基于拍卖模型的移动群智感知网络激励机制

doi:10.11959/j.issn.1000-436x.2019138

Abstract

Abstract:

The selfishness and uncertainty of user behaviors in the mobile crowd sensing network make them unwilling to participate in sensing activities,which may result to a lower sensing task completion rate.To deal with these problems,an incentive mechanism based on auction model was proposed.In order to maximize the utility of each user,the proposed incentive method based on reverse auction (IMRA) leveraged a task-centric method to choose winners,and payed them according to a critical-price strategy.Furthermore,the proposed user-bidirectional interaction incentive mechanism (UBIM) helped drop-out users (buyers) to transfer their unfinished tasks to new users.Simulation results show that,compared with TRAC and IMC-SS,IMRA can achieve a better performance in terms of average user utility and tasks coverage ratio,and the task completion ratio can also be improved by UBIM.

Key words: mobile crowd sensing, incentive mechanism, reverse auction, double auction

CLC Number:

TP393

Yuanni LIU,Yaoxi LI,Huicong LI,Wanlin LI,Jianhui ZHANG,Guofeng ZHAO. Incentive mechanism based on auction model for mobile crowd sensing network[J]. Journal on Communications, 2019, 40(7): 208-222.

Figures/Tables 13

符号标识	说明
U，n，u_i	用户集，总用户数，单个用户
Γ，m，τ_x	任务集，总任务数，单个任务x
s _x，d_x	任务τ_x开始时间，任务τ_x截止时间
t _x，Δt_x	完成任务τ_x所需时间，用户执行任务τ_x的时间
V，V_x	任务价值集合，任务τ_x的价值
Γ _i，v_x	用户u_i上报的任务集，任务集Γ_i中任务的总价值
b_i	用户u_i的竞价
c_i	用户u_i对应于竞价b_i的感知成本
p_i	对用户u_i的支付
S	赢标用户集合
$T_{τ_{i}}$	任务奖励
$U^{b} / u_{i}^{b}, U^{s} / u_{j}^{s}$	买方用户集/买方用户，卖方用户集/卖方用户
B (t)/S(t)	在周期t内到达的买方/卖方用户集
B ^W(t)	在周期t内与卖方匹配成功的买方用户集
S ^W(t)	在周期t内赢得任务的卖方用户集
$\| B (t) \| / \| S (t) \|$	用户集B(t)/S(t)中的用户数量

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名称	参数值
用户数n	50,500]，以50为步长
总任务数m	10,100]，以10为步长
用户竞价b_i	服从[5,10]均匀分布
感知任务成本c_i	服从[1,5]均匀分布
任务的价值V_i	服从[1,50]均匀分布
中途退出的用户数占总用户数的比例p	0.2

名称	参数
买卖双方报价范围	(0,5]均匀分布
买方用户数目α	{10,20,30,40,50}
卖方用户数目β	{10,20,30,40,50,60,70,80,90,100}
拍卖总周期T	100
最大等待耐心I	{0,3 ,6 ,9 ,12,15,18,21,24,27,30}
到达率λ	{1,2,3,4,5,6,7,8,9,10}

Incentive mechanism based on auction model for mobile crowd sensing network

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