Performance Analysis of the Physical and Medium Access Control Layer Parameters with Effect of Varying Transmission Power Using IEEE 802.15.4 Standard for Wireless Body Sensor Networks

Procedia Computer Science(2016)

引用 2|浏览2
暂无评分
摘要
Wireless Body Area Network (WBAN) consists of miniaturized, tiny, low power body sensor nodes communicating with a BAN (Body Area Node) Coordinator through Radio Frequency (RF) interface link. Recent advancements in the field of Information, Communication and microelectronics have led to the realization of WBAN, which will help in hazardous, long term health monitoring especially for elderly people. Most of the present day's Body Area Network node's MAC/PHY protocols are built using IEEE 802.15.4 and ZigBee standard. This standard will surely make an impact through its improvisation especially in MAC/PHY layers in the days to come. Already researchers have been working on the new WBAN standard. This paper emphasizes the basic structure of IEEE 802.15.5 MAC/PHY layers through experimentation on 7 specific, static body sensor nodes placed at appropriate points on the human body transmitting heterogeneous data using Time division multiple access and Contention access period of CSMA/CA protocol. The body area channels are considered with temporal and fixed path loss values. Physical layer parameters such as latency and fade depth distribution, MAC packets received and breakdown packets of MAC layer are presented. Packet with respect to the unique and useful parameters, packet received from Media Access Control layer of each node under various conditions of the channel and with Carrier Sense Multiple Access/Collision Avoidance comparing with Time Division Multiple Access techniques. Experimental results show that a transmitting-12dBm to -15dBm was found to be suitable for energy efficient WBAN system. For TDMA scheme, the algorithm may be fine-tuned to reduce the number of packet failure which improves the energy efficiency for any kind of channel.
更多
查看译文
关键词
WBAN,MAC,PHY,packet delivery,BAN Node
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要