The Internet of Things (IoT) is an interconnection of millions of devices of various types, sensors and surveillance systems to collect and transmit the real-time. There has been a tremendous surge in the IoT with technological advancements in the last decade. The IoT has transformed and is now becoming a part of almost every sector including healthcare, manufacturing, smart cities, agriculture, home appliances and many more. The rapid growth in IoT can be attributed to several factors such as increased connectivity, advancement in technology, proliferation of IoT devices, etc. Although the rapid growth has resulted in several benefits, there has been an increase in the security vulnerabilities including botnet attacks, Denial of Service (DoS) attacks, data privacy concerns, IoT specific malware, etc. To ensure security and reduce the effect of risks associated with it, several solutions have been proposed such as security by design, IoT software regular update and patch management, advanced authentication and encryption mechanisms, network segmentation, Intrusion Detection Systems (IDS), etc. We propose an intrusion detection model based on Deep Learning (DL) strategies to detect the potential intrusions in the IoT network in real-time. In this study we implemented three DL based algorithms which include Generative Adversarial Networks (GAN), Graph Convolution Network (GCN) and a hybrid model implemented by integrating the GAN and GCN. Among the three implemented algorithms the proposed hybrid model achieved the highest performance with an accuracy of 97.5 % followed by GCN gaining an accuracy of 96.3 % and GAN with 89.6 % accuracy. The other performance metrices include precision, recall, f1-score, False Positive Rate (FPR) and False Negative Rate (FNR).
Industries encounter challenges in managing wastewater treatment systems, particularly in controlling aeration, which is vital for maintaining optimal oxygen levels for effective treatment. Frequent malfunctions in aerator control can lead to system damage and operational inefficiencies, impacting environmental compliance and increasing costs. To address these issues, this study examines the potential of an IoT-based monitoring system for aerators. The paper discusses specific IoT applications for aerator monitoring and explores enhancements in monitoring efficiency. The primary objective is to develop a system that maintains optimal oxygen levels by monitoring and controlling aerator operations. The system includes automatic notifications for oxygen level adjustments via the LINE application and manages data through Microsoft SQL Server for centralized storage. The methodology utilizes digital innovation and transformation techniques, integrating IoT technology through sensors, control boards, and Supervisory Control and Data Acquisition (SCADA) software, significantly enhancing monitoring and control efficiency. Automatic notifications facilitate timely issue resolution for operators, reducing their workload in wastewater management. Results indicate substantial improvements in system efficiency and operator workload reduction, highlighting the practical implications of this IoT solution for optimizing wastewater treatment processes in industrial settings.
The Philippines, situated along the Pacific Ring of Fire, faces heightened earthquake vulnerability. To mitigate electrical accidents during earthquakes, it's advised to switch off circuit breakers on distribution panels. However, amidst tremors, people often prioritize seeking shelter or evacuating rather than immediately turning off circuit breakers. This study focuses on designing an earthquake-sensitive circuit breaker with notification features. Its goal is to detect seismic motion, triggering the device to cut off specific loads based on seismic activity and intensity. Notifications serve as safety alerts, mitigating electrical damage and hazards during earthquakes. An automatic calibrating prototype was developed, integrating a 3-axis accelerometer with an Arduino microcontroller and a GSM module to test reliability and accuracy. Observed values aligned with expected sensitivity measurements, validating the prototype's functionality. The reliability of SMS notifications via the GSM module was also tested, yielding a 100% reliability rate in response to seismic movements. Results demonstrate successful automatic tripping across low, medium, and high-risk seismic movements, with a 100% reliability rate for all 30 trials.
Sugarcane is a tropical grass that grows worldwide and is mainly utilized in sugar production. Even in the Philippines, it is one of the contributors for the country's revenue and can be found in most regions, spanning about 422,500 hectares. Sugar production in the Philippines is lower than in most producing countries, which presents a challenge for local sugarcane farmers. One of the reasons for this drop, according to the related studies, is that sugarcane farmers in provinces continue to employ conventional growing methods. Several studies have found that technology is being incorporated into agricultural equipment. utilizing this information, the researchers built a device with a Raspberry Pi and a web camera to classify and detect the quality of sucrose crystals with Yolov5.
Federated learning has gained increasing attention in social applications in recent years. However, concerns have emerged regarding potential threats to the internal information security of learning models. This research addresses these security challenges through several key contributions: We propose a differential privacy implementation for the Federated Averaging (FedAvg) algorithm. This approach aims to create a secure federated learning environment while preserving the benefits of FedAvg's weighted aggregation. We also investigate attack scenarios involving malicious participants in the federated learning process, with a particular focus on model-reversal attacks, where an adversary attempts to extract learning data from the global model. Finally, we evaluate the effectiveness of security measures designed to counter these model-reversal attacks. Through extensive experimentation, we analyze both the ability to protect differential privacy and the potential vulnerabilities in collaborative learning systems. Our findings provide valuable insights into how to safeguard collaborative learning systems while preserving the utility of real-world applications.
