集成低功率广域网以实现增强型Scalability和扩展覆盖
Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage
摘要：
低功耗广域网（LPWAN）正在发展成为物联网（IoT）的支持技术，因为它们能够以非常低的传输功率进行长距离通信。然而，现有的LPWAN技术在满足可扩展性和覆盖非常广泛的区域方面面临限制，这使得它们的采用对于未来的物联网应用具有挑战性，特别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，我们考虑通过集成多个LPWAN来实现可扩展性和扩展覆盖。SNOW（Sensor Network Over White Spaces）是最近提出的一种基于电视白空间的LPWAN架构，它在性能和能效方面都优于现有的LPWAN。在本文中，我们建议通过多个SNOW的无缝集成来扩展LPWAN，从而实现并发的SNOW间和SNOW内通信。然后，我们制定了可扩展性和雪间干扰之间的权衡作为一个约束优化问题，其目标是通过管理多个雪共享的空白频段频谱，以最大限度地提高可扩展性。我们还证明了这个问题的NP-困难。为此，我们提出了一种直观的多项式时间启发式算法来解决可扩展性优化问题，该算法在实践中具有很高的效率。为了理论上的约束，我们还提出了一个简单的多项式时间1/2-近似算法的可扩展性优化问题。通过在（25 x15）平方米的区域内部署进行硬件实验。km以及大规模的模拟证明了我们的算法的有效性和通过无缝集成的SNOW实现可扩展性的可行性，具有高可靠性，低延迟，和能源效率。
英文摘要：
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.
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