Eutrophication in water bodies is a critical problem, particularly in harbors with high nutrient concentrations. This excess nutrient load promotes the growth of harmful algae and bacteria, leading to oxygen depletion and hypoxic conditions, which can disrupt aquatic ecosystems and pose health risks to humans by contaminating drinking water and seafood. The integration of IoT in water quality monitoring enables real-time data collection and analysis, facilitating timely intervention. However, environmental noise, calibration drift, and transmission errors often affect sensor measurements, leading to unreliable data. Accurate water quality data is essential for effective monitoring. In this paper, we propose a Physics-Informed Data Denoising approach to enhance the reliability of water quality data. Empirical evaluations using two factual datasets of Tolo Harbor (Hong Kong) and New York Harbor demonstrate significant performance improvement over state-of-the-art water quality assessment systems. Our method reduces the root mean square error (RMSE) and improves the coefficient of determination (R2) scores for multiple water quality parameters. Our results indicate that integrating physics-based constraints into the data analysis improves prediction accuracy and sensor reliability. Additionally, we conducted an ablation study on the effect of physics-informed denoising. The study reveals that incorporating physical constraints improves stability and predictive performance across monitoring stations, emphasizing the importance of a physics-informed approach in data-driven water quality modeling.

The security threats and risks posed by Internet of Things (IoT) devices have been increasing significantly in recent times. Hence, an Intrusion Detection System (IDS) is required to handle and filter out cyber-attacks. Traditional IDSs face a major challenge in class imbalance within the data, which is the case for many real-world datasets related to intrusion, and a lack of model interpretability. In this paper, we introduce a novel IDS by fusing Generative Adversarial Network (GAN) and Explainable AI (XAI) techniques. Our proposed IDS uses Conditional Tabular GAN (CTGAN) as the synthetic data generator to address class imbalance issues. Additionally, in order to have global and local model interpretability of the proposed IDS, two XAI approaches are followed: SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed IDS achieves accuracy between 97.20% and 100%, F1 score between 89.34% and 100%, test time from 0.0104 s to 0.5686 s, and model size ranging from 2.73 kB to 1510 kB across different datasets. To validate practical applicability, we deploy the best-performing models on a resource-constrained edge device (e.g., Jetson Nano), achieving efficient testing times and demonstrating suitability for real-time applications. We conduct a quantitative comparison with state-of-the-art methods, demonstrating improved performance, enhanced interpretability, and increased model transparency through XAI integration.

In this paper, we consider a cell-free massive multiple input multiple output (CF-MMIMO) communication system, where users are supported by access points (AP) in conjunction with intelligent reflecting surfaces (IRS) mounted on unmanned aerial vehicles (UAVs). Although aerial IRS (aIRS) offers agile support for expanding network coverage in CF communication systems, the effective operation of such a complex network necessitates a channel state information (CSI) acquisition scheme that exhibits low run-time computational complexity. We propose an artificial intelligence (AI)-based approach to design and develop an efficient channel prediction scheme for CSI acquisition in the CF-MMIMO network supported by aIRS considered. Simulation results demonstrate the effectiveness of the proposed scheme in predicting channel gains across a wide range of signal-to-noise ratios (SNR) while maintaining low computational complexity during real-time operations.

High performance computing (HPC) systems are large-scale computing systems with thousands of compute nodes. Massive energy consumption is a critical issue for HPC systems. In this paper, we develop an auction mechanism model for energy consumption reduction in an HPC system. Our proposed model includes an optimized resource allocation scheme for HPC jobs based on processor frequency and a Vickery-Clarke-Groove (VCG)-based forward auction model to enable energy reduction participation from HPC users. The model ensures truthful participation from HPC users, where users benefit from revealing their true valuation of energy reduction. We implement a job scheduler simulator and our mechanism model on a parallel discrete-event simulation engine. Through trace-based simulation, we demonstrate the effectiveness of our auction mechanism model. Simulation shows that our model can achieve overall energy reduction for an HPC system, while ensuring truthful participation from the users.

Mobile crowdsensing, an emerging sensing paradigm, promotes scalability and reduction in the deployment of specialized sensing devices for large-scale data collection in a decentralized fashion. However, its open structure allows malicious entities to interrupt a system by reporting fabricated or erroneous data, making trust evaluation a highly important issue in mobile crowdsensing applications. The goal of this research is to show that an introduction of a reputation system in the process of correlated sensor-based data fusion will enhance the overall quality of the sensed data. To do so, we design a reputation-aware data fusion mechanism to ensure data integrity. We use Gompertz function in our reputation method to rate the trustworthiness of the data reported by a crowdsensing participant. The proposed mechanism, on one hand, is capable of defending a data corruption attack and identifying malicious or honest participants based on their reported data in real time. On the other hand, this mechanism yields more accurate data prediction in terms of lower data prediction error. We conducted experiments using two different real-world datasets. We compare our correlated data and reputation-aware data prediction (CDR) method with other popular methods, and the results show that our effective method incurs lower data prediction error.

With recent widespread usage of state-of-the-art technology (e.g., various mobile devices), environmental sensing is getting popular. The sensors used for sensing are small and due to the mobility they become more error-prone, which results in data corruption or loss from sensor. Therefore, cleaning of the sensed data is of high importance to recover the lost or corrupted data. In this paper, we propose a novel data cleaning mechanism to ensure better accuracy in environmental sensing applications. Based on the sensed data and the context relationship of each sensor, we update the credibility (or alternatively reliability) of the sensed data. We consider mobility pattern of the mobile sensor nodes while selecting the candidate sensor nodes for data stream cleaning. Through simulations, we evaluate the performance of our proposed approach. We compare our proposed sensor data stream cleaning approach with Influence Mean Cleaning (IMC) (a recent algorithm in data stream cleaning) and Mean-based cleaning. Simulation results show up to 24% reduction in root mean square error (RMSE) over IMC and up to 30% over Mean-based cleaning.

Mobile applications can be enhanced to a great extent by using the offloading mechanism in an energy efficient manner to the bounty resourceful clouds. Due to the huge demand of smart phones, the issue of providing more processing capability to this resource constraint device is getting more concern now-a-days. In this paper, a method level offloading mechanism has been proposed where no prior image of the mobile device is needed to be transferred to the cloud. The application is partitioned at different points where the migration of the execution thread is performed from mobile device to nearby resourceful cloud to get the best execution performance in optimal energy cost. The mobile can complete the execution after the partitioned thread returns back from the cloud to the device. This mechanism increases scalability as well as performance in the form of faster execution speed of the mobile devices. Moreover, we consider the mobility of the mobile device and propose a solution to find the best cloud instance on the move. To find out which cloud to offload, the communication latency, capacity, and current load at individual clouds are considered to find out the best cloud to offload to ensure better service for the mobile device. The proposed solution has been simulated and compared against CloneCloud in two different simulation scenarios where we show that our method performs superior to CloneCloud.