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

Sai Krishna Reddy Sathi

TL;DR

This paper proposed a hybrid algorithm that combines Artificial Bee Colony with Levy flight to optimize adaptive sensor placement alongside an important notion of hotspots from domain knowledge experts, which significantly improves the identification of critical hotspots.

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.

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

TL;DR

This paper proposed a hybrid algorithm that combines Artificial Bee Colony with Levy flight to optimize adaptive sensor placement alongside an important notion of hotspots from domain knowledge experts, which significantly improves the identification of critical hotspots.

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.

Paper Structure

This paper contains 10 sections, 11 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. Literature Review
  4. Methodology
  5. Bee-Foraging Algorithms
  6. Artificial Bee Colony (ABC) Algorithm
  7. Particle Swarm Optimization (PSO)
  8. Hybrid ABC with Levy Flight
  9. Results and Discussion
  10. Conclusion and Future Works

Figures (5)

  • Figure 1: Forest-Grid initialization with 20 hotspots with all the UAVs at (50,0)
  • Figure 2: Frames of the simulation video with LevyWeight = 3; Time taken to cover all hotspots: 227.5006 seconds
  • Figure 3: Frames of the simulation video with LevyWeight = 5; Time taken to cover all hotspots: 350.9228 seconds
  • Figure 4: Coverage Heatmap for LevyWeights 1.5, 2, 2.5 and 3 respectively
  • Figure :