Coalitional Graph Game of Multi-Hop Clustering in Vehicular Ad-Hoc Networks

Road traffic information can be collected over a vehicular ad-hoc network (VANET) and utilized in many intelligent traffic system applications. A clustering mechanism is used to create a cluster of vehicles to facilitate the data collection from vehicles to road side units (RSUs) acting as sink nodes. Unlike previous works that focus on cluster lifetime or throughput, we propose a coalitional graph game (CGG) technique to form a multi-hop cluster with a largest possible coverage area for a given transmission delay time constraint to economize on the number of RSUs. Vehicles decide to join or leave the coalition based on their individual utility that is a weighted function of number of members in the coalition, relative velocities, distance to sink nodes, and transmission delay toward the sink nodes. The stability of cluster formation is proved by using a discrete-time Markov chain. Our results show that the proposed game model always yields a stable coalition structure that satisfies the objective, and the solutions vary with the weights given to individual components in the utility function.

On Satisficing in Quantitative Games

Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games. Optimization is one form of analysis. We argue that in many cases it may be better to replace the optimization problem with the satisficing problem, where instead of searching for optimal solutions, the goal is to search for solutions that adhere to a given threshold bound.This work defines and investigates the satisficing problem on a two-player graph game with the discounted-sum cost model. We show that while the satisficing problem can be solved using numerical methods just like the optimization problem, this approach does not render compelling benefits over optimization. When the discount factor is, however, an integer, we present another approach to satisficing, which is purely based on automata methods. We show that this approach is algorithmically more performant – both theoretically and empirically – and demonstrates the broader applicability of satisficing over optimization.