The Internet of Things (IoT) is a new, ongoing revolution. It often uses wireless sensor network (WSN) technologies because these technologies are among the most important solutions for monitoring and controlling the systems. This article provides a model of a smart greenhouse. In this regard, the main contribution of this paper is an innovative implementation of a micro-climate controlled environment for optimal plant growth, based on loT technology and using a fuzzy logic controller. Using this system, we attempted to optimize the functionality of the system proposed by exploiting an Arduino UNO board for data acquisition and processing. The input variables are analog values captured by ZigBee wireless network sensors that are then processed using fuzzy logic control software with heating and extractor control signals. At the same time, all data were sent to the server through a Wi-Fi internet connection, which permitted remote monitoring and analysis of the data via a web browser with tablets, smartphones, and laptops. Results show that the choice of a fuzzy logic controller could promote a comfortable greenhouse micro-climate. Also, we showed the efficiency of our proposed solution for greenhouse climate remote monitoring anywhere via IoT technology.

The Journal of Electronic Imaging (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.

Chronic diseases quickly become broader public health issues because of the difficulty in obtaining appropriate, often long-term health care. So that, it requires the extension of health care for patients with chronic diseases beyond the clinic to include patient’s home and work environment. To reduce costs and provide more appropriate healthcare, we need telehealth care where internet of things (IoT) technology plays an important role. The integration of the IoT and medical science offers opportunities to improve healthcare quality, and efficiency and to better coordinate healthcare delivery at home and in the workplace. In this paper, we present the realization of a remote healthcare system based on the IoT technology. The function of this system is the transmission via a gateway of internet collected data using biomedical sensors node based Arduino board (e.g., temperature, electrical activity of the heart, heart rate monitor). These data will be stored automatically in a cloud. The health can then be monitored by the doctor or patient using a web page in real-time from anywhere at any time in the world using laptops or smart phones, etc. This method also reduces the need for direct interaction between doctor and patient.

This paper investigates the potential improvements in Free Space Optics (FSO) communication systems using Particle Swarm Optimization (PSO) and the Pachycondyla Apicalis Algorithm (API). FSO technology transmits data using laser beams, offering benefits like high bandwidth and enhanced security, with the capability to achieve data rates of up to 10 Gbps over distances of up to 2 km under favorable weather conditions. However, this technology faces challenges due to environmental factors such as rain, fog, and atmospheric turbulence, which can impact its performance. This study specifically addresses link margin degradation caused by these impairments and applies PSO as an efficient baseline metaheuristic, selected for its computational simplicity, rapid convergence, and robustness, while benchmarking its performance against the API algorithm. Simulation results indicate that PSO optimization significantly increases the link margin for various divergence angles compared to the API algorithm. For divergence angles of 1, 1.5, 2.4, and 3 mrad, the link margin improved by 6.4, 11.1, 9.8, and 7.6 dB, respectively, at a fixed transmitted power of 10 dBm. This demonstrates that PSO maintains a higher link margin at divergence angles above 2 mrad compared to both the API algorithm and the conventional scenario. The main contributions of this study are: the development of a bio-inspired optimization framework for FSO systems, enhancement of link robustness against turbulence, beam misalignment, and fading, and demonstration of the framework’s applicability to both current 5G backhaul scenarios and prospective 6G optical wireless networks. Beyond simulation, the framework has been verified for practical feasibility and shown to align with commercially available optical hardware. In addition, PSO can be integrated with real-time atmospheric sensing and predictive weather models to enable proactive parameter tuning, ensuring resilient and efficient 6G FSO deployment. Overall, the results confirm that misalignment-optimized PSO enhances resilience under severe conditions, supporting 6G availability requirements.

Forests are crucial for preserving biodiversity and regulating the global climate. However, they are increasingly at risk from destructive wildfires that threaten the environment and human communities. Accurate prediction models are essential to minimize the impact of forest fires. This study presents a new hybrid model that combines the Apriori association rule mining algorithm with the binary golden ratio optimization method (BGROM) to improve the accuracy of forest fire prediction. The BGROM, based on the golden ratio observed in plant and animal growth and formulated by the renowned mathematician Fibonacci. It is used to select the candidate features, which are then used by the Apriori algorithm to generate classification rules to predict the risk of wildfires. Integrating the Apriori algorithm with BGROM improves the accuracy of forest fire prediction and enhances our understanding of the complex interactions and patterns that influence wildfire behavior. This innovative approach holds great promise for advancing the development of effective forest fire prevention and management strategies. Experimental results show that the proposed model outperforms existing prediction methods, offering a more reliable tool for early forest fire detection and risk management.

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential.