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

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

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

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…