Task-driven MAC protocol for wireless ad hoc networks: architecture and reconfiguration algorithm

Wireless ad hoc networks are widely deployed in emergency response, disaster rescue, and tactical networking scenarios due to their inherent advantages in flexibility, scalability, and dynamic adaptability. An appropriate MAC (multiple access control) protocol can significantly enhance the performance of wireless ad hoc networks. However, existing MAC protocols face a fundamental limitation: no single protocol can achieve optimal performance across all network conditions. Traditional MAC protocols employ hardware-coupled designs, which inherently lack adaptability to dynamic network environments and diverse task requirements. This study seeks to overcome the adaptability constraints of traditional MAC protocols in dynamic wireless ad hoc network environments. We proposed a software-defined system architecture to guide the complete MAC protocol reconfiguration process, aiming to achieve multi-dimensional performance optimization under dynamic network characteristics and evolving task requirements.

This study designed an integrated system architecture combining load monitoring, intelligent decision-making, and dynamic reconfiguration to achieve closed-loop dynamic reconfiguration of MAC protocols for practical applications. This work tackled three key unresolved issues in prior research by introducing three novel solutions.

Traditional MAC protocols are tightly coupled with hardware devices and suffer from inherent inflexibility due to their monolithic design, making them unable to adapt to dynamic network environments and heterogeneous service demands. To address the rigidity of conventional MAC protocols, we proposed a software-defined MAC protocol framework. By introducing a three-layer reconfiguration mechanism (parameter-level, algorithmic, and protocol-level adaptation), our solution enabled dynamic MAC protocol configuration. Building upon existing software-defined MAC protocol techniques, we achieved agile deployment of diverse protocols. Furthermore, we designed a full-stack software-defined system along with comprehensive supporting algorithms and presented a closed-loop software architecture for protocol reconfiguration.

Current research on adaptive MAC protocols predominantly focuses on optimizing individual performance metrics, failing to address the inherent trade-offs between different network performance aspects. Moreover, existing approaches neglect the critical mapping relationship between practical network requirements and protocol performance. Since the adaptability of MAC protocols to dynamic network characteristics and mission-specific communication demands fundamentally determines core performance metrics—including information exchange effectiveness, reliability, and timeliness—this study addresses the limitation of single-dimensional optimization by establishing a task-requirement-performance mapping framework. The proposed methodology enables multi-dimensional performance optimization while supporting adaptive protocol reconfiguration that holistically balances diverse network performance criteria.

Conventional Poisson-process-based traffic assumptions fail to adequately characterize network dynamics or effectively guide protocol reconfiguration, while practical network load parameters are often unobtainable in real-world deployments. To overcome these limitations of existing load modeling approaches, we innovatively propose a distributed observable load model that reconstructs network traffic through three key dimensions: node population, activity intensity, and service characteristics. The proposed model not only establishes the mapping relationship between cluster task execution and communication patterns, but also enables refined characterization of MAC protocol performance attributes.

Simulation results derived from a distributed load observation model reveal the performance characteristics of candidate protocols. It can provide instructive support for the adaptive MAC protocol reconfiguration in ad hoc networks with specific tasks. We create a comprehensive dataset to train an XGBoost classification model with generalization capabilities. The proposed reconfiguration process simultaneously considers multi-dimensional protocol performance and diverse task requirements, enabling optimal MAC protocol selection under given constraints.

This proposed model can accurately describe the three-dimensional characteristics of network load and effectively illustrate the impact of different characteristics on the performance of MAC protocols. The proposed reconfiguration algorithm intelligently adapts MAC protocol selection in response to dynamic network conditions and application-specific requirements, thereby optimizing among three critical performance metrics: throughput, packet loss rate, and delay. This closed-loop architecture establishes a robust framework for deployable solutions in practical wireless networking scenarios.