Enabling Cross-technology Communication from LoRa to ZigBee via Payload Encoding in Sub-1 GHz Bands

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands.

Detection and Early Warning of Toxic Gases Based on Semiconductor Wireless Sensors

This paper studies a semiconductor wireless sensor system, which is composed of a semiconductor wireless sensor sampling circuit, gas-sensitive signal alarm and wireless transmitting circuit, and wireless radio frequency signal receiving circuit. The system is suitable for wireless monitoring of hydrogen fluoride gas in chemical plants. The hydrogen fluoride gas sensor is designed, integrated, and classified according to the polarity and size of the sensor output signal. The signal processing circuit of the sensor output signal is made with an integrated design. This paper developed a simulation experimental system for the wireless monitoring network characteristics of toxic hydrogen fluoride gas and completed the monitoring system’s sensor characteristic calibration and accuracy comparison simulation experiment, the communication distance test experiment of the communication system, and the research experiment on the influence of environmental humidity on the sensor characteristics of the monitoring system. In terms of software, the workflow of network nodes has been optimized. Since the structure of the wireless sensor network is not exactly the same in different application fields, the toxic gas monitoring system based on wireless sensor networks must focus on extending the network’s life cycle. Without affecting the normal operation of the system, distributed compressed sensing can greatly extend the service life of the system. Therefore, this subject combines the compressed sensing technology developed in recent years with the air monitoring system for the processing of transmission data, in order to achieve the purpose of further reducing the energy consumption of the system. The simulation experiment demonstrated that the lmF neural network combined with gas sensor array technology can realize qualitative identification, quantitative analysis of single gas, and quantitative analysis of mixed combustible gas. The research work in this area also provides a new way to further combine the miniature hydrogen fluoride gas sensor unit with sensor technology, integrate the hydrogen fluoride gas sensor unit and the electronic tag, and expand the wireless application of the gas sensor.

A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network

<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance.</p>