traditional methods are not efficient. OBJECTIVES: This paper introduces a software framework called ns-O-RAN. It combines a real-world RAN Intelligent Controller with a network simulator. This allows testing AI solutions without expensive hardware. The study also proposes a smart handover method. METHODS: The goal is to reduce delays and improve speed. The new method uses deep reinforcement learning (DRL). DRL learns the best way to assign users to base stations. The framework collects a large amount of data. It trains the AI system using this data. The model learns from past network conditions. It then makes better decisions for the future. RESULTS: The proposed solution increases network efficiency. The researchers tested their model. They compared it with traditional handover methods. This means faster speeds and fewer connection losses. The framework also enables real-time monitoring. It detects network issues quickly and adapts to changing conditions. This ensures stable and high-quality connections for users. CONCLUSION: This approach supports different types of applications. It works well for video streaming, voice calls and industrial automation. This work has important implications. It helps telecom providers improve service quality. It also reduces operational costs. Researchers and engineers can use this framework for further development.
Copyright (c) 2025 U.S.B.K. Mahalaxmi, Jeevana Sujitha Mantena, V.V. Jaya Rama Krishnaiah, Vatsala Anand, B. Rajani, Inakoti Ramesh Raja, Pavuluri Girija Krishna Sirisha https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Crop Yield Prediction with IoT and Agricultural UAVs: A Comprehensive Review
INTRODUCTION: Rapid development in the field of the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) is allowing them to be utilized across multiple sectors like industrial manufacturing, healthcare, defense, etc. In the agricultural industry, IoT and UAVs are also proving themselves as one of the most promising technologies. These technologies have opened the door to numerous innovative opportunities in precision agriculture, particularly in predicting crop yields more accurately and efficiently. Traditional methods for crop yield prediction were based on manual sampling and statistical models, which proved to be time-consuming and less accurate. OBJECTIVES: This paper mainly contributes to the comprehensive study of IoT and UAVs in crop yield prediction. It highlights how data-driven methods, sensor technologies, and remote sensing enhance decision-making in precision agriculture. METHODS: The paper discusses traditional practices for crop yield prediction and their limitations. It explains the architecture of IoT and its various layers, including a detailed study and comparison of different IoT sensors, microcontrollers, and communication standards. The paper further focuses on the potential of UAVs for yield prediction, including details of different types of UAV platforms, control strategies, and communication standards. Additionally, the paper explains the benefits and limitations of integrating IoT and UAVs for more accurate crop yield prediction. RESULTS: The study demonstrated that IoT-enabled monitoring and UAV-based remote sensing improve crop prediction accuracy. CONCLUSION: Overall, this paper presents the transformative capability of integrating IoT and UAV in modernizing the process of crop yield prediction and other precision agriculture practices. As a future scope, the paper focuses on the use of edge/fog computing, mobile apps, and AI chatbots to enhance the power of IoT and UAVs in crop yield prediction.
Copyright (c) 2025 Purnima Awasthi, Sumita Mishra, Nishu Gupta https://creativecommons.org/licenses/by-nc-sa/4.0
Glaucoma Retinal Image Detection through the Segmentation of OD Using Modified Deep Learning Method
Classifiers are the important processing module in any type of classification systems. This paper uses the proposed Modified LeNET (MLNET) classification architecture along with the standard LeNET to classify the retinal pictures into healthy cases and cases of glaucoma. This research work develops an automated computer aided system which has the following modules as preprocessing, Optic Disk (OD) segmentation, Feature computations and MLNET classification. The Glaucoma classification system has been functioned in two processing phases as training and testing. The training processing phase trains both healthy and Glaucoma retinal images from the known dataset using preprocessing, OD segmentation and feature computations from the segmented OD region. These features from the OD region have been further trained by the proposed MLNET classifier. The testing processing phase tests the unknown retinal image into either Glaucoma or healthy class through the sub processing modules of preprocessing, OD region segmentation and feature computations. The features from the OD region in the unknown test retinal image have been fed into the proposed MLNET classifier with respect to the previous training results.
Copyright (c) 2025 J. Ruby Elizabeth, D. Kesavaraja, S. Ebenezer Juliet, S. Jagadeesh, S. Samsudeen Shaffi, R. Umanesan https://creativecommons.org/licenses/by-nc-sa/4.0
Migration of Microservices Execution Contexts Between Processing Zones in Software-Defined Vehicular Fog Networks
The growing need for time-sensitive applications in vehicular networks makes Fog Computing a promising model to orchestrate cloud-based services consumed by vehicle nodes, as it brings computational resources and decision-making processes near to vehicles. However, vehicular mobility presents significant challenges for implementing Fog Computing Services that require low latency. The dynamic nature of vehicle movement means that the physical location of computational resources (Fog Nodes) relative to vehicles is constantly changing. Consequently, maintaining a consistent and reliable low-latency communication path becomes challenging. Recent research suggests that the Software-Defined Networking paradigm can optimize Vehicular Fog Computing Networks in resource and service management. In this paper, a dynamic methodology is proposed for the migration of the microservice execution context between processing zones in Software-Defined Vehicular Fog Computing Networks (SDVFN). This approach was tested using a simulated use case in our SDVFN Simulation Framework, designed to support research on dynamic microservice orchestration in SDVFN, taking into account vehicular mobility.
Mobility-Responsive Microservice Orchestration
Copyright (c) 2025 Leonel D.C. Alvarenga, Pedro Sousa, António Costa https://creativecommons.org/licenses/by-nc-sa/4.0
Multimedia Encryption Using Rubik’s Cube-Inspired Algorithm: A Novel Approach to Audio and Image Security
This paper presents a fast and secure image and audio encryption scheme based on the principles of a Rubik’s Cube. The proposed method combines scrambling and XOR-based diffusion to ensure confidentiality during storage and transmission. Multimedia data are converted into matrix representations and permuted using randomly generated row and column secret keys. Circular shift operations are applied based on the parity of the key values to achieve effective confusion and diffusion. This paper highlights the application of rubric cube algorithm applied on audio data for the first time for encryption and decryption process for voice biometric system design. The experimental results are also presented on par to image data with execution time. Audio data is processed and modelled as a three-dimensional Rubik’s Cube and scrambled cube using random rotational transformations. Decryption is performed using inverse operations with identical keys. Experimental results confirm strong security, low computational complexity, and high reconstruction accuracy, demonstrating suitability for secure multimedia communication
Copyright (c) 2026 Vani HY, Anusuya M A, Shwethashree G C, KB Drakshayini, Vinod D S https://creativecommons.org/licenses/by-nc-sa/4.0