Student Publications

A Systematic Review of Optimal and Practical Methods in Design, Construction, Control, Energy Management and Operation of Smart Greenhouses

In an era characterized by severe climate change, dwindling resources, and a growing world population, the agricultural industry is facing unprecedented challenges. On the other hand, overuse of natural resources has emerged as a major concern worldwide. Greenhouses (GHs) have been developed as central environments capable of growing a diverse range of high-quality agricultural products throughout the year, regardless of external weather conditions. However, conventional GHs often impose significant costs on energy resources for their heating and cooling operations, thus presenting sustainability challenges. To address these pressing concerns, using new smart technologies as well as the integration and development of renewable energy sources, including photovoltaics (PVs), wind turbines (WT), and geothermal systems, have gained momentum. This integration not only increases the ecological footprint of GHs but also reduces their dependence on conventional energy sources. Furthermore, the adoption of smart GH technologies, characterized by advanced control and automation systems, holds significant promise in energy optimization and efficiency. Hence, this systematic review attempts to carefully examine the optimal and practical methods that include the design, fabrication, control, energy management, and operation of smart GHs. This review includes an in-depth analysis of GH structures, building materials, cooling and heating systems, new dark GH concepts, and smart lighting systems. In addition, it addresses effective strategies to curb energy consumption in smart GHs. By synthesizing and synthesizing existing research and practical experiences, this paper seeks to provide valuable insights and recommendations to facilitate the efficient and sustainable design, construction, and operation of smart GHs. Ultimately, this work aims to promote resource-efficient and environmentally conscious practices in the agricultural energy sector.

https://ieeexplore.ieee.org/abstract/document/10373041

Ghiasi, M., Wang, Z., Mehrandezh, M., & Paranjape, R. (2024). A Systematic Review of Optimal and Practical Methods in Design, Construction, Control, Energy Management and Operation of Smart Greenhouses. IEEE Access.12, 2830-2853

Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach

Efficient energy management is crucial for optimizing greenhouse (GH) operations and promoting sustainability. This paper presents a novel multi-objective optimization approach tailored for GH energy management, aiming to minimize grid energy consumption while maximizing battery state of charge (SOC) within a specified time frame. The optimization problem integrates decision variables such as network power, battery power, and battery energy, subject to constraints based on battery capacity and initial energy, along with minimum and maximum energy from the battery storage system. Through the comparison of a smart energy management system (EMS) with traditional optimization algorithms, the study evaluates its efficiency. Key hyperparameters essential for the optimization problem, including plateau time, prediction time, and optimization time, are determined using the ellipse optimization method. Treating the GH as a microgrid, the analysis encompasses energy management indicators and loads. A simulation conducted via Simulink in MATLAB software (R2021b) demonstrates a significant enhancement, with the smart EMS achieving a more than 50% reduction in the objective function compared to conventional EMS. Moreover, the EMS exhibits robust performance across variations in the load power and irradiation profile. Under partial shading conditions, the EMS maintains adaptability, with a maximum objective function increase of 0.35553%. Aligning the output power of photovoltaic (PV) systems with real-world conditions further validates the EMS’s effectiveness in practical scenarios. The findings underscore the efficiency of the smart EMS in optimizing energy consumption within GH environments, offering promising avenues for sustainable energy management practices. This research contributes to advancing energy optimization strategies in agricultural settings, thereby fostering resource efficiency and environmental stewardship.

https://www.mdpi.com/2624-6511/7/2/36

Jamshidi, F., Ghiasi, M., Mehrandezh, M., Wang, Z., & Paranjape, R. (2024). Optimizing Energy Consumption in Agricultural Greenhouses: A Smart Energy Management Approach. Smart Cities, 7(2), 859-879.

RESP: A real-time early stage prediction mechanism for cascading failures in smart grid systems

Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.

https://ieeexplore.ieee.org/document/10589285

Salehpour, A., & Al-Anbagi, I. (2024). RESP: A real-time early stage prediction mechanism for cascading failures in smart grid systems. IEEE Systems Journal, 18(3), 1593-1604.

https://doi.org/10.1109/JSYST.2024.3420950

Optimized ensemble model with genetic algorithm for DDoS attack detection in IoT networks.

 The growth in Internet of Things (IoT) networks has made them more vulnerable to various cyber threats, including Distributed Denial of Service (DDoS) attacks. Addressing DDoS attacks in resource-constrained IoT environments demands advanced detection methods beyond traditional cybersecurity. Existing machine learning and deep learning models have a tradeoff between accuracy and complexity. Pruning and quantization techniques present challenges related to precision and customization, highlighting the need for more balanced solutions. In response to these challenges, this paper introduces a novel Optimized Ensemble Model with Genetic Algorithm (OMEGA) system designed to detect high- and low-volume DDoS attacks in resource-constrained IoT networks. The system employs a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks in its ensemble model to detect these attacks. In addition, the system employs novel post-training GA-based pruning and Min-Max quantization techniques for optimization. This combination enhances the detection accu-racy of high- and low-volume DDoS and significantly reduces computational demands, making the OMEGA system suitable for deployment in edge devices with limited resources. The OMEGA system is tested using a real-world IoT testbed and various datasets, showing an accuracy of over 90%.

https://doi.org/10.1109/ICCWorkshops59551.2024.10615607

Saiyed, M. F., & Al-Anbagi, I. (2024). Optimized ensemble model with genetic algorithm for DDoS attack detection in IoT networks. In 2024 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 433-438). IEEE.

Deep Ensemble Learning with Pruning for DDoS Attack Detection in IoT Networks

 The upsurge of Internet of Things (IoT) devices has increased their vulnerability to Distributed Denial of Service (DDoS) attacks. DDoS attacks have evolved into complex multi-vector threats that high-volume and low-volume attack strategies, posing challenges for detection using traditional methods. These challenges highlight the importance of reliable detection and prevention measures. This paper introduces a novel Deep Ensemble learning with Pruning (DEEPShield) system, to efficiently detect both high and low-volume DDoS attacks in resource-constrained environments. The DEEPShield system uses ensemble learning by integrating a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network with a network traffic analysis system. This system analyzes and preprocesses network traffic while being data-agnostic, resulting in high detection accuracy. In addition, the DEEPShield system applies unit pruning to refine ensemble models, optimizing them for deployment on edge devices while maintaining a balance between accuracy and computational efficiency. To address the lack of a detailed dataset for high- and low-volume DDoS attacks, this paper also introduces a dataset named HL-IoT, which includes both attack types. Furthermore, the testbed evaluation of the DEEPShield system under various load scenarios and network traffic loads showcases its effectiveness and robustness. Compared to the state-of-the-art deep ensembles and deep learning methods across various datasets, including HL-IoT, ToN-IoT, CICIDS-17, and ISCX-12, the DEEPShield system consistently achieves an accuracy over 90% for both DDoS attack types. Furthermore, the DEEPShield system achieves this performance with reduced memory and processing requirements, underscoring its adaptability for edge computing scenarios.

https://doi.org/10.1109/TMLCN.2024.3395419

2024. F. Saiyed and I. Al-Anbagi, "Deep Ensemble Learning with Pruning for DDoS Attack Detection in IoT Networks," in IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 596-616, 2024.

A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks

Internet and cloud-based technologies have facilitated the implementation of large-scale Internet of Things (IoT) networks. However, these networks are susceptible to emerging attacks. This paper proposes a novel lightweight system for detecting both high- and low-volume Distributed Denial of Service (DDoS) attacks in IoT networks, namely Genetic Algorithm (GA) and t-Test for DDoS Attack Detection (GADAD). The GADAD system employs edge-based technologies and has three phases. In the first phase, it creates and preprocesses an HL-IoT (High- and Low-volume attacks in IoT networks) dataset, which includes both high- and low-volume DDoS attacks. The second phase introduces a novel and lightweight method, called GAStats, for optimal feature selection using the GA and statistical parameters (Stats.). In the third phase, the system trains three tree-based Machine Learning (ML) models: Random Forest (RF), Extra-Tree (ET), and Adaptive Boosting (AdaBoost), along with other ML models, using both the self-generated HL-IoT dataset and the publicly available ToN-IoT dataset. The evaluation includes the assessment of key performance metrics such as accuracy, precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC), computation time, and scalability analysis with overall system performance. The experimental results illustrate the efficacy of the feature selection method in optimizing the system’s efficiency in detecting DDoS attacks in IoT networks, along with a reduction in computation time compared to existing state-of-the-art techniques.

https://doi.org/10.1109/ACCESS.2024.3367357

2024. F. Saiyed and I. Al-Anbagi, "A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks," in IEEE Access, vol. 12, pp. 25623-25641, 2024.

A Novel Computer Vision System for Efficient Flea Beetle Monitoring in Canola Crop

Effective crop health monitoring is essential for farmers to make informed decisions about managing their crops. In canola crop management, the rapid proliferation of flea beetle(FB) populations is a major concern, as these pests can cause significant crop damage. Traditional manual field monitoring for FBs is time consuming and error-prone due to its reliance on visual assessments of FB damage to small seedlings, making conducting frequent and accurate surveys difficult. One of the key pieces of information in assessing if control of FBs is required is the presence of live FBs in the canola crop. This article proposes a novel insect-monitoring framework that uses a solar-powered, intelligent trap called the smart insect trap (SIT), equipped with a high-resolution camera and a deep-learning-based object detection network. Using this SIT, coupled with a kairomonal lure, the FB population can be monitored hourly, and population increases can be identified quickly. The SIT processes images at the edge and sends results to the cloud every 40 min for FB monitoring and analysis. It uses a modified you look only once version 8 small (YOLOv8s) object detection network, FB-YOLO, to improve its ability to detect small FBs. The modification is implemented in the network’s neck, which aggregates features from the deep and early pyramids of the backbone in the neck. Improved attention to small objects is achieved by incorporating spatially aware features from early pyramids. In addition, the network is integrated with an advanced box selection algorithm called confluence nonmax suppression (NMS-C) to prevent duplicate detections in highly overlapped clusters of FBs. The FB-YOLO achieved an average precision (mAP@0.5) of 89.97%, a 1.215% improvement over the YOLOv8s network with only 0.324 million additional parameters. Integrating NMS-C further improved the mAP@0.5 by 0.19%, leading to an overall mAP@0.5 of 90.16%

https://ieeexplore.ieee.org/abstract/document/10589284

Ullah, M., Hasan, M. S., Bais, A., Wist, T., & Sharpe, S. (2024). A Novel Computer Vision System for Efficient Flea Beetle Monitoring in Canola Crop. IEEE Transactions on AgriFood Electronics, 1–14. doi:10.1109/TAFE.2024.3406329

Temporal evolution and thematic shifts in sustainable construction and demolition waste management through building information modeling technologies: A text-mining analysis.

Construction and demolition activities are significant contributors to waste generation worldwide. As population growth accelerates worldwide, the amount of construction and demolition waste (C&DW) will increase proportionally unless proactive measures are implemented. This study analyzes the evolving research landscape on utilizing Building Information Modeling (BIM) technologies to advance sustainable C&DW management practices. A comprehensive text-mining analysis is conducted on 493 scholarly publications covering evolutions from January 2009 to February 2024 using the PRISMA framework. The research objectives are: (i) to identify key themes in domain of BIM technology in C&DW management using VOSviewer, (ii) to map the temporal evolution of research focus using SciMAT, and (iii) to identify emerging thematic trends.Co-occurrence analysis reveals three major research themes: (i) the use of digital twins and prefabrication for waste reduction, (ii) integrating environmental impact assessments, and (iii) data-driven decision-making. Strategic diagrams produced by SciMAT software uncover shifting priorities over the study period, with “reuse and recycling” emerging as motor themes, and “Prefabrication” (CIT = 481), “Decision Making” (CIT = 66), “Material Passport” (CIT = 92), and “Digital Twin” (CIT = 44) emerging as high-centrality and transversal themes. Temporal evolution mapping unveiled progressive integration of BIM tools such as (i) digital twins (TLS = 34, OCC = 9) and (ii) prefabrication (TLS = 40, OCC = 14), presenting opportunities to optimize waste reduction. This study offers a robust overview of the field, aiming to inform a diverse audience, including researchers from various disciplines, policymakers and industry professionals interested in advancing sustainable practices in C&DW management through innovative digital solutions.

https://doi.org/10.1016/j.jenvman.2024.122293

Naghibalsadati, F., Gitifar, A., Ray, S., Richter, A., & Ng, K. T. W. (2024). Temporal evolution and thematic shifts in sustainable construction and demolition waste management through building information modeling technologies: A text-mining analysis. Journal of Environmental Management, 369, 122293. https://doi.org/10.1016/j.jenvman.2024.122293

Performance assessment of novel catalytic spouted-bed vapor jet flow heat exchanger in amine-based carbon capture process

This study introduced a spouted bed and jet flow catalytic heat exchanger (SBJ-EX). This novel non-agitated technology can potentially integrate with a conventional desorption column to enhance heat transfer and CO2 desorption performance in amine-based post-combustion carbon capture systems. As its first research in the carbon capture field, this work experimentally evaluated key factors including overall heat transfer coefficient, logarithmic mean temperature difference, temperature distribution along the reactor, and cyclic loading under varied parameters such as inlet temperature of the heating oil, inlet solvent temperature, rich CO2 loadings, and mass of catalysts to simulate real-world operating conditions using benchmark MEA solvent and solid acid catalyst HZSM-5. Compared to conventional plate heat exchangers, the SBJ-EX demonstrated over a 70 % enhancement in heat transfer performance due to its effective overall heat transfer coefficient. It also exhibited impressive CO2 desorption performance even at lower temperatures with sufficient catalysts. Unlike agitated- type heat exchangers, the SBJ-EX could minimize catalyst attrition and offer more excellent stability. In contrast to fixed-bed catalyst desorbed columns, this equipment offered a more compact design for quicker and simpler catalyst replacement to reduce downtime for operators significantly. The SBJ-EX can also function as an optional backup or add-on unit to provide operators with flexibility. This work further discussed advantages and challenges of the SBJ-EX operation. This work enriched the future research outlook for this technology, and contributed a commercially viable approach to catalysts in carbon capture processes

https://doi.org/10.1016/j.seppur.2024.128393

128393. Yang, T. Li, T. Sema, C. Chan, P. Tontiwachwuthikul, Performance assessment of novel catalytic spouted-bed vapor jet flow heat exchanger in amine-based carbon capture process, Separation and Purfication Technology 353 (2025) 128393.