A Graph-Theoretic Model for Energy-Aware Routing Optimization in MANETs Using Availability Zones

Abstract

Mobile Ad Hoc Networks (MANETs) are characterized by dynamic topologies, limited energy resources, and distributed communication architectures, which pose significant challenges for efficient and reliable routing. Energy conservation remains a critical factor, as the premature depletion of nodes can severely compromise network connectivity and overall performance. This study presents a novel graph-theoretic model for energy-aware routing optimization in MANETs, leveraging the concept of availability zones to systematically manage node participation in packet forwarding. The model represents nodes as graph vertices with dynamic attributes including residual energy, operational availability, and connectivity status, while edges are weighted based on energy costs and availability probabilities. By integrating these factors into a multi-objective optimization framework, the routing algorithm selectively identifies paths that minimize cumulative energy consumption while ensuring robust connectivity, even under conditions of high mobility and node failure.
The proposed approach incorporates heuristic strategies inspired by combinatorial optimization and evolutionary computation, allowing stochastic exploration of alternative paths, which enhances network resilience and load balancing. Extensive simulations across diverse network sizes and mobility scenarios demonstrate significant improvements in key performance metrics, including a 23–27% reduction in total energy consumption, a 23% increase in network lifetime, and markedly lower energy variance among nodes. These results underscore the model’s ability to distribute traffic loads efficiently, prolong network operation, and maintain connectivity under dynamic conditions. Comparative analyses with classical shortest-path and greedy heuristics reveal that the availability-zone-based method consistently outperforms existing strategies in terms of energy efficiency, stability, and fault tolerance.
The findings have broad implications for industrial automation, wireless sensor networks, and robotics, where energy-aware decision-making is essential for continuous operation and reliable system performance. Additionally, the study highlights the relevance of cross-domain optimization principles, drawing parallels between software test-suite reduction methodologies and network routing, demonstrating that selective inclusion and probabilistic exploration can yield optimal resource utilization. This research establishes a comprehensive framework for future investigations into adaptive, energy-efficient routing in MANETs and provides a foundation for integrating predictive mobility models and real-time energy monitoring in next-generation network management systems.