A Packet Scheduling Technique Based on (m, k)-Firm Guarantees for Differential Quality of Service in Tactical Ad-Hoc Networks

Abstract

Tactical ad-hoc networks must provide differentiated Quality of Service (QoS) for heterogeneous traffic, yet classic AODV's single-queue FIFO forwarding cannot prevent priority inversion or starvation under load. We propose a packet scheduling scheme that enforces stream-level $(m, k)$ -firm constraints and uses Distance-Based Priority (DBP) to raise the urgency of flows that are close to violating their $(m, k)$ requirement, with EDF then FIFO tie-breaking at dequeue time. The scheduler updates per-flow $k$ -sequences online and selects the head-of-line packet from the stream with the smallest DBP distance. We evaluate the scheme in ns-3 against RL-AODV and FP-AODV under three tactical scenarios (Mountainous Terrain, Urban CQB, and Landing Operation) using AODV and eight traffic classes. Across all scenarios, FP-AODV maximizes PDR for top classes but severely starves low classes, while RL-AODV yields uniform but mediocre service. In contrast, the proposed $(m, k)$ -firm scheduler preserves high PDR and low end-to-end delay for the highest priorities while preventing starvation and lifting the PDR floor for low priorities, particularly in NLOS and many-to-few aggregation topologies. Throughput trends mirror PDR, and end-penalized delay remains bounded for lower classes. The scheduler operates with $O(N\!\cdot \!k)$ complexity for $N$ streams and window size $k$ , making it practical for resource-constrained nodes (e.g., $N{=}100$ , $k{=}15$ ). Deadlines are assigned per traffic class for consistent meet/miss evaluation across hops, and each flow is tracked independently through metadata. In addition, it incorporates an adaptive routing-level load balancing mechanism that refines AODV's path selection in real time using delay, queue, and PDR metrics, while maintaining full protocol compatibility. These results demonstrate that $(m, k)$ -firm scheduling provides a tunable balance between differentiated service, fairness, and adaptability for tactical MANETs.