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UAV Networks Surveillance Implementing an Effective Load-Aware Multipath Routing Protocol (ELAMRP)
Raja Vavekanand, Kira Sam, Vijay Singh
TL;DR
UAV surveillance networks must provide reliable transmission despite limited bandwidth, changing topology, and high mobility. The paper proposes ELAMR, a load-aware multipath protocol that selects low-load, high-quality paths while considering channel capacity and battery state. Simulations report balanced network load, reduced end-to-end delay, improved packet delivery ratio, and successful optimal-path selection.
Problem
Limited bandwidth, dynamic topology, and high mobility make reliable UAV-network transmission difficult, increasing load and delay while reducing packet delivery ratio and throughput.
Method
ELAMR uses load estimation, multipath construction, channel-capacity and battery calculations, staged drone activation, and path selection based on load and link quality.
Results
ELAMR balanced network load, reduced end-to-end delay, improved packet delivery ratio, and selected paths with minimum load and maximum link quality.
Takeaways & Limitations
The methodology provides a reliable and efficient UAV communication approach for surveillance applications by coordinating coverage, transmission paths, channel capacity, and battery life.
Abstract
from arXiv · showhide
In this work uses innovative multi-channel load-sensing techniques to deploy unmanned aerial vehicles (UAVs) for surveillance. The research aims to improve the quality of data transmission methods and improve the efficiency and reliability of surveillance systems by exploiting the mobility and adaptability of UAVs does the proposed protocol intelligently distribute network traffic across multiple channels, considering the load of each channel, While addressing challenges such as load balancing, this study investigates the effectiveness of the protocol by simulations or practical tests on The expected results have improved UAV-based surveillance systems, more flexible and efficient networks for applications such as security, emergency response and the environment alignment of monitoring -Offering infrastructures, which contribute to efficient and reliable monitoring solutions.
I. INTRODUCTION
UAV surveillance offers continuous monitoring, but limited bandwidth, dynamic topology, and high mobility make reliable data transmission difficult. ELAMR addresses these challenges with load-aware multipath routing, channel and battery considerations, and staged drone activation.
- UAVs support surveillance in military, disaster-management, and environmental-monitoring domains with real-time and continuous monitoring capabilities.
- Limited bandwidth, dynamic topology, and high mobility can increase network load and delay while reducing packet delivery ratio and throughput.
- Effective routing is needed to balance network load, reduce end-to-end delay, and improve packet delivery ratio in UAV surveillance networks.
- ELAMR estimates load using queue length and packets in transmission, then selects paths with minimum load and maximum link quality.
- The proposed approach strategically places drones, evaluates signal-to-noise ratio and channel capacity, monitors battery life, and supports reliable data transmission.
II. RELATED WORK
Prior work addresses wireless-sensor-network reliability and energy constraints through D2D relays, relay selection, power allocation, and UAV deployment optimization. These studies cover communication reliability, energy efficiency, channel modeling, coverage, and surveillance quality.
- WSNs face limited sensor battery life, while relay nodes may have restricted transmission range, unreliable links, and higher energy consumption.
- D2D communication can reduce transmission distance, improve link quality and spectral efficiency, and provide dual connectivity for greater network robustness.
- DSPA selects D2D relay nodes using channel gain and residual energy, while accounting for pairing interference and relay energy use.
- Related D2D studies investigate trust-based relay selection, SNR- and energy-harvesting-based schemes, and hybrid reactive or proactive relay selection.
- Cooperative UAV channel modeling for 5G IoT considers multi-UAV reflection, three-dimensional distance, path loss, and transmit-power control.
- Other UAV surveillance work targets grid topology and video monitoring without cellular coverage, including coverage without blind spots and QoE-based inter-UAV spacing.
- An energy-efficient UAV surveillance scheme jointly optimizes UAV trajectories and compressive-sensing measurement matrices using ADMM simulations.
III. PROPOSED METHODOLOGY
The proposed methodology develops an effective load-aware multipath routing protocol for UAV network surveillance after examining existing projects and frameworks.
- The methodology introduces effective load-aware multipath routing to improve existing UAV network surveillance methods.
A. Algorithm for Effective UAV-based Network Surveillance
The surveillance algorithm maps and covers the target area, positions an anchor drone at the centroid, and evaluates path loss, SNR, capacity, and battery state. It activates a drone subset, selects an optimal path, and updates transmission resources after each transfer.
- The process converts mapped drone coordinates into an analysis coordinate system and places drones to eliminate surveillance blind spots.
- The anchor drone is positioned at the centroid, while log-distance path loss is calculated to support reliable communication.
- Uplink path loss and SNR are calculated using path-loss gain, transmitted power, channel effects, and receiver noise.
- Channel capacity is computed from the available bandwidth to determine the maximum reliable transmission rate.
- Activating 10%, 20%, or 30% of drones at a time balances load and extends battery lifespan during transmission.
- The method calculates battery-adjusted channel capacity using an initial battery range of 80% to 100% and a 11.1V, 2200mAH drone battery configuration.
- After selecting the optimal path, the system updates battery life and channel capacity after each transmission.
B. Algorithms
The algorithm finds all paths through a drone graph between a specified start and end, subject to a path-length threshold.
- Algorithms: FIND ALL PATHS explores routes recursively from the start node toward the destination.Each visited node is appended to the current path before recursion continues.
- Algorithms: The procedure returns the current path when the start and end nodes coincide.This is the recursion’s termination condition for a completed route.
- Algorithms: Nodes already in the current path are excluded to prevent revisiting them.The algorithm also stops extending a route when its length reaches the threshold.
- Algorithms: If the start node is absent from the drone graph, the procedure returns no paths.Otherwise, it recursively examines each neighboring node and aggregates the discovered routes.
VI. IMPLEMENTATION AND SIMULATION OF UAV NETWORK
The implementation constructs a UAV surveillance simulation from geographic area definition through drone activation and communication assessment.
- Implementation and Simulation: The surveillance process begins by identifying the area and obtaining its geographic coordinates.The area is then represented visually using plotted longitude and latitude points.
- Implementation and Simulation: Drone field of view is determined to position UAVs and eliminate blind spots.The implementation includes a reference-area plotting step before deployment decisions.
- Implementation and Simulation: Activating the anchor drone coordinates communication among the other drones.The implementation then assesses transmission parameters, path capacity, and battery levels.
- Implementation and Simulation: The simulation implements UAV surveillance using load-aware multipath routing.The supplied implementation section identifies this as the simulation approach.
- Implementation and Simulation: Geographic points are transformed into a normalized coordinate system for clearer representation.This transformation supports plotting the surveillance shape from map coordinates.
B. Image Processing
The image-processing procedure converts an input image into a binary representation and applies a filter matrix over the resulting grayscale data.
- Image Processing: The procedure converts the input image to grayscale, removing color information from each pixel.Each grayscale pixel is represented by a single intensity value.
- Image Processing: Thresholding is applied to the grayscale image to create a binary matrix.The described sequence includes grayscale conversion, thresholding, and binary-matrix construction.
- Image Processing: A filter matrix is convolved with the grayscale image as it moves across the image.The filter matrix acts as a template during this operation.
- Image Processing: The covered area is the spatial extent over which the filter is applied.This defines the region examined during the filtering step.
C. Drone Deployment
Drone deployment derives spatial relationships among UAVs and uses communication measurements to construct candidate links for path planning.
- Drone Deployment: The method calculates centroid coordinates for each drone from its spatial distribution within the surveillance shape.These centroids provide spatial positions for subsequent deployment calculations.
- Drone Deployment: The deployment workflow includes plotting a reference area and determining drone coverage and adjacent-node distances.These figure captions identify successive spatial-planning operations in the deployment process.
- Drone Deployment: Distances between identified drone centroids are computed to form a distance matrix.The matrix represents pairwise spatial relationships among deployed drones.
- Drone Deployment: Path planning computes pathloss and SNR for every drone pair before constructing the drone graph.Nodes represent drones, while edges represent communication links.
- Drone Deployment: SNR values assigned to graph edges indicate the strength of potential communication connections.Pathloss quantifies signal attenuation through the environment, while SNR measures signal quality relative to background noise.
V. SIMULATION RESULT
The simulation evaluates ELAMR’s path selection and transmission monitoring by tracking channel capacity, battery level, data size, and transmission time. Results indicate improved load management and efficient path selection for UAV surveillance.
- Simulation findings: ELAMR balances network load by distributing transmission responsibilities according to channel capacity and battery level.The protocol selects paths with minimum load and maximum link quality for efficient surveillance-data transmission.
- Simulation findings: For a 600MB transmission on path 4-8-15-23-24, initial link capacities were 3.5-3.5-3.4-3.5 Mbps and each drone started at 100% battery.The example records transmission conditions before data transfer.
- Simulation findings: After transmission, the example reports 172-172-176-172 seconds of transmission time, battery levels of 31%-31%-32%-31%, and capacities of 1.085-1.085-1.088-1.085 Mbps.The observations cover both transmission duration and post-transmission network and battery states.
- Simulation findings: The methodology tracks transmitted data size, selected path, channel capacity, battery levels before and after transmission, and transmission time.These measurements support monitoring network functionality and estimating depletion across different transmission sizes.
- System implications: Strategic drone placement, centroid-based anchor positioning, segmented activation, and optimum-path selection are presented as mechanisms for efficient and reliable data transmission.The conclusion also identifies signal-propagation, signal-to-noise, channel-capacity, and battery monitoring calculations as supporting elements.
VII. FUTURE WORK
The future-work direction combines machine learning, real-time reconfiguration, enhanced communications, adaptive resource allocation, edge computing, and autonomous safety mechanisms for scalable drone networks.
- VII. FUTURE WORK: Machine learning can analyze historical data, weather conditions, and user patterns to optimize drone paths.The proposed direction also includes real-time reconfiguration for changing traffic patterns and communication demands.
- VII. FUTURE WORK: The system explores 5G communication, adaptive bandwidth allocation, edge computing, and scalable deployment with multiple anchor drones.These mechanisms target communication speed, resource utilization, latency reduction, and large coverage areas.
- VII. FUTURE WORK: Autonomous navigation and collision-avoidance algorithms are included to enhance safety and support a robust, secure drone network.