Distributed Scheduling of Network Connectivity Using Mobile Access Point Robots
Distributed Scheduling of Network Connectivity Using Mobile Access Point Robots
复制标题
使用移动接入点机器人的网络连接分布式调度
DOI：
10.1109/tro.2016.2593041
复制
发表时间：
2016
影响因子：
7.8
通讯作者：
M. Zavlanos
中科院分区：
文献类型：
--
作者：
N. Chatzipanagiotis;M. Zavlanos
在本文中，我们考虑了可以利用移动性在无线网络中实现可靠通信的场景，这些无线网络资源稀缺，无法同时为其节点提供服务。具体来说，我们考虑的情况是，一组机器人作为移动接入点（ap）运行，为生成数据的静态源节点的多跳网络提供服务，即足够的端到端通信路由。我们引入了连接调度问题，这是一个将ap的运动规划与源节点的服务调度和网络路由控制相结合的新框架，从而保证了通信的完整性。我们将连接调度问题表述为一个多阶段混合整数规划（MIP）问题，其中路径规划、服务调度和路由决策都在离散时间范围内共同优化。由于MIP问题可以迅速变得难以处理，我们进一步考虑问题的连续凸重构，并采用稀疏优化技术，特别是重新加权的1正则化方案，以恢复解的期望完整性结构。本文提出了一种基于加速分布增广拉格朗日（ADAL）算法的去中心化方法来解决上述松弛问题。具体来说，我们通过在算法中加入重新加权的1格式来修改ADAL，使我们能够在最终解中恢复原始MIP的期望稀疏结构。数值结果验证了该框架的有效性。
In this paper, we consider scenarios where mobility can be exploited to enable reliable communications in wireless networks with scarce resources that are unable to concurrently service their nodes. Specifically, we consider cases where a team of robots operate as mobile access points (APs) that provide service, namely sufficient end-to-end communication routes, to a multihop network of static source nodes which generate data. We introduce the connectivity scheduling problem, a novel framework that combines motion planning of the APs with service scheduling of the source nodes and network routing control so that integrity of communications is guaranteed over time. We formulate the connectivity scheduling problem as a multistage mixed integer programming (MIP) problem, where path planning, service scheduling, and routing decisions are all jointly optimized over a discrete-time horizon. Since MIP problems can grow intractable quickly, we further consider a continuous convex reformulation of the problem and employ sparse optimization techniques, specifically the reweighted ℓ1 regularization scheme, to recover the desired integrality structure of the solution. We propose a decentralized method to solve the above relaxation that is based on the recently developed accelerated distributed augmented Lagrangians (ADAL) algorithm. Specifically, we modify ADAL by incorporating in the algorithm the reweighted ℓ1 scheme, which enables us to recover the desired sparsity structure of the original MIP at the final solution. Numerical results are presented that validate the effectiveness of the proposed framework.