无线代理分布式任务分配的Hedonic联盟形成
Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents
摘要：
自主无线代理，如无人驾驶飞行器或移动基站，在下一代无线网络的部署提出了巨大的潜力。虽然目前的文献主要集中在机器人或软件应用程序中的代理的使用，我们提出了一种新的自组织代理适合无线网络的使用模型。在所提出的模型中，一些代理需要从几个任意位置的任务收集数据。每个任务表示需要由代理收集并随后无线传输到中央接收器的分组的队列。该问题被建模为一个享乐联盟形成游戏之间的代理和任务，互动，以形成不相交的联盟。每个形成的联盟被建模为一个轮询系统，由一些代理之间的不同任务之间的联盟，收集和传输的数据包移动。在每个联盟中，一些代理还可以扮演中继的角色，以提高数据包传输的成功率。所提出的算法允许的任务和代理采取分布式决策加入或离开一个联盟，根据所取得的好处，在有效的吞吐量，和延迟方面的成本。由于这些决定，代理和任务的结构本身成独立的不相交的联盟，构成一个纳什稳定的网络分区。此外，该算法允许代理和任务，以适应拓扑结构的环境变化，如到达/删除的任务或任务的移动性。仿真结果表明，所提出的算法如何提高性能，在平均玩家（代理或任务）的回报，至少30.26%（5个代理与多达25个任务的网络）相对于一个计划，分配附近的任务平均代理。
英文摘要：
Autonomous wireless agents such as unmanned aerial vehicles or mobile base stations present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software applications, we propose a novel usage model for self-organizing agents suited to wireless networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents can also take the role of a relay for improving the packet success rate of the transmission. The proposed algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed algorithm allows the agents and tasks to adapt the topology to environmental changes such as the arrival/removal of tasks or the mobility of the tasks. Simulation results show how the proposed algorithm improves the performance, in terms of average player (agent or task) payoff, of at least 30.26% (for a network of 5 agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
