| _version_ |
1860796853943009280
|
| building |
INTELEK Repository
|
| collection |
Online Access
|
| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
|
| date |
2021-07-14 00:26:57
|
| eventvenue |
Virtual, Online
|
| format |
Restricted Document
|
| id |
10435
|
| institution |
UniSZA
|
| originalfilename |
4410-01-FH03-FIK-21-54260.pdf
|
| person |
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML
like Gecko) Chrome/90.0.4430.212 Safari/537.36
|
| recordtype |
oai_dc
|
| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10435
|
| spelling |
10435 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10435 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 6 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML like Gecko) Chrome/90.0.4430.212 Safari/537.36 2021-07-14 00:26:57 4410-01-FH03-FIK-21-54260.pdf UniSZA Private Access Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT) This paper discusses several issues supporting a knowledge-based methodology for discovery of high volume IoT resources in the simulator NS-3 environment. We found the concept was developed in previous researches, especially based on widely accepted concepts of Q-Learning discovery model. The model is validated using samples from emulations of tested data in the NS-3 simulator. Proper simulation in NS-3 based on the different modules such as checkpoint and restore was used to model and analyse the data. The main feasibility checkpointing concept of simulations in the NS-3 processes were using Distributed Multi-Threaded Checkpointing (DMTCP) to run on a single machine and Message Passing Interface (MPI) under distributed machine to speed up the NS-3 model initialization and execution. As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. Q-learning is one of the successful techniques available for the exploration of IoT nodes, but context based problems have already been established and simplified as issues of dedicated server management, IoT object data acquisition issues, and unique application requirements. The findings empirically support the validation of the Q-Learning model improvement for high-volume IoT resource discovery cases. The study will contribute to the new model development by providing new insights on the conceptualization and validation of knowledge-based methodology based on widely accepted techniques and approaches. 1st International Recent Trends in Engineering, Advanced Computing and Technology Conference Virtual, Online
|
| spellingShingle |
Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT)
|
| summary |
This paper discusses several issues supporting a knowledge-based methodology for discovery of high volume IoT resources in the simulator NS-3 environment. We found the concept was developed in previous researches, especially based on widely accepted concepts of Q-Learning discovery model. The model is validated using samples from emulations of tested data in the NS-3 simulator. Proper simulation in NS-3 based on the different modules such as checkpoint and restore was used to model and analyse the data. The main feasibility checkpointing concept of simulations in the NS-3 processes were using Distributed Multi-Threaded Checkpointing (DMTCP) to run on a single machine and Message Passing Interface (MPI) under distributed machine to speed up the NS-3 model initialization and execution. As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. Q-learning is one of the successful techniques available for the exploration of IoT nodes, but context based problems have already been established and simplified as issues of dedicated server management, IoT object data acquisition issues, and unique application requirements. The findings empirically support the validation of the Q-Learning model improvement for high-volume IoT resource discovery cases. The study will contribute to the new model development by providing new insights on the conceptualization and validation of knowledge-based methodology based on widely accepted techniques and approaches.
|
| title |
Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT)
|
| title_full |
Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT)
|
| title_fullStr |
Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT)
|
| title_full_unstemmed |
Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT)
|
| title_short |
Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT)
|
| title_sort |
determination of suitable resource discovery tool and methodology for high-volume internet of things (iot)
|