Paper Info
Title
Competitive Data Trading in Wireless-Powered Internet of Things (IoT) Crowdsensing Systems with Blockchain.
Abstract
With the explosive growth of smart IoT devices at the edge of the Internet, embedding sensors on mobile devices for massive data collection and collective environment sensing has been envisioned as a cost-effective solution for IoT applications. However, existing IoT platforms and framework rely on dedicated middleware for (semi-) centralized task dispatching, data storage and incentive provision. Consequently, they are usually expensive to deploy, have limited adaptability to diverse requirements, and face a series of data security and privacy issues. In this paper, we employ permissionless blockchains to construct a purely decentralized platform for data storage and trading in a wireless-powered IoT crowdsensing system. In the system, IoT sensors use the power wirelessly transferred from RF-energy beacons for data sensing and transmission to an access point. The data is then forwarded to the blockchain for distributed ledger services, i.e., data/transaction verification, recording, and maintenance. Due to the coupled interference of wireless transmission and the transaction fee incurred by the blockchain’s distributed ledger services, rational sensors have to decide on their transmission rates to maximize their individual payoff. Thus, we formulate a noncooperative game model to analyze this competitive situation among the sensors. We provide the analytical condition for the existence of the Nash equilibria as well as a series of insightful numerical results about the equilibrium strategies in the game.
Year
Venue
Keywords
2018
2018 IEEE International Conference on Communication Systems (ICCS)
crowdsensing,blockchain,energy harvesting,concave games
Field
DocType
Volume
Middleware,Data security,Wireless,Computer science,Computer network,Mobile device,Database transaction,Nash equilibrium,The Internet,Stochastic game
Journal
abs/1808.10217
Citations 
PageRank 
References 
2
0.37
0
Authors
5
Name
Order
Citations
PageRank
Shaohan Feng1605.89
Wenbo Wang2969.38
Niyato Dusit39486547.06
Dong In Kim43784220.90
Ping Wang54153216.93