Affiliations: Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal, India
Correspondence:
[*]
Corresponding author. Miriyala Mahesh, Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal-609609, India. E-mail: [email protected].
Note: [1] The optimal number of RAW slots is equal to the number of groups, since the RAW mechanism assigns each group with a RAW slot.
Note: [2] As per the IEEE 802.11ah draft standard, m = 5, R = 7, and CWmin = 32 [2].
Abstract: IEEE 802.11ah defines amendments to IEEE 802.11 to support the Internet of Things (IoT). IEEE 802.11ah implements restricted access window (RAW) mechanism to reduce the contention and energy consumption in dense IoT networks. The RAW mechanism is a group-based MAC protocol that partitions the devices into various groups and confines the channel access of a group of devices to the restricted time interval known as the RAW slot. However, the standard does not specify, grouping mechanism, duration of RAW slots, and the number of RAW slots while configuring the RAW mechanism. In an IoT network, each device has distinct transmission requirements. Thus, it is necessary to find the optimal number of RAW slots that can maximize the network performance, to group the devices with similar transmission requirements and to assign a RAW slot that adaptively varies with the traffic requirements of the respective group. In this paper, we exploit fuzzy logic to find the optimal number of RAW slots by considering network size, collision probability, and modulation and coding schemes. Further, we propose a traffic-aware adaptive RAW slot allocation (TARA) scheme that uses fuzzy c-means clustering algorithm to group the devices with similar traffic requirements and to assign each group with a RAW slot whose duration adaptively varies with the transmission requirements of the devices. We have also presented a simple yet accurate analytical model to evaluate the performance of the RAW mechanism. Results show that the optimal number of RAW slots found using fuzzy logic significantly enhances the performance of the RAW mechanism in terms of throughput and energy consumption. Further, it is observed that the TARA scheme can effectively meet the traffic requirements of different group of devices when compared to the uniform grouping scheme. Finally, extensive simulations are conducted using ns-3 to validate the analytical results.
Keywords: Internet of Things (IoT), restricted access window (RAW), IEEE 802.11ah, fuzzy logic, Wi-Fi HaLow, fuzzy c-means clustering
