A Task-Driven Sequential Overlapping Coalition Formation Game for Resource Allocation in Heterogeneous UAV Networks

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the Pareto and selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided tabu search (PGG-TS) algorithm is developed to obtain a stable coalition structure. Numerical results verify that the proposed PGG-TS algorithm increases the average utility of tasks by 12.5% and 38.5% compared with the split-merge preferred OCF algorithm and non-overlapping CF algorithm, respectively. The utility of the proposed BMBT order increases by 25.1% and 34.3% compared with selfish and Pareto orders, respectively.

Automated summary

A sequential overlapping coalition formation game model is proposed to optimize the composition and task allocation of heterogeneous UAVs. The model considers the overlapping and complementary relations of resource properties and the task execution order, and introduces a bilateral mutual benefit transfer order to allocate task resources more efficiently.