Research On The Data Collection And Incentive Mechanisms For Applications Of The Internet Of Things

As the star product from a new wave of technology, the internet of things has its philosophy and applications, which already have penetrated into people's daily lives, and quietly changed our way of life and cognitive channels of the world. Along with emerging intelligent applications in the internet of things,wireless sensor networks and crowd sensing networks not only play a central power of technology, but also play the role of real network carriers in various industrial applications. At present, domestic and foreign experts and scholars do a lot of theoretical research and practical system operation on these emerging network technology, but there are still many core issues needed to be further studied. With the explosive growth of network applications, different network carriers are supposed to design new methods to meet the demand for information diversity and time efficiency.Thus, this thesis focuses on the data collection in wireless sensor networks and the related participation incentive problems in crowdsourcing systems for multiple cooperative tasks, and proposes some novel models and methods from two perspectives: how to design time minimum data collection methods, how to provide incentives encourage users to perform tasks cooperatively with personal smart devices. The main contributions of this thesis are as follows:(1) Data collection for time-critical applications in the low-duty-cycle wire-less sensor networks.The main problem is that how fast can raw data be collected from all source nodes to a sink in low-duty-cycle WSNs with general topology? Both lower and upper tight bounds were given for this problem. We presented both centralized and distributed fast data collection algorithms to address this problem. Further-more, when interfering links happen, multi-channel scheduling is exploited to eliminate them. Next, a novel Receiver-based Channel and Time Scheduling(RCTS) algorithm is proposed to obtain the optimal solution. Real trace based simulations and results show that distributed RCTS algorithm is significantly more efficient than the link schedule on the same channel and achieved the lower bound.(2) Stackelberg game based incentive mechanisms for multiple collabora-tive tasks in mobile crowdsourcing.We conduct incentive study on the crowdsourcing system with multiple collaborative tasks. First, two feasible reward functions based on the Number of Users (NU function) and the Value of Tasks (VT function), are designed to solve the reward allocation problem. Next, four incentive mechanisms based on Stackelberg game are proposed to motive users to join the system for the diverse of applications. All proposed mechanisms have been proved to have the Nash equilibrium solutions to maximize the utility of the server, which can be returned in polynomial time. Finally, online mechanisms based on the Markov model are presented for real time tasks. Rigid theoretical analysis and extensive simulations demonstrate that all proposed mechanisms have high computational efficiency and can be applied for online occasions.(3) An incentive framework for peer-to-peer media sharing on public trans-port.In most metropolis, commuters spend a considerable amount of time on public buses, subways or trains on working days. In this paper, we present an incentive framework, called GoSharing to encourage resource owners to share contents collaboratively using p2p (peer to peer) communications that can re-duce transmission delay, and especially cut the huge monetary cost by using the cellular data. To increase commuters'sharing initiatives,GoSharing based on procurement auction allows commuters to express their cost (i.e., battery and contact overhead with server), by submitting bids to the server, as well as avoids bidder's cheating behavior. Both theoretical analysis and extensive simulations demonstrate that GoSharing not only effectively motivates user collaboration,but also achieves the properties of truthfulness, individual rationality, high com-putational efficiency and low overpayment ratio.In summary, this thesis not only proposes a series of models and meth-ods on data collection and incentive mechanisms, but also provides theoretical analysis and extensive experiments to verify the effectiveness of them, provid-ing important theoretical and technical support and reference for the diverse applications of the Internet of things.