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

Felipe Pinheiro Correia (), Samara Ruthielle da Silva, Fabricio Braga Soares de Carvalho, Marcelo Sampaio de Alencar, Karcius Day Rosario Assis and Rodrigo Moreira Bacurau
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Felipe Pinheiro Correia: Graduate Program in Electrical Engineering, Department of Electrical and Computer Engineering, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
Samara Ruthielle da Silva: Graduate Program in Electrical Engineering, Department of Electrical Engineering, Federal University of Paraíba (UFPB), João Pessoa 58051-900, Brazil
Fabricio Braga Soares de Carvalho: Graduate Program in Electrical Engineering, Department of Electrical Engineering, Federal University of Paraíba (UFPB), João Pessoa 58051-900, Brazil
Marcelo Sampaio de Alencar: Graduate Program in Electrical Engineering, Department of Electrical and Computer Engineering, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
Karcius Day Rosario Assis: Graduate Program in Electrical Engineering, Department of Electrical and Computer Engineering, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
Rodrigo Moreira Bacurau: Department of Computational Mechanics (DMC), School of Mechanical Engineering (FEM), State University of Campinas (UNICAMP), Campinas 13083-860, Brazil

Energies, 2023, vol. 16, issue 5, 1-21

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.

Keywords: Internet of Things; wireless sensor networks; agriculture; clustering; gateways; Long Range Wide Area Networks; Performance Evaluation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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