Soft Sensing in Smart Cities: Handling 3Vs Using Recommender Systems, Machine Intelligence, and Data Analytics

Abstract

Today's existing smart city research involves many overtly futuristic applications such as smart transportation, in which smart roads warn drivers of bad traffic conditions ahead, smart parking, which communicates the location of unoccupied parking spaces to drivers, and smart environment, which enables fully automated homes and workplaces to adjust their temperature to conserve energy. The realization of these applications hinges on a data acquisition structure that gathers its data from a countless number of sensors, either deployed for predefined tasks (hard sensing) or built into the mobile devices of smart city residents (soft sensing). At the core of this big data infrastructure lie the 5Vs: veracity, volume, velocity, variety, and value. The soft sensing component of a smart city sensing network is particularly affected by 3Vs: veracity, volume, and velocity. To address the unique challenges of big data, recommender systems, statistical reputation systems, and context analysis are used to ensure the veracity of acquired data, machine learning algorithms are applied to handle the data volume, and data analytics algorithms are implemented to manage data velocity. Despite its seemingly insurmountable size, the acquired data is highly redundant, and systematic use of machine intelligence and data analytics can facilitate processing by extracting only the relevant information; in this article, we study the role of these algorithms through the lens of the 3Vs in facilitating soft sensing within the framework of smart city applications.