Search Results (233)

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

This paper proposes a hardware–software co-design for adaptive lossless compression based on Hybrid Arithmetic–Huffman Coding, a table-driven approximation of arithmetic coding that preserves near-optimal compression efficiency while eliminating the multiplicative precision and sequential bottlenecks that have traditionally prevented arithmetic coding deployment in resource-constrained embedded systems. The compression pipeline is partitioned as follows: flexible software on the processor core dynamically builds and adapts the prefix coding (usually Huffman Coding) frontend for accurate probability estimation and binarization; the resulting binary stream is fed to a deeply pipelined systolic hardware accelerator that performs binary arithmetic coding using pre-calibrated finite state transition tables, dedicated renormalization logic, and carry propagation mitigation circuitry instantiated in on-chip memory. The resulting implementation achieves compression ratios consistently within 0.4% of the theoretical entropy limit, multi-gigabit per second throughput in 28 nm/FinFET nodes, and approximately 68% lower energy per compressed byte than optimized software arithmetic coding, making it ideally suited for real-time embedded vision, IoT sensor networks, and edge multimedia applications. Full article

Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power ($R^2\approx 0.88$) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions. Full article

Over-the-air (OTA) firmware updating has become a fundamental requirement in modern Internet of Things (IoT) deployments, where thousands of heterogeneous embedded devices operate in remote and distributed environments. Manual firmware maintenance in such systems is impractical, costly, and prone to security risks, making automated update mechanisms essential for long-term reliability and lifecycle management. This paper presents a unified OTA update architecture for ESP32-based IoT devices that integrates centralized version control and multi-protocol communication support (Wi-Fi, BLE, Zigbee, LoRa, and GSM), enabling consistent firmware distribution across heterogeneous networks. The system incorporates version-compatibility checks, rollback capability, and a server-driven release routing mechanism for development and production branches. An analytical model of timing, reliability, and energy consumption is provided, and experimental validation on a fleet of ESP32 devices demonstrates reduced update latency compared to native vendor OTA solutions, together with reliable operation under simultaneous device loads. Overall, the proposed solution provides a scalable and resilient foundation for secure OTA lifecycle management in smart-industry, remote sensing, and autonomous infrastructure applications. Full article

This article proposes a complex solution to improve sustainable intelligent building management based on the principles of Environmental, Social, and Governance (ESG) factors. The ESG KPI Framework–Metaverse-Enabled Operations incorporates the latest digital twin solutions, IoT sensor systems, and metaverse platforms to deliver real-time management and optimization of ESG factors. A hybrid solution strategy has been used in this framework, focusing on auto-acquisition of information and multiple validations at different levels through correlation analysis, Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression, and Machine Learning. The designed prototype links all the solutions together in a multi-level dashboard to represent key performance factors such as carbon footprint, energy consumption, renewable energy use, and occupant wellness. Experiments conducted validate the effectiveness of the proposed solution in improving prediction efficiency and user interaction experience during metaverse simulations. Full article

Accurate prediction of building energy loads is essential for smart buildings and sustainable energy management. While machine learning (ML) approaches outperform traditional statistical models at capturing nonlinear relationships, most studies primarily optimize prediction accuracy, overlooking the importance of computational efficiency and feature compactness, which are critical in real-time, resource-constrained environments. This study aims to evaluate whether hybrid nature-inspired feature-selection techniques can enhance the accuracy and computational efficiency of ML-based building energy load prediction. Using the UCI Energy Efficiency dataset, eight ML models (LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, Extra Trees, Linear Regression, Support Vector Regression) were trained under feature subsets obtained from the Butterfly Optimization Algorithm (BOA), Grey Wolf Optimization Algorithm (GWO), and a hybrid BOA–GWO approach. Model performance was evaluated using three metrics (MAE, RMSE, and R²), along with training time, prediction time, and the number of selected features. The results show that gradient-boosting models consistently yield the highest accuracy, with CatBoost achieving an R² of 0.99 or higher. The proposed hybrid BOA–GWO method achieved competitive accuracy with fewer features and reduced training time, demonstrating its suitability for efficient ML deployment in smart building environments. Rather than proposing a new metaheuristic algorithm, this study contributes by adapting a hybrid BOA–GWO feature-selection strategy to the building energy domain and evaluating its benefits under a multi-criteria performance framework. The findings support the practical adoption of hybrid feature-selection-supported ML pipelines for intelligent building systems, energy management platforms, and IoT-based real-time applications. Full article

This study presents models for estimating the charging time and travel time in autonomous electric taxi systems, based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods. In addition to these models, two classical time-series forecasting techniques ARIMA and Prophet were also applied to provide a broader comparative baseline. Unlike traditional time-series prediction methods, the proposed system combines artificial intelligence with Internet of Things (IoT) technologies to perform secure charging operations based on multi-layer cybersecurity mechanisms, including IP authentication, encrypted communication, and charger server validation steps. The models were trained and validated using a comprehensive dataset obtained from 100 electric vehicles with different battery capacities at 50 charging stations located in Kocaeli Province. In the predictions considering parameters such as the vehicle type, battery capacity, and charge level, both models showed high accuracy rates, with the GRU model performing better than the LSTM model in terms of the error rate and temporal consistency. ARIMA and Prophet, on the other hand, produced significantly lower performance compared to deep learning models, confirming that GRU is the most suitable approach for real-time duration estimation. Customers can obtain the estimated time, cost, and charging requirements before their trip, and continuous multi-stage IP-based security controls are performed throughout the charging process as part of the cybersecurity framework. If a foreign or unauthorized connection is detected, the charging operation is automatically stopped. The proposed approach not only increases the efficiency in electric vehicle energy management but also presents an innovative framework that contributes to sustainable and smart transportation. By combining deep learning models, classical statistical forecasting methods, IoT integration, and enhanced cybersecurity controls, this work represents a pioneering step toward autonomous, secure, and eco-friendly urban transportation systems. Full article

LoRa has emerged as a cornerstone of low-power, long-range IoT communication. While highly effective for scalar sensing, its extension to multimedia remains constrained by limited bitrate, payload size, and duty-cycle regulations. This survey reviews research on multimedia transmission over LoRa, revealing that most current efforts are image-centric, with only a few preliminary studies addressing video or audio. We propose a structured taxonomy encompassing compression and fragmentation methods, cooperative and multi-hop architectures, MAC and cross-layer optimizations, and hybrid network designs. These strategies are analyzed in the context of IoT domains such as agriculture, surveillance, and environmental monitoring. Open challenges are highlighted in extending beyond static images, ensuring energy-efficient delivery, and developing spectrum- and ML-aware protocols. The survey provides IoT researchers with both a consolidated reference and a roadmap toward practical and scalable multimedia systems over LoRa. Full article

Internet of Things (IoT) sensor networks comprise diminutive sensor units primarily designed for monitoring phenomena within a designated area. However, reaching the complete potential of this kind of network is extremely difficult due to several challenges, including the fact that the data transmitted by the sensor nodes contains a large amount of duplicates. Data aggregation can be employed to address this issue in routing packets from nodes that send data to the base station (BS). In this study, a novel, hybrid data aggregation framework for IoT sensor networks is proposed by integrating Self-Organizing Maps (SOMs) with the Firefly Optimization Algorithm (FOA). The core motivation for this integration is to address persistent challenges in IoT sensor networks, chiefly energy efficiency, network longevity, and the reliability of data transmission. By combining the adaptive, unsupervised clustering capabilities of SOMs with the robust, multi-objective optimization properties of the FOA, the method aims to achieve more intelligent, adaptive, and practical solutions for real-world IoT systems. This work presents an innovative framework that synergistically leverages the strengths of FOA and SOM, offering a new methodology that addresses key challenges in scalable and energy-efficient IoT sensor network clustering. The suggested algorithm’s validity has been verified using an experimental analysis performed in MATLAB. Experimental results show the proposed method extends network lifetime by 15% and reduces energy consumption by 10% compared to FOA, SOM, and LEACH benchmarks. A notable classification rate was attained after implementing and testing the proposed method using the Intel Berkeley Research Lab dataset. Full article

This study presents the design and validation of an AI-enhanced embedded IoT system for real-time industrial sensor calibration. The proposed platform integrates a PT100 temperature sensor and a 4–20 mA pressure transmitter with an ESP32 microcontroller, enabling on-device data acquisition, processing, and wireless transmission. A lightweight multilayer perceptron (MLP) neural network, trained in Python with a hybrid dataset (synthetic and experimental) and deployed on the ESP32 via JSON weight files, performs local inference to estimate ideal sensor outputs and compute key performance metrics. Experimental tests under controlled laboratory conditions confirmed high accuracy, with efficiency above 98.6%, RMSE below 0.005 V, and absolute uncertainty margins of ±0.5 °C and ±0.07 bar. Additionally, 95% confidence intervals for RMSE and standard deviation demonstrated statistical reliability across all operating points. The prototype also addresses practical constraints, including ESP32 ADC nonlinearity, energy consumption, and multi-sensor scalability, while remaining portable and low-cost. The integration of edge AI capabilities demonstrates the feasibility of executing accurate neural network models directly on embedded microcontrollers, eliminating reliance on cloud-based processing. The proposed solution provides a robust proof-of-concept that is scalable, cost effective, and suitable for industrial IoT applications, predictive maintenance, and Industry 4.0 environments, with future work focusing on long-term drift evaluation and validation under real industrial conditions. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Integrated energy systems (IESs) are increasingly being deployed and expanded, which integrate various energy infrastructures to enable flexible conversion and utilization among different energy forms. To facilitate collaboration among operators of varying scales and fully leverage the economic and environmental benefits of multi-integrated energy systems (MIESs), this study develops a peer-to-peer (P2P) energy sharing framework for MIES based on asymmetric Nash bargaining. First, an IoT-based P2P energy sharing architecture for MIES is proposed, which incorporates coordinated electricity–heat–gas multi-energy synergy within IES models. Carbon capture systems (CCS) and power-to-gas (P2G) units are integrated with carbon trading mechanisms to reduce carbon emissions. Then, an MIES energy sharing operational model is established using Nash bargaining theory, subsequently decoupled into two subproblems: alliance benefit maximization and individual IES benefit distribution optimization. For subproblem 2, an asymmetric bargaining method employing natural exponential functions quantifies participant contributions, enabling fair distribution of cooperative benefits. Finally, the alternating direction method of multipliers (ADMM) is employed to solve both subproblems distributively, effectively preserving participant privacy. The effectiveness of the proposed method is verified by case simulation, demonstrating reduced operational costs across all IESs alongside equitable benefit allocation proportional to energy-sharing contributions. Carbon emission amounts are simultaneously reduced. Full article

Forest fire monitoring is of great significance for ecological protection and public safety. This study proposes a monitoring technology based on LoRa-Mesh (Long Range-Mesh) wireless communication, integrating temperature and humidity sensing, image acquisition, fire identification, data transmission, and energy-saving optimization. To address the limitations of traditional LoRa networks in flexibility and energy consumption, a Layered Dynamic Synchronization Energy-saving (LDSE) protocol is designed. By constructing a hierarchical network, employing implicit route exploration, multi-channel and multi-path communication, and time synchronization optimization, the protocol significantly reduces packet loss rate and system energy consumption. Experimental results demonstrate that the LDSE protocol outperforms the traditional Ad hoc On-Demand Distance Vector Routing Protocol (AODV) in terms of packet loss rate, energy consumption, and latency. Additionally, the proposed energy-saving algorithm significantly reduces system power consumption, with the node sleep-relay mode exhibiting optimal energy efficiency. Experimental verification confirms that the system achieves high reliability, low power consumption, and efficient data transmission, providing an effective IoT solution for forest fire prevention. Full article