Mathematical Models of Mobile Computing Devices and Application Software

移动设备和应用程序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为加拿大无线通信协会（Canadian Wireless Telecommunications Association）准备的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。智能手机、嵌入式设备、可穿戴设备和无线传感器都是移动设备的例子。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。移动设备变得越来越强大，拥有功能丰富的操作系统、多核处理器、千兆内存、多无线电接口和一系列传感器。然而，与台式电脑不同，移动设备依靠电池运行，并且在恶劣的通信环境中运行。设计健壮的移动应用程序的主要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、性能测试的测试套件设计以及延长电池寿命。******通过拟议的研究计划，我们将实现以下目标：(i)确定移动应用程序是否从事可疑活动；（ii）假设设备正在运行一组已知的应用程序，从外部确定它是否正在运行一些未知的应用程序；（iii）估计因可疑活动造成的能量损失；（iv）给定应用程序的运行模式和期望的用户体验质量（QoE）水平，计算应用程序的配置参数（acp）和网络的配置参数（ncp），以便当应用程序配置选定的acp并在网络选定的ncp下执行时，它提供期望的QoE；给定应用的acp、ncp和QoE集合构成性能测试的单个测试用例；(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：外部测量功率，在恶意应用识别中应用独立组件分析和数据分解技术；无线接口传输协议的建模结合应用程序的协议模型和马尔可夫模型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；并设计性能测试充分性的选择标准。******在这项研究中开发的模型、算法和工具将在加拿大移动设备生态系统中为商业和国防应用开发的安全、安全和关键业务应用的强大移动设备的设计和测试中找到应用。********

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.********