Research Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4194

This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.

Browse

Filters

2018 - 201912020 - 20251

Advanced Search

Filter by

Settings

Has files: YesSubject: search.filters.subject.Animal Tracking

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    Integrating machine learning and IoT for real-Time wildlife tracking and crowd sourcing
    (SPIE, 2025-12-09) Saubhagya, S; Wijerathne, N; Sachintha, K; Kumarasinghe, H
    This research introduces a smart system to enhance wildlife safari experiences by integrating real-Time location tracking, animal behavior prediction, and emergency communication technologies. Traditional safari tours rely heavily on human guides, often leading to inefficiencies in wildlife spotting and navigation. Proposed system employs machine learning, GPS tracking, and voice assistance to provide an interactive and informative safari experience. The machine learning algorithm uses past and current movement patterns of animals and generates predictive information to guide tourists to optimal wildlife observation points. The system uses LoRa-based offline communication to ensure seamless connectivity in network-poor regions, facilitating smooth coordination between safari vehicles and park authorities. The voice guidance feature also enhances accessibility by providing real-Time educational content on observed wildlife. This study adds to wildlife tourism with a technology-based framework that improves visitor experience, minimizes environmental footprint by route optimization, and aids conservation through data-driven monitoring of wildlife behavior.
  • Thumbnail Image
    PublicationEmbargo
    Energy Conservation in Animal Tracking
    (IEEE, 2018-03-05) Ayatollahi, H; Tapparello, C; Wijesundara, M; Heinzelman, W
    Wireless animal tracking represents the process of using battery operated wireless collars or tags to monitor and track animals in the wild. Given that it is particularly difficult to tag some species, communication protocols must be designed to be energy efficient, while still ensuring a high packet delivery ratio and low delay. In this paper, we present an energy efficient cross-layer protocol for an animal tracking application. The proposed protocol, MAC-LEAP, is a MIMO based energy adaptive protocol that reduces the energy consumption of the nodes by dynamically selecting their number of antennas for communication. We evaluate this protocol in an elephant tracking application in three different scenarios; when the nodes have limited energy, when the nodes have unlimited energy; and when the tags can be recharged via energy harvesting. Our results show that MAC-LEAP outperforms traditional protocols in terms of packet delivery ratio, and average packet delay and energy consumption.