IoT

Edge computing has emerged as a promising paradigm to minimize latency and energy consumption while improving computational efficiency for mobile devices. Latency-sensitive applications such as autonomous driving, augmented reality, and industrial automation require ultra-low response times, making efficient task offloading a necessity in edge computing. However, distributing optimally computational tasks among edge servers remains a challenge, especially when considering latency, energy consumption, and workload balancing simultaneously. Although existing approaches have focused on one or two of these objectives, they do not provide a holistic solution that incorporates all three factors. In addition, some existing solutions do not take advantage of parallelism at the edge layer, resulting in bottlenecks and inefficient resource usage. In this paper, we propose a novel learning-based task offloading model that integrates parallel processing at the edge layer, adaptive workload balancing, and joint latency–energy optimization. Moreover, by dynamically adjusting the number of selected edge servers for parallel execution, our approach achieves optimal trade-offs between performance and resource efficiency. Our experimental setup includes several edge servers and several randomly deployed devices. It employs Apache HTTP Benchmark (AB) to generate realistic Mobile Edge Computing workloads. The obtained results show that our method outperforms existing approaches by reducing latency, lowering energy consumption, and maintaining a balanced workload across edge nodes. Full article

Thermal hazards in industrial environments often remain undetected until critical failure or injury occurs. Conventional handheld infrared cameras require manual operation and limit continuous situational awareness. This study presents the design and field validation of a wearable helmet-mounted real-time thermal system based on the MLX90640 infrared array sensor and a Raspberry Pi 5 edge computing platform. Experimental validation was performed across multiple scenarios of 400 measurements based on industrial distances of 100 cm and 150 cm. The performance of the system was tested against a pre-calibrated hotspot infrared thermometer using linear regression analysis and standard error metrics to determine proportional agreement. The results indicate a strong proportional relationship between the two systems at both industrial distances, with R² values ranging from 0.9885 to 0.9973 at 100 cm and from 0.9586 to 0.9867 at 150 cm. A moderate increase in mean absolute error (MAE) was observed as the measurement distance increased. Statistically significant increases in error were identified in mechanically dynamic scenarios where statistically significant increases in measurement error were observed (p-value < 0.05), indicating distance-dependent sensitivity under moving mechanical conditions. The higher absolute errors at longer distances mainly result from field-of-view expansion, reduced target occupancy, and mixed-pixel hotspot effects rather than weakened proportional trend stability. An industrial distance-aware linear regression model was developed to describe behavior and support calibrations under different deployment conditions. Despite minor absolute deviations during dynamic operations, the system maintained strong trend-tracking performance, suggesting suitability for daily preliminary hazard monitoring in industrial safety maintenance. Full article

Complex Event Processing (CEP) is essential for real-time analytics in domains such as industrial IoT, cybersecurity, and financial monitoring, yet CEP adoption is still hindered by the difficulty of authoring temporal rules and by rigid redeployment workflows. This paper presents PatternStudio, a neuro-symbolic CEP framework that translates natural language specifications into validated event-processing patterns and executes them on a deterministic Apache Flink-based runtime without interrupting service. The generative layer is constrained to produce a typed intermediate representation, while the symbolic layer enforces validation and runtime execution guarantees. We evaluate the prototype as a single-node system-characterization study on commodity hardware representative of edge and near-edge gateways rather than microcontroller-class devices. Under this setting, PatternStudio reaches 47,910 events per second at 250 active rules while maintaining a bounded memory footprint between 1.6 GB and 1.9 GB during the reported runs. Beyond 500 active rules, throughput degradation is driven primarily by CPU saturation and alert amplification, which also explains the sharp increase in tail latency. Additional measurements with parallelism 4, a static baseline, and a two-stage NL-to-IR evaluation further show that the architecture remains functional under partitioned execution, incurs moderate dynamic-orchestration overhead, preserves rule structure reliably under natural-language authoring, and supports interchangeable LLM backends at the semantic front end. Full article

Emergency vehicle authentication in vehicular ad hoc networks must satisfy strict latency, privacy, and trust constraints. Existing Public Key Infrastructure- and Conditional Privacy-Preserving Authentication-based schemes incur substantial overhead from certificate management and expensive per-hop verification, making them unsuitable for real-time emergency scenarios. We propose a lightweight zero-knowledge- and blockchain-assisted authentication scheme that eliminates certificates, pseudonym pools, and the requirement for online interaction with a trusted authority during the authentication phase. The Certificate Authority (CA) is involved only during offline initialization stages (vehicle enrollment and Merkle tree construction); once provisioning is complete, the runtime authentication process operates without any online CA interaction. Each emergency vehicle registers one-time hash commitments on-chain after proving membership in a category-specific Merkle tree, and authenticates messages by broadcasting a hash along with a zero-knowledge proof of preimage knowledge. Roadside units verify the proof and consult the on-chain state to enforce single-use semantics, creating a tamper-resistant audit trail. Evaluation using the Veins framework (OMNeT++/SUMO) demonstrated a constant 288-byte authenticated payload, millisecond-level end-to-end delay independent of hop count, and stable blockchain processing under sustained load. Full article

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Smart-city IoT ecosystems depend on a large number of devices with limited resources, which often lack built-in security mechanisms. While traditional cloud-based or gateway-centric intrusion detection systems (IDSs) offer essential security, they are still characterized by high detection latency, considerable bandwidth demand, and a lack of precise monitoring of single device actions. This study proposes the Edge AI Bridge, a novel micro-computing security layer positioned between IoT devices and the gateway to enable early-stage threat interception. The architecture integrates embedded AI hardware with a hybrid pipeline, utilizing unsupervised anomaly detection for behavioral profiling and a lightweight signature-matching module to minimize false positives. System operations—including localized traffic inspection, protocol parsing, and feature extraction—are performed before data aggregation, which preserves device-level privacy and reduces the computational burden on the IoT gateway. The contemporary CIC-IoT-2023 dataset, which captures a wide range of smart-city protocols and attack vectors, is used to evaluate the architecture. The Edge AI Bridge leads to a significant reduction in detection latency—≈50 ms on average as opposed to the 500 ms of cloud-based solutions—while the resource footprint is kept low to about 20% CPU utilization. The Edge AI Bridge demonstrates a potential solution that is scalable, modular, and can preserve privacy while improving the cyber resilience of the smart-city infrastructures that are large, heterogeneous, and difficult to manage. Full article

The safety and reliability of railway wagon operations largely depend on the timely detection of degradation in safety-critical components such as axle bearings, wheelsets, and braking systems. Conventional maintenance strategies based on fixed inspection intervals are often inadequate for capturing the actual operating conditions of wagon components, leading to delayed fault detection or unnecessary maintenance actions. To address these limitations, this paper proposes a sensor-based health assessment framework for the continuous monitoring of railway wagons under operational conditions. The proposed framework integrates multi-sensor data acquisition, systematic signal preprocessing, feature-based health indicator construction, and temporal degradation analysis to evaluate component health in real time. A safety-oriented decision logic is employed to classify operating conditions and generate reliable alerts while minimizing false detections caused by transient disturbances. The effectiveness of the proposed approach is validated using a publicly available run-to-failure bearing dataset that exhibits degradation characteristics similar to those observed in railway wagon axle bearings. Experimental results demonstrate that the proposed framework achieves improved classification accuracy, higher detection reliability, reduced false alarm rates, and lower detection latency compared to representative existing condition monitoring approaches. In addition, the computational efficiency of the proposed model confirms its suitability for real-time deployment. The results indicate that the proposed health assessment framework provides a practical and reliable solution for safety-critical railway wagon monitoring and forms a strong foundation for future extensions toward predictive maintenance and remaining useful life estimation. Full article

The Internet of Vehicles (IoV) is a rapidly evolving technology that provides real-time connectivity, enhanced road safety, and reduced traffic congestion; however, its inherently open communication channels expose it to serious security and privacy threats. In 2021, Chaudhry proposed SMEP-IoV, a lightweight message authentication protocol designed to satisfy essential security requirements. This paper presents a comprehensive security analysis of SMEP-IoV and reveals several serious vulnerabilities. Specifically, sensitive credentials are stored in plaintext without tamper-resistant protection, and both authentication and session key derivation depend directly on these credentials. These structural flaws allow an adversary to extract the stored secrets, generate valid authentication messages, and derive the established session key, enabling vehicle impersonation and session key disclosure attacks. Moreover, compromise of long-term secrets facilitates key compromise impersonation attacks. It also fails to ensure anonymity and perfect forward secrecy. To address these issues, we propose an enhanced authentication protocol for resource-constrained IoV environments, leveraging a three-factor authentication mechanism combined with lightweight cryptographic primitives. Formal security analyses using BAN logic, Tamarin, and ProVerif confirm its resilience against known attacks, while NS-3 simulations validate its scalability, high throughput, and low End-to-End Delay (E2ED). The results highlight the protocol as a robust, efficient, and scalable solution for large-scale IoV deployments. Full article

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges. Resource-constrained devices face sophisticated threats but lack the computational capacity for advanced security analysis. This study investigates optimal security task allocation in Cognitive IoT (CIoT) networks. It specifically examines when IoT devices should process security tasks locally or offload them to Mobile Edge Computing (MEC) servers. The problem is formulated as a Continuous-Time Markov Decision Process (CTMDP). The study demonstrates that the optimal offloading policy has a threshold structure. Security tasks are offloaded to MEC servers when the offloading queue length is below a critical threshold, $k^{\ast }$. Otherwise, tasks are processed locally. This structural property is robust to changes in MEC server configurations and threat arrival patterns. It ensures an optimal and easily implementable security policy under the exponential model. Theoretical analysis establishes upper bounds on the performance of AI-based security controllers using the same models. The results also show that standard model-free Q-learning algorithms can recover optimal thresholds without any prior knowledge of the system parameters. Simulations across multiple reinforcement learning architectures, including Q-learning, State–Action–Reward–State–Action (SARSA), and Deep Q-networks (DQN), confirm that all methods converge to the predicted threshold. This empirically validates the analytical findings. The threshold structure remains effective under practical imperfections such as imperfect sensing and parameter estimation errors. Systems maintain 85% to 93% of their optimal performance. This work extends threshold Markov Decision Process (MDP) analysis from classical queuing theory to the context of CIoT security offloading. It provides optimal and practical policies and model-free algorithms for use by resource-constrained devices. Full article

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

This study presents an Internet of Things (IoT)-assisted semi-open hydroponic system for cultivating Andrographis paniculata under tropical conditions, aiming to enhance biomass productivity, andrographolide (AG) yield, and production efficiency. IoT-assisted hydroponics, non-IoT hydroponics, and soil-based cultivation were compared in 10 m² greenhouses. The IoT system enabled real-time monitoring and adaptive regulation of temperature, relative humidity, light intensity, nutrient solution pH, and electrical conductivity (EC). IoT-assisted hydroponics achieved earlier harvest (≈90 days) and the highest fresh biomass yield (0.409 ± 0.014 kg m⁻²) while maintaining per-plant productivity (15.74 ± 0.54 g plant⁻¹) comparable to soil-based cultivation. Andrographolide concentration reached 25.58 ± 3.36 mg g⁻¹ DW (2.56% w/w), meeting pharmacopeial requirements. Owing to stable environmental regulation and tolerance to high planting density, the IoT system produced the highest areal AG productivity (209.5 mg m⁻²), representing a four- to tenfold increase over the other systems. Despite higher operational costs, IoT-assisted hydroponics achieved the lowest AG unit cost (≈6.77 USD g⁻¹). While most previous studies emphasize tissue-level AG concentration, system-level productivity and cost efficiency under realistic cultivation conditions remain insufficiently explored. Overall, IoT-enabled semi-open hydroponics provides a scalable and economically viable approach for medicinal plant production, bridging the gap between open-field cultivation and fully controlled plant factory systems. Full article

The pervasive adoption of AI and AIoT applications at the network edge presents both opportunities and challenges for smart cities. With a focus on the energy efficiency of AI in urban environments, this paper provides a systematic comparative analysis of representative edge hardware platforms, i.e., embedded GPUs, FPGAs, and ultra-low-power microcontroller-/sensor-class devices, assessing their suitability for AI workloads in IoT-driven smart city infrastructures. The evaluation, based on direct characterization of diverse neural networks and relevant datasets, quantifies computational performance and energy behavior through inference latency, throughput, and energy/per inference measurements. Across the evaluated network–board pairs, the measured inference power spans several orders of magnitude, ranging from 0.1–10 mW for ultra-low-power Intelligent Sensor Processing Units (ISPUs) up to 1–10 W for embedded GPUs, highlighting the wide design space between the least and most power-demanding configurations. Results indicate that embedded GPUs provide a favorable performance-to-power ratio for computationally intensive workloads, while MCU/ISPU-class solutions, despite throughput limitations, offer compelling advantages in ultra-low-power scenarios when combined with quantization and pruning, making them well-suited for distributed sensing and actuation typical of smart city deployments. Overall, this comparative analysis guides hardware selection for heterogeneous, sustainable AI-enabled urban services. Full article

The continuous expansion of the Internet of Things (IoT) has intensified the need to evaluate and guarantee the quality of entropy sources used in random number generation, an essential element in securing communications used in IoT ecosystems. This work presents an automated and web-based framework designed to execute and analyze the results of statistical tests defined in the NIST SP 800-22 standard, enabling systematic assessment of entropy sources and random numbers generators in IoT devices and environments. The proposed system integrates a Python-based backend built upon an optimized implementation of the original NIST suite, along with an intuitive web interface that facilitates configuration, monitoring, and parallel execution of tests through Representational State Transfer (REST) endpoints. Session management based on Redis ensures reliable and concurrent operation of multiple users or devices while maintaining isolation and data integrity. To demonstrate its applicability, an emulated IoT ecosystem was implemented in which multiple virtual devices periodically and asynchronously request real-time validation of their local random numbers generators. The obtained results confirm the system’s capability to detect deficiencies in pseudo random generators and validate true random number sources, highlighting its potential as a diagnostic and verification tool for distributed IoT security systems. The tool developed in this work is fully accessible to the public, allowing researchers, engineers, and practitioners to evaluate random number generators without requiring specialized hardware or proprietary software. Full article

Occupant presence is a primary driver of Heating, Ventilation, and Air Conditioning (HVAC) and lighting energy consumption in office environments. Existing occupancy-sensing solutions often rely on privacy-sensitive modalities or require costly infrastructure, limiting their applicability in Small and Medium Offices (SMOs). To address these limitations, this study proposes a lightweight CSI-based occupancy-sensing framework based on a dual-core ESP32-S3 architecture, enabling concurrent CSI processing, environmental sensing, and cloud communication. A multi-stage signal preprocessing pipeline compresses raw CSI streams into a compact $56\times 8$ statistical feature matrix, achieving 98.86% classification accuracy for multi-level occupancy estimation. Compared with image-based baselines such as DenseNet121, the proposed approach reduces input data size to 24 kB and model parameters to 138 K, yielding over 129× reduction in transmission volume without sacrificing performance. These results demonstrate that the proposed framework provides a practical, privacy-preserving, and edge-deployable solution for occupancy-aware energy management in SMOs. Full article

Malware’s proliferation in the Internet of Things (IoT) ecosystem requires precise, efficient detection systems capable of operating on IoT devices. Existing static analysis approaches often fail due to computational inefficiency stemming from high feature dimensionality inherent in raw opcode features. This research addresses this limitation by proposing a novel machine-learning (ML)-driven Intelligent Hybrid Feature Selection (IHFS) framework with two distinct architectures. IHFS1 combines a filter method (variance threshold) with an embedded method (LGBM feature importance). Conversely, IHFS2 integrates variance thresholding with a wrapper method (Recursive Feature Elimination with Cross-Validation using LGBM) for optimal selection. This framework is specifically designed to select an optimally stable and minimal feature subset from the initial 1183 opcode frequency vector extracted from ARM binaries. Applying this framework to a multi-family IoT malware dataset, the IHFS architectures yielded distinct and highly efficient feature subsets: IHFS1 achieved a 95.77% reduction (to 50 features), while IHFS2 attained a 98.06% reduction (to 23 features). Evaluation across eight ML models confirmed that the Random Forest (with IHFS1 subset) and Decision Tree (with IHFS2 subset) classifiers were the best performing, achieving robust classification metrics that outperform current state-of-the-art solutions. The Decision Tree model demonstrated exceptional detection capabilities, with an accuracy of 99.87%, a precision of 99.82%, a recall of 99.88%, and an F1-score of 99.85%. It achieved an average inference time of 0.058 ms per sample. Experimental results attained on a native ARM64 environment validate the deployment feasibility of the proposed system for resource-constrained IoT devices, such as the Raspberry Pi. The proposed system achieves a high-throughput, low-overhead security posture while maintaining host operational stability, processing a single ELF binary in just 3.431 ms. Full article

The growing demand for IoT applications in highly dynamic environments with multiple connected devices introduces significant scalability and low-latency challenges. In the context of software-defined networking (SDN) integrated with Edge environments, adopting machine learning (ML) techniques has emerged as a promising approach to meet these requirements. This study presents a Systematic Literature Review (SLR) that identifies and analyzes ML-based solutions applied to Software-Defined Internet of Things (SD-IoT) infrastructures in Edge environments, emphasizing improving low latency and scalability. Following established methodological best practices, we conducted the review, including a clear definition of research questions, well-defined inclusion and exclusion criteria, a structured search protocol, and multiple scientific databases. Based on the analysis of selected studies, the main strategies employed to enhance network performance are categorized, along with the level of fidelity and complexity of the experimental environments used, and the realism and applicability of the proposed solutions are discussed. Furthermore, drawing from the context of the selected studies, the most recurrent ML approaches are presented—including supervised, unsupervised, and reinforcement learning methods—along with a discussion of their advantages and limitations in dynamic network scenarios. By compiling and organizing the contributions from the literature, this paper provides a comprehensive overview of the state of the art in applying ML to SD-IoT networks, shedding light on current trends, existing gaps, and research opportunities aimed at building more intelligent and adaptable solutions for IoT environments. Full article

Traffic congestion and travel time uncertainty remain major challenges to the operational efficiency of Bus Rapid Transit (BRT) systems in urban areas of developing countries. This study proposes an integrated solution for the Trans Metro Bandung (TMB) system by leveraging Internet of Things (IoT)–based GPS data and tree-based ensemble machine learning algorithms. Spatio-temporal data collected from on-board GPS modules are processed to predict traffic congestion levels and estimate travel time across route segments. The performance of Decision Tree, Random Forest, and XGBoost models is evaluated in terms of prediction accuracy, interpretability, and computational efficiency, with particular consideration for deployment on resource-constrained hardware. Experiments conducted on 20,156 data samples show that the Decision Tree model achieves the highest congestion classification accuracy of 96.8%, while Random Forest outperforms other models in travel time regression, achieving an R² value of 0.95 and a root mean square error (RMSE) of 5.80 min. The trained models are successfully deployed on a Raspberry Pi 3B microcontroller for real-time inference, enabling fleet management and travel planning without reliance on cloud connectivity. The results demonstrate that cost-effective and interpretable machine learning solutions can deliver reliable performance in heterogeneous urban infrastructures while providing a replicable framework for medium-sized cities seeking to implement affordable smart transportation systems. Full article

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems. Full article

The prevalence of radio frequency signals in indoor environments has in recent years given rise to new technologies across many domains such as robotics, healthcare, and surveillance. Radio frequency signals propagate in the wireless medium through multiple paths and carry useful environment-dependent information. Capturing and analyzing these signal patterns can offer new solutions for a number of applications relevant to ranging, tracking, perception and recognition. In this work we propose a novel architecture, separating physical, back-bone networks, and inference layers, towards fully ubiquitous passive recognition systems that scale with the number of environments and applications. We propose a back-bone architecture that utilizes a novel Cross Dual-Path Attention (CDPA) block to capture spatial and temporal correlations from Channel State Information (CSI) for device-free, multi-task applications. Subsequently, a distill and transfer algorithm is proposed to generalize the inference capabilities of CDPA over multiple target environments for scalable training and reduced computational costs. By sharing knowledge between models across a shared network, experimentation shows that edge devices can be deployed with improved performance while simultaneously meeting strict computation and memory requirements. Our distributed learning paradigm demonstrates that CDPA-based models are capable of using passive signals in a non-intrusive and privacy-protecting manner, in order to achieve ubiquitous recognition at scale in smart environments. Full article