International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Call for Paper Volume 7 Issue 4 April 2026 Submit your research before last 3 days of to publish your research paper in the issue of April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 4
April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

A Multi-Agent Reinforcement Learning Framework for Drone Swarm Border Surveillance

Author(s) Mr. Anmol Kumar Singh, Dr. Upendra Kumar Srivastava
Country India
Abstract Abstract
Border surveillance presents unique challenges requiring persistent monitoring, rapid response, and adaptive coordination across large, dynamic environments. Drone swarms, with their scalability and flexibility, offer a promising solution, but effective deployment demands intelligent decision-making under uncertainty. This paper proposes a multi-agent reinforcement learning (MARL) framework for drone swarm border surveillance, where each drone acts as an autonomous agent capable of cooperative sensing, patrolling, and threat detection. The framework leverages decentralized policies with shared global objectives, enabling drones to balance exploration and exploitation while adapting to adversarial intrusions and environmental variability. A reward structure is designed to encourage coverage efficiency, minimize energy consumption, and maximize detection accuracy. Simulation experiments demonstrate that the proposed MARL approach outperforms baseline strategies in terms of surveillance coverage, resilience to agent loss, and adaptability to dynamic border conditions. The results highlight the potential of MARL-driven drone swarms as a scalable, intelligent, and robust solution for next-generation border security operations.
Keywords MALR, Drone swarms, Surveillance , MADDPG
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, March 2026
Published On 2026-03-30
DOI https://doi.org/10.70528/IJLRP.v7.i3.2027
Short DOI https://doi.org/hbvwc4
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 12 Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com