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An incentive game based evolutionary model for crowd sensing networks

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Abstract

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61379110, 61073104, 61272494, 61272149), the National Basic Research Program of China (973 Program) (2014CB046305), JSPS KAKENHI Grant Number 25880002, 26730056, JSPS A3 Foresight Program.

Ethical statement

On behalf of, and having obtained permission from all the authors, I declare that:

(a) The material has not been published in whole or in part elsewhere;

(b) The paper is not currently being considered for publication elsewhere;

(c) This study is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time (e.g. “salami-publishing”).

(d) No data have been fabricated or manipulated (including images) to support our conclusions

(e) All authors have been personally and actively involved in substantive work leading to the report, and will hold themselves jointly and individually responsible for its content;

(f) No data, text, or theories by others are presented as if they were the author’s own (“plagiarism”). Proper acknowledgements to other works have been given.

(g) All relevant ethical safeguards have been met in relation to patient or subject protection, or animal experimentation.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, ChangSha, 410083, China

    Xiao Liu, Anfeng Liu & Zhigang Chen

  2. Muroran Institute of Technology, Muroran, Japan

    Kaoru Ota

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  1. Xiao Liu
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  2. Kaoru Ota
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  3. Anfeng Liu
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  4. Zhigang Chen
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Correspondence to Anfeng Liu.

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Liu, X., Ota, K., Liu, A. et al. An incentive game based evolutionary model for crowd sensing networks. Peer-to-Peer Netw. Appl. 9, 692–711 (2016). https://doi.org/10.1007/s12083-015-0342-2

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