基于效用感知的移动代理选择的驾车路过感知时空覆盖增强
Spatio-Temporal Coverage Enhancement in Drive-By Sensing Through Utility-Aware Mobile Agent Selection
摘要：
近年来，驾车感应模式已经变得越来越受欢迎，以实现对城市地区的经济有效的监测。驾车感应是一种形式的人群感应，其中配备传感器的车辆（又名，移动代理）是主要的数据收集代理。提高驾驶感测的功效提出了许多挑战，其中一个重要的挑战是选择非专用的移动代理，在其上安装有限数量的传感器。这个问题，我们称之为移动代理选择问题，有显着的时空覆盖的驱动传感平台和由此产生的数据集的影响。这方面的挑战是在考虑到地理区域的相对重要性的同时实现最大的时空覆盖。在本文中，我们解决这个问题的背景下，SCOUTS项目，其目标是准确地映射和分析城市热岛现象。我们的工作做出了几项重大的技术贡献。首先，我们描绘了一个模型，代表移动代理选择问题。该模型考虑了车辆（在我们的情况下是公共交通巴士）的轨迹和城市区域的相对重要性，并将其公式化为优化问题。其次，我们提供了两个算法，是基于实用程序（覆盖）值的移动代理，即，基于热点的算法，限制搜索空间的重要子区域和实用程序感知的遗传算法，使后者的算法，使无偏的选择。第三，我们设计了一个高效的覆盖冗余最小化算法，在每一步，选择移动代理，这提供了最大的改善时空覆盖。本文报告了一系列的实验，从雅典，GA，美国的真实世界的数据集，以证明所提出的方法的有效性。
英文摘要：
In recent years, the drive-by sensing paradigm has become increasingly popular for cost-effective monitoring of urban areas. Drive-by sensing is a form of crowdsensing wherein sensor-equipped vehicles (aka, mobile agents) are the primary data gathering agents. Enhancing the efficacy of drive-by sensing poses many challenges, an important one of which is to select non-dedicated mobile agents on which a limited number of sensors are to be mounted. This problem, which we refer to as the mobile-agent selection problem, has a significant impact on the spatio-temporal coverage of the drive-by sensing platforms and the resultant datasets. The challenge here is to achieve maximum spatiotemporal coverage while taking the relative importance levels of geographical areas into account. In this paper, we address this problem in the context of the SCOUTS project, the goal of which is to map and analyze the urban heat island phenomenon accurately. Our work makes several major technical contributions. First, we delineate a model for representing the mobile agents selection problem. This model takes into account the trajectories of the vehicles (public transportation buses in our case) and the relative importance of the urban regions, and formulates it as an optimization problem. Second, we provide two algorithms that are based upon the utility (coverage) values of mobile agents, namely, a hotspot-based algorithm that limits the search space to important sub-regions and a utility-aware genetic algorithm that enables the latter algorithm to make unbiased selections. Third, we design a highly efficient coverage redundancy minimization algorithm that, at each step, chooses the mobile agent, which provides maximal improvement to the spatio-temporal coverage. This paper reports a series of experiments on a real-world dataset from Athens, GA, USA, to demonstrate the effectiveness of the proposed approaches.
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。
