Blockchain and deep Q-learning for trusted cloud-enabled drone network in smart forestry: A Survey
| dc.contributor.author | Ali, Guma | |
| dc.contributor.author | Wamusi, Robert | |
| dc.contributor.author | Mijwil, Maad M. | |
| dc.contributor.author | Al-Hamzawi, Hassan A. Hameed | |
| dc.contributor.author | Al Sailawi, Ali S. Abed | |
| dc.contributor.author | Salau, Ayodeji Olalekan | |
| dc.date.accessioned | 2026-01-12T08:08:36Z | |
| dc.date.available | 2026-01-12T08:08:36Z | |
| dc.date.issued | 2025-12-14 | |
| dc.description | This research supports Uganda’s National Development Plan IV by promoting climate-smart natural resource management, digital innovation, and environmental protection. The study highlights how secure drone networks, cloud computing, blockchain, and intelligent learning systems can strengthen forest monitoring, reduce illegal logging, improve wildfire detection, and support reforestation efforts. These outcomes align with NDP IV priorities on green growth, technology-driven development, innovation, and sustainable use of natural resources. The paper also contributes to key Sustainable Development Goals, particularly SDG 9 (Innovation), SDG 12 (Responsible Resource Use), SDG 13 (Climate Action), and SDG 15 (Life on Land), by advancing transparent, secure, and intelligent forestry management systems. | |
| dc.description.abstract | The convergence of drone technology, cloud computing, and intelligent decision-making is revolutionizing precision forestry. However, deploying large-scale drone networks in smart forestry faces challenges such as trust, security, data integrity, and autonomous coordination. This survey examines how combining Blockchain technology with deep Q-learning (DQL) can address these issues within cloud-enabled drone networks. Drawing on 102 peer-reviewed sources published between 2022 and 2025 from reputable platforms such as ACM Digital Library, Frontiers, Wiley Online Library, PLoS, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, and Google Scholar, this work highlights recent advancements in secure and intelligent drone ecosystems. Blockchain provides a decentralized, tamper-resistant framework for validating transactions and securing data exchange among autonomous drones, ensuring the integrity, confidentiality, and authenticity of environmental data. This is critical in forestry, where data manipulation and unauthorized access pose significant risks. Complementing this, DQL enables drones to make autonomous decisions by interpreting real-time environmental data and learning from past experiences, allowing drones to adjust their flight paths, optimize resource utilization, and enhance data collection in dynamic forest environments, such as wildfires or illegal logging operations. Together, Blockchain and DQL create a resilient, scalable architecture that supports secure, real-time, and intelligent forest monitoring. This framework lays the groundwork for developing autonomous and trustworthy drone networks that promote sustainable and climate-smart forestry management. | |
| dc.identifier.citation | Ali, G., Wamusi, R., Mijwil, M. M., Al-Hamzawi, H. A. H., Al Sailawi, A. S. A., & Salau, A. O. (2025). Blockchain and deep Q-learning for trusted cloud-enabled drone network in smart forestry: A Survey. Babylonian Journal of Networking, 2025, 207-241. | |
| dc.identifier.issn | 3006-5372 | |
| dc.identifier.uri | https://dir.muni.ac.ug/handle/20.500.12260/861 | |
| dc.language.iso | en | |
| dc.publisher | Imam Ja’afar Al-Sadiq University | |
| dc.subject | Smart Forestry | |
| dc.subject | Cloud Computing | |
| dc.subject | Drone Network | |
| dc.subject | Blockchain Technology | |
| dc.subject | Deep Q-Learning. | |
| dc.title | Blockchain and deep Q-learning for trusted cloud-enabled drone network in smart forestry: A Survey | |
| dc.type | Article |