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A Low Complexity Relaxation for Minimizing Bandwidth Use in IoT Storage Without Newcomers

机译:低复杂度松弛,可在没有新手的情况下最大限度地减少物联网存储中的带宽使用

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This paper proposes a low-complexity solution for the data protection problem without newcomer nodes in Internet of Things (IoT) scenarios, i.e., when device losses cannot be replaced by new devices. Application scenarios include environmental monitoring, data collection, and industrial automation. Although the optimal solution and optimization framework have been studied in previous work to minimize the network costs and storage capacity requirements, this paper shows that the optimal solution has a high complexity as the number of devices increases. Given the massive number of IoT devices, we propose a relaxation to the cut capacity constraints that (a) guarantees data recoverability, (b) achieves the minimum network use, and (c) reduces the problem's complexity dramatically. Our numerical results show that the proposed relaxation allows us to change the computational scaling of the problem. More specifically, we show that the time taken to compute the optimal transmission policy with the relaxation for a system with 800 devices is the same as the time it takes the optimal solution to solve the case of 15 devices.
机译:本文提出了一种低复杂度的数据保护问题解决方案,该解决方案在物联网(IoT)场景中(即设备丢失无法用新设备代替时)没有新节点的情况下。应用场景包括环境监控,数据收集和工业自动化。尽管在先前的工作中已经研究了最佳解决方案和优化框架,以最大程度地降低网络成本和存储容量要求,但本文显示,随着设备数量的增加,最佳解决方案具有很高的复杂性。鉴于物联网设备的数量众多,我们建议放宽对削减容量的限制,以(a)保证数据可恢复性,(b)最大限度地减少网络使用,并且(c)大大降低问题的复杂性。我们的数值结果表明,所提出的松弛使我们能够改变问题的计算尺度。更具体地说,我们表明,对于具有800个设备的系统,通过松弛计算最佳传输策略所花费的时间与解决15个设备的情况所花费的最佳解决方案所花费的时间相同。

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