Method of Assigning Spreading Factor to Improve the Scalability of the LoRaWan Wide Area Network

Abstract

In modern world Low Power Wide Area Network (LPWAN) technologies have become the key enabler for a sheer diversity of the Internet of Things (IoT) applications involving massive deployment of resource-limited devices. In this paper we address the problem of improving the network scalability for one of the most widely used today licensee-free-band LPWAN technologies, named LoRaWAN. We show that the conventional method for assigning the spreading factor (SF) parameter to the devices in a LoRaWAN network, which effectively minimizes the consumption of individual devices, has some drawbacks when this comes to the scalability of the network as whole. Therefore, in this paper we propose another method of assigning the SFs to the nodes, which improves the probability of data delivery in an LPWAN at a cost of minor increase of the devices' consumption. The results of the conducted simulations confirm and characterize the utility of the proposed method.

Using a Small Number of Devices to Experimentally Estimate the Packet Delivery Ratio on a Lorawan Network with a Large Number of End Devices

“…Result of experiment shows that idea from work [7] can up the probability of success . The optimal combination of devices with SF7 and SF8 with fewer of them is changed from optimal value in work [7].…”

“…In work [7] is scenario with 1000 devices in identical radio conditions send short messages on BS with SF7. With some assumption was shown what part of devices can be change SF from 7 to 8 and it will increase the likelihood of delivery messages.…”

“…The optimal combination of devices with SF7 and SF8 with fewer of them is changed from optimal value in work [7]. This is due to the fact that in [7] a number of assumptions are used that are not valid for a real system. Fig.…”

Improving Quality-Of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

“…In ref. [16], the authors proposed an optimized SF parameter allocation approach, stating that it increases the probability of uplink data delivery by 20-40% at the cost of increasing power consumption by 1-8%, respectively. Due to its relevance in the context of this paper, we considered this algorithm as a benchmark (dubbed "Tiurlikova") in the comparative performance analysis outlined in Section 6.…”

“…• Equal-distribution: With a similar goal to random, the equal-distribution distributes the number of end-devices equally between CF, SF pairs. • Tiurlikova: The Tiurlikova policy is based on the work of [16], which creates a dynamic allocation method of SF. This policy determines the number of nodes distributed in each SF n i through Equation (13), where i is the index of SF, T is the airtime (according to Table 2), and N is the nodes numbers:…”