Receiver-Initiated Data Collection in Wake-Up Radio Enabled mIoT Networks: Achieving Collision-Free Transmissions by Hashing and Partitioning

2021

To achieve ultra-low energy consumption and decade-long battery lifetime for Internet of Things (IoT) networks, wake-up radio (WuR) appears as an eminent solution. While keeping devices in deep sleep for most of the time, a WuR enabled IoT device can be woken up for data transmission at any time by a wake-up call (WuC). However, collisions happen among WuCs for transmitter-initiated data reporting and among data packets for receiver-initiated data collection . In this article, we propose three novel hashing-based schemes in order to achieve collision-free data transmissions for receiver-initiated data collection. We consider first a simple scenario where all devices in a region of interest …

Achieving Ultra Energy-efficient and Collision-free Data Collection in Wake-up Radio Enabled mIoT

2020

Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning

2021

Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence o…