The Internet of Things (IoT) holds great promise for transforming rural applications such as precision agriculture, infrastructure monitoring, and environmental sensing. However, enabling reliable and scalable wireless connectivity in rural areas remains a fundamental challenge due to sparse infrastructure, energy constraints, and wide-area coverage requirements. This dissertation presents a system-level exploration into building practical, low-power, and cost-effective LoRa-based networks tailored for rural IoT.Specifically, I address four key challenges: (1) unreliable backhaul due to the absence of cellular or wired infrastructure, (2) weak LoRa signal coverage in complex and obstructed rural terrains, (3) limited scalability of existing backscatter systems for battery-free communication, and (4) inflexible physical layer encoding that fails to meet the diverse demands of rural applications. I propose and validate a series of novel techniques, including opportunistic satellite backhaul using lightweight link estimation and routing, polarization-aligned underground communication for cross-soil sensing, concurrent non-linear chirp backscatter for scalable battery-free transmission, and reconfigurable chirp encoding for adaptive coverage, throughput, and energy balancing. These techniques are evaluated through real-world deployments, hardware prototypes, and empirical experiments across rural-scale testbeds.Together, these contributions advance the design of robust and scalable LoRa networks for rural IoT. Looking forward, this work motivates future research on integrating space–air–ground architectures, embedding joint sensing and communication capabilities, and co-designing cross-layer protocols that adapt to the dynamic and heterogeneous nature of rural environments. This dissertation lays the foundation for next-generation rural IoT systems that are not only technically efficient but also practically deployable across underserved regions.
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