Liyanage Chandratilak De Silva’s research while affiliated with Universiti Brunei Darussalam and other places

Current technology has permitted various technological advancements and enhanced how we live and work as the world works toward a sustainable economy, contributing to zero hunger, climate action, responsible consumption, and production of the United Nation's sustainable development goals. By integrating smart agriculture technologies, the Internet of Things (IoT), which connects equipment over the Internet, has changed the agricultural industry. This study delivers an IoT applications overview in agriculture, focusing on the layout encompassing devices and sensors, data collection and analysis, automation and control systems, connectivity and communication, case studies, and future trends. Smart agriculture uses IoT sensors to track and improve agricultural metrics, enabling accurate irrigation and fertilization management. Real-time agricultural data collecting is made possible by data collection techniques like wireless sensor networks and remote sensing, while data analysis and informed decision-making are made possible by cloud computing and predictive analytics. Resource efficiency is improved by automation systems like smart irrigation and precision farming, while data transmission is made easy by connectivity systems like wireless fidelity (Wi-Fi) and low-power wide-area networks (LPWAN). The advantages of IoT in agriculture have been demonstrated in a few case studies, and IoT integration with developing technologies is expected to be a future trend. Ultimately, the implementation of IoT in smart farming holds the potential to revolutionize the sector, improve productivity, and promote sustainable practices.

Significant time delays and energy inefficiencies are frequently caused by implementing Industrial Control Systems (ICS) in Industrial Wireless Sensor Networks (IWSN). To overcome these issues, this paper suggests two optimization techniques: Harris Hawk Optimization (HHO) and Gradient-Based Optimization (GBO). The GBO technique uses gradient data to optimize ICS deployment to reduce delay times and increase IWSN energy efficiency. The system achieves an efficient communication flow and minimizes transmission delays by incrementally altering the arrangement of ICS components based on gradient descent. The allocation of resources and the elimination of unnecessary data transmissions optimize energy use. Similarly, the HHO approach optimizes ICS deployment in terms of time delay reduction and energy economy by modeling the cooperative hunting approach of Harris Hawks. The HHO algorithm effectively balances exploration and exploitation to find the best deployment configurations. Extensive simulations assess the proposed method in simulation model ICS scenarios. The outcomes show how well GBO and HHO work to cut down on delays and increase energy effectiveness. The enhanced ICS deployment reduces time delays and uses less energy than traditional deployment techniques, improving IWSN performance. The simulation code can be accessed via: [https://github.com/Muzamil282/HHOfor-time-reduction-in-an-ICS-System-]

Security is a complex issue in critical infrastructure like industrial control systems (ICS) since its leakages cause critical damage. Protecting the ICS environment from external threats, cyber-attacks, and natural disasters is important. Various works have been done on anomaly detection in ICS, and it has been identified that these proposed approaches are the sole models associated with them. Although there is a research gap in anomaly detection methodologies because of their computational complexity. To overcome the research gap of high false positive rate (precision), accuracy, and computational complexity in the literature, the study presents an Improved autoencoder (ImpAE) anomaly detection methodology for anomaly detection in ICS. The proposed methodology is a deep learning-based model to build anomaly detectors that alarm the attacks affecting ICS security. This methodology follows a flexible and modular design that permits a group of numerous detectors to get suitable detection. To express the suitability of the proposed model, we implemented it on the Secure water testbed (SWat) dataset, which is collected from a working water treatment plant. Experimental work shows that by using ImpAE, gaining a precision of 0.993 and an accuracy of 96%, in comparision to the existing results in the literature. With precision and accuracy, we gained a recall of 0.673 and an F1-Score of 0.771, which is better than the average of the other works. The used dataset was attained from ITrust Center, Singapore University of Digital Science, reliable for anomaly detection in an ICS environment.

Technological advancement plays a crucial role in our daily lives and constantly transforms the industrial sector. However, these technologies also introduce new security vulnerabilities to Industrial Control Systems (ICS). Attackers take advantage of these weaknesses to infiltrate the ICS environment. The size of the targeted industry and the attacker’s knowledge of the internal ICS environment are crucial factors in determining the degree of impact. Researchers and industry professionals have taken several initiatives to identify and address security problems in the ICS environment; however, to our knowledge, a comprehensive survey of this landscape has yet to be conducted. Existing surveys have limitations since they mainly focus on specific aspects of ICS security rather than covering the security aspects holistically. This paper aims to cover all aspects of security in ICS by classifying the ICS environment into its components, such as SCADA, PLC, DCS, RTU, HMI, MTU, etc. The paper then discusses the vulnerabilities in the modern ICS environment, including those of the specific components. The article also presents a classification of ICS-specific attack types. Furthermore, the study examines real-world attack scenarios in the industrial critical infrastructure sectors, including energy, power, water, and wastewater. This study provides an in-depth analysis of ICS security that empowers researchers and industry practitioners to comprehend the complexities of ICS security and to strengthen the ICS environment’s resilience proactively.

Plant pests and diseases are a significant threat to almost all major types of plants and global food security. Traditional inspection across different plant fields is time-consuming and impractical for a wider plantation size, thus reducing crop production. Therefore, many smart agricultural practices are deployed to control plant diseases and pests. Most of these approaches, for example, use vision- based artificial intelligence (AI) or deep and machine learning methods to provide perfect solutions. However, existing open issues must be considered and addressed before AI methods can be used. In this study, we conduct a systematic literature review and present a detailed survey of the studies employing data collection techniques and publicly available datasets. To begin the review, 1349 papers were chosen from five academic databases. After deploying a comprehensive screening process, the review considered 176 studies based on the importance of the method. Convolutional neural network (CNN) methods are typically trained on small datasets and are only intended for a few selected plant diseases. Finally, the lack of large-scale, publicly available datasets from the plant field is one of the main obstacles to solving plant disease identification and related problems, among others.

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.