Most existing image-based parking space detection and classification methods assume that all training data reside in a single, centralized location—an unrealistic scenario that yields models unable to generalize to new parking lots. Furthermore, privacy concerns prevent lot owners from sharing raw images, limiting collaboration. To overcome these challenges, we present ParkFL, the first federated learning framework for parking space detection and classification that trains models across distributed client sites without exchanging raw image data. Built on NVFlare, ParkFL demonstrates model-agnosticism through evaluation on two deep-learning architectures. We benchmark four federated algorithms—FedAvg, FedProx, FedOpt, and SCAFFOLD—using real-world datasets. Despite training on non-centralized, heterogeneous data, ParkFL achieves 99.5% mAP, matching the accuracy of state-of-the-art centralized models on the same parking lots. When evaluated on images from different parking lots, ParkFL significantly outperforms models trained solely on individual-site data, which achieve 25.6% mAP, even though ParkFL never accesses raw images from other sites. Communication overhead remains below 3% of total training time, demonstrating a scalable, privacy-preserving solution with nearly state-of-the-art performance. We release ParkFL code at https://github.com/ahmedmbakr/ParkFL.

In the effort of making a more realistic simulation environment for VANET systems, we employ real traffic data to create a mobility scenario for ns-3. We then test how our simulated system performs when running different variations for network protocols. One version is optimized for general use, while the other is optimized for a VANET environment. We then evaluate how effective the optimizations for VANET are as well as the accuracy of our test system. We found that 802.11p performed better than 802.11ac in our simulated system in terms of both packet delivery and computational efficiency, and that our system seemed to be a reasonable model of the real world.

Until recently, industrial control systems (ICSs) used “air-gap” security measures, where every node of the ICS network was isolated from other networks, including the Internet, by a physical disconnect. Attaching ICS networks to the Internet benefits companies and engineers who use them. However, as these systems were designed for use in the air-gapped security environment, protocols used by ICSs contain little to no security features and are vulnerable to various attacks. This paper proposes an approach to detect the intrusions into network attached ICSs by measuring and verifying data that is transmitted through the network but is not inherently the data used by the transmission protocol-network telemetry. Using simulated PLC units, the developed IDS was able to achieve 94.3 percent accuracy when differentiating between machines of an attacker and engineer on the same network, and 99.5 percent accuracy when differentiating between attacker and engineer on the Internet.