Cross-Layer Protocol Design and Performance Evaluation of LoRa Ad Hoc Networks for Heterogeneous Traffic

Abstract

To address the severe coverage blind spots and concurrent collision bottlenecks faced by LoRa networks in dense deployments and complex three-dimensional (3D) occlusion environments, this paper proposes a distributed cross-layer protocol framework for LoRa ad hoc networks supporting heterogeneous traffic. To break through the limitations of a single star architecture, this framework constructs a 3D penetration loss model at the physical layer and designs a distributed relay deployment algorithm based on hybrid simulated annealing, achieving blind-spot-free connectivity in complex spaces. At the MAC layer, a non-preemptive priority access mechanism based on symbol energy detection is introduced. Through differentiated backoff windows with time-domain isolation, it precisely guarantees the quality of service (QoS) requirements of heterogeneous traffic and significantly suppresses concurrent collisions. At the network layer, the CAM-AODV routing algorithm is proposed, which integrates hop count, link quality, MAC queue congestion, and nodal residual energy to achieve dynamic traffic diversion and network-wide energy balancing under bursty high loads. Simulation results demonstrate that this cross-layer framework effectively breaks the traditional network capacity bottlenecks. In a large-scale, high-density scenario with 300 nodes, CAM-AODV reduces the average end-to-end delay by 19.46% compared to the traditional AODV. Under high-concurrent loads, the packet delivery ratio (PDR) of the proposed framework improves by 16.32% over the traditional protocol, while the system delay is reduced by 13.66%. Furthermore, under the two aforementioned evaluation scenarios, the Energy Balancing Index (EBI) is significantly improved by 11.13% and 10.57%, respectively, compared to the traditional protocol. This study provides an efficient joint optimization scheme for building high-capacity, wide-coverage, and long-lifespan complex Internet of Things (IoT) networks.