IEEE 802.15.4 LR-WPAN is considered one of the most commercially adopted medium access control (MAC) protocol in wireless sensor networks, much research have been conducted in IEEE 802.15.4 to improve the performance of this standard by adjusting the duty cycle (DC) which is defined the active to inactive period, as IEEE 802.15.4 does not define adjustable DC. In this deployment, it is important to get a sufficient DC based on the network condition and behavior to adjust the trade-off among the power consumption, transmission delay and network throughput. This thesis proposes an optimization for the MAC parameter and the DC for IEEE 802.15.4 using fuzzy logic inference to dynamically adjust the DC through traffic estimation by the coordinator. Since all transmission are destined to the coordinator, its distinguished features enable it to observe the communication behavior of all clients, then formulating these actions and behavior to get the inputs of fuzzy controller, which are Superframe Utilization (SFU) and Failure Node Rate (FNR), in order to adjust the DC. The simulation results show the improvements to the network performance in term of energy consumption, transmission delay and throughput.

IEEE 802.15.4 LR-WPAN is considered one of the most commercially adopted medium access control (MAC) protocol in wireless sensor networks, much research have been conducted in IEEE 802.15.4 to improve the performance of this standard by adjusting the duty cycle (DC) which is defined the active to inactive period, as IEEE 802.15.4 does not define adjustable DC. In this deployment, it is important to get a sufficient DC based on the network condition and behavior to adjust the trade-off among the power consumption, transmission delay and network throughput. This thesis proposes an optimization for the MAC parameter and the DC for IEEE 802.15.4 using fuzzy logic inference to dynamically adjust the DC through traffic estimation by the coordinator. Since all transmission are destined to the coordinator, its distinguished features enable it to observe the communication behavior of all clients, then formulating these actions and behavior to get the inputs of fuzzy controller, which are Superframe Utilization (SFU) and Failure Node Rate (FNR), in order to adjust the DC. The simulation results show the improvements to the network performance in term of energy consumption, transmission delay and throughput.

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