Cooperative driving is a promising approach to increase traffic efficiency and safety but requires high Vehicle-to-Everything (V2X) communication performance for coordinating and executing a cooperative maneuvers, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized V2X networks maintain the radio communication performance under congested channel conditions but severely degrade the communication performance from the application perspective, such as for cooperative driving, as focused in this paper. Thus, we propose a relevance-aware resource allocation mechanism for decentralized V2X networks that prioritizes vehicles with relevant information and counteracts channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-aware resource allocation and content-agnostic congestion control mechanisms.

Moving data across communication networks is often subject to deadline requirements. An example is early warning of disasters of natural origin, where sensor measurements at the disaster location must be communicated across a network within a predefined maximum delay in order for a consequent warning to be timely. In this work, we present a probabilistic model that allows for characterizing the delay experienced by sensor measurements in a wireless sensor network from source to sink depending upon the routing metric used for forwarding the data through the network. Using link delay probability distributions and the probabilities of following different paths to the sink, source-to-sink delay distributions are found for routing policies based on minimum hop-count, minimum mean delay and the Joint Latency (JLAT) protocol. An algorithm for calculating the end-to-end source to sink delay probability density function (PDF) is presented for the general case of networks that use routing tables whose input for routing decisions is the remaining time-to-deadline. The work provides a general tool for routing delay analysis, allowing for comparison of the deadline miss probability between different routing policies. An improved form of JLAT is proposed. Its deadline miss probability is found using the presented algorithm and compared to the ones determined for minimum hop-count, minimum mean delay and JLAT by means of an example.

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios.