Abstract

Packet size optimization, with the purpose of minimizing the wireless packet transmission energy consumption, is crucial for the energy efficiency of the Internet of Things nodes. Meanwhile, energy scavenging from ambient energy sources has gained a significant attraction to avoid battery issues as the number of nodes increasingly grows. Packet size optimization algorithms have so far been proposed for battery-powered networks that have limited total energy with continuous power availability to prolong their lifetime. On the other hand, batteryless networks based on energy harvesting offer unlimited total energy with the interruption in availability. This is due to changing ambient conditions or the required time for harvesting and storing in small capacitors. Packet size optimization of batteryless networks has not been addressed so far. In this paper, an AI-based packet size optimization algorithm is proposed for batteryless networks that consider the amount of harvested energy at each node. Therefore, packet size is optimized dynamically for each round of data transmission. The proposed method is then evaluated via numerical simulations for a heterogenous wireless body area sensor network as a case study, considering 1-hop, cooperative, and 2-hop communication networks. Cooperative topology yields optimum energy efficiency for highly dynamic sensors, such as ECG, while 2-hop has shown to be optimum for the same type of sensors in battery-powered networks. Also, for sensors with slower dynamics such as body temperature, 1-hop turns out to be optimum in networks solely dependent on energy scavenging while cooperative topology is optimum for battery-powered networks. The algorithm applies to any heterogeneous fully batteryless networks to dynamically optimize packet size at each transmission instance.

中文翻译：

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用于无电池IoT节点的动态数据包大小优化的AI：无线人体区域传感器网络的案例研究

摘要

为了最小化无线分组传输能耗，分组大小优化对于物联网节点的能源效率至关重要。同时，随着节点数量的增长，从环境能源中清除能量已成为避免电池问题的重要吸引力。迄今为止，已经提出了针对电池供电的网络的分组大小优化算法，该电池具有有限的总能量并且具有连续的功率可用性以延长其寿命。另一方面，基于能量收集的无电池网络可提供无限的总能量，并且会中断可用性。这是由于环境条件的变化或在小型电容器中收集和存储所需的时间所致。迄今为止，尚未解决无电池网络的分组大小优化问题。在本文中，针对无电池网络提出了一种基于AI的数据包大小优化算法，该算法考虑了每个节点的能量收集量。因此，每轮数据传输都会动态优化数据包大小。然后，通过数值模拟，以一类无线人体区域传感器网络为例，通过考虑一跳，协作和两跳通信网络对提出的方法进行评估。对于高动态传感器（例如ECG），协作拓扑可产生最佳的能源效率，而对于电池供电网络中的相同类型的传感器，2跳已证明是最佳的。同样，对于诸如人体温度等动力学较慢的传感器，在仅依赖于能量清除的网络中，一跳是最佳的，而对于电池供电的网络，协作拓扑是最佳的。