A cloud-assisted crowdsourcing machine-to-machine networking platform for vehicular applications

机器对机器(M2M)网络最近引起了工业界和学术界的极大关注。突出的例子是车辆通信网络，它可以持续监测大量道路上的车辆的状态，以及明智地规划智能交通系统，将交通拥堵和废气排放降至最低。虽然车辆的短程和宽带蜂窝接入能力越来越强，但它们的通信带宽和机会仍然有限。此外，由于它们的中央处理单元(CPU)能力有限，它们无法以在线方式高效地处理数据。然而，云计算平台最擅长的是繁重的计算。可以通过众包的方式收集大量的车辆数据，如位置、速度、加速度、瞬时里程和发动机状态，以便在云中进行处理。 这一拟议的战略项目旨在设计和实施一个高效的众包M2M网络平台，并完全集成云，重点是车载设备的集成 网络。从理论的角度来看，该项目的一个最初的亮点是研究车载和路边通信节点的编码技术和存储能力的组合，以便在车辆应用中最大化信息流的同时，能够适应各种延迟约束。从实践的角度来看，我们的新平台将根据处理众包数据的结果，帮助快速开发一系列新的应用程序。 该项目中拟议的研究将与两个行业合作伙伴密切合作进行：世界知名的无线通信技术创新者高通加拿大公司和专门从事车载网络云应用的初创公司Moj.io。

Machine-to-Machine (M2M) networks have recently attracted significant attention from both industry and academia. Prominent examples are vehicular communication networks, which enable continuous status monitoring of a massive number of on-road vehicles, as well as smart planning towards an intelligent transport system with minimized traffic congestion and exhaust gas emission. Though vehicles are increasingly capable of both short-range and broadband cellular access, they still have limited communication bandwidth and opportunities. Further, due to their limited Central Processing Unit (CPU) power, they are not able to process data efficiently in an online fashion. Heavy-lifting computation, however, is where cloud computing platforms excel. Large volumes of vehicular data, such as location, speed, acceleration, instantaneous mileage and engine status, can be collected in a crowdsourcing fashion, such that they can be processed in the cloud. This proposed Strategic Project seeks to design and implement an efficient crowdsourcing M2M networking platform with full cloud integration, with a specific focus on the integration of vehicular networks. From a theoretical perspective, one original highlight of this project is to study the combination of coding techniques and storage capabilities of both vehicular and road-side communicating nodes, so that a variety of delay constraints can be accommodated as we maximize information flow rates in vehicular applications. From a practical perspective, our new platform will assist rapid development of a new array of applications based on the results of processing crowdsourced data. The proposed research in this project will be undertaken in close collaboration with two industry partners: Qualcomm Canada a world-renowned innovator in wireless communication technologies; and moj.io, a start-up company specializing in cloud applications for vehicular networks.