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Adaptive Data Rate (ADR) is used by multi-channel LoRaWANs to meet the demanding capacity needs of LoRa networks. The network server running ADR in each channel determines the optimum data rate and assigns the appropriate spreading factor for each LoRa device to maximize the network throughput. This in turn requires the gateway to be capable of receiving LoRa packets of all possible spreading factors. Existing gateways achieve this by using multiple RF front ends, increasing the overall cost and complexity. In this work, we propose BYOG (Bring Your Own Gateway), a LoRaWAN receiver that can receive and decode 10 channels simultaneously in real-time. Towards this pipeline, we develop self-dechirping, an SF-agnostic packet detection algorithm that also detects the spreading factor of the packet. This computationally lightweight algorithm can be implemented on any general-purpose software-defined radio, bringing down the cost and ease of LoRaWAN gateway implementations. BYOG will enable research and development in LoRaWAN ADR. Using experimental, real-world datasets, we show that the proposed algorithm can detect the spreading factor accurately and operate over a wide range of SNRs using three different SDRs (RTL-SDR, HackRF One, USRP B210). BYOG performs as well as a high-end LoRaWAN gateway in terms of network throughput. 

The Cloud Radio Access Network (CRAN) architecture has been proposed as a way of addressing the network throughput and scalability challenges of large-scale LoRa networks. CRANs can improve network throughput by coherently combining signals, and scale to multiple channels by implementing the receivers in the cloud. However, in remote LoRa deployments, a CRAN's demand for high-backhaul bandwidths can be challenging to meet. Therefore, bandwidth-aware compression of LoRa samples is needed to reap the benefits of CRANs. We introduce Cloud-LoRa, the first practical CRAN for LoRa, that can detect sub-noise LoRa signals and perform bandwidth-adaptive compression. To the best of our knowledge, this is the first demonstration of CRAN for LoRa operating in real-time. We deploy Cloud-LoRa in an agricultural field over multiple days with USRP as the gateway. A cellular backhaul hotspot is then used to stream the compressed samples to a Microsoft Azure server. We demonstrate SNR gains of over 6 dB using joint multi-gateway decoding and over 2x throughput improvement using state-of-the-art receivers, enabled by CRAN in real-world deployments. 

The Cloud Radio Access Network (CRAN) architecture has been proposed as a way of addressing the network throughput and scalability challenges of large-scale LoRa networks. CRANs can improve network throughput by coherently combining signals, and scale to multiple channels by implementing the receivers in the cloud. However, in remote LoRa deployments, a CRAN’s demand for high-backhaul bandwidths can be challenging to meet. Therefore, bandwidth-aware compression of LoRa samples is needed to reap the benefits of CRANs. We introduce Cloud-LoRa, the first practical CRAN for LoRa, that can detect sub-noise LoRa signals and perform bandwidth-adaptive compression. To the best of our knowledge, this is the first demonstration of CRAN for LoRa operating in real-time. We deploy Cloud-LoRa in an agricultural field over multiple days with USRP as the gateway. A cellular backhaul hotspot is then used to stream the compressed samples to a Microsoft Azure server. We demonstrate SNR gains of over 6 dB using joint multi-gateway decoding and over 2x throughput improvement using state-of-the-art receivers, enabled by CRAN in real-world deployments. 

Abstract Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as day-to-day applications such as vital sign monitoring. Despite this urgent need, existing solutions, such as laser vibrometers, commercial Wi-Fi devices, and cameras, lack wide practical deployment due to their limited sensitivity and functionality. Here we proposed a fully passive, metamaterial-based vibration processing device, fabricated prototypes working at different frequencies ranging from 5 Hz to 285 Hz, and verified that the device can improve the sensitivity of wireless vibration measurement methods by more than ten times when attached to vibrating surfaces. Additionally, the device realizes an analog real-time vibration filtering/labeling effect, and the device also provides a platform for surface editing, which adds more functionalities to the current non-contact sensing systems. Finally, the working frequency of the device is widely adjustable over orders of magnitudes, broadening its applicability to different applications, such as structural health diagnosis, disaster warning, and vital signal monitoring. 

Understanding and handling interference across multiple active cameras. 

Wireless communication over long distances has become the bottleneck for battery-powered, large-scale deployments. Low-power protocols like Zigbee and Bluetooth Low Energy have limited communication range, whereas long-range communication strategies like cellular and satellite networks are power-hungry. Technologies that use narrow-band communication like LoRa, SigFox, and NB-IoT have low spectral efficiency, leading to scalability issues. The goal of this work is to develop a communication framework that is energy efficient, long-range, and scalable. We propose, design, and prototype WiChronos, a communication paradigm that encodes information in the time interval between two narrowband symbols to drastically reduce the energy consumption in a wide area network with large number of senders. We leverage the low data-rate and relaxed latency requirements of such applications to achieve the desired features identified above. We design and implement chirp spread spectrum transmitter and receiver using off-the-shelf components to send the narrowband symbols. Based on our prototype, WiChronos achieves an impressive 60% improvement in battery life compared to state-of-the-art LPWAN technologies in transmission of payloads less than 10 bytes at experimentally verified distances of over 4 km. We also show that more than 1,000 WiChronos senders can co-exist with less than 5% collision probability under low traffic conditions. 

The food and drug industry is facing the need to monitor the quality and safety of their products. This has made them turn to low-cost solutions that can enable smart sensing and tracking without adding much overhead. One such popular low-power solution is backscatter-based sensing and communication system. While it offers the promise of battery-less tags, it does so at the cost of a reduced communication range. In this work, we propose PACT - a scalable communication system that leverages the knowledge asymmetry in the network to improve the communication range of the tags. Borrowing from the backscatter principles, we design custom PACT Tags that are battery-less but use an active radio to extend the communication range beyond standard passive tags. They operate using the energy harvested from the PACT Source. A wide-band Reader is used to receive multiple Tag responses concurrently and upload them to a cloud server, enabling real-time monitoring and tracking at a longer range. We identify and address the challenges in the practical design of battery-less PACT Tags using an active radio and prototype them using off-the-shelf components. We show experimentally that our Tag consumes only 23μJ energy, which is harvested from an excitation Source that is up to 24 meters away from the Tag. We show that in outdoor deployments, the responses from an estimated 520 Tags can be received by a Reader concurrently while being 400 meters away from the Tags.