Paper Info
Title
Asynchronous Finite Sum optimization for Task Pricing in Crowdsourcing-Based Internet of Things : (Invited Paper)
Abstract
The unprecedented growth of Internet of Things (IoT) enables smart devices to connect with each other, leading to a wide range of ubiquitous applications. In the Lot paradigm, crowdsourcing is considered to be a promising approach for providing efficient sensing, computing, and processing service to a particular task generated by customers, efficiently integrating the power of crowd. In this paper, we investigate the crowdsourcing platform utility maximization by finding the optimal pricing policy for requested tasks. Such a pricing strategy can be formulated as a finite sum optimization problem in which the nodes try to achieve a global consensus on the pricing policy of each task. During the process of optimization, however, some nodes may be in the sleeping mode so that they cannot perform instantaneous updates, and it is too time-and resource-consuming to proceed synchronously centralized optimization due to the large scale networks. To address this issue, we use the stochastic gradient descent (SGD) type algorithm nonconvex primal-dual splitting with exact minimization (NESTT-E) to solve the optimization problem distributedly and asynchronously. Numerical results show the NESTT-E is more efficient than synchronous ADMM and conventional SGD with a larger number of working nodes.
Year
DOI
Venue
2018
10.1109/ICCS.2018.8689251
2018 IEEE International Conference on Communication Systems (ICCS)
Keywords
Field
DocType
Crowdsourcing,Internet of Things,Task Pricing,Finite sum optimization,Stochastic gradient descent
Asynchronous communication,Stochastic gradient descent,Mathematical optimization,Computer science,Crowdsourcing,Internet of Things,Minification,Utility maximization,Optimization problem
Conference
ISBN
Citations 
PageRank 
978-1-5386-7864-0
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Ruoguang Li1273.22
Li Wang2574.93
Mei Song326544.50
Zhu Han400.34