Gagangeet Singh Aujla

Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.

Blockchain technology is acclaimed for eliminating the need for a central authority while ensuring stability, security, and immutability. However, its integration into Internet of Things (IoT) environments is hampered by the limited computational resources of IoT devices. Consensus algorithms, vital for blockchain safety and efficiency, often require substantial computational power and face challenges related to security, scalability, and resource demands. To address these critical issues, we propose a novel model that significantly enhances the security and performance of blockchain in IoT environments. Our model introduces three key innovations: (1) a bidirectional-linked blockchain system that strengthens security against long-range attacks by exploiting dual reference points for block validation; (2) the integration of user preferences into the Committee Member Auction (CMA) consensus algorithm, optimizing miner selection to balance resource efficiency with security; and (3) a comprehensive performance and frequency analysis that demonstrates the system’s resilience against double-spend, long-range, and eclipse attacks. The proposed model not only reduces block validation delays but also enhances overall system performance, as evidenced by simulations comparing its effectiveness with existing CMA algorithms. These advancements have the potential to significantly impact the deployment of blockchain in resource-constrained IoT environments, offering a more secure and efficient solution.

With the widespread adoption of healthcare Internet of Things (IoT) devices, the need for effective intrusion and anomaly detection has become pivotal in ensuring network security. However, the optimization of machine learning (ML) and deep learning (DL) models for these detection tasks frequently necessitates extensive computational resources, adversely affecting both temporal and energy efficiency. This article introduces an automated ML (AutoML) framework specifically tailored to enhance anomaly detection models, with a strategic focus on energy efficiency throughout the optimization process. The process commences with data preprocessing, followed by feature selection employing a combination of recursive feature elimination (RFE) and SHapley Additive exPlanations (SHAP) to identify important features for anomaly detection. Subsequently, a baseline multilayer perceptron (MLP) neural network model is trained, and hyperparameter optimization is executed in a constrained search space to mitigate energy consumption. The framework produces optimized models, which are assessed based on accuracy and energy consumption at various checkpoints, with the models demonstrating inferior performance systematically excluded based on predefined accuracy or energy consumption objectives. Experimental outcomes reveal that the pipeline effectively balances detection performance with energy consumption, with certain cases showing minimal accuracy losses (less than 1%) accompanied by substantial energy savings (over 60%), presenting a sustainable and resource-efficient approach to anomaly detection in IoT systems.

Sensor-enabled distributed energy resources (DERs) provide various advantages, including a lower carbon footprint, yet effective management of millions of DERs is still an issue. Virtual power plants (VPP) can integrate several DERs into a unified operational digital twin to enable real-time monitoring, analysis, and control. VPP may utilize advanced solutions to improve operational efficiency by combining substantial measurement data from DERs. However, effectively managing the quantity and complexity of data flows, whether streaming data or high-impact low-frequency data, is essential in maintaining the performance of DERs at sustained levels. The vast amounts of diverse data generated from various DERs pose significant challenges for storage, processing, and resource management. This paper proposes a comprehensive framework that employs a distributed big data cluster to ensure scalable and reliable data storage and utilizes a robust message broker system for efficient data queuing. Additionally, we present innovative load-balancing strategies within the VPP Digital Twin system. A decision tree algorithm is implemented to calculate the forthcoming workload collected by various deployed sensors at various DERs. The required resources are identified per workload, and the numbers are forwarded to the Orchestrator. The Orchestrator scales up and down resources based on resource utilization suggested by the decision tree algorithm when the resources or nodes are insufficient to handle the sensor data, optimizing the utilization of computing resources. The framework also features a failure detection component that performs root cause analysis to provide actionable insights for system optimization. Experimental results show that this framework ensures high efficiency, reliability, and real-time operational capability in VPP digital twin by addressing critical challenges in data storage, streaming data analysis, and load balancing.

With renewable energy sources (RESs) integrated into modern power systems, energy consumers participate in the energy market making the entire network more complex and uncertain. Therefore, adaptive strategies are needed to address the various uncertainties associated the energy network to maintain reliability and achieve sustainability. In this research paper, we propose an integrated two-fold approach that combines competitive market mechanisms cooperative strategies to enhance reliability and resilience in distribution networks under uncertainty. In the normal operational stage, we adopt a competitive approach wherein an optimal price is determined to meet the required consumer demand, facilitating energy sharing among areas and minimising reliance on the grid, thereby reducing carbon emissions associated conventional energy generation. To detect and identify an uncertain event within the network, a heuristic algorithm is proposed, which determines the hidden inter-dependencies among the different network input parameters. Finally, to mitigate the impact of these identified uncertainties, we introduce a cooperative approach wherein all areas leverage the battery storage facilities to ensure continuity of energy supply and minimise overall losses. During an uncertain event, a maximum reduction of 97% is observed in the carbon footprints for the proposed scheme while maintaining the overall profit.

The COVID-19 pandemic continues to challenge healthcare systems globally, necessitating advanced tools for clinical decision support. Amidst the complexity of COVID-19 symptomatology and disease severity prediction, there is a critical need for robust decision support systems to aid healthcare professionals in timely and informed decision-making. In response to this pressing demand, we introduce BayesCovid, a novel decision support system integrating Bayesian network models and deep learning techniques. BayesCovid automates data preprocessing and leverages advanced computational methods to unravel intricate patterns in COVID-19 symptom dynamics. By combining Bayesian networks and Bayesian deep learning models, BayesCovid offers a comprehensive solution for uncovering hidden relationships between symptoms and predicting disease severity. Experimental validation demonstrates BayesCovid ’s high prediction accuracy (83.52–98.97%). Our work represents a significant stride in addressing the urgent need for clinical decision support systems tailored to the complexities of managing COVID-19 cases. By providing healthcare professionals with actionable insights derived from sophisticated computational analysis, BayesCovid aims to enhance clinical decision-making, optimise resource allocation, and improve patient outcomes in the ongoing battle against the COVID-19 pandemic.

Drones are sophisticated machines that can hover over extreme locations, conduct aerial surveillance, collect surveillance data, and disseminate it to the distributed edge for processing and analysis. The distributed edge deploys advanced Artificial Intelligence (AI) models to detect any unwarranted activity or object based on surveillance data. However, these lightweight and low-power Unmanned Aerial Vehicles (UAVs) may experience faults due to unprecedented workload when deployed in extreme surveillance domains. In this paper, we have designed an AI framework to detect any safety concerns with drones deployed for aerial surveillance in extreme locations based on real-time drone critical parameters. We also propose a MapReduce-based object recognition and classification module to process large-scale images captured by drones efficiently. However, conventional AI systems behave like black box systems, leading to a lack of trust and transparency. Thus, we convert the traditional framework of AI into an explainable edge AI framework using SHapley Additive exPlanations (SHAP) that opens Pandora’s black box. The experimental results show the effectiveness of the proposed framework in detecting drone safety concerns through explainable health status tracking alongside ensuring an effective object detection mechanism.