An incentive game based evolutionary model for crowd sensing networks
- 844 Downloads
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.
KeywordsGame model Incentive mechanism Cooperation rates Crowd sensing networks
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.
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.
Conflict of interest
There are not potential conflicts of interest.
- 3.Mianxiong D, Kimata T, Sugiura K et al (2014) Quality-of-Experience (QoE) in emerging mobile social networks[J]. IEICE Trans Inf Syst 97(10):2606–2612Google Scholar
- 5.Dong M, Ota K, Li X, et al (2011) HARVEST: A task-objective efficient data collection scheme in wireless sensor and actor networks. Communications and Mobile Computing (CMC), 2011 Third International Conference on. IEEE pp 485–488Google Scholar
- 6.Thiagarajan A, Ravindranath L, LaCurts K, et al (2009) VTrack: Accurate, energy-aware road traffic delay estimation using mobile phones[C]. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. ACM pp 85–98Google Scholar
- 7.Maisonneuve N, Stevens M, Niessen ME, et al (2009) NoiseTube: Measuring and mapping noise pollution with mobile phones[M]. Information Technologies in Environmental Engineering. Springer, Berlin Heidelberg pp 215–228Google Scholar
- 14.Robert Ad, William DH (1981) The evolution of cooperation. http://www.life.umd.edu/faculty/wilkinson/BIOL608W/Axelrod&Hamilton81.pdf
- 15.Robbins H (1985) Some aspects of the sequential design of experiments. Herbert Robbins Selected Papers. Springer, New York, pp 169–177Google Scholar
- 21.Liu G, Ji S, Cai Z (2014) Strengthen nodal cooperation for data dissemination in mobile social networks. Pers Ubiquit Comput 1–15Google Scholar
- 22.Chun-Mei GUI, Qiang JIAN, Huai-Min WANG, Quan-Yuan WU (2010) Repeated game theory based penalty-incentive mechanism in internet-based virtual computing environment. J Softw 21(12):3042–3055Google Scholar
- 25.Chen BB, Chan MC (2010) Mobicent: A credit-based incentive system for disruption tolerant network. 2010 Proceedings IEEE INFOCOM pp 1–9Google Scholar
- 26.Ning T, Yang Z, Xie X, et al (2011) Incentive-aware data dissemination in delay-tolerant mobile networks. 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON) pp 539–547Google Scholar
- 30.Liu Y, Liu A, Chen Z (2014) Analysis and improvement of send-and-wait automatic repeat-reQuest protocols for wireless sensor networks. Wirel Pers Commun 1–37. doi: 10.1007/s11277-014-2164-6