Research On Incentive Technology In Participatory Sensing Network

Nowadays,with the development of hardware in mobile devices, various kinds of sensors are embedded in the smart mobile terminals, so that some mobile sensing technologies based on smartphone heat up recently,and a new kind of data collection method called participatory sensing comes into sight. Participatory sensing is a sensing process of people collecting and analyzing data by using mobile devices and cloud servers. People's participation is essential in this process, and it establishes an interactive sensor network by deploying the sensing tasks to mobile users to make people collect and share data conveniently.However, some smartphone users are not likely to participate in the sensing activities because it will cost time and energy to sense data or communicate with the server. Therefore,an incentive mechanism which tends to provide some monetary payments as the reward is essential to provoke and guide people to take the active participatory sensing task.To enhance users' participation and network scale in participatory sensing, this paper proposes sensing utility based dynamic incentive mechanism, SUBDIM, which aimed at urging users to carry out sensing activities while maintaining a reasonable cost. SUBDIM implies the concept of demand and supply in micro-economics, and uses sensing utility to measure the data value, which will change dynamically with the process of task execution.SUBDIM consists of three main players: consumers, producers and a server. Consumer would like to have data being sensed at a remote area, while a producer is willing to carry out such sensing tasks. A user in can serve as both the consumer and producer. Server is responsible to manage the interactions between consumers and producers. At the last of chapter three, we conduct a simulation to study the performance of SUBDIM, and results show that the algorithm can promote the sensing activities and users' participation under a reasonable incentive cost with a comparison to non-incentive and RADP.On the basis of SUBDIM, we propose an optimization strategy based on directional distribution to make the energy cost more efficient, which named STDD. Before dispatching sensing tasks, the server will calculate the producer subset and distribute sensing tasks directionally according to it, so that can reduce the needless energy consumption on the server end. In addition, we conduct evaluation on STDD, the results show that the algorithm has a better performance on energy efficiency and network throughput.In the end, this paper presents a reward and return mechanism on the basis of previous work, which is aimed at maintaining the stability of network scale and user participation. The evaluation results indicate that the two strategies can promote number of sensing activities and reduce the situations of users dropping out.