LoRaWAN Gateway Placement in Smart Agriculture: An Analysis of Clustering Algorithms and Performance Metrics

Abstract

The use of Wireless Sensor Networks (WSN) in smart agriculture has emerged in recent years. LoRaWAN (Long Range Wide Area Networks) is widely recognized as one of the most suitable technologies for this application, due to its capacity to transmit data over long distances while consuming little energy. Determining the number and location of gateways (GWs) in a production setting is one of the most challenging tasks of planning and building this type of network. Various solutions to the LoRaWAN gateway placement problem have been proposed in the literature, utilizing clustering algorithms; however, few works have compared the performance of various strategies. Considering all these facts, this paper proposes a strategy for planning the number and localization of LoRaWAN GWs, to cover a vast agricultural region. Four clustering algorithms were used to deploy the network GWs: K-Means and its three versions: Minibatch K-Means; Bisecting K-Means; and Fuzzy c-Means (FCM). As performance metrics, uplink delivery rate (ULDR) and energy consumption were used, to provide subsidies for the network designer and the client, with which to choose the best setup. A stochastic energy model was used to evaluate power consumption. Simulations were performed, considering two scenarios: Scenario 1 with lower-medium concurrence, and Scenario 2 with higher-medium concurrence. The simulations showed that the use of more than two GWs in Scenario 1 did not lead to significant improvements in ULDR and energy consumption, whereas, in Scenario 2, the suggested number of GWs was between 11 and 15. The results showed that for Scenario 1, the FCM algorithm was superior to all alternatives, regarding the ULDR and mean energy consumption, while the K-Means algorithm was superior with respect to maximum energy consumption. In relation to Scenario 2, K-Means caused the best ULDR and mean consumption, while FCM produced the lowest maximum consumption.

Multi-Objective Optimization of Gateway Location Selection in Long-Range Wide Area Networks: A Tradeoff Analysis between System Costs and Bitrate Maximization

“…The TOPSIS method constructs a normalized decision matrix using Equations ( 19) and (20) to transform the attribute dimensions into non-dimensional attributes. Here, U lv represents the non-dimensional attribute value for point l and objective v, while r lv denotes the normalized value of the objective function.…”

“…Furthermore, the particle swarm optimization (PSO) approach effectively identifies optimal gateway locations, yielding superior outcomes with an increasing number of gateways [19]. The placement of gateways significantly influences the energy consumption of end devices, a critical factor for preserving battery life and autonomy [20]. The meticulous identification of optimal gateway locations is crucial for ensuring comprehensive coverage [21,22].…”

LoRaWAN Metering Infrastructure Planning in Smart Cities

“…Paper [41] analyzed the impact of adding new clusters (resulting in new gateways) on the power efficiency of LoRaWANs. As the number of gateways increased, the distances shortened, leading to an increase in SNR, causing the ADR algorithm to recommend smaller values for the transmit power P t and SF factor.…”

“…It is worth noting that as the SF increases, the measurement nodes may remain in range of more gateways and reduce the transmission efficiency within these gateways due to a higher probability of collisions. By knowing the radio parameters of each node, it is possible to determine the transmission airtime based on the length of the messages sent and, as a result, the energy efficiency of the entire system [41]. The results shown in Figure 6 show the dependence of the coverage ratio on both the SF and the number of gateways used.…”