Participatory Sensing: Key Technologies And Applications

Information and communications technology (ICT) deeply impacts peo-ple's life and work. ICT will enhance both traditional and emerging industries.Meanwhile, big data, cloud computing and etc. will promote the evolution of ICT. Furthermore, more and more big data applications, such as intelligent city,will integrate and improve people's life and work. In order to deal with the increasing of data requirements, Internet-of-Things (IoT) has been applied to many fields. Nevertheless, large-scale deployment of IoT devices leads to huge consumption of resource, installation, maintenance and upgrading.Meanwhile, smart devices have become an indispensable part of people's daily life. The high-performance built-in sensors, such as GPS, accelerometer and etc., make smart devices have great power of sensing and computing. High speed mobile Internet also greatly enhances the communication capabilities of smart devices. In this critical period, a novel data collection method: Participa-tory Sensing, comes into being. In participatory sensing systems, people collect and share the surrounding environmental data with their smart devices. Thus,participatory sensing can reduce the cost of data collection, make data collection become flexible, and make people more connected and their life more fun.However, participatory sensing also faces some problems to be resolved,such as how to select the participants for improving data quality, how to im-prove participants' participation willingness for sensing tasks, how to reduce participants' bad behaviors, how to protect participant privacy and etc.. This paper addresses these problems, and the main contributions are as follows:First, in order to improve the quality of data effectively, a metric of Qol satisfaction ratio is proposed to quantify the degree of dispersion of the collected data. A participant selection strategy based on task weight is also designed to select participants for multi-task-based participatory sensing systems, in order to reduce the cost of data collection and ensure the amount of collected data can match the offered inventive budgets.Second, in order to improve participants' participation willingness for sens-ing tasks, a participant sampling behavior model based on energy consumption is proposed to recommend the work intensity according to the devices' initial energy level of participants. And a participant selection strategy based on the rejection probability for sensing tasks is also designed to ensure the degree of task completion and prevent the loss of participants from sensing tasks.Third, in order to reduce the risk of privacy leakage, a contribution assess-ment method based on participant coordination is proposed to estimate the po-tential contributions of participants without letting the application server know the personal information of participants. And a participant selection strategy based on Borda count is also designed to ensure the degree of task completion and enhance the security of participants' personal information. Furthermore, a data aggregation method based on participant collaboration is proposed for ag-gregating the collected data safely. Meanwhile, an authentication mechanism is used for prevent irregularities behaviors of participants.Finally, in order to verify the collected data when using privacy protection methods, a multi-role and participant coordination based participatory sensing architecture is designed to verify the collected data by assigning participants to different work roles and allocating different work content. And a participant reputation algorithm based on the accuracy of data collection is proposed to calculate the degree of participants' reliability during data collection. Then a dynamic event discovery application based on participatory sensing is designed to obtain the event boundary using the min-cut based algorithm with the col-lected data by participants.