Network planning seeks to determine base station parameters that maximize coverage and capacity in cellular networks. However, achieving optimal planning remains challenging due to the diversity of deployment scenarios and the significant simulation-to-reality discrepancy. In this paper, we propose AutoPlan, a new automatic network planning framework by leveraging digital radio twin (DRT) techniques. We derive the DRT by finetuning the parameters of building materials to reduce the sim-to-real discrepancy based on crowdsource real-world user data. Leveraging the DRT, we design a Bayesian optimization based algorithm to optimize the deployment parameters of base stations efficiently. Using the field measurement from Husker-Net, we extensively evaluate AutoPlan under various deployment scenarios, in terms of both coverage and capacity. The evaluation results show that AutoPlan flexibly adapts to different scenarios and achieves performance comparable to exhaustive search, while requiring less than 2% of its computation time.

Solar-powered roadside edge nodes face a fundamental trade-off between V2X service coverage and energy sustainability under seasonal irradiance variation. In this poster, we propose a new edge site operation framework with a two-tier model predictive control (MPC). At the upper tier, we plan hourly energy budgets in a receding horizon, based on predictive solar energy and traffic profiles. At the lower tier, we optimize minute-level site operation modes (e.g., active and sleep) based on real-time traffic observations. Preliminary results show that the proposed framework sustains an average of 17.4 active hours per day and achieves 79.2% vehicle coverage over the year, representing a 2.3× improvement in active time compared to the baseline under a suburban simulation scenario.

This study presents a simulated transceiver with a microstrip patch antenna (MPA) designed to operate at 150 GHz and embedded in a dielectric paint. The in-paint MPA (IP-MPA) is designed for the Internet of Paint (IoP) paradigm, which envisions seamless device communication through a paint layer on walls. Due to the paint's dielectric properties, the resonant frequency of the IP-MPA lowers to 141.3 GHz, with lower spreading loss. The study introduces a comprehensive channel model between transceivers in paint at arbitrary depths as well as IP-MPA angle orientations. The best transceiver orientations are analyzed for IoP channel performance. Our extensive evaluations shows that the lateral waves, which propagate along the air-paint interface, have the lowest loss, making this path the most reliable communication path between transceivers in paint. Further, all multipaths, except the reflected paths, are depth-independent when the received power is maximized. The findings suggest that the proposed network of IP-MPA-enabled transceivers for IoP has the potential to transform conventional walls into an integrated high-speed wireless communication infrastructure.

The proliferation of nano-technology has enabled novel applications in various fields, including the potential for miniaturized terahertz (THz)-enabled devices. The extra-large bandwidth available in the THz spectrum can facilitate high-speed communication, even for transmission through lossy media. Culminating these capabilities, this paper introduces a new paradigm: Internet of Paint (IoP), transforming the 40,000-year-old concept of paint with extensive connectivity and sensing capabilities. IoP will enable seamless, massively parallel, and high-capacity communication and sensing capabilities, enabling innovative applications. IoP is expected to offer communication and sensing using nano-devices embedded in paint. In this paper, challenges towards the realization of IoP are discussed. Simulations indicate that THz signals in this stratified medium can form lateral waves that propagate along the Air-Paint interface and that relatively high channel capacity can be achieved by optimizing the transceiver locations, the paint thickness, and color. Addressing the challenges regarding nano-devices, nano-transceivers, materials, antennas, and power for IoP holds the potential to transform communication technologies and their seamless integration with living spaces.

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Climate change resulting from releasing greenhouse gases into the atmosphere continues to affect the Earth's ecosystem. This pressing issue is driving the development of novel technologies to sense and measure harmful gas emissions. In parallel, the evolution of wireless communication networks requires the wider deployment of mobile telecommunication infrastructure. The terahertz (THz) spectrum is currently under-utilized but is expected to feature in 6G. The use of this spectrum is explored simultaneously for ultra-broadband communication and atmospheric sensing. For atmospheric sensing, the absorption of THz signals by gas molecules is used to estimate atmospheric gas composition. Molecular absorption loss profiles for each gas isotopologue are taken from the HITRAN database and compared with data from transceivers in sensing mode. Preliminary results are presented, showing the effects of signal path loss and power spectral density. A 6G network architecture is proposed to indicate how 6G infrastructure can perform climate change sensing, in addition to its primary purpose of wireless communication.

In our earlier work, we introduced the concept of Gene Regulatory Neural Network (GRNN), which utilizes natural neural network-like structures inherent in biological cells to perform computing tasks using chemical inputs. We define this form of chemical-based neural network as Wet TinyML. The GRNN structures are based on the gene regulatory network and have weights associated with each link based on the estimated interactions between the genes. The GRNNs can be used for conventional computing by employing an application-based search process similar to the Network Architecture Search. This study advances this concept by incorporating cell plasticity, to further exploit natural cell's adaptability, in order to diversify the GRNN search that can match larger spectrum as well as dynamic computing tasks. As an example application, we show that through the directed cell plasticity, we can extract the mathematical regression evolution enabling it to match to dynamic system applications. We also conduct energy analysis by comparing the chemical energy of the GRNN to its silicon counterpart, where this analysis includes both artificial neural network algorithms executed on von Neumann architecture as well as neuromorphic processors. The concept of Wet TinyML can pave the way for the new emergence of chemical-based, energy-efficient and miniature Biological AI.

This work opens a new chapter in the 100, 000 year-old concept of paint, by leveraging innovations in nano-technology in the sub-THz frequency range. More specifically, the groundbreaking concept of Internet of Paint (IoP) is introduced along with a comprehensive channel model and a capacity analysis for nano-scale radios embedded in paint and communicating through paint. Nano-network devices, integrated within a paint medium, communicate via a multipath strategy, encompassing direct waves, reflections from interfaces, and lateral wave propagation. The evaluation incorporates three distinct paint types to assess path losses, received powers, and channel capacity. Analysis of path loss indicates a slight non-linear increase with both frequency and Line of Sight (LoS) distance between transceivers. Notably, paints with high refractive indexes result in the highest path loss. Moreover, burying transceivers at similar depths near the Air-Paint interface showcases promising performance of lateral waves with increasing LoS distance. Increasing paint layer depth leads to amplified attenuation, while total received power exhibits promising results when in close proximity to the Air-Paint interface but steeply declines with burial depth. Additionally, a substantial reduction in channel capacity is observed with LoS distance and burial depth, so transceivers need to be close together and in proximity of the A-P interface to communicate effectively. Comparing paint and air mediums, IoP demonstrates approximately two orders of magnitude reduction in channel capacity compared to air-based communication channels. This paper provides valuable insights into the potential of IoP communication within paint mediums and offers a foundation for further advancements in this emerging field.

Wireless networks in agricultural environments are unique in many ways. Recent measurements reveal that the dynamics of crop growth impact wireless propagation channels with a long-term seasonal pattern. Additionally, short-term environmental factors, such as strong wind, result in variations in channel statistics. Next-generation agricultural fields, populated by autonomous tractors, drones, and high-throughput sensing systems, require high-throughput connectivity infrastructure, resulting in the future deployment of high-frequency networks, where they have not been deployed before. More specifically, when millimeter-wave (mmWave) communication systems, a viable candidate for 5G and 6G high-throughput solutions, are deployed for higher throughput, these issues become more prominent due to the relatively small wavelength at this frequency band. To improve coverage in the mmWave spectrum in agricultural settings, reconfigurable intelligent surfaces (RISs) are a promising solution with low energy consumption and high cost efficiency when compared to half-duplex active relays with multiple antennas. To ensure link resiliency under dynamic channel behavior, an adaptive RIS for broadband wireless agricultural networks (AgRIS) at mmWave band is designed in this work. AgRIS relies on output from a time-series model that forecasts the short-term wind speed based on measured wind data, which is readily available in most farms. The temporal correlation between link reliability and wind speed is demonstrated through extensive field experiments. Our simulation results demonstrate that AgRIS with a small footprint of 11 × 11 elements can help mitigate the adversarial effects of wind-induced signal level drop by up to 8 dB and provides high energy efficiency of 1 Gbits/joule.

This paper explores the nexus of two emerging Internet of Things (IoT) components in precision agriculture, which requires vast amounts of agriculture fields to be monitored from air and soil for food production with efficient resource utilization. On the one hand, unmanned aerial vehicles (UAVs) have gained interest in agricultural aerial inspection due to their ubiquity and observation scale. On the other hand, agricultural IoT devices, including buried soil sensors, have gained interest in improving natural resource efficiency in crop production. In this work, the path loss and fading characteristics in wireless links between a UAV and underground (UG) nodes (Air2UG link) are studied to design a UAV altitude optimization solution. A path loss model is developed for the Air2UG link, including fading in the channel, where fading is modeled using a Rician distribution and validated using the Kolmogorov-Smirnov test. Moreover, Rician-K is found to be dependent on the UAV altitude, which is modeled with a Gaussian function with an RMSE of 0.4 − 1.3 dB. Furthermore, a novel altitude optimization solution is presented to minimize the bit error rate (BER). Results show that the lowest possible altitude does not always minimize the BER. Optimizing the altitude reduces the Air2UG link BER by as much as 8.6-fold. Likewise, altitude optimization can minimize the impacts of increasing burial depth on the BER. Our results and analysis are the first in this field and can be exploited to optimize the altitude and resources of a UAV node to communicate with the sensors embedded in the soil efficiently.

Reliable Terahertz (THz) links are necessary for outdoor point-to-point communication with the exponential growth of wireless data traffic. This study presents a modified Monte Carlo simulation procedure for estimating THz link attenuation due to multiple scattering by charged dust particles on the THz beam propagation path. Scattering models are developed for beams through dust, based on Mie and Rayleigh approximations for corresponding frequencies on Earth (0.24 THz) and Mars (0.24 & 1.64 THz). The simulation results are compared, considering parameters such as the number of Monte-Carlo photon (MCP) packets, visibility, dust particle placement density along the beam, frequency, and distance between the transmitter and the receiver. Moreover, a channel capacity model was proposed, considering THz link attenuation due to dust storms, spreading loss, and molecular absorption loss for Earth and Mars outdoor environments. Simulation results for Earth show that the link attenuation increases with dust particle placement density, distance, and frequency, and attenuation decreases with visibility and MCP packets. On Mars, similar results are obtained for both frequencies, except that the attenuation varies around a constant value with the frequency increase. Moreover, attenuation is slightly higher at 0.24 THz frequency compared to 1.64 THz when more dust particles are present on the beam propagation path. Channel capacity is estimated for Earth and Mars environments considering time and distance-dependent scenarios. Time windows that show a sudden drop of dust particles along the beam provide opportunities to communicate with high reliability. Moreover, increasing the distance between the transmitter and receiver severely reduces the channel capacity measurement in strong dust storm conditions in both environments. Our study has found that weak dust storms have relatively little effect on Mars but much more significant effects on Earth.

Millimeter-wave (mmWave) spectrum offers wide bandwidth resources that are promising to realize high-throughput wireless communications in agricultural fields. Due to the relatively small wavelength at this frequency band, mmWave signals tend to be scattered when the wireless link is established above the crop canopy. However, little is known about the scattering effect caused by crop canopy at mmWave. In this work, the scattering loss in the mmWave spectrum is quantified for different crop canopy states that are represented by the leaf area index. In particular, an approach based on a Rayleigh roughness criterion is utilized, coupled with canopy height statistics, to calculate the scattering loss. The results of the model agree well with empirical data collected from agricultural field experiments conducted in Summer 2021. The results demonstrate that as the leaf area index decreases with crop maturity, the scattering loss also decreases. This is the first work that illustrates the feasibility of using the mmWave communication links to perform sensing on the leaf area index, which is a critical metric in estimating crop conditions.

There has been much focus on the potential for wireless links that use THz frequencies. Despite their theoretical advantages, the very high channel path loss here on Earth presents practical challenges. This paper compares the path loss performance of THz links in atmospheric gas compositions and environmental conditions on Mars and Earth. Simulations using data from the Mars Climate Database and HITRAN indicate that conditions on Mars ensure that path loss between surface-based transceivers is reduced compared to Earth. Greater effective transmission distances for THz can be achieved on Mars: at frequencies of 1.67 THz and 1.64 THz, the transmission distance is 60–70 times longer than Earth. However, severe dust storms that are common on Mars can increase path loss, so the maximum transmission distance reduces by 1–2 orders of magnitude during such storms. Some of this additional path loss can be reduced by raising antennas higher above the ground and by configuring antennas to suit different usage scenarios.

Emerging agricultural internet-of-things (Ag-IoT) is increasing the efficiency of farming. The data collected by the wireless-enabled Ag-IoT infrastructure is highly sensitive as corrupting the data can cause significant damages to farm production and the livelihood of growers. The trust of the data can be established by initial secure bootstrapping of the wireless underground end nodes. This paper tackles the problem of scalable and secret-free trust-establishment for commercial off-the-shelf (COTS) underground nodes with an aboveground gateway applicable to heterogeneous end nodes. Secure boot-strapping requires authentication and secret establishment, which are achieved in-band, aided by a trusted underground node by exploiting the unique and hard-to-forge underground wireless signal propagation laws. The secret-free trust-establishment for underground wireless networks (STUN) protocol is resistant to active signal injection attacks and is scalable with an increasing number of underground nodes. Further, it is theoretically proven that STUN has security equivalent to the unbalanced oil and vinegar scheme in public cryptography. STUN is validated based on experimental data from an underground wireless testbed.

Extensive agricultural field experiments in the millimeter-wave spectrum have been conducted for the first time in corn and soybean fields to study the impacts of crop type, crop growth stage, and antenna height on the path loss. Path loss analysis is conducted based on the field measurements and control experiment data. Based on experiments with over 6,860 data points collected over a five-month period in three crop fields, our results suggest that common crops, such as corn and soybean, impact the mmWave channel. The observations provide essential guidelines for the deployment of agricultural millimeter-wave wireless networks.

Millimeter-wave (mmWave) spectrum promises high throughput links for next-generation wireless agricultural networks, which will be characterized by teams of autonomous ground vehicles, unmanned aerial vehicles (UAVs), and connected agricultural machinery. However, channel models at mmWave frequencies in agricultural environments remain elusive. Moreover, due to the dynamic crop growth behavior, agricultural field channels bear notable distinctions from urban and rural macrocellular network channels. In this work, the most extensive agricultural field experiments on the mmWave spectrum are reported and a channel model is developed to characterize the large-scale path loss, coherence bandwidth, and link quality under the effect of various environmental factors. In particular, this study investigates the effects of wind on signal-to-noise ratio, and the diffuse scattering of electromagnetic waves due to near-canopy propagation at different crop growth stages. Accordingly, (1) during the growing season, the crop canopy surface acts as a “new ground”. This new ground creates multipath components and results in a higher path loss exponent, which is correlated with the relative height between the crop canopy surface and the radios, (2) An increase of 4 m/s in gust speed results in a half-power drop (3-dB SNR degradation) due to beam misalignment and increased scattering, (3) the channel coherence bandwidth increases as the water content in the crop decreases, and (4) the beam-level spatial consistency allows for micro-mobility support for agricultural robotic applications. It is also shown that the impacts of humidity and water vapor on the mmWave channel are insignificant in the absence of rain and irrigation. Such characteristics are fundamental for designing advanced channel estimation and signal processing algorithms in advanced agricultural Internet-of-Things solutions. The extensive experiment dataset is made public for future reproducible research (https://ieeedataport.org/documents/mmwave-farm-channel-modelingwireless-agricultural-networks-broadband-millimeter-wave).

Danmu (a.k.a., barrage videos or bullet comments) is a novel type of interactive video streaming, which displays instantaneous user comments flying across the screen during the video playback to better engage the users. However, such fancy experience brings a considerable burden to the battery of mobile user devices that have limited capacity. For example, WiFi testbed experiments show 15% to 35% increase in WiFi network energy consumption because of the large amount of additional network traffic for user comments. On the other hand, current network energy minimization methods adversely impact the Quality of Service (QoS) of Danmu users, because they put off the transmission and then delay the display of the user comments that should match with the timeline of the corresponding videos. In this paper, for the first time, a heuristic QoS-aware network energy optimization algorithm is proposed to reduce the WiFi network energy consumption while still maintaining the desired QoS of Danmu users. Comprehensive testbed experiments using an open-source Danmu streaming system and with real Danmu user traces indicate up to 28% WiFi network energy saving depending on different system, network, and user settings.

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.

Vehicle-to-barrier (V2B) communications is an emerging communication technology between vehicles and road-side barriers to mitigate run-off-road crashes, which result in more than half of the traffic-related fatalities in the United States. To ensure V2B connectivity, establishing a reliable V2B channel is necessary before a potential crash, such that real-time information from barriers can help (semi-)autonomous vehicles make informed decisions. However, the characteristics of the V2B channel are not yet well understood. Therefore, in this paper, aV2B channel model is developed with three channel metrics: received power, root mean square (RMS) delay spread, and RMS Doppler spread based on experiments during controlled vehicle crash tests. Experimentation, empirical analyses, and mathematical models are introduced to capture the impacts of antenna height, barrier type, and vehicle type in V2B channel characteristics. Vehicle-height barrier antennas experience 6.4% (540ns) less reference delay spread while encountering 10% (13Hz) higher reference Doppler spread and 10dB more received power than the barrier-height barrier antennas. Moreover, steel barrier deployment results in a 21% (2, 040ns) larger reference delay spread and 2.4% (2.35Hz) smaller reference Doppler spread than concrete barrier deployment. Finally, the impact of the crash in the communication channel is investigated with these empirical metrics. To the best of our knowledge, this is the first V2B communication channel model that captures received power, RMS delay spread, and RMS Doppler spread validated with the most extensive set of vehicular crash tests. The experimental code and experiment dataset are made public to support reproducible research (https://github.com/UNL-CPN-Lab/Crashing-Waves).