Battery-powered wireless IoT devices are now widely seen in many critical applications. Given the limited battery capacity and inaccessibility to external power recharge, optimizing energy efficiency (EE) plays a vital role in prolonging the lifetime of these IoT devices. However, a sheer amount of existing works only focus on the EE design at the infrastructure level such as base stations (BSs) but with little attention to the EE design at the device level. In this paper, we propose a novel idea that aims to shift energy consumption to a grid-powered cognitive radio mesh network thus preserving energy of battery-powered devices. Under this line of thinking, we cast the design into a cross-layer optimization problem with an objective to maximize devices' energy efficiency. To solve this problem, we propose a parametric transformation technique to convert the original problem into a more tractable one. A baseline scheme is used to demonstrate the advantage of our design. We also carry out extensive simulations to exhibit the optimality of our proposed algorithms and the network performance under various settings.

中文翻译：

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优化边缘物联网能源效率 (EEE)：认知网状网络中的跨层设计

电池供电的无线物联网设备现在广泛用于许多关键应用中。鉴于电池容量有限且无法使用外部电源充电，优化能效 (EE) 在延长这些物联网设备的使用寿命方面发挥着至关重要的作用。然而，现有的大量工作仅关注基站（BS）等基础设施级别的 EE 设计，而很少关注设备级别的 EE 设计。在本文中，我们提出了一种新颖的想法，旨在将能源消耗转移到电网供电的认知无线电网状网络，从而保护电池供电设备的能量。在这种思路下，我们将设计转化为跨层优化问题，目标是最大限度地提高设备的能源效率。为了解决这个问题，我们提出了一种参数转换技术，将原始问题转换为更易于处理的问题。基线方案用于展示我们设计的优势。我们还进行了广泛的模拟，以展示我们提出的算法的最优性和各种设置下的网络性能。