Capacity Planning of Aggregators, Provisioning of Software Defined Network and Cloud Resources to Applications of Wireless Sensor Networks
Recently, Internet of Things (IoT) has become an emerging technology of worldwide network which links various smart objects through the Internet for exchanging data. In IoT, wireless sensor networks (WSNs) play the essential roles for sensing data in real world applications. The rapid growth of data...
Saved in:
| Main Author: | |
|---|---|
| Format: | บทความวารสาร |
| Language: | English |
| Published: |
Science Faculty of Chiang Mai University
2019
|
| Online Access: | http://it.science.cmu.ac.th/ejournal/dl.php?journal_id=9163 http://cmuir.cmu.ac.th/jspui/handle/6653943832/64126 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Language: | English |
| Summary: | Recently, Internet of Things (IoT) has become an emerging technology of worldwide network which links various smart objects through the Internet for exchanging data. In IoT, wireless sensor networks (WSNs) play the essential roles for sensing data in real world applications. The rapid growth of data from WSN requires the substantial amount of storage and processing power for data archiving and data analytics. For this requirement, cloud computing can support abundant storage and processing power resources. However, WSN can experience bandwidth constraint and memory constraint when transferring data directly to the cloud. Therefore, this paper proposes a system architecture called IoT based Sensor-Cloud with aggregator approach and software defined network (SDN) approach for various IoT applications. The aggregator approach can lessen bandwidth constraint and memory constraint from sensors. The SDN approach can improve network connectivity between aggregators and cloud. Moreover, the proposed system is applied in healthcare monitoring. To apply the system in healthcare monitoring, the optimization approaches are also applied to minimize the total cost for capacity planning of aggregators, provisioning of SDN bandwidth, and cloud resources under data demand uncertainty. |
|---|