Recommendation of tasks with Multiple Incentives in Mobile Crowdsensing
Saurabh Anand, Anant Ram, Manas Kumar Mishra · 2021
In Mobile Crowdsensing, often it is observed that the quality of data; that are obtained from the participants of the crowdsensed network is not the same as the desired data. There can be multiple reasons for this; firstly the reward given to the participants is not enough; secondly even in voluntary participation, the participants do not get involved in the cause. In either case, it is highly required to cater the needs of the users considering it as the backbone of the mobile crowdsensing network. So this paper focuses on both the aspects. Recommended tasks will be given to the contributors of the system based on their categorization, and they will be allowed to choose the task. Also, for rewarding the users; they will be given premium for their participation work, as well for sensing the environment. Knowing that the crowdsensing works in a stochastic environment; an effort has been done for sensing the abode considering the dynamic and mobile nature of the system. The results also show that the overall quality of the data get improved in this.