Abstract
The Internet of Things (IoT) has attracted the interests of both academia and industry and enables various real-world applications. The acquirement of large amounts of sensing data is a fundamental issue in IoT. An efficient way is obtaining sufficient data by the mobile crowdsensing. It is a promising paradigm which leverages the sensing capacity of portable mobile devices. The crowdsensing platform is the key entity who allocates tasks to participants in a mobile crowdsensing system. The strategy of task allocating is crucial for the crowdsensing platform, since it affects the data requester’s confidence, the participant’s confidence, and its own benefit. Traditional allocating algorithms regard the privacy preservation, which may lose the confidence of participants. In this paper, we propose a novel three-step algorithm which allocates tasks to participants with privacy consideration. It maximizes the benefit of the crowdsensing platform and meanwhile preserves the privacy of participants. Evaluation results on both benefit and privacy aspects show the effectiveness of our proposed algorithm.
Highlights
The Internet of Things (IoT) is an efficient network that connects various devices on the Internet
A task allocation algorithm with privacy preservation (TAPP) is proposed. It consists of three phases, allocating tasks without privacy preservation, modifying allocations with privacy consideration, and merging the allocations
We evaluate the performance of TAPP towards a real-world dataset from Yelp
Summary
The IoT is an efficient network that connects various devices on the Internet. Most of the IoT applications require large amounts of sensing data for monitoring and computing. An efficient way to acquire large amounts of sensing data is using the mobile crowdsensing. It is a promising sensing paradigm which encourages crowds to use mobile devices to collect sensing data. There is a wide range of IoT applications based on mobile crowdsensing, such as environmental monitoring [13, 14], healthcare [15], and smart cities [16, 17]
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