In Wireless Sensor Networks, sensor nodes are deployed to ensure continuous monitoring of the environment which requires high energy utilization during the data transmission. To address the challenge of high energy consumption through frequent independent data transmission, the IEEE 802.11b provides a backoff window that reduces collisions and energy losses. In the case of Internet of Things (IoTs), billions of devices communicate with each other simultaneously. Therefore, adapting the contention/backoff window size to data traffic to reduce congestion has been one such approach in WSN. In recent years, the IEEE 802.11b MAC protocol is used in most ubiquitous technology adopted for devices communicating in the IoT environment. In this paper, we perform a thorough evaluation of the IEEE 802.11b standard taking into consideration the channel characteristics for IoT devices. Our evaluation is aimed at determining the optimum parameters suitable for network optimization in IoT systems utilizing the IEEE 802.11b protocol. Performance analysis is made on the sensitivity of the IEEE 802.11b protocol with respect to the packet size, packet delivery ratio (PDR), end-to-end delay, and energy consumption. Our studies have shown that for optimal performance, IoT devices using IEEE 802.11b channel require data packet of size 64 bytes, a data rate of 11Mbps, and an interpacket generation interval of 4 seconds. The sensitivity analysis of the optimal parameters was simulated using NS3. We observed PDR values ranging between 27% and 31%, an average end-to-end delay ranging within 10-15 ms while the energy remaining was between 5.59 and 5.63Joules. The results clearly indicate that scheduling the rate of packet generation and transmission will improve the network performance for IoT devices while maintaining data reliability.
CITATION STYLE
Engmann, F., Adu-Manu, K. S., Abdulai, J. D., & Katsriku, F. A. (2021). Network Performance Metrics for Energy Efficient Scheduling in Wireless Sensor Networks (WSNs). Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/9635958
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