Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

TL;DR

This paper introduces DAACA, a family of ant colony algorithms designed for energy-efficient data aggregation in wireless sensor networks, improving network lifetime and transmission success.

Contribution

The paper proposes four novel pheromone adjustment strategies within DAACA that enhance energy efficiency and network longevity compared to existing algorithms.

Findings

DAACA outperforms other algorithms in energy efficiency and network lifetime

DAACA maintains higher average node degree and success ratio of one-hop transmissions

The algorithms demonstrate robustness, fault tolerance, and scalability

Abstract

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.