Data aggregation for pest identification in coffee plantations using WSN: A hybrid model

Coffee production stands important asset in developing the economy of various countries. The serious effect to the coffee plantation is caused by the pest named Coffee White Stem Borer (CWSB). Removal of the CWSB pest by identifying its existence using fixed Wireless Sensor Networks (WSNs) results in high-yield productivity of coffee. For the enhancement in a lifetime of the WSN, optimum energy consumption is required that is a challenging task. More of the energy is consumed for data transmission and data computation. In the past, many data aggregation techniques for WSN have been proposed to lower the energy consumption used for forwarding the data from the sensor node to the Base Station (BS). In this paper, we propose a technique named Cluster-Based Data Aggregation (CBDA) for transmitting the information of CWSB existence in the Coffee Arabica plants, from the sensor nodes to the BS. Ultrasonic Active Sensors (UAS) are made use off to detect these pests in the coffee field. The UAS are placed according to their transmission range. A WSN hybrid model is designed that consists of the time-driven and event-driven model for transmitting the information procured. The proposed technique involves three phases: WSN initialization with clustering, data aggregation and routing of the aggregated data to its destination. For each sensor node in the WSN, the clustering technique adopted uses, i-band and o-band range for selecting cluster members and assigns one particular state (i-band, o-band, request for cluster-head, idle, cluster-head). To check the performance of the proposed technique simulation experiments is conducted. The result shows that the technique is effective in terms of energy consumption and aggregation ratio.