[论文解读] Crowdsensing in Opportunistic Mobile Social Networks: A Context-aware and Human-centric Approach

Phuong T. Nguyen, Klara Nahrstedt|arXiv (Cornell University)|Apr 27, 2017

Opportunistic and Delay-Tolerant Networks参考文献 16被引用 9

一句话总结

本文提出了一种上下文感知、以人为本的近似算法，用于在机会型移动社交网络中优化群体感知，通过将问题建模为顶点覆盖任务。通过引入节点可观测性和覆盖效用评分，该方法在真实社交活动数据轨迹上显著优于基线近似算法，提升了感知覆盖范围。

ABSTRACT

In recent years, there have been efforts to collect human contact traces during social events (e.g., conferences) using Bluetooth devices (e.g., mobile phones, iMotes). The results of these studies have enabled the ability to do the crowd-sourcing task from within the crowd, in order to answer questions, such as: what is the current density of the crowd, or how many people are attending the event? However, in those studies, the sensing devices are usually distributed and configured in a certain manner. For example, the number of devices is fixed, people register for the devices on a volunteering basis. In this paper, we treat the above problem as an optimization problem and draw the connection to the vertex cover problem in graph theory. Since finding the optimal solution for minimum vertex cover problem is NP-complete, approximation algorithms have to be used. However, we will show that the well-known approximation algorithms do not perform well with the crowd-sensing task. In this paper, we propose the notions of node observability and coverage utility score and design a new context-aware approximation algorithm to find vertex cover that is tailored for crowd-sensing task. In addition, we design human-centric bootstrapping strategies to make initial assignment of sensing devices based on meta information about the participants (e.g., interests, friendship). The motivation is to assign the sensing task to a more "socialized" device to obtain better sensing coverage. We perform comprehensive experiments on real-world data traces obtained from previous experimental studies in conference and academic social context. The results show that our proposed approach significantly outperforms the baseline approximation algorithms in terms of sensing coverage.

研究动机与目标

• 解决通用近似算法在动态、基于社交信息的群体感知场景中效率低下的问题。
• 通过将任务建模为顶点覆盖问题，在固定设备数量和时间间隔约束下优化感知覆盖范围。
• 将以人为本的元信息（如社交关系和个人兴趣）整合到感知设备的初始分配中。
• 通过优先分配社交关联度更高的个体担任感知角色，提升感知覆盖范围。

提出的方法

• 将群体感知任务建模为图论中的顶点覆盖问题，其中节点代表设备，边代表机会性接触。
• 引入节点可观测性概念，量化节点随时间对其邻居的观测能力。
• 定义覆盖效用评分，用于评估节点的综合感知贡献，优先选择能减少冗余并提升覆盖范围的节点。
• 提出一种上下文感知的近似算法，优先将感知任务分配给覆盖效用和可观测性较高的节点。
• 采用以人为本的初始化策略，基于社交关系和共同兴趣分配感知角色，以增强初始覆盖效果。
• 采用集中式感知模型，设备定期上报数据并接收更新的感知指令。

实验结果

研究问题

• RQ1 如何将现有的顶点覆盖近似算法适配到动态、基于社交信息的移动群体感知场景中？
• RQ2 人类互动的社会特征和时空特性在多大程度上影响机会网络中的感知覆盖范围？
• RQ3 将友谊和兴趣等元信息纳入是否能改善感知设备的初始分配？
• RQ4 所提出的上下文感知算法在覆盖效率方面与基线近似方法相比表现如何？
• RQ5 在真实场景中，为最大化覆盖范围，感知间隔和设备数量的最佳配置是什么？

主要发现

• 所提出的上下文感知近似算法在真实世界移动轨迹数据集上的感知覆盖范围方面显著优于标准近似算法。
• 引入以人为本的初始化策略（基于社交关系和兴趣）可实现更优的初始设备分配，并带来更高的感知覆盖范围，优于随机或贪心分配方法。
• 感知间隔的长度对覆盖范围有显著影响，最优性能可通过上下文自适应的间隔设置实现。
• 即使在可用感知设备数量有限的情况下，该算法仍能保持优异性能，展现出在资源受限条件下的鲁棒性。
• 节点可观测性和覆盖效用评分能有效捕捉动态交互模式，使感知节点的选择优于通用图论度量。