No abstract

Wireless sensor networks have been proposed as a solution to obtain soil and environment information in large distributed areas. The main economic activity of the São Francisco Valley region in the Northeast of Brazil is the irrigated fruit production. The region is one of the major agricultural regions of the country. Grape plantations receive large investments and provide good financial return. However, the region still lacks electronic sensing systems to extract adequate information from plantations. Considering these facts, this paper presents a study of path loss in grape plantations for a 2.4 GHz operating frequency. In order to determine the position of the sensor nodes, the research dealt with various environmental factors that influence the intensity of the received signal. It has been noticed that main plantation aisles favor the guided propagation, and the vegetation along the secondary plantation aisles compromises the propagation. Diffraction over the grape trees is the main propagation mechanism in the diagonal propagation path. Transmission carried out above the vineyard showed that reflection on the top of the trees is the main mechanism.

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.

In this paper, we propose a novel hybrid strategy that combines mixed integer linear programming (MILP) formulation with different alternative routing approaches that is capable of solving, simultaneously, the routing, modulation format and spectrum assignment problem in the design of elastic optical networks. We extend our proposal to a path-link formulation that, although it is not guaranteed to find the optimal solution, it has shown reasonable solutions within inferior simulation time, allowing it to be used in not very short networks. We also compare our proposal with another MILP formulation and three adapted heuristics, all available in the literature. The results show the benefits of our proposal considering diverse simulation scenarios and different number of modulation formats over 19 realistic networks in terms of simulation run time and maximum number of used set of slots.