Over the years, there has been an increase in the use of wearable sensors for high-precision Human Activity Recognition (HAR), ranging from personal fitness to remote patient monitoring. However, the continuous collection of biometric information poses a privacy risk, potentially leading to user profiling and data misuse. Today’s state-of-the-art approaches use standard encryption techniques to protect data in transit but leave it vulnerable during computation. For accurate HAR while maintaining the privacy of users’ data during collaborative analysis, we propose a hybrid, domain-agnostic framework that integrates Homomorphic Encryption (HE) with Secure Multi-Party Computation (SMPC). The proposed approach enables healthcare providers and device manufacturers to collaboratively analyze data without ever disclosing raw biometrics by utilizing HE for data encryption and secret sharing for collaborative computation. While this framework applies to general HAR scenarios, it will be crucially useful in the high-stakes domain of eldercare, where data privacy and regulatory compliance are critical. Reliable, Privacy-Aware Human Activity Sensing (RAHAS) achieved \(89.24\pm 0.95\%\) accuracy when tested on the widely used PAMAP2 dataset for HAR. Our analysis demonstrates that our privacy-preserving design provides side-channel resilience while maintaining utility comparable to clear-text baselines.

The freshness of information related to status updates is crucial in time-critical applications like disaster response and search and rescue operations. We can enhance network connectivity by using electric vertical take-off and landing vehicles (eVTOLs) operating in the affected region as portable wireless repeaters as part of the operation. In this work, we study the spectrum allocation in ultra-reliable low latency communication (URLLC) networks assisted by eVTOLs while minimizing the age of information (AoI). The optimal spectrum allocation for eVTOL-assisted networks is a challenging problem that depends on various dynamic factors, such as bit error rate (BER), data rate, power consumption, and the flight trajectory of the eVTOLs. Therefore, we propose a dynamic approach that can efficiently allocate spectrum among the eVTOLs to minimize the total AoI between the source and the destination, as well as the individual AoI of each eVTOL acting as a relay node. Simulations exhibit that the proposed algorithm can outperform the classical approaches in terms of AoI by improving the AoI by $57.42 \%$ over the classical fairness approach and $43.43 \%$ over the classical optimal relay selection approach. We also find an improvement in data rate by $8.89 \%$ and $6.76 \%$, respectively, along with a marginal improvement in the BER. Our approach offers an efficient solution for next-generation AoI-aware spectrum management in eVTOL-assisted networks.

Controlling the carbon footprint and operating costs of 5G and nextG networks remains a venerable problem among network designers aiming for high spectrum efficiency and communication performance. This paper introduces a Game-Theoretic solution known as GeTOA (Game-Theoretic Age of Information), which leverages Nash bargaining (NB) to optimize the Age of Information (AOI) for multi-user electric vertical take-off and landing (eVTOL) communication. Considering the unique trajectory of the e VTOLs, which have substantial alterations in the channel conditions, coupled with the variation in the AOI during critical phases of flight, we calculate the Pareto optimal solutions for fair and efficient use of available resources while increasing the information content in our communication. We compare the cooperative GeTOA approach against the non-cooperative utility maximization (UM) approach, resulting in a notable 12.76% improvement while ensuring equitable resource allocation among the nodes. In contrast to the traditional UM approach, GeTOA significantly enhances energy allocation efficiency for multiple e VTOLs operating with diverse trajec-tories while enabling zero-touch fair resource management in open-access spectrum scenarios. In particular, results show that GeTOA handles fairness among the eVTOLs by ensuring an equal rate of information and fair distribution of power at the eVTOLs, which is especially relevant for Citizens Broadband Radio Service (CBRS) and C- Band applications. The flexibility and prioritization of AOI-based optimization allow a significant number of e VTOLs to operate efficiently within a congested spec-trum, facilitating Ultra-Reliable Low Latency Communication (URLLC) and improving power efficiency for the advancement of large-scale Urban Air Mobility (UAM) services which the limited flight range of e VTOLs has historically restricted.

The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area. To detect coexisting jamming and subtle interference, we introduce a Bayesian Inference-assisted machine learning (ML) methodology. Our methodology uses cross-layer Key Performance Indicator data collected on a Non-Standalone (NSA) 5G NR testbed to leverage supervised learning models, further assessed, calibrated, and revealed using Bayesian Network Model (BNM)-based inference. The models can operate on both instantaneous and sequential time-series data samples, achieving an Area under Curve above 0.954 for instantaneous models and above 0.988 for sequential models including the echo state network (ESN) from the Reservoir Computing (RC) family, across various jamming scenarios. The 180 ms instantaneous detection time allows for continuous tracking of the dynamic jamming condition due to UE mobility. Our approach serves as a validation method and a resilience enhancement tool for ML-based jamming detection while also enabling root cause identification for observed performance degradation. The introduced BNM-based inference proof-of-concept is successful in addressing 72.2% of the erroneous predictions of the RC-based sequential detection model caused by insufficient training data samples, thereby demonstrating its near real-time applicability in 5G NR and Beyond-5G networks.

In the process of developing software, code review is crucial. By identifying problems before they arise in production, it enhances the quality of the code. Finding the best reviewer for a code change, however, is extremely challenging especially in large scale, especially open source software stacks with cross functioning designs and collaborations among multiple developers and teams. Additionally, a review by someone who lacks knowledge and understanding of the code can result in high resource consumption and technical errors. The reviewers who have the specialty in both functioning (domain knowledge) and non-functioning areas of a commit are considered as the most qualified reviewer to look over any changes to the code. Quality attributes serve as the connection among the user requirements, delivered function description, software architecture and implementation through put the entire software stack cycle. In this study, we target on auto reviewer assignment in large scale software stacks and aim to build a self-learning, and self-correct platform for intelligently matching between a commit based on its quality attributes and the skills sets of reviewers. To achieve this, quality attributes are classified and abstracted from the commit messages and based on which, the commits are assigned to the reviewers with the capability in reviewing the target commits. We first designed machine learning schemes for abstracting quality attributes based on historical data from the OpenStack repository. Two models are built and trained for automating the classification of the commits based on their quality attributes using the manual labeling of commits and multi-class classifiers. We then positioned the reviewers based on their historical data and the quality attributes characteristics. Finally we selected the recommended reviewer based on the distance between a commit and candidate reviewers. In this paper, we demonstrate how the models can choose the best quality attributes and assign the code review to the most qualified reviewers. With a comparatively small training dataset, the models are able to achieve F-1 scores of 77% and 85.31%, respectively.