Record Details

Self-Adaptive Learning and Cellular Automata based Mobile Crowdsensing

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Self-Adaptive Learning and Cellular Automata based Mobile Crowdsensing
 
Creator Anand, Saurabh
Ram, Anant
Mishra, Manas Kumar
 
Subject Markov decision process
Optimization
Reinforcement learning
Smart crowdsensing
 
Description 94-102
Mobile Crowdsensing (MCS) is frequently utilized for computation assignments, but it is particularly useful for sensing
complicated environments. Previously, the MCS platform spent a lot of time and effort establishing incentive mechanisms
and task assignment algorithms to encourage mobile users to participate. In actuality, because of their sensing environment
and other participants' methodologies, MCS participants face numerous uncertainties, and it is unknown how they interact
with one another and make sensing decisions. This study uses the perspectives of MCS participants to develop a web
detection arrangement that will maximize their payoffs through MCS participation. Self-adaptive cellular automata-based
Markov decision process exhibits interactions among mobile clients and detecting contexts. With the help of Self-Adaptive
Support Learning (SASL) and Cellular Automata (CA), we developed a novel method that uses the ideal detecting technique
for each client to improve the predicted payoff against random detecting scenarios in a stochastic multi-agent environment.
With distinct dynamic sensing, the SASL and CA based smart Crowdsensing enhances user’s payoff, as shown in the
simulation.
 
Date 2022-01-07T09:33:19Z
2022-01-07T09:33:19Z
2022-01
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58868
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(01) [January 2022]