Abstract
With the rapid development of smart phones and wireless communication, mobile sensing has become an efficient environmental data acquisition method capable of accomplishing large-scale and highly complex sensing tasks. Currently, participants want to collect continuous data over a period of time. However, the number of participants varies widely in some periods. In view of this application background, this paper proposes a new incentive mechanism of extra rewards: premium and jackpot incentive mechanism (PJIM), and a new participant selection method based on time window: participant selection for time window dependent tasks (PS-TWDT). In the PJIM, the platform divides the time period of sensing tasks according to the time distribution of task participants and adopts different incentive strategies in different situations; at the same time, it introduces the prize pool mechanism to attract more participants to participate in the sensing task with fewer participants. In the PS-TWDT, we design a participant selection method based on dynamic programming algorithm. The goal is to maximize the data benefit while the sensing time of the selected participants covers the task time period. In addition, the updating strategy of participants’ credit value is added, and the credit value of participants is updated according to their willingness to participate in the task and data quality. Finally, simulation experiment verifies that the incentive mechanism and participant selection method proposed in this paper have good performance.
Highlights
With the development of wireless network and the progress of embedded sensors technology, there are many sensors embedded in people’s smart devices, such as microphone, camera, temperature sensor, light sensor, and positioning sensor
When the participants at night meet our requirements, we need to select the participants based on the differences in their credit, participation time, and other aspects. erefore, this paper proposes two aspects: One is the participant selection method based on dynamic programming algorithm, which aims to maximize the data benefit on the basis of covering the task time window. e second is that the credit value updating mechanism of participants updates the credit value of participants according to the willingness of participants to perform tasks and the quality of data collected
As the number of time windows for task execution increases from 1 to 10, we can see that the average data cost of participant selection for time window dependent tasks (PS-TWDT) and MST proposed in this paper is not much different and much lower than that of random participant selection (Random) method. is is because, in the process of selecting participants, both the participant selection mechanism and MST proposed in this paper adopt the dynamic programming algorithms, which take the bid price into account when selecting participants, while the Random method does not take the bid price into account; its average data cost is higher than the other two methods, and its fluctuation range is the largest
Summary
With the development of wireless network and the progress of embedded sensors technology, there are many sensors embedded in people’s smart devices, such as microphone, camera, temperature sensor, light sensor, and positioning sensor. We consider a mobile crowdsensing network consisting of task publishers, task platform, and task participants with smart devices. In the practical application of mobile crowdsensing, after ensuring that enough participants engage in the sensing task, the task platform puts forward certain value requirements for the collected data. These tasks require participants to collect continuous sensing data for a period of time. An optimization algorithm based on dynamic programming is proposed to select participants, and an extra reward mechanism is proposed It is based on the participation time of each participant to carry out different incentive strategies. (3) e proposed incentive mechanism ensures that there will be enough participants to participate in the sensing task, and the participant selection strategy is to select the appropriate participants to meet the task requirements from enough participants. ese two parts of work provide a complete working mode for the task platform
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