Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle
Jun Xu, Gurkan Solmaz, Rouhollah Rahmatizadeh, Damla Turgut, and, Ladislau Boloni

TL;DR
This paper presents a novel UAV and wireless sensor network-based animal monitoring system that uses MDP and Q-learning for efficient path planning to detect and track animals in large wildlife areas without attaching devices.
Contribution
It introduces an MDP-based path planning method for UAVs in animal monitoring, optimizing information gathering and movement prediction without animal-mounted devices.
Findings
Outperforms greedy and random heuristics in simulations.
Effectively detects animal movements using real-world datasets.
Reduces message delays and maximizes value of information.
Abstract
In animal monitoring applications, both animal detection and their movement prediction are major tasks. While a variety of animal monitoring strategies exist, most of them rely on mounting devices. However, in real world, it is difficult to find these animals and install mounting devices. In this paper, we propose an animal monitoring application by utilizing wireless sensor networks (WSNs) and unmanned aerial vehicle (UAV). The objective of the application is to detect locations of endangered species in large-scale wildlife areas and monitor movement of animals without any attached devices. In this application, sensors deployed throughout the observation area are responsible for gathering animal information. The UAV flies above the observation area and collects the information from sensors. To achieve the information efficiently, we propose a path planning approach for the UAV based on…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsUAV Applications and Optimization · Opportunistic and Delay-Tolerant Networks · Energy Efficient Wireless Sensor Networks
MethodsQ-Learning
