Vehicle-to-Everything (V2X) communication requires continuous exchange of mobility data, but this information is highly sensitive and must be protected. Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data and has recently been explored for Intelligent Transportation Systems (ITS), yet its practical feasibility for specific V2X tasks remains unclear. This paper experimentally evaluates three lattice-based schemes (BFV, BGV, CKKS) using OpenFHE on SUMO-generated mobility data. Three representative traffic-related tasks are considered: collision-risk detection, average speed estimation, and traffic density. For each scheme, we measure encryption time, homomorphic computation time, decryption time, ciphertext size, and correctness relative to plaintext baselines. The results show that all schemes achieve near-perfect correctness, and that aggregated analytics (average speed, density) complete well within non-safety-critical time budgets. However, all schemes consume a significant portion of the 100 ms safety message threshold. These findings quantify the current performance–privacy trade-offs of FHE in V2X scenarios and indicate that hardware acceleration or lighter cryptographic configurations are needed for real-time safety applications.
Louise Wegrelius, Theodor Johansson, Elias Gustafsson et al.· International Mediterranean...· 0 citations
Traditional Internet of Things (IoT) security solutions often rely on heavy cloud-based or gateway-class infrastructure, which is frequently unsuitable for resource-limited hardware due to latency, power, and memory constraints. This paper proposed a resource-aware behavioral Intrusion Detection System (IDS) designed for highly constrained IoT devices. To address these challenges, the proposed system implements real-time application-layer monitoring on an ESP32 Microcontroller Unit (MCU) and utilizes an offline-trained logistic regression model for autonomous, on-device inference. The detection mechanism extracts behavioral features, such as request rates, failed authentication attempts, and invalid resource access within sliding time windows to estimate attack probabilities. Experimental evaluations under controlled scenarios involving flood, brute force, and scan attacks demonstrate that the system achieves high accuracy, precision, and recall. These findings indicate that effective cyber intrusion detection and local mitigation can be successfully executed directly on a single MCU while preserving stable runtime performance and minimal memory usage. Finally, this paper highlights the need for further optimizations to improve robustness and scalability.
Sofyan Bisher, Anas Fawaza, Tarek Mawed et al.· International Conference on...· 0 citations
Vehicle-to-Everything (V2X) communication enables vehicles to exchange safety-critical messages, but its reliance on temporary pseudonymous identities makes it vulnerable to Sybil attacks, where a single attacker fabricates multiple identities to inject false information into the network. This paper presents a lightweight cryptographic mechanism that combines SHA-256 Proof-of-Work token mining with a time-windowed ratecontrol layer to limit the number of new identities a vehicle can activate within a given interval. The mechanism was implemented in Java and evaluated through a parametric simulation across three independent variables: PoW difficulty, rate limit, and attack intensity. Results show that a difficulty of 4 and a rate limit of 5 tokens per 10-second window provide an effective balance between Sybil resistance and legitimate vehicle access in an 8-vehicle scenario, with an average mining time of 0.227 seconds. The evaluation identifies the rate-control layer as the primary security mechanism, while PoW difficulty increases the computational cost per identity without independently capping accepted tokens. The proposed approach is infrastructure-free and suitable for regulated V2X deployments where attackers represent a minority of the network.
Maher Fayyad, Abdullah Awad, E. P. de Freitas et al.· International Conference on...· 0 citations
The rapid growth of Internet of Things (IoT) deployments has intensified the need for efficient, decentralized computation management at the network edge. This paper presents a lightweight, neighbor-aware one-hop task offloading framework designed for resource-constrained IoT networks. The proposed adaptive scheme combines Exponential Weighted Moving Average (EWMA) load estimation with a queue-depth gate to prevent unnecessary offloading under transient load spikes, and an assignment-pressure mechanism to distribute tasks more evenly across neighboring nodes. We evaluate the framework using a custom-developed discrete-event simulator on a 90-node ringplus-chord topology with heterogeneous hotspot and light nodes, comparing against three baselines: local-only execution, random offloading, and least-loaded neighbor selection. Results show that a load-aware but pressure-unaware least-loaded strategy surprisingly produces the highest load variance (377.25), worse than random offloading (114.84), due to severe task funneling toward persistently fast nodes. The proposed scheme eliminates task drops entirely, achieves an average latency of 148.7 ms, and reduces task-count variance to 44.33 - an $8.5 \times$ improvement over the least-loaded baseline and 4.6× over local-only execution - while requiring only 28.21% of tasks to be offloaded. These results demonstrate that assignment-pressure tracking is essential for fair load distribution in energy-limited IoT deployments.
Faizan Haider, Alexandre dos Santos Roque, E. P. de Freitas· International Conference on...· 0 citations