Mobile social network analytics and mobile edge solutions for trustworthy and reliable urban sensing

移动社交网络分析和移动边缘解决方案，实现值得信赖和可靠的城市感知

• 批准号: RGPIN-2017-04032
• 负责人: Kantarci, Burak
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=655060
• 关键词: Mobile; social; network; analytics; mobile

There is a growing need for smart methodologies to enable rapid and reliable sensing, analysis and presentation of information regarding emergency preparedness, public safety, public health, environment and quality of life in urban environments. These methodologies are needed to improve urban sensing and ensure a smart crowdsensing infrastructure. Examples include implicit recruitment of smartphone users to sense traffic conditions, environmental pollution, noise levels or temperature, capture images at certain locations, and analyze collected data rapidly so that immediate reports can be prepared for citizens and/or authorities. However, in crowdsensing, data sources are noisy, unreliable, erroneous, and largely unknown. In our previous work, we addressed the user recruitment problem via auction-theoretic and game-theoretic approaches with the objective of maximizing trustworthiness of the recruited user set in crowdsensing applications. We have shown that the use of statistical and collaborative reputation systems can significantly improve the usefulness of acquired data. Despite the progress in crowdsensing research, a reliable, secure, scalable and robust framework that can be used to effectively collect, quantify and analyze data is lacking. As anyone is allowed to participate in urban sensing upon downloading a mobile app, the pool of sources is not examined thoroughly, and the reliability of individual participants is generally unknown to the data collector. Hence, particularly in mission critical applications such as public safety or emergency preparedness, the problem of refining reliable information becomes challenging. The objective of this research program is to lay the foundations for mobile social network-based data acquisition in urban settings and address its reliability, scalability, and privacy challenges. Indeed, mobile social network denotes interaction links between users, which can be in various forms such as data communications, co-location and mobility. The research will leverage methods and results from the fields of network science, data analytics, modelling and optimization, and multidisciplinary analysis and design. The research findings will be transformative for social network-assisted urban sensing systems, especially in sustainability and public safety areas owing to the following novel components: 1) continuous social identification and authentication algorithms for effective user recruitment, 2) novel data fusion algorithms to improve reliability, 3) scalable implementation of fog computing architecture to handle unexpectedly increasing volume and velocity of crowd-sensed data, 4) cloaked contextual patterns to improve privacy of the participants. The research findings will lead to a novel framework consisting of social network-driven urban sensing system offering scalability, trustworthiness and privacy.

对智能方法的需求日益增长，以便能够快速、可靠地监测、分析和提供有关应急准备、公共安全、公共卫生、环境和城市环境生活质量的信息。这些方法是改善城市感知和确保智能人群感知基础设施所必需的。例如，隐含招募智能手机用户来感知交通状况、环境污染、噪音水平或温度，捕捉特定地点的图像，并快速分析收集的数据，以便为公民和/或当局准备即时报告。然而，在人群感知中，数据源是嘈杂的、不可靠的、错误的，并且基本上是未知的。在我们之前的工作中，我们通过拍卖理论和博弈论的方法来解决用户招募问题，目标是最大化被招募用户集合的可信性。我们已经证明，使用统计和协作声誉系统可以显著提高所获得数据的有用性。尽管在人群感知研究方面取得了进展，但缺乏一个可靠、安全、可扩展和健壮的框架，可以用于有效地收集、量化和分析数据。由于任何人都可以在下载移动应用程序时参与城市遥感，因此没有彻底检查来源池，数据收集者通常不知道单个参与者的可靠性。因此，特别是在公共安全或应急准备等关键任务应用中，提炼可靠信息的问题变得具有挑战性。该研究计划的目标是为城市环境中基于移动社交网络的数据采集奠定基础，并解决其可靠性、可扩展性和隐私方面的挑战。事实上，移动社交网络指的是用户之间的交互链接，可以是各种形式，如数据通信、主机代管和移动性。这项研究将利用网络科学、数据分析、建模和优化以及多学科分析和设计领域的方法和结果。这些研究成果将对社会网络辅助的城市感知系统产生变革，特别是在可持续发展和公共安全领域，这是因为以下新组件：1)用于有效招募用户的连续社会识别和认证算法，2)用于提高可靠性的新颖数据融合算法，3)可扩展实施的雾计算体系结构，以处理意外增加的人群感知数据的数量和速度，4)隐蔽的上下文模式，以改善参与者的隐私。研究结果将导致一个由社会网络驱动的城市感知系统组成的新框架，提供可伸缩性、可信性和隐私。