Mana Saleh Al Reshan

Due to technological advancements, today’s world is rapidly changing from traditional and legacy approaches to smarter and more autonomous techniques. Intelligent systems and super-connectivity make possible the era of pervasive services, where these services can be used by anyone, anywhere, and at any time (AAA). These advancements have completely transformed modern living, increasing convenience and providing greater comfort. Robotic cleaning is one such revolutionized area where self-driving robots and automated systems are used to navigate and clean dirt. 3D mapping adapts changes based on visual conditions to detect and remove waste obstacles from the location. These robots are useful for collecting waste and cleaning the floor. In this research work, we have proposed a LiDAR-based cleaning robotic system called ”CleanAll: Efficient Object Collection Route Planning from LiDAR Point Clouds in the Cleaning Scenario.” It is an automotive system that calculates an optimized path from its 3D point-cloud data to collect some objects through its object collector. CleanAll moves over the optimized path using point cloud data along with a dual-antenna GNSS receiver and an IMU. A defined data set is already used to compare and detect object shapes, and an extended Kalman filter is used to obtain the robot’s position. CleanAll is more responsive, and its quick decision-making makes it a better option for cleaning scenarios where dirt and trash are scattered throughout areas. The optimized path used by the robot is not only useful in finding the shortest path, but it also saves time and consumes the minimum energy by following these short paths for trash collection. CleanAll is evaluated in three different scenarios, collecting all objects (scattered/line-by-line) with an average standard error rate of 0.4593. To assess performance on other parameters, it has been tested with two standard datasets, ACIN and IPA. The TPRs have 0.75, and the FPRs have been recorded as 0.40 in ACIN, while the TPRs in IPA have 0.7 and 0.4 in the same scenario. This means that CleanAll performs better using the ACIN data set, achieving higher TPRs with lower FPRs. All these experiments illustrate that CleanAll performs better using the ACIN data set, which aims to recognize and clean trash more precisely and efficiently.

This paper studies the influence of vehicle mass on electric vehicle (EV) energy consumption with the Black Box Energy Consumption Model (BBECM) in a Python-based simulation. Incorporating the TomTom and Open-Meteo API for real-time routes and weather data made the experiment in line with the realistic, predictable results. This framework enables controlled mass perturbation under identical driving conditions, allowing the explicit extraction of mass-dependent energy sensitivity that is not directly observable in fixed historical or standardized drive-cycle datasets. With different masses and constant aerodynamic, frontal area, and rolling resistance, a quadratic relationship (R ${}^{2} =0.9921$ ) was obtained. The energy consumption grows by 0.029 Wh/kg/km, which is about 0.29 kWh/100 km in + 100kg. Sensitivity analysis across mass and coefficient variations showed deviations under 8% and 4%. The novelty of the study is in the integration of BBECM with real-time data and regression in the context of generalizable EV energy information.

The development of internet-connected environments to facilitate people to handle a variety of tasks is being made possible by the Internet of Things (IoT). Technology advancements offer businesses a numerous conveniences and benefits. However, they provide hackers and intruders more opportunities to investigate and take advantage of different methods to get through the security of IoT networks. Therefore, the primary challenges with the IoT based environments is security. It is essential to secure computer and Internet of Things platforms against a variety of threats and attacks. Furthermore, traditional intrusion detection systems (IDS) frequently handle massive amounts of data from IoT networks that contain redundant and irrelevant data, which results in decreased accuracy and longer response times. Therefore, this paper proposed an IDS to detect different types of attacks on IoT networks by employing the CIC-IoT-2023 dataset. The most relevant IoT network features are extracted by integrating the Improved Binary Grey Wolf Optimisation (IBGWO) and Particle Swarm Optimisation (PSO). Additionally, the Random Forest (RF) algorithm is used to analyse the selected features, using several decision trees to produce an accurate response. Therefore, the suggested Hybrid IBGWO-PSO-RF model has significantly outperformed the other IDS algorithms with an accuracy of 99.96% for multi-class classification.

Insider threats are a widespread and severe issue of critical infrastructure and cyber‐physical systems (CPSs), in which traditional security measures are frequently not sufficient. Despite these machine learning (ML) methods being utilized in detecting insider threats, most of the data mining techniques utilized are based either on misuse or anomaly methods, which limits their flexibility and resistance in dynamic operations. In order to overcome these shortcomings, this study presents a hybrid deep learning framework that is built on ensemble learning, and it can detect the presence of insider threat by integrating the spatial and temporal behavioral features. The architecture suggested is based on the convolutional neural networks (CNNs), long short‐term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi‐LSTM) models. The conventional layers can be extracted to obtain spatial features of input sequences that are based on logs, whereas recurrent layers are used to forecast temporal dynamics and changing user behavior. The heterogeneous models are combined via a soft‐averaging ensemble approach, which boosts the generalizability and robustness. Recursive feature elimination (RFE) is used to reduce dimensionality to enhance interpretability using a random forest‐based ranking of feature importance. Moreover, synthetic minority over‐sampling technique (SMOTE) is applied to address the issue of the existing class imbalance, making the model more sensitive to infrequent insider threats. Evaluation of the cyber emergency response team (CERT) insider threat dataset using experimental methods confirms that the proposed ensemble model is much more effective than traditional baselines, with a detection accuracy of 90% and a ROC area under the curve (AUC) score of 0.96, a true positive rate (TPR) of 0.96 and a fairly low false positive rate (FPR) of 0.128. This article helps to fill the gap between approaches based on misuse and others on anomaly, and a scalable, real‐time‐capable detection solution, as well as provides a solid architecture of human‐centric security in digitized and complex CPS environments.

The existence of Bushfire is a significant problem. So not only are environmental threats to life and society a risk, but also the threatening of the safety of all. Thus it is extremely critical to detect these fires in time. This identification can assist in the successful application of fire management. Existing approaches might not be effective under conditions such as bad weather. A decrease in visible light also can affect accuracy. These techniques focus on the identification of fires during early stages. We introduce an innovative method in this study. The early detection of bushfires is achieved through UAVs. Each drone in the network has a thermal image camera onboard to provide real‐time surveillance of the assigned area. The drones are also equipped with sensors that detect smoke and other indications of potential fire. A control network receives up‐to‐the‐minute information from drones. Machine learning algorithms are used to interpret this data. The aim is to detect potential fires. The above detailed evaluations were captured using the eBee X and DJI Matrice 300 RTK. This study was carried out in a dedicated area provided by a National Park and involved the use of fixed and multirotor UAVs. State of the art sensor system could be successfully deployed on board the UAVs. The integrated technology was comprised of a Sequoia+ multispectral camera, H20T Quad Sensor and PGS 813 Gas Sensor. These were test burns, including controlled prescribed burning and fire simulations. They highlighted the system's impressive ability to detect bushfires in their early stages. The system has provided a quantum leap in preventive fire management. And it has heightened cost‐mindedness in real‐world settings. This enhancement is due to its high‐quality monitoring and precise detection of fire signs. The proposed method might be a useful and powerful tool for proactive fire fighting and early warning, such as fighting wildfire and related scenarios.