Cluster-based data aggregation for pest identification in coffee plantations using wireless sensor networks

Identification of Coffee White Stem Borer (CWSB) pest in the Arabica coffee plantation is a huge menace.Cluster-Based Data Aggregation (CBDA) is proposed with the use of Ultrasonic Active Sensors (UAS) for identifying CWSB pests.Clustering scheme is designed using solid-disc clustering for selection of Cluster-Head (CH).Data aggregation with redundancy elimination using Kolmogorov's zero-one law is carried at the CH.The aggregated data are delivered to the BS by establishing the route through the standard AODV protocol for further processing. Display Omitted This paper proposes a Cluster-Based Data Aggregation (CBDA) method for identifying pests in Arabica Coffee plantation using Wireless Sensor Networks (WSNs). Acoustic signals that are generated with biting sound by the pests inside stem are captured by WSN. Information regarding existence of pests is aggregated at Cluster-Head (CH) and is conveyed to base station. CH is selected using five states of each node: i-band, o-band, cluster-head request, idle and cluster-head. CH performs data aggregation with residual energy, time stamp using Kolmogorov's zero-one law to eliminate redundancy. Simulation analysis of CBDA is compared with fast local clustering, energy-efficient reliable data aggregation technique and energy-efficient data aggregation transfer in terms of aggregation ratio, message overhead, control overhead, packet delivery ratio, algorithmic complexity, delay, energy consumption, time-out period and clustering time. The CBDA simulation results outperform compared to the corresponding techniques.