Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

Abstract

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this article, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.

A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

“…Consider two ACWs referring to the same time instant t, but of different sizes, C Here we suppose that the agent has previously collected enough information to calculate both ACWs. The agent evaluates the estimate of information t by simulating its absence using both C (4) t , C (7) t . This results in two different estimates that are compared to the real value.…”

“…Thanks to their increasing computational power and accessibility, smart devices can be exploited to make the data acquisition in cities a participatory activity. This is the key concept of Mobile Crowd Sensing (MCS), which leverages device mobility and sensing capabilities, as well as human collaboration and intelligence to distributively perform tasks and provide cost-efficient applications and services [7]. MCS enables integrating different types of smart devices into a large scale sensing infrastructure.…”

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

“…At the same time, UEs are also increasingly equipped with more powerful computing and storage capabilities, which allow them to participate in the edge-C3 as well. Moreover, mobile crowdsourcing [32,33] and device-to-device (D2D) communication [34,35] enable UEs in close proximity to share their resources with each other, eventually reducing the network congestion and the resources to be used at edge servers. Thus, UEs can also be considered as part of the edge-C3, despite their limited resources compared to edge servers.…”

HUAD: Hierarchical Urban Anomaly Detection Based on Spatio-Temporal Data

“…These available data lay a foundation for the anomaly analysis and detection based on the laws of human movement. For metropolis with hundreds or even tens millions of people, the movement of crowd follows complex but stable pattern [3]. Obviously, a serious stampede that occurred during the New Year celebration in Shanghai will bring huge loss of life and property if there is no timely warning and treatment.…”

“…In addition, we also calculate the Pearson Correlation Coefficient (PCC) of the traffic sequence of the current adjacent period and the traffic sequence corresponding to the adjacent period one week ago in each region [10], while paying attention to the trend of traffic changes in the adjacent region [11]. In the above, a series of related indicators are aggregated to form the characteristic input of OC-SVM, thereby anomalous events in the anomalous area screened [3]. This process aims to improve the accuracy of the model.…”