Edge computing in wildlife behavior and ecology

Abstract

Modern sensor technologies increasingly enrich studies in wildlife behavior and ecology. However, constraints on weight, connectivity, energy and memory availability limit their implementation. With the advent of edge computing, there is increasing potential to mitigate these constraints, and drive major advancements in wildlife studies.

Keywords: automation; biologging; energy and storage efficiency; low latency; tiny machine learning.