Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage
Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage
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DOI：
10.1109/tnet.2020.2963886
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发表时间：
2020-01
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影响因子：
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通讯作者：
Mahbubur Rahman;Abusayeed Saifullah
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文献类型：
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作者：
Mahbubur Rahman;Abusayeed Saifullah
低功耗广域网 (LPWAN) 因其能够以极低的传输功率进行长距离通信而不断发展成为物联网 (IoT) 的支持技术。然而，现有的 LPWAN 技术在满足可扩展性和覆盖非常广泛的领域方面面临局限性，这使得它们的采用对未来的物联网应用充满挑战，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范围。 SNOW（白色空间传感器网络）是最近提出的一种基于电视白色空间的 LPWAN 架构，它在性能和能源效率方面展示了其相对于现有 LPWAN 的优势。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，我们将可扩展性和 SNOW 间干扰之间的权衡制定为约束优化问题，其目标是通过管理跨多个 SNOW 的空白频谱共享来最大化可扩展性。我们还证明了这个问题的 NP 难度。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，该算法在实践中非常高效。为了理论界限，我们还提出了一种用于可扩展性优化问题的简单多项式时间 $\frac {1}{2}$ 近似算法。通过在 ( $25\times 15$ )km2 区域部署的硬件实验以及大规模模拟证明了我们算法的有效性以及通过无缝集成 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。
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 scalability 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 $\frac {1}{2}$ -approximation algorithm for the scalability optimization problem. Hardware experiments through deployment in an area of ( $25\times 15$ )km2 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.