无线体域网的快速鲁棒最优设计
Towards the fast and robust optimal design of Wireless Body Area Networks
摘要：
无线体域网是无线传感器网络，其采用最近出现并在重要的医疗保健应用中传播，例如远程监测患者的健康状况。与这种网络的部署相关联的主要问题是由能量消耗表示的：通常，传感器的电池不能容易地更换和充电，因此通过网络和路由的合理设计来控制能量的使用是至关重要的。另一个问题是交通不确定性：身体传感器可能会以事先不完全知道的可变速率产生数据，例如，因为数据的生成是事件驱动的。忽视流量的不确定性可能会导致错误的设计和路由决策，这可能会损害网络的功能，并对患者的健康产生非常不良的影响。为了解决这些问题，在这项工作中，我们提出了第一个强大的最优化模型联合优化的拓扑结构和路由在交通不确定性的身体区域网络。由于问题可能会导致具有挑战性的，即使是最先进的优化求解器，我们提出了一个原始的优化算法，利用适当的线性松弛，以指导随机固定的变量，支持一个确切的大变量邻域搜索。对实际实例的实验表明，我们的算法比一个国家的最先进的求解器，快速产生的解决方案与改进的最优性差距。
英文摘要：
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.
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