Adaptive Sensor Placement Inspired by Bee Foraging: Towards Efficient Environment Monitoring

TL;DR

This paper introduces a hybrid algorithm inspired by bee foraging behavior, combining Artificial Bee Colony with Levy flight to optimize sensor placement for environmental monitoring, enhancing hotspot detection and applicability in rescue operations.

Contribution

The paper presents a novel hybrid algorithm that improves adaptive sensor placement by integrating domain knowledge and advanced exploration techniques.

Findings

Enhanced hotspot detection accuracy

Improved exploration and exploitation balance

Potential applications in search and rescue operations

Abstract

This paper aims to make a mark in the future of sustainable robotics, where efficient algorithms are required to carry out tasks like environmental monitoring and precision agriculture efficiently. We proposed a hybrid algorithm that combines Artificial Bee Colony (ABC) with Levy flight to optimize adaptive sensor placement alongside an important notion of hotspots from domain knowledge experts. By enhancing exploration and exploitation, our approach significantly improves the identification of critical hotspots. This algorithm also finds its usecases for broader search and rescue operations applications, demonstrating its potential in optimization problems across various domains.