The Study of AMCA System Performance of IEEE 802.15.4e
Date Issued
2016
Date
2016
Author(s)
Ling, Li-Min
Abstract
IEEE 802.15.4 is a suitable standard to be adopted for wireless sensor networks. It defines the communication physical layer (PHY) and media access control layer (MAC) for wireless personal area network (WPAN). Recently, with the multidisciplinary development of Internet of Things (IoT) the WSN becomes more powerful for its functions, e.g. the wireless sensor and actuator network (WSAN). IEEE 802.15.4e standard is designed to support the complicated development of WSN/WSAN. It officially defines multi-channel communication protocols as part of the standard. In the standard, multi-channel communication can be categorized into beacon-enabled as example of Deterministic & Synchronous Multi-Channel Extension (DSME) and non-beacon enabled as example of Asynchronous Multi-Channel Adaption (AMCA). Traditionally it has flexibility on variable unit data length for MAC layer on non-beacon enabled WSN but less performance balanced due to unpredictable waiting time for network access to every node in the network. With the introduction of the AMCA, it can run with different MAC unit data lengths in different channels in parallel so it’s getting more use cases. We propose enhancements based on native AMCA of the IEEE 802.15.4e and build up an energy analysis model for comparing of native AMCA and enhanced AMCA modes. There are main four approaches to our proposition and the algorithms are easy to be implemented on a micro controller system as a practical method for real deployment of WSN. We finally use OPNET network simulator software to form networks with different node sizes under mesh and cluster-tree hybrid topologies as simulation scenarios. The results present our E-AMCA approaches have better performance than N-AMCA especially in the networks with high volume nodes.
Subjects
WSAN
802.15.4e
AMCA
Multi-Channel WSN
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-105-P99943005-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):c038766ccc8b7218b948fa654fd543ee