Mathematical Models of Mobile Computing Devices and Application Software

移动的设备和应用程序（app）的广泛使用正在为加拿大人带来大规模的经济效益。根据市场研究公司Nordicity为加拿大无线电信协会编写的2016年报告，加拿大无线通信生态系统中的公司在2015年创造了489.6亿美元的收入。智能电话、嵌入式设备、可穿戴设备和无线传感器是移动的设备的示例。它们在新兴应用中发挥着关键作用：个人通信、金融交易、资产监控、自动驾驶汽车、基于无人机的国界和冲突地区监控，以及武装部队执行的传感和通信。移动的设备已经变得越来越强大，具有功能丰富的操作系统、多核处理器、千兆字节的存储器、多无线电接口和传感器阵列。然而，与台式机不同，移动的设备依靠电池运行，并且在恶劣的通信环境中工作。设计健壮的移动的应用程序的关键挑战包括恶意代码的建模和识别、单个应用程序和设备的功耗建模、设计用于性能测试的测试套件以及延长电池寿命。** 通过拟议的研究计划，我们将追求以下目标：（i）确定移动的应用程序是否参与可疑活动;（ii）假设设备正在运行一组已知的应用程序，外部确定它是否正在运行一些未知的应用程序;（iii）估计由于可疑活动而造成的能量损失;（iv）给定应用的操作模型和用户体验质量（QoE）的期望水平，计算应用的配置参数（ACP）和网络的配置参数（NCP）因此，当应用配置有所选的ACP并在网络的所选NCP下执行时，它会提供所需的QoE;给定应用的ACP、NCP和QoE集合构成了性能测试的单个测试用例;以及（v）制定测试覆盖标准，以充分覆盖应用程序、ACP、NCP和QoE。 ** 我们实现目标的方法包括：外部功率测量，独立组件分析和数据分解技术在恶意应用程序识别中的应用;无线接口上传输协议的建模;通过结合应用程序的协议模型和马尔可夫模型来设计应用程序的性能模型;通过开发模型反演的概念从性能模型生成测试用例;设计性能测试充分性的选择标准。 ** 本研究中开发的模型、算法和工具将应用于设计和测试强大的移动的设备，以实现加拿大移动终端生态系统中开发的商业和国防应用的安全性、安全性和关键业务应用。**

The widespread use of mobile devices and applications (apps) is translating into large-scale economic benefits for Canadians. According to a 2016 report prepared by the market research firm Nordicity for the Canadian Wireless Telecommunications Association, companies in the Canadian wireless communications ecosystem generated $48.96 billion in revenue in 2015. Smartphones, embedded devices, wearable devices, and wireless sensors are examples of mobile devices. They play key roles in emerging applications: personal communication, financial transactions, asset monitoring, autonomous vehicles, drone-based monitoring of national borders and conflict zones, and sensing and communications performed by the armed forces. Mobile devices have become increasingly powerful, with feature-rich operating systems, multi-core processors, gigabytes of memory, multi-radio interfaces, and an array of sensors. However, unlike desktops, mobile devices run on batteries and operate in harsh communication environments. Among the key challenges in designing robust mobile apps are modeling and identification of malicious code, modeling of the power cost of individual apps and devices, designing test suites for performance testing, and extending battery life. ******Through the proposed research program, we will pursue the following objectives: (i) determine if mobile apps are engaging in suspicious activities; (ii) assuming that a device is running a known set of apps, externally determine if it is running some unknown apps; (iii) estimate the loss of energy due to suspicious activities; (iv) given the operational model of an app and a desired level of user's quality of experience (QoE), compute the app's configuration parameters (ACPs) and the network's configuration parameters (NCPs) so that when the app is configured with the selected ACPs and executed under the network's selected NCPs, it delivers the desired QoE; the set of ACPs, NCPs, and QoE for a given app constitute a single test case for performance testing; and (v) develop test coverage criteria to adequately cover the app, ACPs, NCPs, and QoE. ******Our methodologies to achieve the objectives include: external measurement of power, application of independent component analysis and data disaggregation techniques in the identification of malicious apps; modeling of transport protocols over wireless interfaces; designing performance models of apps by combining the protocol models and Markov models of apps; generating test cases from performance models by developing a concept of model inversion; and designing selection criteria for performance test adequacy. ******The models, algorithms, and tools to be developed in this research will find applications in the design and test of robust mobile devices for security, safety, and business critical applications developed in the Canadian mobile device ecosystem for both commercial and defense applications.********