# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 2 Related Research  
The bulk data transmission problem has been investigated in the literature in the context of opportunistic or delay tolerant networks [12]. Different types of communication protocols have been used, from IEEE802.11 [13] to IEEE802.15.4 [14] and RFIDs [15]. In contrast to those technologies, our work focuses on the data collection from devices placed in large geographical areas, thus, a long range protocol such as LoRa is required. Supporting bulk data transmissions over LoRa-based networks is constrained by various limitations and unique characteristics (e.g., duty cycle, multiple pseudo-orthogonal spreading factors, etc.) that do not apply to other conventional networking solutions.  
We have recently studied the bulk data collection problem in LoRa-based networks from different perspectives [11; 16; 17; 18]. In [11; 16], heuristic approaches were proposed to compute the time-slotted schedule with online and offline schemes, respectively. The offline approach assumes that a set of information such as the number of devices and their SFs is known in advance by the gateway. In contrast, in the online approach [16], only partial knowledge is assumed, allowing this approach to adapt to dynamic changes such as frequent topology changes. Nevertheless, this comes at the expense of higher energy consumption and longer data collection time than the offline approach. Both approaches are centralized, where the schedule is computed at the gateway and disseminated to the end devices. An alternative approach was proposed for Industrial IoT deployments [18], where the authors investigated a decentralized approach, where end devices can autonomously compute their schedule by receiving the minimum possible information from the gateway. This work was designed to meet specific industrial network criteria, such as fast joining times, low power consumption, and low overhead. However, the schedule length tends to increase substantially as the number of devices increases, which greatly increases the data collection time. Finally, the problem of collecting data using LoRaWAN over a single channel was examined in [17]. In this work, optimal SF assignments are proposed so that the average probability of successful packet delivery is maximized. However, this approach is still Aloha-based and does not eliminate collisions. Unlike the previous works, in this paper, we present the complete picture of the bulk data collection problem, we formulate and solve the optimal scheduling problem to minimize the data collection time considering collision-free scheduling and allocation of SFs, and we study practical implementation aspects such as the schedule transmission.  
We rely on a time-slotted medium access control (MAC) approach to enable efficient bulk data transmissions in LoRa-based networks. In the literature, similar MAC approaches have been considered as they typically offer higher reliability than that of Pure Aloha [19]. In [20; 21; 22], various time-slotted MAC strategies are used to mitigate the collisions in dense LoRa/LoRaWAN networks and, thus, improve their scalability. Similar MAC strategies are also considered in [23; 24; 25; 26] to meet the requirements of soft real-time industrial applications. In [27; 28; 29], time slots are used to enable low-power multi-hop communications in LoRa-based networks to extend the coverage in harsh environments like underground deployments. In addition to that, the Time Slotted Channel Hoping mechanism of the IEEE802.15.4e standard is employed to enable LoRa-based mesh networking [30]. Unlike the profound advantages of the aforementioned time-slotted solutions in terms of reduced packet collisions, they can barely be used for bulk data collection because of the absence of a scheduler to allocate slots to the nodes or because they follow a slotted-Aloha approach which does not eliminate collisions. Since in bulk data collection, the nodes tend to use a high data rate to quickly expedite all transmissions, non collision-free approaches may suffer for long data collection times.