A Modified IEEE 802.11 MAC for Optimizing Broadcasting in Wireless Audio Networks

Abstract

The use of network infrastructures to replace conventional professional audio systems is a rapidly increasing ield which is expected to play an important role within the professional audio industry. Currently, the market is dominated by numerous proprietary protocols which do not allow interoperability and do not promote the evolution of this sector. Recent standardization actions are intending to resolve this issue excluding, however, the use of wireless networks. Existing wireless networking technologies are considered unsuitable for supporting real-time audio networks, not because of lack of bandwidth but due to their ineicient congestion control mechanisms in broadcasting. In this paper, we propose an amendment of the IEEE 802.11 MAC that improves the performance of the standard for real-time audio data delivery. The proposed amendment is ofering a solution for the balancing of data low density in wireless ad-hoc networks for a multi-broadcasting environment. It is based on two innovative ideas. First, it provides a protection mechanism for broadcasting and second, it replaces the classic congestion control mechanism, based in random backof, with an alternative traic adaptive algorithm, designed to minimize collisions. The proposed MAC is able to operate as an alternative mode allowing regular Wi-Fi networks to coexist and interoperate eiciently with audio networks, with the last ones being able to be deployed over existing wireless network infrastructures.

Adaptive IEEE 802.11ah MAC protocol for Optimization Collision Probability in IoT smart city data traffic Based Machine Learning models

“…In aperiodic traffic patterns, Figure 4 shows a relationship between the collision probability and the number of IoT nodes in both legacy IEEE 802.11 and IEEE 802.11ah. In legacy IEEE 802.11 that follows the formula (1), the collision probability increases exponentially with the increasing number of IoT nodes until it reaches 2000 nodes [13]. On the other hand, the IEEE 802.11ah collision probability increases slightly until it reaches 8192 nodes.…”

“…Therefore, the obtained accuracy for each feature is promising, and more specifically, the one obtained by employing the Payload size (class 3) shows great promise. have a great impact on optimizing "minimizing" the collision probability for both the aperiodic and periodic traffic patterns by about 80%~90% compared to the legacy IEEE 802.11 in the works [13,14]. Finally, each IoT smart city traffic patterns were adapted to allocate its associated IEEE 802.11ah access channel using the supervised ML algorithms with high accuracy range from 91.62% to 99.45% and short processing time from 0.764 sec to 1.066 sec.…”

“…Mehmet Karaca, et al [12] proposed a new backoff scheme rather than the current IEEE 802.11 MAC to obtain a low collision probability for about 100 users. Christos Chousidis, et al [13] presented the collision probability analysis for IEEE 802.11 MAC and proposed a modified IEEE 802.11 MAC to improve the collision probability compared to a classical IEEE 802.11 MAC. Nevertheless, the collision probability is still increasing with an increasing number of stations.…”

“…Firstly, existing related works addressed an evaluation of the collision probability in the legacy 802.11 Distributed Coordination Function (DCF) access channel [11][12][13][14]. S. Spinsante, et al [11] derived an expression for the collision probability based on the time interval between the consecutive transmission in the unidirectional WSN.…”

“…The collision probability 𝑃 𝑐𝑜𝑙𝑙 in the DCF access technique is affected by the number of stations 𝑛 − 1 and the access channel probability 1 𝐶𝑊 𝑎𝑣𝑔 as [13]:…”