# A fast ILP-based Heuristic for the robust design of Body Wireless Sensor Networks

## TL;DR

This paper introduces a novel heuristic algorithm for designing robust body wireless sensor networks, effectively handling data uncertainty and outperforming existing solvers in solution quality.

## Contribution

The paper presents an original heuristic combining deterministic and probabilistic strategies, guided by strengthened relaxations, to efficiently solve a complex min-max ILP problem.

## Key findings

- Heuristic outperforms state-of-the-art solvers in solution quality.
- The approach effectively manages data uncertainty in sensor network design.
- Computational tests demonstrate significant improvements over benchmarks.

## Abstract

We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.