Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture

Abstract

The smart management of freshwater for precision irrigation in agriculture is essential for increasing crop yield and decreasing costs, while contributing to environmental sustainability. The intense use of technologies offers a means for providing the exact amount of water needed by plants. The Internet of Things (IoT) is the natural choice for smart water management applications, even though the integration of different technologies required for making it work seamlessly in practice is still not fully accomplished. The SWAMP project develops an IoT-based smart water management platform for precision irrigation in agriculture with a hands-on approach based on four pilots in Brazil and Europe. This paper presents the SWAMP architecture, platform, and system deployments that highlight the replicability of the platform, and, as scalability is a major concern for IoT applications, it includes a performance analysis of FIWARE components used in the Platform. Results show that it is able to provide adequate performance for the SWAMP pilots, but requires specially designed configurations and the re-engineering of some components to provide higher scalability using less computational resources.

Architecting and Deploying IoT Smart Applications: A Performance–Oriented Approach

“…This paper differs from previous work showing a performance analysis study of six deployment configurations of FIWARE and LoRaWAN/LPWAN for smart agriculture and smart city, involving different placement strategies of components over fog and cloud infrastructures. It extends previous preliminary performance results presented in [4] and [48], which focused on more specific scenarios of smart irrigation.…”

“…Smart agriculture (or more broadly smart farming), depends on innovative technologies to increase the productivity of crops and animal products, to make efficient use of precious resources like water, and to decrease the amount of chemical substances used (e.g., medicines and pesticides) [5]. Notably, we have been involved in using IoT and machine learning solutions within the Sensors 2020, 20, 84 6 of 24 SWAMP project, which develops IoT-based approaches for smart water management for a precision irrigation application, and pilots them in Italy, Spain, and Brazil [4]. Healthcare is an essential area for introducing IoT, given its enormous potential to provide quality of life to the society in applications such as elderly monitoring, chronic diseases, fitness programs, and remote treatment [8].…”

“…Yet, most current reported experiences show: (a) small-scale pilots [7,44]; (b) simulation-based or analytical results [21,45]; (c) measurement-based results with limited scope [19,46]; (d) no quantitative results at all [2,3,5,8,9,11,12,16,24,37,47]. Also, different architectural and deployment choices for IoT systems affect scalability, and real-time decision-making is possible in an environment composed of thousands of sensors [4,48]. Therefore, understanding the tradeoffs involved in planning and deploying different software components of a specific scenario on different location infrastructures-such as cloud, fog, or mist-requires careful consideration of the performance and scalability of the solution.…”

“…The number of devices connected to the Internet has been increasing steadily [1,2], so smart applications will typically have to deal with thousands or even millions of devices. Customized configurations, automated reconfigurability, and scalability are critical challenges in any successful deployment of IoT smart applications [3], in areas such as agriculture [4,5], cities [6,7], healthcare [8] and industry [9].…”

“…In this paper, we introduce a new IoT architecture for smart applications called IoTecture, comprising five layers, namely L1-Device, L2-Transport, L3-Data, L4-Model, and L5-Service. Our experience with deploying an IoT platform derived from the implementation of this architecture in four pilots with different characteristics in three countries [4] taught us that there is no one-size-fits-all approach for this process. The need for our IoTecture is due to the acknowledgment that the traditional 5-layer architecture is not prone to having an active role in the deployment of new applications since the business, application, and middleware layers are too generic with broader scopes.…”