Efficient Crowdsourcing-Aided Positioning and Ground-Truth-Aided Truth Discovery for Mobile Wireless Sensor Networks in Urban Fields

In urban fields, Mobile Wireless Sensor Networks (MWSNs) become ubiquitous. Accurate GPS positioning for sensors is a fundamental problem for MWSNs. To solve this problem, this paper proposes a Crowdsourcing-Aided Positioning scheme, which takes an ideal situation and a more realistic situation into account. In the ideal situation, all participants are considered accurate. Then, two optimization objectives are addressed for the efficient Crowdsourcing-Aided Positioning task. Their utility functions are proven to be submodular and a greedy algorithm is given to solve them. In the more realistic situation, randomly selected participants cannot guarantee the accuracy of the data. We propose a data-accuracy-calibration-based participant selection framework to solve this dilemma. Through data accuracy calibration, participants gain their data accuracy and reliability with the help of wireless sensor networks. First, we design three kinds of data accuracy calibration methods based on probabilistic models. Then, we propose a Truthful-Data-Driven Participant Selection problem, which tends to raise the data accuracy and reliability. The optimization problem is proved to be NP-hard and its optimization function has submodular property. We give a greedy algorithm with 1- 1/e approximation ratio to solve this problem. Simulation experiments are conducted to validate the algorithmic effectiveness at last.

Automated summary

This paper proposes a Crowdsourcing-Aided Positioning scheme for Mobile Wireless Sensor Networks, considering both ideal and realistic situations. The paper addresses optimization objectives and provides a greedy algorithm for the ideal situation, and proposes a data-accuracy-calibration-based participant selection framework for the realistic situation. Simulation experiments are conducted to validate the effectiveness of the algorithms.