To fully realize the potential of vehicular networks, several obstacles and challenges need to be addressed. Chief among the obstacles are strict QoS requirements of applications and differentiated service requirements in different situations. Although DSRC and WAVE have been adopted as the de facto standards, they do not address all the problems and there is room for improvements. In this study, we propose a generic prioritization and resource management algorithm that can be used to prioritize processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4 percent in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9 to 67 percent more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

In vehicular ad-hoc networks, due to high mobility, vehicles usually communicate for short periods of time with several neighboring vehicles and are required to process data fast; sometimes in the order of few milliseconds. This urgency of data processing is further heightened in safety-critical scenarios that involve many vehicles. Such scenarios require data to be prioritized and processed with minimum delay. While packet scheduling has been extensively studied, these studies focus on channel scheduling, our work focuses on processing received packets by a vehicle in dense scenarios. In this paper, we formulate the prioritized data processing problem as an integer linear program given a prior knowledge of the request sequence and prove that it is NP-complete. Due to the difficulty of predicting the traffic patterns and obtaining the request sequence in advance, we propose an online algorithm that does not require the prior knowledge of the request sequence and achieves an O(1) competitive ratio. The proposed online algorithm strives to accept higher severity packets for processing in order to maximize the cumulative severity given vehicular communications/computation capacity constraints. Using real traffic traces, we evaluate the performance of the online algorithm against three online algorithms, in which two of them use an exponentially weighted moving average-based threshold while the other one accepts requests as capacity permits. Our evaluation shows that our algorithm achieves up to 492% more cumulative severity compared to the three other baseline algorithms.