دانلود رایگان مقاله مکانیزم تشویقی با حفاظت حریم خصوصی در سیستم جمع سپاری همراه

Abstract

In order to improve the efficiency and utility of mobile crowdsourcing systems, this paper proposes an incentive mechanism with privacy protection in mobile crowdsourcing systems. Combining the advantages of offline incentive mechanisms and online incentive mechanisms, this paper proposes an incentive mechanism that selects the worker candidates statically, and then dynamically selects winners after bidding. The proposed incentive mechanism includes two algorithms which are an improved two-stage auction algorithm (ITA) and a truthful online reputation updating algorithm (TORU). Through simulations, we verify the efficiency and effectiveness of the proposed incentive mechanism, which can solve the free-riding problem and improve the efficiency and utility of mobile crowdsourcing systems effectively.

5. Conclusion

The market of smartphones has proliferated rapidly in the recent years and continues to expand. In order to improve the ef- ficiency and utility of mobile crowdsourcing systems, this paper proposes an incentive mechanism with privacy protection in mobile crowdsourcing systems. Combining the advantages of offline incentive mechanisms and online incentive mechanisms, this paper proposes an incentive mechanism that selects the worker candidates statically, and then dynamically selects winners after bidding. An improved two-stage auction algorithm is proposed in order to determine the winners in real-time and overcome the unfairness problem. According to the free-riding problem, this paper proposes a truthful online reputation updating algorithm (TORU) to update workers’ reputations effectively. Through simulations, we verify the efficiency and effectiveness of the proposed incentive mechanism, which can solve the free-riding problem and improve the efficiency and utility of mobile crowdsourcing systems effectively. As future works, we will focus on the malicious fluctuation behavior of workers, which indicate that workers accumulate reputations in some transactions then behave unreliably in later transactions. We will further investigate how to solve the malicious fluctuation problem.