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.
['Roshan Zameer Ahmed', 'Rajashekhar C. Biradar', 'Shilpa Shashikant Chaudhari']
Cluster-based data aggregation for pest identification in coffee plantations using wireless sensor networks