Flying ad hoc networks (FANETs) offer flexible, real-time wireless communication solutions for multi-drone systems by utilizing drones as network routers. However, FANETs’ unique characteristics, including high mobility, unstable network topology, and intermittent connectivity, pose significant challenges in designing efficient and reliable routing protocols. Traditional routing protocols for mobile ad hoc networks often fall short in highly dynamic airborne environments due to excessive control overhead, increased latency, and inefficient route maintenance. To address these issues, this paper proposes an enhanced on-demand predictive (EDP) routing protocol that integrates a neighbor coverage-based predictive flooding mechanism and an adaptive link quality-based route maintenance strategy. The flooding mechanism mitigates directional deafness by using a Kalman filter-based probabilistic forwarding model, while the route maintenance method optimizes path selection based on distance, traffic load, and link lifetime. Simulation results show that EDP significantly improves packet delivery rate, reduces network delay, and lowers overhead compared to benchmarks, making it well-suited for applications in FANETs.

Flying ad hoc networks (FANETs) provide high flexibility and real-time wireless communication solutions for multiunmanned aerial vehicle (UAV) systems by utilizing UAVs as routers. However, conventional routing protocols are inadequate for FANETs due to high mobility and dynamic topology of UAV networks. To address these challenges, this paper proposes an enhanced on-demand predictive (EDP) routing protocol for UAV networks. The EDP protocol incorporates a neighbor-coverage-based predictive flooding mechanism and an adaptive link-quality-based route maintenance method. The flooding mechanism utilizes Kalman filter theory to predict mobility and the route maintenance method selects optimal route by evaluating multiple factors. Simulation results demonstrate that the EDP protocol significantly improves the packet delivery rate while reducing network delay and overhead under varying environments, outperforming benchmark routing protocols for FANETs.

Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy, which have been used in an increasing number of mission scenarios. However, unstable link quality and frequently changing network topology pose significant challenges for adopting existing routing protocols in mobile ad hoc networks (MANETs). In this paper, we propose a time-slotted on-demand predictive (TSDP) routing protocol designed specifically for UAV networks. The TSDP protocol introduces a novel approach to route selection by incorporating multiple criteria, including delivery ratio, adjacent degree, and mobility prediction factor, to ensure reliable and efficient data transmission. By addressing high latency in route discovery and excessive broadcast overhead, TSDP employs a time-slotted communication mechanism that reduces packet drop rates and enhances route stability. Simulation results demonstrate that TSDP consistently outperforms ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) protocols in terms of throughput, packet delivery ratio, end-to-end delay, and overhead, particularly in highly dynamic network environments.

In this paper, we consider a wireless network enabled by multiple unmanned aerial vehicles (UAVs), which observe physical processes and transmit status updates to a monitor node over an error-prone communication channel. The communication scenarios are classified into two modes based on monitor types: UAV-to-UAV (U2U) and UAV-to-network (U2N) scenarios. Specifically, the U2N scenario is capable of covering a larger area compared to U2U scenario with a high signal-to-noise ratio (SNR). However, the U2U scenario constructs communication links more quickly, resulting in lower latency and higher rates. To evaluate the timeliness of the UAV-aided network, we utilize the age of information (AoI) as a fundamental indicator. AoI measures the time delay between the most recent data generation and the current moment. To compensate for the error-prone channel, this study employs a combination of hybrid automatic repeat request (HARQ) protocols, which include fixed-redundancy HARQ (FR-HARQ) and infinite incremental redundancy HARQ (IIR-HARQ) protocols. Furthermore, the average and peak Age of Information (AAoI and PAoI) of UAV-aided networks are derived for both U2U and U2N scenarios, and the theoretical expressions agree with simulation results. Additionally, FR-HARQ performs a better time performance than IIR-HARQ, and such observation van be verified through simulations.