Leveraging Big Sensed Data over Ubiquitous Networks

我们的日常生活越来越依赖于信息的吸收。在我们的数字世界的到来，收集了大量的数据来帮助我们的活动，预防和减轻危险，并同步复杂的系统(例如，交通)。然而，越来越多的人采用传感系统，在本已紧张的通信网络中产生了前所未有的数据量。随着我们计划更好的连接、丰富的数据收集和可扩展的传感，越来越多地依赖互联网来提供通信骨干是不现实的。更重要的是，海量数据及其来源的异构性和不同质量，正在将我们推向大传感数据(BSD)时代。因此，建立在这些数据之上的服务阻碍了决策和信息的实时处理。*我将以我在动态资源管理方面的研究为基础，为物联网(IoT)时代的BSD搭建平台。具体地说，我将在异构物联网系统上设计新的数据收集、清理、修剪和管理方案，以向物联网服务提供高质量的数据。我们的范例将从三个维度解决信息服务问题。首先，在多个异类传感系统上收集的数据将在源上进行修剪，并在资源质量指标下进行评估，以确定它们相对于所有其他源的生存能力。因此，在遍历(和加载)网络基础设施之前，统一的度量将规定数据质量(QOD)。其次，我将设计自适应反馈协议，以实现对传感数据源的负载平衡管理，降低多余/劣质传感器的数据生成率，并使更快地访问高质量信息。第三，我将通过引入能够实时融合QOD测量数据的边缘处理来采用本地化数据管理，从而产生我们的边缘融合BSD(EF-BSD)框架。然后，高质量的数据将被推送到下一代互联网的一个有希望的候选者上，即信息中心网络(ICN)，它内在地处理内容传播。*本计划培训的高素质人员将包括先进的大数据管理和下一代物联网技术融合技术方面的经验。他们将接受实时数据融合、动态资源管理、ICN协议设计、管理异类网络和缓存技术方面的培训。我预计将有两名博士、两名硕士和两名本科生接受这一研究项目的培训。信息技术和电信行业对HQP的需求旺盛，他们未来的就业将加快向加拿大行业传播下一代通信技术，并在全球物联网服务和系统计划中为加拿大带来竞争优势。

Our everyday lives are growing ever more dependent on the assimilation of information. In the advent of our digital world, a monumental amount of data is collected to aid our activities, prevent and mitigate dangers, and synchronize complex systems (e.g., transportation). However, the increasing adoption of sensing systems has created unprecedented amounts of data that traverse an already strained communication network. As we plan for better connectivity, abundance in data collection and scalable sensing, a growing dependence on the Internet to provide the communication backbone is unrealistic. More importantly, the sheer volume of data and the heterogeneity of its sources with varying quality, are pushing us into the era of Big Sensed Data (BSD). As such, building services on top of such data impedes decision making and real-time processing of information. ******I will build on my research in dynamic resource management to build a platform for BSD in the Internet of Things (IoT) era. Specifically, I will devise novel data collection, cleaning, pruning and management schemes over heterogeneous IoT systems, to feed high-quality data to IoT services. Our paradigm will address information services from three dimensions. First, data collected over a multiplicity of heterogeneous sensing systems will be pruned at the source, and evaluated under Quality of Resource metrics to dictate their viability relative to all other sources. Thus, a uniform metric will dictate the Quality of Data (QoD) before traversing (and loading) the network infrastructure. Second, I will devise Adaptive feedback protocols to enable load-balanced management of sensed data sources, reduce the data production rate of superfluous/inferior sensors, and enable faster access to high-quality information. Third, I will adopt localized data management by introducing Edge processing that enables real-time fusion of QoD-measured data, yielding our Edge Fusion BSD (EF-BSD) framework. High quality data will then be pushed onto a promising candidate for the next-generation Internet, namely Information Centric Networks (ICN), which inherently handle content dissemination. ******Training of highly qualified personnel in this program will include experience in advanced Big Data management and fusion techniques over next generation IoT technologies. They will be trained in real-time data fusion, dynamic resource management, ICN protocol design, managing heterogeneous networks, and caching techniques. I expect that two PhD, two Master's and two undergraduate students will receive training in this research program. There is a strong demand for HQP in the information technology and telecommunications sectors and their future employment will accelerate disseminating next-generation communications technology to Canadian industry, and present a competitive edge for Canada in global IoT initiatives for services and systems.