# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 2 Related Work  
Even before the first draft of IEEE 802.11 ah appeared in October 2013, the research community had already demonstrated its interest in its performance. That interest led to early evaluations and overviews of the key features of the technology, describing the advantages and challenges in the physical and MAC layer design [5; 6; 7; 8; 9; 10]. Furthermore, evaluations of key features led to novel solutions for performance improvement of the technology, namely improvements in energy efficiency, throughput, latency and hidden node mitigation, especially on the MAC layer. Available research regarding optimization mechanisms on the MAC layer are summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Year** & **Traffic** & **MAC Features** & **Objective** & **Validation Tool** \\ \hline _This article_ & 2018 & UL/DL & TIM \& RAW & reliability, latency, throughput \& energy & ns-3 \\ [PERSON] et al. [11] & 2017 & UL/DL & TIM \& RAW & energy & throughput & analytical \& unknown simulation \\ [PERSON] and [PERSON] [12] & 2017 & UL/DL & TIM \& RAW & energy & Matlab \\ [PERSON] et al. [13] & 2016 & UL/DL & TIM \& RAW & energy \& throughput & analytical \& unknown simulation \\ [PERSON] et al. [14] & 2014 & UL/DL & TIM \& RAW & energy & Matlab \\ [PERSON] et al. [15] & 2016 & DL & TIM & latency & unknown simulation \\ [PERSON] et al. [16] & 2016 & DL & TIM \& RAW & hidden node mitigation & Matlab \\ [PERSON] et al. [17] & 2017 & UL & TIM \& RAW & latency and energy & Matlab \\ [PERSON] et al. [18] & 2017 & UL & RAW & throughput & analytical \\ [PERSON] et al. [19; 20] & 2017 & UL & RAW & throughput & ns-3 \\ [PERSON] et al. [21] & 2017 & UL & RAW \& latency and energy & analytical \\ [PERSON] et al. [22] & 2017 & UL & RAW & energy & Matlab \\ [PERSON] et al. [23] & 2016 & UL & RAW & hidden node mitigation & analytical \& unknown simulation \\ [PERSON] et al. [24] & 2016 & UL & RAW & hidden node mitigation & Matlab \\ [PERSON] et al. [25] & 2015 & UL & RAW & throughput & unknown simulation \\ [PERSON] et al. [22; 26] & 2015 & UL & RAW & energy & Matlab \\ [PERSON] et al. [27] & 2015 & UL & RAW & throughput & analytical \\ [PERSON] et al. [28] & 2015 & UL & RAW & energy and throughput & analytical \\ [PERSON] et al. [29] & 2014 & UL & RAW & throughput & analytical \& unknown simulation \\ [PERSON] et al. [30] & 2014 & UL & RAW & throughput and energy & analytical \\ [PERSON] et al. [31] & 2014 & UL & RAW & throughput & analytical \& unknown simulation \\ [PERSON] et al. [32] & 2013 & UL & unknown & throughput \& energy & unknown \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The majority of available IEEE 802.11 ah MAC optimization algorithms optimize only RAW and consider only uplink traffic. Being the most popular MAC layer feature according to the number of published papers, the RAW feature is utilized in most MAC layer algorithms that improve network performance in some way. However, even though RAW can improve scalability in highly dense deployments, its performance depends on current network conditions. Most of the available algorithms and evaluations consider uplink traffic only. In that manner, [PERSON] et al. [17] developed the Delay and Energy Aware RAW Formation (DEARF) scheme to support delay sensitive devices along with other delay tolerant devices. They eliminated contention for transmission by employing Resource Allocation feature of RAW for delay sensitive stations. However, only uplink traffic is considered for both delay sensitive and insensitive stations. Regarding throughput optimization, [PERSON] et al. [19; 20] developed a real-time Traffic-Adaptive RAW Optimization Algorithm (TAROA) that improves uplink throughput in dynamic and heterogeneous WSNs. [PERSON] et al. [18] argue that the duration of each RAW slot should be chosen according to the size of the group, which has proven to increase uplink throughput. [PERSON] et al.