Abdelfattah Ali Eladl

The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform.

False Data Injection Attacks (FDIAs) represent a significant cybersecurity threat to smart grids (SGs), compromising both system stability and operational reliability. Conventional detection approaches frequently prove inadequate, largely due to challenges such as data imbalance and suboptimal model parameterisation. To overcome these limitations, this study proposes a proactive detection framework that integrates ensemble learning, adaptive oversampling, and a novel metaheuristic optimization algorithm, termed FalsEye. At the core of the proposed framework is a Voting Classifier ensemble, which strategically combines heterogeneous base learners, including ExtraTrees, CatBoost, and LightGBM. The performance of this ensemble is further enhanced through the IceCube Optimization (IO) algorithm, a physics-inspired metaheuristic technique employed to fine-tune the hyperparameters of the individual base models. Additionally, the framework incorporates adaptive oversampling using the Adaptive Synthetic method to effectively mitigate class imbalance within the dataset, thereby improving the detection rate of minority FDIA instances. Experimental results demonstrate that the IO Voting Classifier achieves superior F1-scores and exhibits a more balanced precision–recall trade-off compared to conventional ensemble approaches. The optimized framework attains an accuracy of 99%, with a precision of 92%, a recall of 98%, and an F1-score of 95%, marking a substantial improvement over traditional methods. These findings highlight the considerable potential of combining metaheuristic optimization with ensemble learning to develop robust and cyber-resilient SG infrastructures.