# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 2 Related Work  
Even though the IEEE 802.11 ah standard was only officially published recently, research on IEEE 802.11 ah has been conducted for several years. Several articles [3; 4; 5; 6; 7; 8] provide a deep overview of the key features of the new technology, fully describing the advantages and challenges in the design of PHY and MAC layer schemes. Moreover, performance assessment on IEEE 802.11 ah in four common machine-to-machine (M2M) scenarios, i.e., smart metering, agriculture monitoring, animal monitoring, and industrial automation, has been conducted by [PERSON] et al. [5]. [PERSON] et al. [8] thoroughly evaluated performance of IEEE 802.11 ah in comparison to the most notable IEEE 802.11 standards, and exposed the capabilities of IEEE 802.11 ah in supporting different IoT applications. Several recent works study physical layer aspects of 802.11 ah and sub-1 GHz communications [9; 10; 11; 12; 13; 14]. [PERSON] [9] conducts assessment on the link budget, and derives the achievable data rate and optimal packet size of 802.11 ah. [PERSON] and [PERSON] [10] study three urban propagation path loss models of 802.11 ah for carrier frequencies at 900 MHz. [PERSON] and [PERSON] [14] compare the indoor coverage and time delay performance between IEEE 802.11g and IEEE 802.11 ah in M2M communications. [PERSON] and [PERSON] [12] proposed an IEEE 802.11 ah prototype that is configured as a self-contained M2M wireless sensor system and allows an over-the-air protocol performance assessment. [PERSON] and [PERSON] [13] introduced a design for a programmable 802.11 ah station based on the Cadence-Tensilica Fusion digital signal processor (DSP). [PERSON], [PERSON] and [PERSON] [11] built a real-time Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) testing platform for evaluating narrow-band sub-1 GHz transmission characteristics. Moreover, [PERSON] et al. [15] developed an 802.11 ah fully-digital polar transmitter, this hardware prototype passes all the PHY requirements of the mandatory modes in IEEE 802.11 ah with 4.4% error-vector-magnitude (EVM), while consuming only 7.1 mW with 0 dBm output power. More relevant to the research presented in this article is work focusing on RAW analysis and optimization. Several studies have been conducted on the optimality of RAW configurations given specific network and traffic conditions [1; 16; 17]. [PERSON] [16] showed the effectiveness of RAW to mitigate the hidden node problem. [PERSON] et al. [17] evaluated RAW in terms of energy efficiency, showing that increasing the number of RAW groups significantly improves energy efficiency for sensor stations. Finally, in our own previous work [1], we evaluated the optimal RAW station grouping configuration under a variety of traffic conditions, proving the need for a dynamic regrouping algorithm, that takes into account changes in network and traffic conditions. Several such station grouping algorithms have been proposed in literature, as shown in Table 1. The goal of these algorithms is to determine RAW parameters (i.e., number of groups and slots, group duration, and station partitioning among groups), given the current network conditions (e.g., number of stations, traffic demand, station location). For each algorithm, Table 1 shows the assumed upstream traffic conditions, the optimization objective, and the used algorithmic method. The surveyed algorithms focus on upstream traffic, as station grouping mainly improves upstream scalability, and it is the main type of traffic in sensor networks. The traffic conditions consist of two parts. First, the traffic intensity can be categorized as either (i) one packet per station per slot; (ii) saturated traffic for each station; (iii) a static finite number of packets per station per slot and (iv) a dynamic (i.e., changing from slot to slot) finite number of packets per station per slot.