An energy-efficient IoMT three-tier architecture for continuous monitoring of endangered bird species

Abstract

The alarming decline in animal populations, particularly birds, due to environmental degradation necessitates close monitoring of endangered migratory waterbirds in their natural habitats. This can be accomplished through the continuous capture and transmission for population estimation, habitat analysis, and various relevant studies. This paper introduces a three-tier IoMT (Internet of Multimedia Things) deployed along the Edge-Cloud continuum for automated bird monitoring systems aimed at safeguarding endangered waterbird populations. At the edge level, Wireless Multimedia Sensor Networks (WMSN) are used to periodically capture and transmit images to a central collection station (fog level). Challenges such as limited bandwidth and power in Low-Power and Lossy Networks (LLNs) are addressed through local audio identification of endangered bird calls, which activates cameras only for target birds. This significantly reduces data transmission and conserves energy. To tackle ambient noise issues in audio recognition, especially in complex environments such as wetlands, an appropriate noise reduction technique is employed to augment our automatic bird call recognition system. This paper details an energy-efficient approach addressing LLNs’ challenges and incorporates robust noise reduction techniques to improve local audio recognition. The research includes a thorough analysis of potential technical solutions prior to implementation, establishing a critical phase in the system development.