Abstract
In this paper, we consider joint pricing and task allocation in a unified mobile crowdsensing system, where all task initiators and mobile users are viewed as peers. From an exchange market point of view, the pricing and task allocation in such a unified system depend only on the supply and demand since no one can dominate the process, with the optimal solution being characterized by the Walrasian equilibrium. This is quite different from existing approaches, where each task initiator builds a specific mobile crowdsensing system and provides an incentive mechanism to maximize his/her own utility. We design distributed algorithms to compute the Walrasian equilibrium under the scenario where one cloud platform is available in the system. We propose to maximize social welfare of the whole system, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by task initiators and mobile users. We prove that the proposed algorithm converges to the optimal solution of social welfare maximization problem. Further, we show that the prices and task allocation obtained by the algorithm also yields a Walrasian equilibrium. Also, the proposed algorithm does not need the cloud to collect private information such as utility functions of task initiators and cost functions of mobile users. Extensive simulations demonstrate the effectiveness of the proposed algorithms.
| Original language | English (US) |
|---|---|
| Article number | 7797476 |
| Pages (from-to) | 4048-4057 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Industrial Electronics |
| Volume | 64 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 1 2017 |
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Keywords
- Mobile crowdsensing (MCS)
- Optimization
- Social welfare
- Walrasian equilibrium
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
Cite this
Distributed algorithms to compute walrasian equilibrium in mobile crowdsensing. / Duan, Xiaoming; Zhao, Chengcheng; He, Shibo; Cheng, Peng; Zhang, Junshan.
In: IEEE Transactions on Industrial Electronics, Vol. 64, No. 5, 7797476, 01.05.2017, p. 4048-4057.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Distributed algorithms to compute walrasian equilibrium in mobile crowdsensing
AU - Duan, Xiaoming
AU - Zhao, Chengcheng
AU - He, Shibo
AU - Cheng, Peng
AU - Zhang, Junshan
PY - 2017/5/1
Y1 - 2017/5/1
N2 - In this paper, we consider joint pricing and task allocation in a unified mobile crowdsensing system, where all task initiators and mobile users are viewed as peers. From an exchange market point of view, the pricing and task allocation in such a unified system depend only on the supply and demand since no one can dominate the process, with the optimal solution being characterized by the Walrasian equilibrium. This is quite different from existing approaches, where each task initiator builds a specific mobile crowdsensing system and provides an incentive mechanism to maximize his/her own utility. We design distributed algorithms to compute the Walrasian equilibrium under the scenario where one cloud platform is available in the system. We propose to maximize social welfare of the whole system, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by task initiators and mobile users. We prove that the proposed algorithm converges to the optimal solution of social welfare maximization problem. Further, we show that the prices and task allocation obtained by the algorithm also yields a Walrasian equilibrium. Also, the proposed algorithm does not need the cloud to collect private information such as utility functions of task initiators and cost functions of mobile users. Extensive simulations demonstrate the effectiveness of the proposed algorithms.
AB - In this paper, we consider joint pricing and task allocation in a unified mobile crowdsensing system, where all task initiators and mobile users are viewed as peers. From an exchange market point of view, the pricing and task allocation in such a unified system depend only on the supply and demand since no one can dominate the process, with the optimal solution being characterized by the Walrasian equilibrium. This is quite different from existing approaches, where each task initiator builds a specific mobile crowdsensing system and provides an incentive mechanism to maximize his/her own utility. We design distributed algorithms to compute the Walrasian equilibrium under the scenario where one cloud platform is available in the system. We propose to maximize social welfare of the whole system, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by task initiators and mobile users. We prove that the proposed algorithm converges to the optimal solution of social welfare maximization problem. Further, we show that the prices and task allocation obtained by the algorithm also yields a Walrasian equilibrium. Also, the proposed algorithm does not need the cloud to collect private information such as utility functions of task initiators and cost functions of mobile users. Extensive simulations demonstrate the effectiveness of the proposed algorithms.
KW - Mobile crowdsensing (MCS)
KW - Optimization
KW - Social welfare
KW - Walrasian equilibrium
UR - http://www.scopus.com/inward/record.url?scp=85018957757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018957757&partnerID=8YFLogxK
U2 - 10.1109/TIE.2016.2645138
DO - 10.1109/TIE.2016.2645138
M3 - Article
VL - 64
SP - 4048
EP - 4057
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 5
M1 - 7797476
ER -