Wireless technologies have been evolved rapidly, whose infrastructures are built and delivered speedily. The emerging wireless technologies offer new solutions for data communication, monitoring, sensing, and edge computing, etc. The fast growth of wireless networks generates not only opportunities for new applications, but also issues in high energy consumption, unexpected latency, and potential privacy breach. In this dissertation, we propose two novel cyber-physical systems to demonstrate the possibility of empowering new IoT services and applications by leveraging the emerging wireless charging infrastructures and benchmarking the energy performance of end nodes in low-power wireless networks, respectively. First, we present QID, the first system that identifies a Qi-compliant device during wireless charging in real-time using wireless charging fingerprints. QID employs a 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. With the novel mobile coil design and a set of novel fingerprints from oscillator and controller patterns, QID achieves high device recognition accuracy by using ensembled Machine Learning algorithms. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy. Second, we develop a novel benchmarking ecosystem, called \textit{NB-Scope}, to study the energy performance of the Narrowband Internet of Things (NB-IoT) network. NB-Scope adopts a hierarchical design, resolving the heterogeneity in network operators, node module vendors, and location profiles, to allow for the fusion of fine-grained diagnostic traces and current measurement. We then conduct a large-scale field measurement study consisting of 30 nodes deployed at over 1,200 locations in 3 regions for three months. Our in-depth analysis of the collected 49 GB traces showed that NB-IoT nodes yield significantly imbalanced energy consumption in the wild, up to a ratio of 75:1, which may lead to short battery lifetime and frequent network partition. By extensive data analysis, we identify several key factors, including diverse network coverage levels, long-tail power profile, and excessive control message repetitions, that lead to high variance in the energy performance. Finally, we explore the optimization of NB-IoT base station settings on a software-defined eNodeB testbed and suggest several important design aspects that can be considered by future NB-IoT specifications and chipsets. Our study on the NB-IoT network provides important insights into the energy consumption of low-power wide-area networks and empowers the IoT applications.
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