Original papersContext-aware control and monitoring system with IoT and cloud support
Introduction
“Internet of Things” (IoT) is a concept that has become increasingly disputed in the past decade and half. Although this concept does not have a definition which has been widely accepted in the engineering field until the present moment, it refers to a network of objects (devices) interconnected in a wireless network that have the ability to communicate and exchange information between them. Such networks of interconnected equipments began to be increasingly used nowadays in industrial applications.
This trend began to spread extensively in automation when it was found the need to introduce automation equipment in hospitals, office buildings, housings, manufacturing enterprises and so on. In particular, IoT is being used more and more often in association with precision agriculture (PA) technologies, for example sensing technologies for automated data collection and yield monitoring. One of these is nutrient management, often linked to the variable rate application (VRA) fertilizer spreading.
IoT platforms used in PA must be designed considering both the increase in the number of interconnected devices and the particular demands of users in different applications contexts. Therefore, an IoT platform will be successful only if it recognizes the context in which applications are operating.
This becomes possible by using context-aware computing, which acquires and analyzes relevant context information, and produces appropriate decisions to respond at contextual changes.
Context-aware systems (CAS) are systems that are able to adapt their operations to context changes without explicit user intervention. A CAS platform must explicitly provide by its components’ functionalities, context information and management action and also offers services to users using context information where relevance depends on the user’s task.
So, a context-aware platform can be considered a middleware support which allows the transfer of environmental information from the lowest infrastructure level to a higher level for interpretation and decision. This multi-layered architecture is typical for Cloud computing hierarchy, which allows to place a middleware platform as part of a Sensor-Cloud interface in the PaaS (Platform as a Service) layer.
The aim of the paper is to integrate these three advanced technologies (IoT, CAS and Cloud) to allow the real-time control of a remote automatic system. For reasons which will be thoroughly grounded as proof of the concept has been chosen an application designed to monitor important parameters in precision agriculture.
Section snippets
Related work
Since our work aims at integrating in the field of control systems three technologies (Internet of Things, Context-awareness, and Cloud computing) we chose to analyze for the state of the art recent works (published in the last three years) in each area but related to at least one of the other two. At the same time, we searched for works that refer to precision agriculture applications, and as far as possible in the field of irrigation and fertilization.
Paradoxically, although Precision
The architecture of the industrial system
Our goal was to ensure control of an irrigation system designed for small and medium size farms (1–5 ha) and monitor parameters related to soil properties, crops’ type and essential environment factors in order to achieve improved results in terms of quality, quantity and costs. In this regard, we rely on combining the specific advantages of several modern technologies. Specifically, the classic solution for automatic control of technological installation for irrigation performed by a central
Context-aware middleware platform
The CMU ensures maintaining the prescribed value (with deviations within acceptable limits) of three process variables: pressure, flow and level. However, changing the confinement values can be done automatically, based on the context information.
In the discussed application the values of the four parameters of context are considered: two for soil (soil pH and soil conductivity EC) and two for the external environment (air temperature and wind speed).
Retrieving context information from sensors,
Implementation of Internet of Things application
At the moment, there are many “Internet of Things” development environments made available by large companies offering Internet and communication solutions (Google, Microsoft, IBM etc.). The appropriate choice of an IoT platform is a challenge for everyone. Above all, it must be known that the chosen environment will not represent the user’s desired IoT application itself. Such an environment represents a set of algorithms, data engines, application programming interfaces (API) and wireless
Experimental results
The irrigation system is in operation since 2016 in an Experimental Research Station. Several tests were performed both in simulated and real environment. Different scenarios based on specific recipes of the water and nutrient mixture were performed in order to determine their efficiency. Unfortunately, the context-aware decision procedure cannot be exemplified (otherwise than by presenting program code lines, which would make the presentation excessively difficult). We chose to present only a
Conclusions
The main conclusions derived from the analysis of experimental results of the application which has been utilized as a proof of concept, for controlling and monitoring an irrigation system connected to an IoT platform. For those tasks were elaborated a hardware Command Unit (CU) and a software application running on IoT platform.
The purpose of this paper was to propose a competitive architecture of a context aware system that allow real-time control and monitoring of an industrial process by
References (25)
- et al.
Improving automatic climate control with decision support techniques to minimize disease effects in greenhouse tomatoes
Inf. Process. Agric.
(2017) - et al.
Using Cloud IOT for disease prevention in precision agriculture
Proc. Comput. Sci.
(2018) - et al.
Managing irrigation and fertilization for the sustainable cultivation of greenhouse vegetables
Agric. Water Manag.
(2018) - et al.
Evaluating performances of the first automatic system for paddy irrigation in Europe
Agric. Water Manag.
(2018) - et al.
Internet of Things (IoT) for smart precision agriculture and farming in rural areas
IEEE Internet Things J.
(2018) - et al.
Automatic control for greenhouse farming
Int. J. Eng. Res. Technol.
(2017) - et al.
IoT solutions for precision agriculture
Automation of lettuce seedlings irrigation with sensors deployed in the substrate or at the atmosphere
Scientia Agricola
(2017)- et al.
Multi-agent based context aware information gathering for agriculture using Wireless Multimedia Sensor Networks
Egypt. Inf. J.
(2018) - et al.
Context aware wireless sensor network suitable for precision agriculture
Wirel. Sens. Netw.
(2016)