Search Results (127)

The rapid advancement of quantum computing poses significant threats to classical cryptographic methods, such as Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC), which currently secure Internet of Things (IoT) and cloud communications. Post-Quantum Cryptography (PQC), particularly lattice-based schemes, has emerged as a promising alternative. CRYSTALS-Kyber, standardized by the National Institute of Standards and Technology (NIST) as ML-KEM, has shown efficiency and practicality for constrained IoT devices. Most existing research has focused on PQC within the Transport Layer Security (TLS) protocol. Consequently, a critical gap exists in understanding PQC’s performance in lightweight IoT protocols. These are Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP), particularly under adverse network conditions. To address this gap, this paper provides a systematic review of the literature on the network resilience and performance of CRYSTALS-Kyber when integrated into these protocols operating over lossy and high-latency networks. Additional challenges include non-standardized integration, resource limitations, and side-channel vulnerabilities. This review provides a structured synthesis of current knowledge, highlights unresolved trade-offs between security and efficiency, and outlines future research directions, including protocol-level optimization, lightweight signature schemes, and resilience testing of PQC-secured IoT protocols under realistic conditions. Full article

This study designed a message queuing telemetry transport (MQTT)-based communication framework to acquire environmental data with stable, low-latency response (soft real-time capability) and detect anomalies in smart livestock housing. We validated the performance of the proposed framework using actual sensor data. It comprises environmental sensor nodes, a Mosquitto MQTT broker, and a GRU-based anomaly detection model, with data transmission via a WiFi-based network. Comparing quality of service (QoS) levels, the QoS 1 configuration demonstrated the most stable performance, with an average latency of ~150 ms, a data collection rate ≥ 99%, and a packet loss rate ≤ 0.5%. In the sensor node expansion experiment, responsiveness (≤200 ms) persisted for 10–15 nodes, whereas latency increased to 238.7 ms for 20 or more nodes. The GRU model proved suitable for low-latency analysis, achieving 97.5% accuracy, an F1-score of 0.972, and 18.5 ms/sample inference latency. In the integrated experiment, we recorded an average end-to-end latency of 185.4 ms, a data retention rate of 98.9%, processing throughput of 5.39 samples/s, and system uptime of 99.6%. These findings demonstrate that combining QoS 1-based lightweight MQTT communication with the GRU model ensures stable system response and low-latency operation (soft real-time capability) in monitoring livestock housing environments, achieving an average end-to-end latency of 185.4 ms. Full article

The advent of fifth-generation (5G) networks has enabled cellular vehicle-to-everything (C-V2X) communication, requiring the efficient delivery of large volumes of real-time vehicular data under stringent latency and reliability constraints. At the application layer, Message Queuing Telemetry Transport (MQTT) and ZeroMQ have emerged as candidate protocols; however, their comparative performance in vehicular networking contexts remains insufficiently examined. This work presents a simulation-based evaluation of MQTT and ZeroMQ using OMNeT++, integrating INET for protocol modeling, Veins for vehicular mobility, and Simu5G for cellular network operations. We developed custom protocol modules and assessed them under diverse traffic conditions, analyzing key metrics such as end-to-end latency, message overhead, and scalability. Our results reveal that ZeroMQ achieves lower latency in moderate traffic scenarios, whereas MQTT demonstrates superior reliability and efficiency under high traffic loads, offering valuable insights for selecting application-layer protocols in C-V2X environments. Full article

The proliferation of drones across precision agriculture, disaster response operations, and delivery services has accentuated the critical need for secure communication frameworks. Due to the limited computational capabilities of drones and the fragility of real-time wireless communication networks, the cellular connected drones confront mounting cybersecurity threats. Traditional authentication mechanisms, such as public-key infrastructure-based authentication, and identity-based authentication, are centralized and have high computational costs, which may result in single point of failure. To address these issues, this paper proposes a blockchain-enabled authentication and key agreement scheme for cellular-connected drones. Leveraging identity-based cryptography (IBC) and the Message Queuing Telemetry Transport (MQTT), the scheme flow is optimized to reduce the communication rounds in the authentication. By integrating MQTT brokers with the blockchain, it enables drones to authenticate through any network node, thereby enhancing system scalability and availability. Additionally, cryptographic performance is optimized via precompiled smart contracts, enabling efficient execution of complex operations. Comprehensive experimental evaluations validate the performance, scalability, robustness, and resource efficiency of the proposed scheme, and show that the system delivers near-linear scalability and accelerated on-chain verification. Full article

This study presents a novel federated learning (FL) methodology implemented directly on STM32-based microcontrollers (MCUs) for energy-efficient smart irrigation. To the best of our knowledge, this is the first work to demonstrate end-to-end FL training and aggregation on real STM32 MCU clients (STM32F722ZE), under realistic energy and memory constraints. Unlike most prior studies that rely on simulated clients or high-power edge devices, our framework deploys lightweight neural networks trained locally on MCUs and synchronized via message queuing telemetry transport (MQTT) communication. Using a smart agriculture (SA) dataset partitioned by soil type, 7 clients collaboratively trained a model over 3 federated rounds. Experimental results show that MCU clients achieved competitive accuracy (70–82%) compared to PC clients (80–85%) while consuming orders of magnitude less energy. Specifically, MCU inference required only 0.95 mJ per sample versus 60–70 mJ on PCs, and training consumed ∼70 mJ per epoch versus nearly 20 J. Latency remained modest, with MCU inference averaging 3.2 ms per sample compared to sub-millisecond execution on PCs, a negligible overhead in irrigation scenarios. The evaluation also considers the payoff between accuracy, energy consumption, and latency through the Energy Latency Accuracy Index (ELAI). This integrated perspective highlights the trade-offs inherent in deploying FL on heterogeneous devices and demonstrates the efficiency advantages of MCU-based training in energy-constrained smart irrigation settings. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Effective detection of unexpected events in wide-area surveillance remains a critical challenge in the development of intelligent monitoring systems. Recent advancements in Narrowband Internet of Things (NB-IoT) and 5G technologies provide a robust foundation to address this issue. This study presents an integrated architecture for real-time event detection and response. The system utilizes the Constrained Application Protocol (CoAP) to transmit encapsulated JPEG images from NB-IoT modules to an Internet of Things (IoT) server. Upon receipt, images are decoded, processed, and archived in a centralized database. Subsequently, image data are transmitted to client applications via WebSocket, leveraging the Message Queuing Telemetry Transport (MQTT) protocol. By performing temporal image comparison, the system identifies abnormal events within the monitored area. Once an anomaly is detected, a visual alert is generated and presented through an interactive interface. The test results show that the image recognition accuracy is consistently above 98%. This approach enables intelligent, scalable, and responsive wide-area surveillance reliably, overcoming the constraints of conventional isolated and passive monitoring systems. Full article

The rapid growth of generative artificial intelligence (AI) has enabled diverse applications but also introduced new attack techniques. Similar to deepfake media, generative AI can be exploited to create AI-generated traffic that evades existing intrusion detection systems (IDSs). This paper proposes a Dual Detection System to detect such synthetic network traffic in the Message Queuing Telemetry Transport (MQTT) protocol widely used in Internet of Things (IoT) environments. The system operates in two stages: (i) primary filtering with a Long Short-Term Memory (LSTM) model to detect malicious traffic, and (ii) secondary verification with a Transformer–MLP ensemble to identify AI-generated traffic. Experimental results show that the proposed method achieves an average accuracy of $99.1 \pm 0.6\%$ across different traffic types (normal, malicious, and AI-generated), with nearly 100% detection of synthetic traffic. These findings demonstrate that the proposed dual detection system effectively overcomes the limitations of single-model approaches and significantly enhances detection performance. Full article

IoT (Internet of Things)-enabled solar irradiance sensors are evolving toward energy harvesting, interoperability, and open-source availability, yet current solutions remain either costly, closed, or limited in robustness. Based on a thorough literature review and identification of future trends, we propose an open-source smart wireless sensor that employs a small photovoltaic module simultaneously as sensing element and energy harvester. The device integrates an ESP32 microcontroller, precision ADC (Analog-to-Digital converter), and programmable load to sweep the PV (photovoltaic) I–V (Current–Voltage) curve and compute irradiance from electrical power and solar-cell temperature via a calibrated third-order polynomial. Supporting Modbus RTU (Remote Terminal Unit)/TCP (Transmission Control Protocol), MQTT (Message Queuing Telemetry Transport), and ZigBee, the sensor operates from batteries or supercapacitors through sleep–wake cycles. Validation against industrial irradiance meters across 0–1200 W/m² showed average errors below 5%, with deviations correlated to irradiance volatility and sampling cadence. All hardware, firmware, and data-processing tools are released as open source to enable reproducibility and distributed PV monitoring applications. Full article

The Internet of Things (IoT) has surfaced as a revolutionary technology, enabling ubiquitous connectivity between devices and revolutionizing traditional lifestyles through smart automation. As IoT systems proliferate, securing device-to-device communication and server–client data exchange has become crucial. This paper presents a novel security framework that integrates elliptic curve cryptography (ECC) with artificial neural networks (ANNs) to enhance the Message Queuing Telemetry Transport (MQTT) protocol. Our study evaluated multiple machine learning algorithms, with ANN demonstrating superior performance in anomaly detection and classification. The hybrid approach not only encrypts communications but also employs the optimized ANN model to detect and classify anomalous traffic patterns. The proposed model demonstrates robust security features, successfully identifying and categorizing various attack types with 90.38% accuracy while maintaining message confidentiality through ECC encryption. Notably, this framework retains the lightweight characteristics essential for IoT devices, making it especially relevant for environments where resources are constrained. To our knowledge, this represents the first implementation of an integrated ECC-ANN approach for securing MQTT-based IoT communications, offering a promising solution for next-generation IoT security requirements. Full article

The rapid expansion of the Internet of Things (IoT) demands scalable, efficient, and user-friendly gateway solutions that seamlessly connect resource-constrained edge devices to cloud services. Low-cost, widely available microcontrollers, such as the ESP32 and its ecosystem peers, offer integrated Wi-Fi/Bluetooth connectivity, low power consumption, and a mature developer toolchain at a bill of materials cost of only a few dollars. For smart-home deployments where budgets, energy consumption, and maintainability are critical, these characteristics make MCU-class gateways a pragmatic alternative to single-board computers, enabling always-on local control with minimal overhead. This paper presents the design and implementation of an embedded IoT gateway powered by the ESP32 microcontroller. By using lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and REST APIs, the proposed architecture supports local control, distributed intelligence, and secure on-site data storage, all while minimizing dependence on cloud infrastructure. A real-world deployment in an educational building demonstrates the gateway’s capability to monitor energy consumption, execute control commands, and provide an intuitive web-based dashboard with minimal resource overhead. Experimental results confirm that the solution offers strong performance, with RAM usage ranging between 3.6% and 6.8% of available memory (approximately 8.92 KB to 16.9 KB). The initial loading of the single-page application (SPA) results in a temporary RAM spike to 52.4%, which later stabilizes at 50.8%. These findings highlight the ESP32’s ability to serve as a functional IoT gateway with minimal resource demands. Areas for future optimization include improved device discovery mechanisms and enhanced resource management to prolong device longevity. Overall, the gateway represents a cost-effective and vendor-agnostic platform for building resilient and scalable IoT ecosystems. Full article

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

With the rapid development of the Internet of Things (IoT) applications, the ability to automatically discover and retrieve resource information has become increasingly enhanced. Despite being one of the most commonly used IoT communication protocols, Message Queuing Telemetry Transport (MQTT) does not natively support resource discovery. To address this limitation, MQTT-resource discovery (MQTT-RD), a resource discovery mechanism based on MQTT, has been used for resource management. In this study, we tested and evaluated MQTT-RD using the Sniffer system that manages the resource directory and synchronizes data via MQTT. When too many Sniffers are activated, the MQTT-RD system becomes unsustainable. However, the experimental results in this study revealed that frequent updates to the resource directory (RD) and high-frequency heartbeat messages (pingalive) significantly increase network traffic and system load. In this study, we identified performance and stability issues to propose improvement strategies, including refining the topic design, reducing message transmission frequency, and improving the synchronization mechanism. Additionally, the feasibility of incorporating centralized management was explored to enhance system efficiency. Full article

Water quality is a key factor in environmental and agronomic sustainability. Due to the influence of human activity and industrial development, the composition of rivers or lakes can experience significant variations both immediately and over time. In order to obtain a more accurate and documented assessment of these data, distributed monitoring with multiple sampling points is necessary. This paper presents the design and implementation of a scalable monitoring network based on long range (LoRa) and Message Queuing Telemetry Transport (MQTT), integrating a submersible sensor module (SSM) that works as a static measuring station or as a complement to sediment collectors, capable of measuring key water quality parameters such as TDS, turbidity, pH, temperature, and river kinematics with a gyroscope. The system includes a LoRa repeater (LRR) and a gateway, in addition to the SSM, which manages information transmission to a monitoring server (MS) using a tree topology. This configuration allows for dynamic antenna power adjustment based on the Received Signal Strength Indicator (RSSI) between the LRR and the gateway. Evaluations were performed on the Chil River in Arequipa, Peru, a rapid river that demonstrated ideal characteristics for validating the system’s efficacy. The results confirm the design’s efficacy and its capacity for real-time remote water quality monitoring. Full article

The rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi 4B, commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi 4B devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform. Full article