基于iMesh和拍卖的多跳无线传感器和机器人网络中基于agent的任务分配

B. Gasparovic, I. Mezei
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引用次数: 1

摘要

本文采用基于局部距离敏感信息网格的服务发现算法(iMesh),对多跳无线传感器和机器人网络中现有的基于agent的任务分配(k-aSAAP)拍卖聚合协议进行了改进。通过引入代理来减少通信开销,并通过以下方式引入iMesh服务发现来进一步改进:将传感器监控的区域划分为扇区,其中一个静态代理位于该扇区的中心,拥有该扇区中机器人的所有信息。其中一个传感器检测到事件信息。然后,该传感器在传感器矩阵中进行查找过程,以从iMesh结构中获取有关附近服务提供商(机器人)的信息。在找到最近的服务提供商后,它将信息转发给该部门的代理,该代理触发在邻近代理中搜索成本最低的机器人的拍卖。将现有的基于代理的解决方案与iMesh进行比较,然后将其结合起来,以提高最优分配的百分比、平均最优与最近距离的比率,并保持较低的交换消息数量。仿真结果表明,在k(跳数)较低的情况下,引入iMesh可以将最优分配的百分比从50%提高到90%以上,同时使每个机器人的平均交换消息数保持在1左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agent-based task assignment in multi-hop wireless sensor and robot networks improved by iMesh and auctions
In this article, an improvement to the existing agent-based auction aggregation protocol for the task assignment (k-aSAAP) in multi-hop wireless sensor and robot networks is proposed by applying the localized distance-sensitive information mesh based service discovery algorithm (iMesh). The agents are introduced to reduce the communication overhead and further improved by introducing the iMesh service discovery in the following way: the area monitored by sensors is divided into sectors where one static agent, having all information about the robots in that sector, is located in the center. Event information is detected by one of the sensors. That sensor then conducts the lookup process in the sensor matrix to obtain the information about nearby service providers (robots) from the iMesh structure. After the nearest service provider is found, it now forwards the information to the agent in that sector which triggers the auction searching for the robot with the lowest cost among the neighboring agents. The existing agent-based solutions and the iMesh were compared and later combined to improve the percentage of optimal assignments, the average optimal-to-closest distance ratio and to maintain low the number of exchanged messages. The performance of the protocols are shown by the simulation results which show that introducing the iMesh can improve the percentage of optimal assignments from 50 to over 90% for low values of k (number of hops), while keeping the number of average exchanged messages to around 1 per robot.
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