Next-generation smart city applications, attributed by the power of Internet of Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on the quality of sensing data. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-aservice these days, it is imperative to provision for a shared sensing infrastructure for better utilization of resources. However, a shared sensing infrastructure that leverages low-cost sensing devices for a cost effective solution, still remains an unexplored territory. A significant research effort is still needed to make edge based data shaping solutions, more reliable, feature-rich and costeffective while addressing the associated challenges in sharing the sensing infrastructure among multiple collocated services with diverse Quality of Service (QoS) requirements. Towards this, we propose a novel edge based data pre-processing solution, named UniPreCIS that accounts for the inherent characteristics of lowcost ambient sensors and the exhibited measurement dynamics with respect to application-specific QoS. UniPreCIS aims to identify and select quality data sources by performing sensor ranking and selection followed by multimodal data pre-processing in order to meet heterogeneous application QoS and at the same time reducing the resource consumption footprint for the resource constrained network edge. As observed, the processing time and memory utilization has been reduced in the proposed approach while achieving upto 90% accuracy which is arguably significant as compared to state-of-the-art techniques for sensing. The effectiveness of UniPreCIS has been evaluated on a testbed for a specific use case of indoor occupancy estimation that proves its effectiveness.
翻译:由物联网(Iot)和网络物理系统(Cyber-physical Systems)的力量所赋予的下一代智能城市应用,在很大程度上依赖遥感数据的质量。随着城市发展智能应用和提供遥感服务企业的智能应用的指数指数式增长,现在迫切需要提供共享的遥感基础设施,以便更好地利用资源。然而,利用低成本遥感设备实现成本效益解决方案的共享遥感基础设施仍是一个尚未探索的领域。仍然需要做出重大研究努力,使基于边缘的数据形成解决方案、更可靠、功能丰富和具有成本效益,同时应对多个具有不同服务质量(Qos)要求的同地服务共享遥感基础设施的相关挑战。为此,我们提出了一个新的基于前沿的数据预处理解决方案,称为UniPrecrime CIS,其中说明了低成本环境传感器的固有特点和在应用特定Qos-S方面的计量动态。 Unipreprecrial旨在确定和选择高质量的数据源,随后进行多式联运数据预处理,以便满足具有不同质量的应用程序(Qos)的效益。我们提出了一个新的基于资源测试方法,在准确度方面,在准确度方面,对资源使用前的精确度方面进行了评估后,在所观察到的准确度方面,在使用前的准确度方面,对资源使用率进行了评估后,对资源进行了评估,在达到了标准化的精确度方面,在达到了为精确度评估。