Distributed data reduction with decision-making for IoT-based smart farming irrigation systems

The more crucial process and one of the world's major consumers of water in agriculture is irrigation, which has been growing as a result of population growth and the resulting rise in demand for food. The irrigation system is one of the most important components of a system of agriculture. The crop's productivity will be directly influenced by the efficiency and applicability of the irrigation system. Water scarcity is one of the greatest challenges for an irrigation system. The efficient use of land, water, and energy resources depends on Internet of Things (IoT)-based smart agriculture systems. In this article, a distributed data reduction and decision-making (DiDaReD) approach for IoT-based smart farming irrigation systems is proposed. The DiDaReD approach is implemented on two levels: sensor devices and edge gateway. At the sensor device level, we implemented a lightweight scoring method for removing the redundant collected soil moisture readings before sending them to the edge gateway. At the edge of the gateway, a voting technique is applied to the scores of received readings from sensor devices to produce a decision regarding irrigation of the monitored farming field according to the soil moisture state of this field. Finally, since the DiDaReD approach is periodic and works in real-time, we implement a decision reduction algorithm to prevent sending the same decision notifications to the actuator, thus saving the energy of the IoT network. Several experiments are implemented using real sensed data from the farming field, and the conducted results show that the proposed DiDaReD outperforms other methods in terms of data reduction ratio, number of sent readings, energy consumption, network lifetime, data integrity, decisions, and total energy consumed by the edge-gateway node.