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Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, and reducing cost. However, the mass collection of farm data in IoT applications raises serious concerns about potential privacy leakage that may harm the farmers’ welfare. In this work, we propose a novel scalable and private geo-distance evaluation system, called SPRIDE, to allow application servers to provide geographic-based services by computing the distances among sensors and farms privately. The servers determine the distances without learning any additional information about their locations. The key idea of SPRIDE is to perform efficient distance measurement and distance comparison on encrypted locations over a sphere by leveraging a homomorphic cryptosystem. To serve a large user base, we further propose SPRIDE+ with novel and practical performance enhancements based on pre-computation of cryptographic elements. Through extensive experiments using real-world datasets, we show SPRIDE+ achieves private distance evaluation on a large network of farms, attaining 3+ times runtime performance improvement over existing techniques. We further show SPRIDE+ can run on resource-constrained mobile devices, which offers a practical solution for privacy-preserving precision agriculture IoT applications. 

To facilitate research in dynamic spectrum access, 5G, vehicular networks, underground wireless communications, and radio frequency machine learning, a city-wide experimental testbed is developed to provide realistic radio environment, standardized experimental configurations, reusable datasets, and advanced computational resources. The testbed contains 5 cognitive radio sites, and covers 1.1 square miles across two campuses of the University of Nebraska-Lincoln and a public street in the city of Lincoln, Nebraska. Each site is equipped with a 4x4 MIMO software-defined radio transceiver with 20Gbps fronthaul connectivity. Additional cognitive radio transceivers with an underground 2x2 MIMO antenna are included in a site. High speed fronthaul network based on dedicated fiber connects the 5 sites to a cloud-based central unit for data processing and storage. The testbed provides researchers rich computational resources such as arrays of CPUs and GPUs at the cloud and FPGAs at both the edge and fronthaul network. Developed via the collaboration of the university, city, and industrial partners, this testbed will facilitate education and researches in academic and industrial communities. 

Current regulations leave a few television (TV) white spaces in populated urban areas where spectrum shortage is mostly experienced. As TV set feedback becomes essential in the next generation terrestrial TV standard, an opportunistic TV spectrum sharing based on TV receiver activity information and transmit power control is proposed to exploit the underutilized active TV channels. Based on investigation of the spatial–spectral–temporal characteristics of TV receiver activities, analytical models are developed to capture the spatio-temporal distributions of available spectrum and corresponding capacity. The influence of multiple factors, such as feedback delay, spectrum handover overhead, ranking order, and distribution of TV channel popularity are discussed and modeled. The proposed power control mechanism is verified through experiments at representative campus and residential environments. Empirical data-based simulations and geographic analyses are conducted to evaluate the developed models and further profile the spectrum opportunities within a cell, across New York city (NYC) and other 273 cities in the United States. In NYC, the proposed solution provides a 3.8 – 11.7 -fold increase of average spectrum availability, and 2.5 – 6.6 -fold increase of capacity from current regulations. By investigating the feasibility and prospects of this approach, this paper intends to motivate further discussions in policy, business, and privacy aspects to reach its significant potential. 

Problem definition: Inspired by new developments in dynamic spectrum access, we study the dynamic pricing of wireless Internet access when demand and capacity (bandwidth) are stochastic. Academic/practical relevance: The demand for wireless Internet access has increased enormously. However, the spectrum available to wireless service providers is limited. The industry has, thus, altered conventional license-based spectrum access policies through unlicensed spectrum operations. The additional spectrum obtained through these operations has stochastic capacity. Thus, the pricing of this service by the service provider has novel challenges. The problem considered in this paper is, therefore, of high practical relevance and new to the academic literature. Methodology: We study this pricing problem using a Markov decision process model in which customers are posted dynamic prices based on their bandwidth requirement and the available capacity. Results: We characterize the structure of the optimal pricing policy as a function of the system state and of the input parameters. Because it is impossible to solve this problem for practically large state spaces, we propose a heuristic dynamic pricing policy that performs very well, particularly when the ratio of capacity to demand rate is low. Managerial implications: We demonstrate the value of using a dynamic heuristic pricing policy compared with the myopic and optimal static policies. The previous literature has studied similar systems with fixed capacity and has characterized conditions under which myopic policies perform well. In contrast, our setting has dynamic (stochastic) capacity, and we find that identifying good state-dependent heuristic pricing policies is of greater importance. Our heuristic policy is computationally more tractable and easier to implement than the optimal dynamic and static pricing policies. It also provides a significant performance improvement relative to the myopic and optimal static policies when capacity is scarce, a condition that holds for the practical setting that motivated this research. 

With increasing needs of fast and reliable commu- nication between devices, wireless communication techniques are rapidly evolving to meet such needs. Multiple input and output (MIMO) systems are one of the key techniques that utilize multiple antennas for high-throughput and reliable communication. However, increasing the number of antennas in communication also adds to the complexity of channel esti- mation, which is essential to accurately decode the transmitted data. Therefore, development of accurate and efficient channel estimation methods is necessary. We report the performance of machine learning-based channel estimation approaches to enhance channel estimation performance in high-noise envi- ronments. More specifically, bit error rate (BER) performance of 2 × 2 and 4 × 4 MIMO communication systems with space- time block coding model (STBC) and two neural network-based channel estimation algorithms is analyzed. Most significantly, the results demonstrate that a generalized regression neural network (GRNN) model matches BER results of a known-channel communication for 4 × 4 MIMO with 8-bit pilots, when trained in a specific signal to noise ratio (SNR) regime. Moreover, up to 9dB improvement in signal-to-noise ratio (SNR) for a target BER is observed, compared to least square (LS) channel estimation, especially when the model is trained in the low SNR regime. A deep artificial neural network (Deep ANN) model shows worse BER performance compared to LS in all tested environments. These preliminary results present an opportunity for achieving better performance in channel estimation through GRNN and highlight further research topics for deployment in the wild.