Resource aware task scheduling in wireless sensor networks
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.
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BRAC University
2016
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| Online adgang: | http://hdl.handle.net/10361/5414 |
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10361-54142022-01-26T10:08:18Z Resource aware task scheduling in wireless sensor networks Mahjabeen, Maksura Khan, Md .Muhidul Islam Department of Computer Science and Engineering, BRAC University CSE Computer science and engineering Task scheduling Wireless sensor networks Reinforcement learning Trade off Resource aware Reward function This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. Cataloged from PDF version of thesis report. Includes bibliographical references (page 37-39). Wireless Sensor Networks (WSNs) consist of so many sensor nodes capable of sensing, processing and transmitting sensed information to a remote station. Among the application of wireless sensor networks target tracking is considered as the most significant and pre-eminent application. While tracking multiple objects, it is very important to schedule the task in an efficient manner such that we can get optimal result by maintaining energy constraint. Here, we develop a method that would be able to find out the best possible result in accordance with the prominent trade-off between these two factors. Furthermore, tracking multiple objects is more formidable than single target tracking as the speed, position and movement of targets can be different. Also, there are issues of connectivity failure and high power consumption that could lead us to data loss in a system. In our paper, we consider the sensor nodes as intelligent agents that will adapt the next task by observed application behavior by using cooperative reinforcement learning where we introduce a reward function based on our specific condition that includes energy efficiency and performance. Simulation results show that our proposed methods provide better trade-off between power consumption and performance comparing with the existing methods Maksura Mahjabeen B. Computer Science and Engineering 2016-05-31T09:55:51Z 2016-05-31T09:55:51Z 2016 2016-04 Thesis ID 13101297 http://hdl.handle.net/10361/5414 en BRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 39 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
CSE Computer science and engineering Task scheduling Wireless sensor networks Reinforcement learning Trade off Resource aware Reward function |
| spellingShingle |
CSE Computer science and engineering Task scheduling Wireless sensor networks Reinforcement learning Trade off Resource aware Reward function Mahjabeen, Maksura Resource aware task scheduling in wireless sensor networks |
| description |
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. |
| author2 |
Khan, Md .Muhidul Islam |
| author_facet |
Khan, Md .Muhidul Islam Mahjabeen, Maksura |
| format |
Thesis |
| author |
Mahjabeen, Maksura |
| author_sort |
Mahjabeen, Maksura |
| title |
Resource aware task scheduling in wireless sensor networks |
| title_short |
Resource aware task scheduling in wireless sensor networks |
| title_full |
Resource aware task scheduling in wireless sensor networks |
| title_fullStr |
Resource aware task scheduling in wireless sensor networks |
| title_full_unstemmed |
Resource aware task scheduling in wireless sensor networks |
| title_sort |
resource aware task scheduling in wireless sensor networks |
| publisher |
BRAC University |
| publishDate |
2016 |
| url |
http://hdl.handle.net/10361/5414 |
| work_keys_str_mv |
AT mahjabeenmaksura resourceawaretaskschedulinginwirelesssensornetworks |
| _version_ |
1814307390139400192 |