Towards the fast and robust optimal design of Wireless Body Area Networks

TL;DR

This paper introduces a robust optimization model for designing energy-efficient and reliable Wireless Body Area Networks under uncertain traffic conditions, along with an innovative algorithm that outperforms existing solvers.

Contribution

It presents the first robust optimization framework for joint topology and routing design in body area networks considering traffic uncertainty, with a novel algorithm demonstrating superior performance.

Findings

The proposed algorithm outperforms state-of-the-art solvers.

Solutions have improved optimality gaps and faster computation times.

The model enhances network reliability and energy efficiency.

Abstract

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.