Abstract
Recently, crowdsensing, which can provide various sensing services using consumer mobile devices, is attracting considerable attention. The success of these services depends on active user participation and, thus, a proper incentive mechanism is essential. However, if the sensing information provided by a user includes personal information, and an attacker compromises the service provider, participation will be less active. Accordingly, personal information protection is an important element in crowdsensing services. In this study, we resolve this problem by separating the steps of sensing data processing and the reward payment process. An arbitrary node in a sensing data processing pool consisting of user nodes is selected for sensing data processing, and only the processing results are sent to the service provider server to reward the data providing node. The proposed user-participatory crowdsensing system is implemented on the Kaa Internet of things (IoT) platform to evaluate its performance and demonstrate its feasibility.
Highlights
Crowdsensing is a technique that uses sensing devices, such as smartphones and wearable devices, to receive, process, and utilize sensing data from the public [1]
We propose a user-participatory crowdsensing system in which user nodes in a data processing node pool participate in sensing data processing, which is assigned to randomly selected data processing nodes, whereas the service provider server (SP) only performs reward data
The user application at a data processing node has a built-in Kaa Software Development Kit (SDK) that includes a reward data schema, the address of the SP, and so on, and it communicates with the SP, which corresponds to a Kaa cluster and the analysis system in the Kaa Internet of things (IoT) architecture
Summary
Crowdsensing is a technique that uses sensing devices, such as smartphones and wearable devices, to receive, process, and utilize sensing data from the public [1]. The payment of financial rewards requires proper identification of the data provider and, sensitive personal information, such as user location, may be exposed to an attacker who compromises the service provider or its server. Crowdsensing is being studied and developed in a wide range of applications that require large amounts of data, such as real-time saturation of public transportation (e.g., as buses and subways) [4], vehicle flow information [5], and store operation hours The provider of such a service, as a central management entity, performs sensing data processing and pays rewards. It can access and store sensitive personal information, such as user location and identification information, which is included in the sensing data. An attacker must attack the SP and all data processing nodes to obtain the personal information of a specific user
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
