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