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  1. 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. 
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  2. Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs. 
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