Smartphones have become the most pervasive devices in people's lives and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work. Not only are today's smartphones ubiquitous devices, but also they are equipped with a plethora of embedded multimodal sensors, integrate wireless communication technologies such as 4G/WiFi Internet connectivity, and possess complex processing capabilities. For example, the cameras on smartphones can be used as video and image sensors [29] , the microphone can be used as an acoustic sensor [39, 154, 206] , and the embedded global positioning system (GPS) receiver can be used to gather accurate location information, while gyroscopes, accelerometers, and proximity sensors can be used to extract contextual information about the users, such as driving or walking states [75, 122] . Further, additional sensors can be easily interfaced with smartphones via Bluetooth or wired connections, such as temperature, air quality, and humidity [69, 168] . These technological features, combined with the advanced sensing capability of humans, have spurred a significant amount of research from both academia and industry, which together have proposed over the last 10 years myriad applications based on the emerging mobile crowdsensing paradigm. 1 Mobile crowdsensing empowers ordinary citizens (or users 2 ) with the capability to actively monitor various phenomena pertaining to themselves (e.g., health, social connections) or their community (e.g., environment). This rich information or inference about themselves or the community may also be sent back to the participating or other concerned users to improve their life experiences, thus influencing their choices. Real-life applications, which can take advantage of both low-level sensor data and high-level user activities, range from real-time traffic monitoring applications to environmental pollution monitoring, crime monitoring, and social networking, just to name a few. For a survey on mobile crowdsensing applications, we refer the reader to [92] . Although mobile crowdsensing allows the creation of disruptive technologies and applications, it also comes with novel and unique research challenges, which were absent in less complex sensing paradigms such as wireless sensor networks. The most important challenges that we will analyze in this article can be summarized by the following three questions: 1 Henceforth, we will refer to the term "mobile crowdsensing" to designate applications where participants voluntarily contribute sensor data for their own benefit or for the benefit of the community by using their phones. Such a notion therefore includes participatory sensing [20], mobiscopes [1], opportunistic sensing [23] , and equivalent terms such as mobile phone sensing and smartphone sensing [98] or mobile crowdsensing [65] . It also covers specific terminologies such as urban sensing [23], citizen sensing [20], people-centric sensing [23], [24] , and community sensing [97] .
We refer the reader to our recent article REF , in which we extensively survey existing work and research challenges in this field.
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
TOSN
Computer Science