A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Abstract

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

Crowdsourcing Without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping Toward an SDG Indicator

“…Loreto et al (2017) emphasized that modern participatory sensing, which is one of three sub-categories of citizen cyberscience (Grey, 2009), has witnessed signi cant progress related to the fast development and social networking tools of ICT (Information and Communication Technologies), which "allow effective data and opinion collection and real-time information sharing processes". In that context, Guo et al (2015) and Capponi et al (2019) introduced mobile crowdsensing (MCS), which focuses on sensing and collecting data with mobile devices and aggregating data in the cloud. However, there are pollutants which are still exclusive for IoT 'sensor dust'.…”

Mobile Crowd Sensing RPL-based Routing Protocol for Smart City

“…Mobile Crowd Sensing (MCS) has gained popularity in recent years due to its advantages over the traditional wireless sensing network (WSN). The large spreads of smartphones, which have set of built-in sensors (GPS, gyroscope, accelerometer, microphone, and camera), and communication interfaces (Wifi, Bluetooth) is the key factor of the success of MCS [23]. It offers lower costs and extended sensing covers compared to WSN since it depends on the human that carries those devices.Two classes of user contribution exist in MCS: participatory and opportunistic sensing.…”

Game Theory in Mobile CrowdSensing: A Comprehensive Survey

“…Sensed data plays a predominant role in IoT: According to Cisco, 2.5 quintillion bytes of daily data generation and 30 billion IoT-connected devices are forecast by the year 2020 [1]. Thus, it is vital to supplement the existing dedicated sensing infrastructures via sustainable and cost-efficient non-dedicated sensing solutions such as participatory or opportunistic sensing via smartphone sensors [2]. By taking advantage of embedded sensors in smart phones, sensed data can be collected in high volumes, and can be processed in real time in support of the IoT-based services.…”