A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms

Abstract Prolonging wireless sensor networks (WSNs) longevity and minimizing energy expenses represent the primary considerations in transmitting sensor data. Sensor nodes typically function on restricted battery power, so overall energy consumption and network lifespan become crucial concerns. Data aggregation is key to diminishing bottlenecks, burdens, and energy usage, extending the lifetime of WSNs. This paper introduces a new tree-based data aggregation technique, TDAC, which utilizes two popular metaheuristic algorithms, Ant Colony Optimization (ACO) and Cuckoo Search (CS), to deal with the NP-hardness of the data aggregation problem. A primary constraint of the ACO algorithm is the sluggish local search procedure. To address this shortcoming, the TDAC algorithm incorporates the CS algorithm to optimize the local search of the ACO algorithm, thereby enhancing the quality of solutions obtained. A series of comprehensive tests were carried out using the MATLAB simulator to assess the effectiveness of TDAC in comparison with previous techniques. The results indicate that TDAC outperforms benchmark techniques regarding network delay, longevity, energy consumption, and overhead. The integration of ACO and CS in TDAC greatly enhances data aggregation efficiency in WSNs.