论文标题
集成低功率宽面积网络，以增强可扩展性和扩展覆盖范围
Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage
论文作者
论文摘要
低功率广阔的区域网络（LPWANS）由于其能力以非常低的变速箱功率在长距离上进行沟通，因此正在发展成为一种促进技术的能力（IoT）。但是，现有的LPWAN技术在满足可伸缩性和涵盖非常广泛的领域方面面临限制，这使得其对未来的物联网应用的挑战，尤其是在基础设施有限的农村地区。为了解决这一限制，在本文中，我们考虑通过整合多个LPWAN来实现尺度性和扩展覆盖范围。 Snow（白色空间上的传感器网络）是最近提议的LPWAN建筑在电视白色空间上，已经证明了其在性能和能源效率方面的现有LPWAN的优势。在本文中，我们建议通过多个雪的无缝整合来扩展LPWAN，这可以并发播种机间和鼻内通信。然后，我们将可伸缩性和抢断干扰之间的权衡为约束优化问题，其目的是通过管理多个雪的白空间频谱共享来最大化可伸缩性。我们还证明了这个问题的NP硬度。在此范围内，我们提出了一种直观的多项式启发式算法，用于解决实践中高效的可伸缩性优化问题。为了理论结合，我们还提出了一种简单的多项式时间1/2- approximation算法，以解决可伸缩性优化问题。通过部署在（25x15）平方英尺的区域中进行的硬件实验。 KM以及大规模的模拟证明了我们算法的有效性以及通过无缝整合雪的可行性，具有高可靠性，低潜伏期和能量效率。
Low-Power Wide-Area Networks (LPWANs) are evolving as an enabling technology for Internet-of-Things (IoT) due to their capability of communicating over long distances at very low transmission power. Existing LPWAN technologies, however, face limitations in meeting scalability and covering very wide areas which make their adoption challenging for future IoT applications, especially in infrastructure-limited rural areas. To address this limitation, in this paper, we consider achieving scal-ability and extended coverage by integrating multiple LPWANs. SNOW (Sensor Network Over White Spaces), a recently proposed LPWAN architecture over the TV white spaces, has demonstrated its advantages over existing LPWANs in performance and energy-efficiency. In this paper, we propose to scale up LPWANs through a seamless integration of multiple SNOWs which enables concurrent inter-SNOW and intra-SNOW communications. We then formulate the tradeoff between scalability and inter-SNOW interference as a constrained optimization problem whose objective is to maximize scalability by managing white space spectrum sharing across multiple SNOWs. We also prove the NP-hardness of this problem. To this extent, We propose an intuitive polynomial-time heuristic algorithm for solving the scalability optimization problem which is highly efficient in practice. For the sake of theoretical bound, we also propose a simple polynomial-time 1/2-approximation algorithm for the scalability optimization problem. Hardware experiments through deployment in an area of (25x15)sq. km as well as large scale simulations demonstrate the effectiveness of our algorithms and feasibility of achieving scalability through seamless integration of SNOWs with high reliability, low latency, and energy efficiency.
