Optimal Control Based on Scheduling for Comfortable Smart Home Environment

Smart home environments account to a major portion of the total energy consumption in today's world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in s...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.218245-218256
Main Authors: Malik, Sehrish, Lee, KyuTae, Kim, DoHyeun
Format: Article
Language:English
Subjects:
Actuators
Admission control
Algorithms
Control tasks
Energy consumption
Energy costs
Fuzzy logic
Fuzzy sets
Indoor environments
Load management
Markov chains
Optimal control
Optimization
Particle swarm optimization
Program processors
Residential energy
Smart buildings
smart home
Smart homes
Task analysis
Task scheduling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Smart home environments account to a major portion of the total energy consumption in today's world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in smart home systems is efficiency of tasks management and control commands' execution for smart home actuators. In this paper, we propose an optimal control solution for smart home environment based on smart home energy optimization and control tasks' load dispatching and scheduling. Optimal control is achieved by first defining an objective function for minimizing energy cost which is implemented using VB-PSO (velocity boost particle swarm optimization) algorithm. Next, the control tasks are generated using rule set implemented in fuzzy logic; defined based on optimal values achieved from VB-PSO. A Markov model based mechanism dispatches control tasks at scheduler, for efficient scheduling and optimal control. The results show that the proposed optimization scheme saves up to 29.73% energy costs on average, in comparison to baseline scheme. The proposed tasks' load dispatching scheme of admission control, makes the job of load balancing among the processors efficient while giving priority to the urgent tasks. The results for scheduler evidently show the low dropping probabilities for urgent tasks along with showing 34.9% reduction in tasks' starvation rate and 36.82% reduction in average tasks' instances missing rates.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3042534