Achieving an efficient and collision-free path remains a critical challenge for unmanned aerial vehicles (UAVs) due to their high mobility, dynamic environments, and energy constraints. This paper proposes a novel hybrid path-planning model that integrates Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to address these challenges. PSO is used to identify energy-efficient, destination-aware paths, while GWO enhances obstacle avoidance and threat adaptability in real time. The model is evaluated using metrics such as path optimality, energy consumption, robustness, time efficiency, and collision avoidance. Simulation results in MATLAB demonstrate that the proposed PSO-GWO model outperforms existing methods, producing shorter, safer, and more cost-efficient paths. Additionally, statistical validation through ANOVA confirms the model’s superiority in optimizing key performance indicators. The approach also supports improved quality of service (QoS) in UAV communication networks.

The conservation of energy in flying ad-hoc networks (FANETs) is a crucial issue that needs to be addressed to make clustering efficient and effective for these networks. However, selecting energy-efficient cluster heads (CHs) is vital for optimal clustering. Improperly chosen CHs can lead to excessive energy consumption during data transmission. It reduces network lifetime and overall performance. To address these challenges, we have developed a new algorithm for selecting the cluster head for UAVs using grey wolf optimization called CH-GWO (Cluster Head through Grey Wolf Optimization). We have proposed an objective function and weight parameters to facilitate efficient cluster head selection and formation. The proposed CH-GWO protocol is extensively analyzed in this research using the MATLAB 2021b environment for simulation. It enables us to evaluate its performance against other well-known clustering algorithms, namely K-means, low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), distributed energy-efficient clustering (DEEC), enhanced energy-efficient unequal clustering (EEUC), and stable election protocol (SEP). The results demonstrate that the CH-GWO algorithm significantly enhances the network lifetime by 20%, 18.9%, 14.8%, 12.5%, 7.8%, and 3.8% compared to K-means, LEACH, HEED, DEEC, EEUC, and SEP, respectively. As a result of the proposed method, the average energy consumption of the system is reduced by 37.5%, 33.3%, 29.78%, 19.7%, 16.6%, and 6.25% compared to the conventional algorithms. Based on the experimental data obtained through simulations, the CH-GWO algorithm outperforms K-means, LEACH, HEED, DEEC, EEUC, and SEP in various performance metrics, including network lifetime, packet delivery ratio, throughput, bit error rate (BER), time analysis, and end-to-end delay. These findings establish the effectiveness and superiority of the CH-GWO algorithm for cluster head selection in FANETs.

Achieving an exact localization is a complex and essential issue for autonomous aerial vehicles (AAVs) due to their three-directional high-speed mobility. Identifying the accurate flying position of AAVs for resource management and task reallocation is still challenging. In these scenarios, the position of the AAVs must be identifiable in a timely and precise manner. A bioinspired metaheuristic hybrid model was proposed to overcome the shortcomings of inaccurate altitude and improve the AAVs' flying positional coordinates. The proposed model incorporates the particle swarm optimization (PSO) with a fuzzy logic technique. PSO is used to find the optimal or near-optimal positions for the AAVs by minimizing localization error across a wide search space. Once the PSO has determined a feasible solution, fuzzy logic is applied for fine tuning the position based on real-time environmental factors (e.g., signal strength, sensor data, or global positioning system errors). This combination achieved both global efficiency (via PSO) and local precision (via fuzzy logic), ensuring robust localization even in noisy or dynamic conditions during AAVs flight operations. The model, compared to the state-of-the-art model, shows more accuracy in AAV localization with real-time operational data.

Communication among drones and diverse devices, known as D2X (Drone-to-Everything) communication, faces challenges within traditional drone setups, including routing complexities, interference issues, and susceptibility to single points of failure. These shortcomings hinder network scalability and overall performance. This paper introduces the decentralized cooperative multi-layer drone to everything (DCM-D2X) architecture with integrated hybrid bioinspired grey wolf optimization-waypoint tracking (GWO-WPT) mobility model. DCM-D2X architecture incorporates GWO-WPT mobility patterns that are explicitly integrated for decentralized cooperative multi-layer for effective communication in D2X environments. This model is purposefully integrated and designed to enhance communication efficacy in D2X environments by mitigating single points of failure, optimizing resource allocation, managing interference, and improving cooperative routing. Extensive simulations have been conducted using the optimized link state routing protocol (OLSR) within the network simulator (NS2) to evaluate the proposed architecture and mobility model. Performance metrics, including network diameter, average clustering coefficient, energy consumption, delay, throughput, and packet delivery ratio (PDR), have been assessed. Compared to the latest literature, the proposed model demonstrated an average percentage difference of 19.195%reduction in routing delay, 27.335%reduction in energy consumption, 22.18%increase in packet delivery ratio, 21.25%increase in throughput, and reducing interference up to 23%in high mobility scenarios. The DCM-D2X model demonstrates robustness against node failures in large-scale drone networks, significantly improving interference mitigation, routing efficiency, and network connectivity. These advancements increase D2X communication network performance.

Routing data between unmanned aerial vehicles (UAVs) involves identifying node locations, analyzing residual energy levels, evaluating temporal throughput and packet delivery performance, and identifying other network and node parameters. It assists in forming quality of service (QoS) aware routes. Existing routing models require large data samples to find the optimized path or are highly complex, increasing their computational requirements. Low-complexity models showcase low-performance routing QoS when deployed on large-scale networks. To solve these limitations, a hybrid bioinspired model is proposed for fault-aware UAV routing that uses destination awareness with a fan-shaped clustering process (BMUDF). The model initially collects data from different UAV nodes and their node-level and network-level constraints. These parameters are processed through the particle swarm optimization (PSO) model, which enforces fan-shaped clustering (FSC) for effective routing operations. Our scheme uses the PSO model to identify the initial routing paths cascaded with a genetic algorithm (GA) based destination-aware routing model. These routing paths are evaluated through the QoS matrices like maximum temporal throughput, packet delivery ratio (PDR), delay, and energy consumption. A grey wolf optimization (GWO) model further scrutinizes this routing performance, integrating fault tolerance and route optimization during continuous operations. The GWO model evaluates a trust-based fitness function, which helps to identify faulty nodes, and reconfigures the network with non-faulty nodes to improve its QoS performance under node failures and faults. Integrating the bioinspired models into the proposed system maximizes performance under different network scenarios. This performance compares and validated with an analysis of variance (ANOVA) test with various state-of-the-art models. It is observed that the proposed model showcased an 8.3% lower routing delay, 5.9% lower energy consumption, 1.5% higher packet delivery ratio, and 9.1% higher throughput, which makes it useful for a wide variety of real-time UAV routing scenarios.

Despite the power constraints, UAVs (Unmanned aerial vehicles) have an inherent advantage of lower air traffic, making them an attractive alternative to high-speed transportation and logistics. Many algorithmic models are used for empirical analysis based on network architecture, data forwarding, and comprehensive performance variation regarding routing delay, energy efficiency, throughput, network overheads, scalability, bandwidth, link failure probability, etc. Due to such a wide variation in protocol availability, and respective performance measures, it is difficult for researchers and network designers to select the best possible models suited for their network application. Moreover, this wide variation increases network design time and cost-to-market, which affects UAV network viability. Thus, there is a need to simplify this process of routing model selection. This motivates us to frame this survey article. A comprehensive survey of recently proposed UAV routing models is proposed. This survey includes a description of reviewed models and their nuances, advantages, limitations, and future research possibilities. Upon referring to this survey, readers could contemplate the characteristics of respective models and identify improvement areas in each. Based on observation, researchers can select the best-suited routing models of UAVs for their applications. This review is accompanied by an in-depth statistical analysis of these models and their comparison concerning computational complexity, throughput, energy efficiency, end-to-end delay, and routing efficiency. It will assist researchers and UAV network designers in selecting the most optimum context-specific models for their network deployments, thereby lowering network design time and cost of deployment.