Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture

2026-03-25

AI Summary

This work addresses the limitations of precision agriculture imposed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methodologies. It proposes a cost-aware design space exploration framework tailored for multimodal drone–ground vehicle collaborative platforms, where cost is explicitly treated as a primary optimization objective for the first time. The framework holistically integrates multiple constraints—including budget, energy consumption, sensing coverage, payload capacity, computation, and communication—and leverages integer linear programming (ILP) coupled with SAT-based verification to enable efficient multi-objective trade-offs. Experimental results demonstrate that the approach consistently achieves full field coverage within budget across farms of varying scales, significantly improves payload utilization, and outperforms existing CPS design space exploration methods.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-aware design space exploration (DSE) framework for multimodal drone-rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT-based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude of alternatives. We conduct case studies on smaller and larger-sized farms to show that our method consistently achieves full coverage within budget while maximizing payload efficiency, outperforming state-of-the-art CPS DSE approaches.

Problem

• Precision Agriculture
• Cyber-Physical Systems
• Design Space Exploration
• Cost-aware Design
• Affordability

Innovation

• Design Space Exploration
• Cyber-Physical Systems
• Precision Agriculture
• Integer Linear Programming
• SAT-based Verification