Commercial Unmanned Aerial Vehicles Research Articles

Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV

In contemporary logistics, the deployment of fixed-wing unmanned aerial vehicles (UAVs) as a transportation platform is experiencing rapid advancements, garnering substantial application within numerous logistic operations with pronounced efficacies. There are notable impediments to the utilization of commercial logistic-oriented fixed-wing UAVs, including elevated procurement and maintenance costs, extensive maintenance intervals, and unsuitability for small-volume, low-altitude transport tasks. These factors collectively exacerbate the risk associated with enterprise procurement and elevate the cost–benefit ratio. This study introduces the design and fabrication of a cost-efficient UAV for logistic delivery purposes, constructed primarily from cost-effective wood materials. This UAV is engineered to ferry payloads of up to 1000 g across a predefined aerial route at an altitude of 40 m. Upon reaching the designated location, the UAV is programmed to initiate the identification of the drop zone, thereafter descending to facilitate the release of the cargo. To mitigate the impact force during the landing phase, the payload was encapsulated within a sponge-damping layer, thereby preserving the integrity of the transported items. The empirical findings from outdoor delivery trials underscore the UAV’s ability to precisely execute payload drops at the targeted locations, confirming its potential to fulfill the logistical requirements for the transportation and delivery of small-volume items in a cost-effective, low-altitude framework. This investigation contributes to the burgeoning discourse on leveraging ultra-low-cost UAVs in logistics, offering a feasible solution to the challenges of cost and efficiency in UAV-operated delivery systems.

Development of an algorithm for detecting commercial unmanned aerial vehicles using machine learning methods

Development of an algorithm for detecting commercial unmanned aerial vehicles using machine learning methods

Development and validation of graphics‐based digital twin framework for UAV‐aided post‐earthquake inspection of high‐rise buildings

SummaryTraditional post‐earthquake inspection of civil infrastructure is conducted manually, taking a considerable amount of time and often putting inspectors in harm's way. This problem is exacerbated in modern cities, where millions of people can be left homeless until their residences are deemed safe to reinhabit. Image collection enabled by commercial unmanned aerial vehicles (UAVs) combined with computer vision‐based techniques has provided an alternative with high potential for rapid post‐earthquake inspection. However, the extracted images of the damage alone are inadequate to evaluate the system‐level safety condition of a structure. The quality of the visual information also heavily relies on the effectiveness of the UAV inspection scheme which is susceptible to environmental uncertainties. To this end, a graphics‐based digital twin (GBDT) framework is developed for UAV‐aided post‐earthquake inspection of high‐rise buildings and validated using a high‐rise building in Guangzhou, China. The GBDT is comprised of a finite element (FE) model and a photorealistic computer graphics (CG) model, with the latter being informed by the former, jointly providing as a comprehensive virtual representation of the structure so that every step of the post‐earthquake inspection procedure can be planned and evaluated virtually. First, to avoid the cumbersome nature of constructing the graphical representation of the numerous components in high‐rise buildings, the CG model in the GBDT is created by automatically importing structural components from the FE model and adding nonstructural components according to the dimensions of the as‐built structure. This fast modeling process as well as the accuracy of the virtual presentation are validated by point cloud comparisons between the CG model and the as‐built structure. Subsequently, the GBDT is used to showcase the evaluation of UAV flight schemes for post‐earthquake inspection of high‐rise buildings. To shorten flight time and place more emphasis on potential damage, FE analysis is conducted to determine the earthquake‐induced damage locations. Consistent damage hotspots are then marked on the CG model, along with restrictions from the real environment such as obstacles, weak satellite signal, wind speed, and lighting conditions considered in the synthetic environment. Finally, applying the synthetic environment as the testbed, three UAV‐aided inspection schemes are implemented virtually and the best UAV flight scheme is determined for the assumed field inspection. This example demonstrates the flexibility of the GBDT in representing the real‐world structure and environmental conditions and its efficacy in assisting decision making for rapid and effective structural inspection in the aftermath of an earthquake.

Automated and repeated aerial observations of GPS‐collared animals using UAVs and open‐source electronics

AbstractTelemetry has enabled ecologists to link animal movement trajectories and environmental features at a fine spatiotemporal resolution; however, the effects of social interactions on individual choice within large mobile groups remain largely unknown. Estimating the effect of social interaction in the wild remains challenging because existing long‐term tracking tools such as GPS collars focus on the movements of a single individual and cannot observe the behavior of other individuals within the group. The progression of socially informed movement models requires measuring simultaneous trajectories of many individuals at once, as well as the instantaneous social cues to which individuals may be responding. The availability of low‐flying unmanned aerial vehicles (UAVs) and low‐cost open‐source electronics presents a promising opportunity to collect fine‐scale data on social interactions in order to advance our understanding of collective behavior. Here, we present a tracking system that enables the repeated localization and observation of a collared individual and its near neighbors using nadir video footage collected from a commercial UAV. We make use of open‐source electronics combined with the UAV's in‐built functionality that allows it to follow a stream of GPS locations to create an automated system that can follow a specific individual without user control. We demonstrate the tracking systems' performance by studying the group movements of a herd of Exmoor ponies (Equus ferus caballus), and as a proof of concept, we examine the position of the focal individual (collared animal) in relation to the center of the video frame. We also collect information about the focal individual's nearest neighbors. The automated animal observation tool is effective at consistently keeping the focal individual close to the center of the video frame, offering a new dimension to existing remote telemetry tools. For instance, the repeated observation of the same individual in different physiological states, seasons, and demographic groups potentially opens new avenues in collective movement ecology research. By making our design, software, and firmware freely available, we aim to encourage continuous improvements to collective behavior research and to facilitate replicable approaches across other species and ecosystems.

Genetic-Algorithm-Aided Deep Reinforcement Learning for Multi-Agent Drone Delivery

The popularity of commercial unmanned aerial vehicles has drawn great attention from the e-commerce industry due to their suitability for last-mile delivery. However, the organization of multiple aerial vehicles efficiently for delivery within limitations and uncertainties is still a problem. The main challenge of planning is scalability, since the planning space grows exponentially to the number of agents, and it is not efficient to let human-level supervisors structure the problem for large-scale settings. Algorithms based on Deep Q-Networks had unprecedented success in solving decision-making problems. Extension of these algorithms to multi-agent problems is limited due to scalability issues. This work proposes an approach that improves the performance of Deep Q-Networks on multi-agent delivery by drone problems by utilizing state decompositions for lowering the problem complexity, Curriculum Learning for handling the exploration complexity, and Genetic Algorithms for searching efficient packet-drone matching across the combinatorial solution space. The performance of the proposed method is shown in a multi-agent delivery by drone problem that has 10 agents and ≈1077 state–action pairs. Comparative simulation results are provided to demonstrate the merit of the proposed method. The proposed Genetic-Algorithm-aided multi-agent DRL outperformed the rest in terms of scalability and convergent behavior.

Data Acquisition Tree Height and Areal Parameters of Mangrove Vegetation Using UAV

Unmanned aerial vehicles (UAVs) can be used to assess the biophysical parameters of mangroves (particularly tree height and area). UAVs can produce high-resolution imagery, in this study UAV imagery was used to calculate tree height distribution and mangrove area. UAV imagery was taken using a commercial UAV with a planned flight path. The obtained UAV images are then compiled based on coordinate information and z-elevation values, and then 3D modelling is created from the point cloud. The 3D model will be detailed by a dense point cloud. The surface area and texture are obtained from the 3D mesh. Finally, a high-resolution DSM can be generated. DSM is the elevation of the overall surface including vegetation, buildings, and others, while DTM is the elevation data of the ground surface, so the next process is to separate bare ground and above-ground objects. Tree height is obtained from the intersection of DSM and DTM elevation data. The area of mangrove vegetation was obtained using the on-screen digitisation method. Based on the results of the analysis conducted by comparing tree height data from UAV images and in-situ measurement tree height data has a difference with a range of 0.24 m to 0.94 m, with an average of 0.49m. This remote sensing survey research using commercial UAVs can be concluded as one of the solutions to acquire the parameters of tree area and height distribution with good accuracy.

A Benchmarking of Commercial Small Fixed-Wing Electric UAVs and RGB Cameras for Photogrammetry Monitoring in Intertidal Multi-Regions

Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.

Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Unmanned Aerial Vehicles (UAVs) are getting more and more uses in recent times. However, low-cost commercial UAVs may not possess enough computational power to run state of the art algorithms in order to perform certain tasks, negatively affecting performance. Remote computational systems, where heavy processing tasks can be offloaded emerge as a solution. However, they introduce latency, which can be undesirable for real-time tasks. Furthermore, if the task is simple, using a local algorithm with worse performance may be acceptable to avoid latency. As such, a method to decide which algorithm to use is of great importance. We consider the use case of computer vision tasks, in particular detection and tracking. In these tasks, image properties such as brightness, contrast, motion blur and clutter affect the algorithm performance. Our proposed methods use a combination of neural networks and kernel machines to estimate the performance of the algorithm given the input image. An appropriate cost function is then used to identify the best algorithm for the task given the input image, the task deadline, and the uncertainty in the variables of the algorithm, in particular computing time and error rate. Results show that our method matches or outperforms similar state of the art methods, complying with time restrictions while delivering increased performance.

High-Power Microwave Pulse-Induced Failure on Unmanned Aerial Vehicle System

With the wide application of unmanned aerial vehicles (UAVs), more attention has been paid to the safe operation of UAVs in complex electromagnetic environments. In this article, we investigate the impact of high-power microwave (HPM) pulses on three types of typical commercial UAVs. We established the HPM irradiation experiment system first and obtained the responses of UAVs under the HPM attack with different electric field strengths. The flight logs and the damage analysis reports indicate that the damaged components of the type I UAV and the type II UAV are the rotor motor and the electronic speed controller. The experiment results prove that L-band narrowband HPM microwave has strong backdoor coupling effects on UAVs. The damage mechanism of UAVs by the HPM is studied by simulation. The simulation results show that an HPM pulse will cause a high-voltage pulse coupled to the cable connecting the electronic speed controller and the rotor motor, which caused the electronic speed controller to send out a false signal that caused the rotor motor speed to rise sharply, eventually making the rotor motor to burn out. Experiment results show that strengthening electromagnetic protection on the cable can effectively prevent HPM damage to UAVs. The simulation results match the experiment results and the damage analysis reports. Finally, some suggestions are provided to improve the UAV’s anti-HPM capability.

UAS-LC-GNSS: Precision Surveying with a Low-Cost GNSS System for Commercial Drones

INGV (National Institute of Geophysics and Volcanology, Italy) is one of the institutions that studies and monitors the geophysical phenomena (earthquakes, volcanic eruptions, landslides, etc.) that occur on Earth. During these events, it is essential to carry out a large, detailed and fast map of the affected areas.If we think of the difficulties encountered during the mapping of the fault sources for the 2016 earthquake in central Italy, we can understand how the UAS (Unmanned Aircraft System) can be a valid “low-cost” alternative to the traditional methods of surveys.These devices, thanks to precision instrumentation such as GNSS (Global Navigation Satellite Systems) receivers and IMU (Inertial Measurement Unit) control units, allow a detailed reconstruction of the investigated areas, especially for small-scale analysis applications. These aircraft are based on multiple technologies and show great investigative capabilities, therefore they must be considered as complete systems.Starting from these concepts, we have developed a low-cost RTK/PPK (Real Time Kinematic/Post Processing Kinematic) GNSS survey system on “commercial” UAVs (Unmanned Aerial Vehicles), i.e. professional drones that are not created to be modified.We have demonstrated how the integration of a GNSS RTK/PPK module on commercial UAVs makes the system efficient for the reconstruction of a highly detailed and precise DEM (Digital Elevation Model), without using GCP (Ground Control Point), allowing to make precision measurements in areas that are difficult to explore and investigate.

Computer Vision Techniques for Military Surveillance Drones

Commercial unmanned aerial vehicles (UAVs), also referred to as drones, have proliferated recently, raising concerns about security threats and the need for effective countermeasures. To address these concerns, various technologies have been explored, including radar, acoustics, and RF signal analysis. However, computer vision, particularly deep learning approaches, has emerged as a robust and widely used method for autonomous drone identification. The goal of this research is to create an autonomous drone identification and surveillance system that makes use of a mix of static wide-angle cameras and a lower-angle camera placed on a revolving turret. To optimize memory and processing time, we suggested a novel multi-frame DL identification model. In this approach, the frames captured by the turret's magnified camera are stacked on top of the frames from the wide-angle still camera. Utilizing this technique, we can create an efficient pipeline that conducts initial identification of small-sized aerial invaders on the primary picture plane and identification on the expanded image plane at the same time. This approach significantly reduces the computational burden associated with detection algorithms, making it more resource-efficient. Furthermore, we present the complete system architecture, which includes DL classification frameworks, tracking algorithms, and other essential components. By integrating these elements, we create a comprehensive solution for drone identification and tracking. The system leverages the power of deep learning to accurately classify and track drones in real-time, enabling prompt response and mitigating potential security threats. Overall, this research offers a novel and effective approach to autonomously identify and track drones using computer vision and deep learning techniques. By combining static and dynamic camera perspectives and employing a multi-frame detection method, we provide a resource-efficient solution for drone identification. This work contributes to the ongoing efforts in enhancing security measures against potential drone-related risks

Software system for laser targeting dropped ammunition

For last few years usage of small commercial unmanned aerial vehicles (UAV) on battlefield was drastically increased. In addition to use UAVs as spotter, they more and more frequently applied to drop small ammunition on enemy forces. In this paper laser targeting system that suitable for use on lightweight, mobile drones that drop freefalling ammunition was developed. Laser targeting systems are used for detecting and tracking the targets in the battlefield by obtaining the position information of these targets which is being designated and illuminated by UAV’s laser designator then the laser light reflected from these targets and come to the input of the optical systems of the dropped ammunition. An optical system for collimating the reflected beam from the target is one of the main components of the laser targeting system (LTS). The Quad Detector (QD) as a part of this optical system is simply consists of four photodiodes capable of detecting light spot projected on its surfaces and determine the deviation position of the laser spot from its center. It converts the incident laser spot to its corresponding photocurrent, the readout circuit that filter and convert the photocurrent to its corresponding voltage and the tracking system that is controlling the laser seeker movement to track the intended target based on the feedback information of the QD depending on the real position of the tracking platform. Developed system will ensure that munitions are accurately hit the target, reducing unnecessary casualties and damage, as well as reducing the number of sorties required and the time spent in hazardous airspace for the unmanned aerial vehicle.

UAV Implementations in Urban Planning and Related Sectors of Rapidly Developing Nations: A Review and Future Perspectives for Malaysia

The rapid growth of urban populations and the need for sustainable urban planning and development has made Unmanned Aerial Vehicles (UAVs) a valuable tool for data collection, mapping, and monitoring. This article reviews the applications of UAV technology in sustainable urban development, particularly in Malaysia. It explores the potential of UAVs to transform infrastructure projects and enhance urban systems, underscoring the importance of advanced applications in Southeast Asia and developing nations worldwide. Following the PRISMA 2020 statement, this article adopts a systematic review process and identifies 98 relevant studies out of 591 records, specifically examining the use of UAVs in urban planning. The emergence of the UAV-as-a-service sector has led to specialized companies offering UAV operations for site inspections, 3D modeling of structures and terrain, boundary assessment, area estimation, master plan formulation, green space analysis, environmental monitoring, and archaeological monument mapping. UAVs have proven to be versatile tools with applications across multiple fields, including precision agriculture, forestry, construction, surveying, disaster response, security, and education. They offer advantages such as high-resolution imagery, accessibility, and operational safety. Varying policies and regulations concerning UAV usage across countries present challenges for commercial and research UAVs. In Malaysia, UAVs have become essential in addressing challenges associated with urbanization, including traffic congestion, urban sprawl, pollution, and inadequate social facilities. However, several obstacles need to be overcome before UAVs can be effectively deployed, including regulatory barriers, limited flight time and range, restricted awareness, lack of skilled personnel, and concerns regarding security and privacy. Successful implementation requires coordination among public bodies, industry stakeholders, and the public. Future research in Malaysia should prioritize 3D modeling and building identification, using the results of this study to propel advancements in other ASEAN countries.

Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass

Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs). Images captured with red–green–blue cameras mounted on UAVs in two missions varying in ground sample distances were used as input for generating three-dimensional point clouds using the structure-from-motion approach. These point clouds were then compared to manual ground measurements. The results showed that the agreement between the methods was closest when CC presented dense and smooth canopies. However, stands with rough canopies or gaps showed substantial differences between the UAV method and ground measurements. We conclude that the UAV method is substantially more precise and accurate in determining CH than measurements taken with a ruler since the UAV introduces additional dimensions with greatly increased resolution. CH can be a reliable indicator of biomass yield, but no differences between the investigated methods were found, probably due to allometric variations of different CC species. We propose the presented UAV method as a promising tool to include site-specific information on CC in crop production strategies.

Android Application to Control Commercial UAV

Unmanned Aerial Vehicles (UAVs) have been used in many industries because they can fly out of sight, maximize production, reduce costs and risks, while ensuring visual security and safety for human workers during pandemics. A commercial drone is typically controlled and navigated using a remote control and a mobile application. Mobile apps are used to visualize the drone's coordinates on a map. There is no direct mobile app for controlling a developer drone like the DJI Matrice 100. This research presents a mobile application for controlling a DJI drone as well as a sensor system for providing information about obstacles encountered during the flight.

Effective UAV patrolling for swarm of intruders with heterogeneous behavior

AbstractThe phenomenal growth in the utilization of commercial unmanned aerial vehicles (UAVs) or drones leads to an urgent need for new approaches to ensure safety in the sky. Effective aerial surveillance requires patrolling swarms to react according to the various behaviors demonstrated by intruding swarms, but existing approaches are not practical when dealing with a large number of drones. Specifically, predicting the behaviors or planned paths of the intruding swarms is highly challenging as intruders may perform evasive strategies to avoid detection. Therefore, this work utilizes heuristic search strategies and investigates how various intruder behaviors affect the search performance. To investigate the search performance, a swarm versus swarm simulator is developed. Using the simulator, first, a comparative study is performed to evaluate how intruders’ behaviors can affect the performance of the patrolling swarm. Subsequently, three approaches, including single-objective optimization, multi-objective optimization, and Lévy flight, are compared in terms of their detection performance in a bounded space. The results suggest that multi-objective optimization outperforms both single-objective optimization and Lévy flight-based approaches. Furthermore, our results show that intruders have a lower chance of being tracked when moving in a dense crowd, and this finding reaffirms the schooling behaviors of fish. In a specific simulation scenario, the total percentage of detection is above 90%. However, the detection percentage is highly related to other factors such as search space, number of patrolling UAVs, and the intruders’ behaviors.

Mechatronic gripper system and delivery platform for the autonomous cargo transport with unmanned aerial vehicles

For intralogistics material transport with unmanned aerial vehicles (UAVs), special pickup and release devices are necessary to guarantee a reliable and efficient transport process in the field of application. Therefore, the paper presents a process-reliable transport system comprising a mechatronic gripper and a landing platform to conduct autonomous cargo pick-up and delivery. The presented configuration was used with a commercial UAV for the autonomous product handover between defined locations. To compare the performance against conventional methods of transporting small goods, different scenarios were carried out. The potential time saving is verified through comparisons with conventional conveyor systems. The cooperation of drone fleets and multi-UAV monitoring by a single operator are also discussed.

Energy and Nvidia Sionna-Based Signal Integrity: Selection Design for Off-the-Shelf Synthetic Aperture Radar-Capable Unmanned Aerial Vehicles

This paper presents the relationship between energy and signal integrity of Unmanned Aerial Vehicles (UAVs) with Synthetic Aperture Radar (SAR) capabilities for a more informed off-the-shelf selection design. The energy is expressed in the UAV and SAR power consumption, whereas signal integrity is expressed in Bit-Error Rate (BER). Low-end, mid-end, and highend—categorized in terms of their market cost—commercial UAVs and SAR modules were considered. The energy relations are based on the UAV flight path and power ratings. The signal processing and decomposition of the reconstructed objects for the model simulations are based on a transmit channel-receive chain of 16-quadrature-amplitude, low-density parity check, Rayleigh response, and single-antenna directivity done through Nvidia Sionna. The results indicate that far-reaching flight spatial path scanning is achieved with high-end UAVs and low-cost SARs, but the accuracy is relatively low. Higher accuracy is achieved with low-end UAVs with high-cost SARs. Further, the results point out that selecting UAV-SAR is critical in terms of energy and accuracy for a specific target application. The outcomes show tradeoffs in the selection design.

THE PRINCIPLE OF AD COELOM IN RESPECT TO THE LOW-ALTITUDE AIRSPACE FOR THE COMMERCIAL UNMANNED AERIAL VEHICLES (UAVs) IN MALAYSIA

The development and innovation of unmanned aerial vehicles (UAVs) or drones especially in the commercial arena are predicted to give a positive impact on socio-economic growth in Malaysia. Malaysia has been chosen in this discussion to evaluate the development of UAV laws and initiatives by Malaysia's Civil Aviation Authority to uphold commercial UAVs in line with the International Civil Aviation Organization's (ICAO) mission to provide the fundamental international regulatory framework through Standards and Recommended Practices (SARPS) in addressing UAVs. Nevertheless, the existing laws in Malaysia regarding the right of way and right to use low-altitude airspace did not offer much for commercial UAVs due to the ad coelom principle. The right to use airspace for commercial UAVs shall be the first and major concern in moulding the UAV laws. This article is aimed to analyse and evaluate the relationship of the ad coelom principle with the laws available in Malaysia which might honour the right to low-altitude airspace for commercial UAVs. This article adopts doctrinal research by referring to the available laws and regulations in Malaysia to see the integration of the ad coelom principle with the right of way and right to low-altitude airspace for commercial UAVs. The concept of ‘avigation easement’, ‘drone corridor’, and ‘drone-port’ could be the best solutions in determining the right of ways for commercial UAVs to reflect the ad coelom principle. Therefore, the Civil Aviation Authority of Malaysia (CAAM), the Federal government, all States, municipal councils, and private individuals shall be ready to draw zoning of airspace horizontally or vertically without neglecting public rights and breaching other laws. This is to realize the CAAM mission for the integration of Air Traffic Control (ATC) in controlling the navigable airspace of manned aircraft which is to be extended to the UAVs by the year 2035.

UAV Charging Station Placement in Opportunistic Networks

Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including a key role within opportunistic wireless networks. These types of opportunistic networks are considered well suited for infrastructure-less areas, or urban areas with overloaded cellular networks. For these networks, UAVs are envisioned to complement and support opportunistic network performance; however, the short battery life of commercial UAVs and their need for frequent charging can limit their utility. This paper addresses the challenge of charging station placement in a UAV-aided opportunistic network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and random clustering, with each clustering approach being examined in combination with Epidemic, Spray and Wait, and State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining the charging station locations using K-means clustering with three clusters showed lower message delay and higher success rate than deciding the charging station location either randomly or using DBSCAN regardless of the routing strategy employed between nodes.