Energy-Aware Task Scheduling in Heterogeneous Mobile Ad-Hoc Networks Using Ant Colony Optimization

International Journal of Computer Science and Technology | Vol. 7, No. 9, September 2005 | pp. 65–72

DOI: 10.46882/2005/IJCST/070912

Research Article

Title: Energy-Aware Task Scheduling in Heterogeneous Mobile Ad-Hoc Networks Using Ant Colony Optimization

Names of Authors: Fatoumata Diallo¹, Diego Silva²

Authors’ Affiliations: ¹Département de Génie Informatique, École Nationale d'Ingénieurs de Bamako, Bamako, Mali; ²Departamento de Engenharia de Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

Abstract:
Mobile Ad-hoc Networks (MANETs) consist of battery-powered terminal devices that are resource-constrained and highly prone to premature power exhaustion under intense distributed computing workloads. This paper details a novel energy-aware task scheduling architecture that optimizes workload allocation across heterogeneous mobile groups using a modified Ant Colony Optimization (ACO) heuristic. The scheduling algorithm monitors residual battery energy vectors alongside wireless link quality metrics to assign computational sub-tasks dynamically without over-stressing critical routing nodes. Network modeling configurations with node densities varying between 50 and 200 units showed that the energy-aware ACO model extended the overall operational lifespan of the network topology by 26.8% compared to traditional minimum-laxity scheduling methods. The computational throughput rose by 14.3%, and the packet delivery ratio remained resilient above 96.5% even during rapid spatial changes in the network structure, establishing a durable paradigm for ad-hoc grid computations.

Keywords: Mobile Ad-Hoc Networks, Task Scheduling, Ant Colony Optimization, Energy Harvesting, Network Lifetime

Manuscript Timeline: Received June 04, 2005; Revised July 18, 2005; Accepted August 12, 2005; Published September 23, 2005

Citation: Diallo, F., & Silva, D. (2005). Energy-Aware Task Scheduling in Heterogeneous Mobile Ad-Hoc Networks Using Ant Colony Optimization. International Journal of Computer Science and Technology, 7(9), 65–72.

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