Interest in smart cities is rapidly rising due to the global rise in urbanization and the wide-scale instrumentation of modern cities. Due to the considerable infrastructural cost of setting up smart cities and smart communities, researchers are exploring the use of existing vehicles on the roads as "message ferries" for the transport data for smart community applications to avoid the cost of installing new communication infrastructure. In this paper, we propose an opportunistic data ferry selection algorithm that strives to select vehicles that can minimize the overall delay for data delivery from a source to a given destination. Our proposed opportunistic algorithm utilizes an ensemble of online hiring algorithms, which are run together in passive mode, to select the online hiring algorithm that has performed the best in recent history. The proposed ensemble based algorithm is evaluated empirically using real-world traces from taxies plying routes in Shanghai, China, and its performance is compared against a baseline of four state-of-the-art online hiring algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm can reduce the overall delay compared to the baseline by an impressive 13% to 258%.

翻译：由于全球城市化进程加速和现代城市大规模设备化，对智慧城市的关注度正迅速提升。鉴于建设智慧城市和智慧社区所需的基础设施成本高昂，研究人员正探索利用道路上现有车辆作为“消息摆渡车”，为智慧社区应用传输数据，以避免新建通信基础设施的成本。本文提出一种机会性数据摆渡选择算法，旨在筛选能够最小化数据从源节点到指定目的地整体传输延迟的车辆。该机会性算法采用在线雇佣算法集成策略，通过并行运行多个被动模式的在线雇佣算法，选取近期历史表现最优的算法进行决策。基于中国上海出租车真实轨迹数据，对所提出的集成算法进行了实证评估，并与四种前沿在线雇佣算法基线进行性能对比。多组实验结果表明，相较于基线算法，本文算法能够将整体延迟降低13%至258%，性能提升显著。