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 \emph{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 \emph{AutoPlan} under various deployment scenarios, in terms of both coverage and capacity. The evaluation results show that \emph{AutoPlan} flexibly adapts to different scenarios and achieves performance comparable to exhaustive search, while requiring less than 2\% of its computation time.

This study presents a simulated transceiver with a microstrip patch antenna (MPA) designed to resonate at 150 GHz and embedded in 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. This study introduces a comprehensive channel model for transceivers in paint at arbitrary depths and IP-MPA orientations. The best antenna orientations are analyzed for IoP channel performance. Extensive simulations indicate that the lateral waves, which propagate along the air-paint interface, exhibit the lowest loss, making this path the most reliable for communication between transceivers in paint. Furthermore, the maximum received power for each propagation path, except for the direct path, depends on depth. 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 and sensing infrastructure.

Spectrum usage is increasing daily, necessitating new methods for efficient utilization. Spectrum sharing allows the coexistence of multiple wireless communication systems in the same spectrum. Effective spectrum segmentation and classification are essential for this, yet existing methods treat them as separate processes and often focus on specific communication techniques. Our application addresses these issues by jointly segmenting and classifying narrowband signals in wideband IQ samples. This demo paper presents the application, demonstrating its end-to-end approach of spectrum segmentation and classification. The application achieves an accuracy of 92.6% on the over-the-air (OTA) wireless communication spectrum. This represents an improvement of over 9% compared to the state-of-the-art solution, highlighting its effectiveness.

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of the Internet of Things (IoT) requires innovative solutions that maximize the use of the available spectrum to support the increasing number of connected devices. The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real-time and utilize unused frequencies. In this work, Seek and Classify, an end-to-end framework for joint spectrum segmentation and classification for narrowband signals from wideband IQ samples, is developed. Seek and Classify includes a novel intersection of unions-based training methodology and machine learning architectures that advances this unique area. Evaluations performed through both synthetically generated radio signals and over-the-air experiments with software defined radios reveal that the proposed training strategy and models increase the classification accuracy from 41% to 99%. Moreover, the end-to-end framework reduces the sensing time for narrowband signals by 2-10 times, depending on hardware capabilities. The extensive evaluations provide guidance for the choice of training methods, machine learning architectures, and preprocessing tools for the most effective joint segmentation and classification performance.

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.

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.

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).

System-level timing fluctuations caused by unstable low-cost clocks and end-to-end communication delays are the main sources of uncertainties in existing synchronization mechanisms that rely on timestamp exchanges. This paper introduces a timestamp-free clock syntonization approach, carrier frequency offset (CFO)-assisted syntonization (CFOSynt), to estimate the clock skew between a pair of nodes by utilizing carrier frequency offset. To enable CFOSynt, we leverage the fact that RF oscillators in the radio can be used as the reference to calibrate the system clock oscillators, and the pairwise RF clock information is carried in the transmission carrier frequency. By incorporating CFO and capturing the clock frequency relationship in system clock skew estimation, CFOSynt can eliminate the need for timestamping and the impact of delay uncertainties. To validate the design, CFOSynt is implemented on two common off-the-shelf (COTS) IoT platforms with access to the CFO estimation from the radio chip. Extensive experiments are conducted to evaluate CFOSynt, and CFOSynt can estimate the clock skew of 32 kHz low-cost electronic oscillators with a mean error of$-2.46$Hz. In comparison with timestamp-based approaches, CFOSynt achieves up to 70-90 percent improvement in skew estimation error and shows significant reliability when low-cost oscillators are used.

Long-range (LoRa) is a suitable candidate for underground wireless communications due to its capability of communicating over a long range. However, due to the uniqueness of soil properties at a given geographical location, and the varying nature of soil moisture, it is challenging to apply a universal approach to characterize LoRa in wireless underground channels. In this paper, the performance of LoRa in underground channels is studied both theoretically and empirically. The range and bit error rate (BER) formulation of LoRa is derived as a function of soil parameters based on statistical underground channel models. To validate the model, path loss measurements are conducted under different moisture levels in two soil types (sandy and silty clay loam soil). In addition, as underground communication is also dependent on the return loss of buried antennas, the path loss measurements are performed using two different types of underground antennas. Results show that the underground channel models agree well with empirical LoRa measurements, resulting in R-squared values of 0.87-0.89. The results suggest that the performance of LoRa in underground channels can be predicted using the models developed in this paper.

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.

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).

The (inverse) discrete Fourier transform (DFT/ IDFT) is often perceived as essential to orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex-valued convolutional network (DCCN) is developed to recover bits from time-domain OFDM signals without relying on any explicit DFT/IDFT. The DCCN can exploit the cyclic prefix (CP) of OFDM waveform for increased SNR by replacing DFT with a learned linear transform, and has the advantage of combining CP-exploitation, channel estimation, and intersymbol interference (ISI) mitigation, with a complexity ofO(N2). Numerical tests show that the DCCN receiver can outperform the legacy channel estimators based on ideal and approximate linear minimum mean square error (LMMSE) estimation and a conventional CP-enhanced technique in Rayleigh fading channels with various delay spreads and mobility. The proposed approach benefits from the expressive nature of complex-valued neural networks, which, however, currently lack support from popular deep learning platforms. In response, guidelines of exact and approximate implementations of a complex-valued convolutional layer are provided for the design and analysis of convolutional networks for wireless PHY. Furthermore, a suite of novel training techniques are developed to improve the convergence and generalizability of the trained model in fading channels. This work demonstrates the capability of deep neural networks in processing OFDM waveforms and the results suggest that the FFT processor in OFDM receivers can be replaced by a hardware AI accelerator.

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.

Wireless underground sensor networks (WUSNs) are becoming ubiquitous in many areas. The design of robust systems requires an extensive understanding of the underground (UG) channel characteristics. In this article, the UG channel impulse response is modeled and validated via extensive experiments in indoor and field testbed settings. Three distinct types of soils are selected with sand contents ranging from 13% to 86%, and clay contents ranging from 3% to 32%. The impacts of changes in soil texture and soil moisture are investigated with more than 1, 200 measurements in a novel UG testbed at the University of Nebraska-Lincoln that allows flexibility in soil moisture control. Moreover, the time-domain characteristics of the channel, such as the RMS delay spread, coherence bandwidth, and multipath power gain, are analyzed. The power delay profile analysis validates the three main components of the UG channel: direct, reflected, and lateral waves. Furthermore, it is shown that the RMS delay spread follows a log-normal distribution. The coherence bandwidth ranges between 650 kHz and 1.15 MHz for soil paths of up to 1 m and decreases to 418 kHz for distances above 10 m. Soil moisture is shown to affect the RMS delay spread non-linearly, which provides opportunities for soil moisture-based dynamic adaptation techniques. A statistical channel model for the wireless underground channel has been developed based on the measurements and analysis. The statistical model shows good agreement with the measurement data. The model and analysis pave the way for tailored solutions for data harvesting, UG sub-carrier communication, and UG beamforming.

In this paper, the problem of optimal scheduling of network synchronization in the Internet of Things is studied. The optimal scheduling problem is transformed into a stochastic optimization problem by modeling clock skew and offset process as virtual queues. Synchronization is therefore achieved by controlling the virtual queue backlog sizes to satisfy constraints on synchronization errors. Accordingly, an optimal synchronization scheduling algorithm (OSSA) is designed to optimize network utility functions. Optimal policies are considered to minimize the energy cost and the age of information using OSSA, and it is proven that the performance can be controlled to be arbitrarily close to optimal. The performance of OSSA is compared against periodic and on-demand synchronization algorithms.

Efficient and accurate time synchronization is critical to various applications in the Internet of Things and has been extensively studied. The emergence of large-scale, duty-cycled sensor networks ruins the previous assumptions used for protocol design, leading to the necessity of developing new synchronization protocols to achieve better energy efficiency and reliability. In this paper, the correlation-based time synchronization protocol (CorTiS), which exploits the spatiotemporal correlation between clocks, is developed. To the best of our knowledge, this is the first effort that exploits spatiotemporal correlation for time synchronization. A theoretical spatiotemporal model is presented to capture the collective behaviors of clocks. With this model, a probabilistic prediction scheme to infer synchronization errors based on correlation is developed without additional message exchanges. The CorTiS protocol is then developed incorporating community detection technique to cluster clocks that are tightly correlated as synchronization communities, such that different communities can be adaptively synchronized. Simulation results show that with CorTiS, participating nodes in each synchronization round can be significantly reduced in dynamic environments.

Construction workers who usually work in high altitudes and hazardous construction zones are prone to accidents that may lead to injuries and fatalities. This calls for advanced monitoring technologies that can locate workers and hazardous areas within a dynamically evolving outdoor/indoor area. More accurate localization techniques indicate more safety for protecting them from an accident. In this paper, a multiple transmit power assisted sequence-based localization (MPSBL) solution is developed to achieve high localization accuracy. The theoretical analysis and system model design illustrate the feasibility of MPSBL. Simulations and empirical experiments in construction zones have been conducted, which show that MPSBL outperforms state-of-the-art approaches.

Internet of underground things (IOUT) is an emerging paradigm which consists of sensors and communication devices, partly or completely buried underground for real-time soil sensing and monitoring. In this paper, the performance of different modulation schemes in IOUT communications is studied through simulations and experiments. The spatial modularity of direct, lateral, and reflected components of the UG channel is exploited by using multiple antennas. First, it has been shown that bit error rates of 10-3can be achieved with normalized delay spreads (τd) lower than 0.05. Evaluations are conducted through the first software-defined radio-based field experiments for UG channel. Moreover, equalization has a significant impact on the performance improvement of an IOUT system. An 8-Tap DFE (decision-feedback equalizer) adaptive equalizer achieves better performance. It is also found that DBPSK, and DPSK are more suitable for digital communications in the UG channel without adaptive equalization. Then, two novel UG receiver designs, namely, 3W-Rake and Lateral-Direct-Reflected (LDR) are developed and analyzed for performance improvement. It has been shown that with a three antenna LDR design, BER of lower than 10-5can be achieved. The BER of these two approaches are compared and the LDR has been shown to perform better.

System-level timing inconsistency and wireless communication delays are the main uncertainties of existing synchronization mechanisms for wireless sensor networks. Existing solutions mainly rely on timestamp exchanges to estimate clock offset and skew, which results in frequent synchronization, high overhead, and high energy consumption to maintain a well-synchronized network. This paper introduces a novel clock syntonization approach to estimate the differences between clock frequencies of network nodes without the need for timestamp exchanges. The carrier frequency offset (CFO) assisted syntonization (CFOSynt) utilizes the carrier information obtained from wireless packet transmission for clock skew compensation. The key idea of CFOSynt is that, in any wireless communication system, where carrier modulation is employed, carrier frequency delivers information about the transmitter RF clock. Consequently, clock frequency offset between a pair of sensor nodes will result in a carrier frequency offset detected by the receiver node. By leveraging the CFO information, CFOSynt can estimate the system clock skew based on digital counter theory. Extensive experiments and numerical analysis have been demonstrated to evaluate clock skew estimation.

Current wireless underground (UG) communication techniques are limited by their achievable distance. In this paper, a novel framework for underground beamforming using adaptive antenna arrays is presented to extend communication distances for practical applications. Based on the analysis of propagation in wireless underground channel, a theoretical model is developed which uses soil moisture information to improve wireless underground communications performance. Array element in soil is analyzed empirically and impacts of soil type and soil moisture on return loss (RL) and resonant frequency are investigated. Accordingly, beam patterns are analyzed to communicate with underground and above ground devices. Depending on the incident angle, refraction from soil-air interface has adverse effects in the UG communications. It is shown that beam steering improves UG communications by providing a high-gain lateral wave. To this end, the angle, which enhances lateral wave, is shown to be a function of dielectric properties of the soil, soil moisture, and soil texture. Evaluations show that this critical angle varies from 0° to 16° and decreases with soil moisture. Accordingly, a soil moisture adaptive beamforming (SMABF) algorithm is developed for planar array structures and evaluated with different optimization approaches to improve UG communication performance.

In this work, two access point (AP) selection mechanisms called STOOP and MD-STOOP are presented for enterprise wireless local area networks (WLANs). STOOP utilizes first- order stochastic dominance (FSD) tools to develop a runtime algorithm that probes the network to compare the quality of service from multiple APs in a stochastic manner. MD-STOOP utilizes the concept of mean dominance (MD) to select APs, which provides good average case performance with less overhead than STOOP. Performance of the two algorithms is evaluated through extensive simulations in comparison to the state-of-the-art. It is shown that STOOP and MD-STOOP outperform existing solutions and directions for further improvements are discussed.

Unique interactions between soil and communication components in wireless underground communications necessitate revisiting fundamental communication concepts from a different perspective. In this paper, capacity profile of wireless underground (UG) channel for multi-carrier transmission techniques is analyzed based on empirical antenna return loss and channel frequency response models in different soil types and moisture values. It is shown that data rates in excess of 124 Mbps are possible for distances up to 12 m. For shorter distances and lower soil moisture conditions, data rates of 362 Mbps can be achieved. It is also shown that due to soil moisture variations, UG channel experiences significant variations in antenna bandwidth and coherence bandwidth, which demands dynamic subcarrier operation. Theoretical analysis based on this empirical data show that by cyber-physical adaption to soil moisture variations, 180% improvement in channel capacity is possible when soil moisture decreases. It is shown that compared to a fixed bandwidth system; soil-based, system and sub-carrier bandwidth adaptation leads to capacity gains of 56%-136%. The analysis is based on indoor and outdoor experiments with more than 1,500 measurements taken over a period of 10 months. These semi-empirical capacity results provide further evidence on the potential of underground channel as a viable media for high data rate communication and highlight potential improvements in this area.

Wireless underground sensor networks (WUSNs) are becoming ubiquitous in many areas and designing robust systems requires extensive understanding of the underground (UG) channel characteristics. In this paper, UG channel impulse response is modeled and validated via extensive experiments in indoor and field testbed settings. Three distinct types of soils are selected with sand and clay contents ranging from 13% to 86% and 3% to 32%, respectively. Impacts of changes in soil texture and soil moisture are investigated with more than 1,200 measurements in a novel UG testbed that allows flexibility in soil moisture control. Time domain characteristics of channel such as RMS delay spread, coherence bandwidth, and multipath power gain are analyzed. The analysis of the power delay profile validates the three main components of the UG channel: direct, reflected, and lateral waves. It is shown that RMS delay spread follows a log-normal distribution. The coherence bandwidth ranges between 650 kHz and 1.15MHz for soil paths of up to 1m and decreases to 418 kHz for distances above 10m. Soil moisture is shown to affect RMS delay spread non-linearly, which provides opportunities for soil moisture-based dynamic adaptation techniques. The model and analysis paves the way for tailored solutions for data harvesting, UG sub-carrier communication, and UG beamforming.

Today, more than half of the traffic fatalities are a result of run-off- road (RoR) crashes, which usually involve a single vehicle. Roadside barriers are often the last means to mitigate the severity of a RwD crashes into hazardous objects or features. While the recent research on vehicular communications primarily focus on safety related wireless communications for vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) scenarios, the interactions between vehicles and barriers in next-generation vehicular systems have not been well- studied. In this paper, vehicle-to-barrier (V2B) wireless communication paradigm is introduced as a potential missing link in preventing single-vehicle RoR fatalities. V2B communications, which take place between vehicles and radios embedded in roadside barriers can contribute to keeping cars on the road and help mitigate RwD crashes. The realization of V2B communication services necessitates an in-depth understanding of the underlying physical characteristics of the environment and channel. To this end, in this paper, some of the first real world field test measurement results of V2B communications are presented. More specifically, the effects of two types of commonly-utilized barriers (rigid concrete barrier and corrugated-beam guardrail) on the V2B channel communications are illustrated. The results show that guardrail barriers exhibit a waveguiding effect on signal transmission, while higher signal attenuation is observed with rigid barriers. Moreover, experiments illustrate the characteristics of V2B orthogonal frequency-division multiplexing (OFDM) communication during vehicle encroachment and braking. The results highlight the adverse effects of vehicle braking on OFDM signal transmission in terms of received signal strength, peak to average power ratio, and error vector magnitude.

Current TV whitespace regulations mainly benefit rural areas where large amounts of TV whitespace exist. Thus, the spectrum scarcity problem is yet to be addressed in urban locations, where it is most experienced. To further improve the spectrum efficiency, a new framework for cognitive radio network operation is presented, which can coexist with current broadcast TV networks. Through geographical evaluations based on distribution of TV towers and population dynamics, it is shown that by leveraging the TV viewership statistics, 5.6-7.7-fold increase in available channels can be provided to mobile users in populated areas such as New York City. Furthermore, daily dynamics of TV viewership can be exploited to provide up to 96 MHz additional bandwidth during prime time and 162-228 MHz additional bandwidth during non-peak hours. The additional TV spectrum can provide additional channel capacities in both rural and urban areas. To the best of our knowledge, this is the first work that analyzes TV whitespace availability based on TV viewership statistics in space and time.

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2(S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations.

Wireless underground sensor networks (WUSNs) have recently been investigated for a wide range of applications. One challenge in wireless underground communications is its high bit error rate, especially at long distances. In WUSNs, the communication quality is substantially affected by the environment, especially the soil moisture. Thus, the underground channel quality can be effectively estimated based on local soil moisture readings. By utilizing the local soil moisture values to estimate the channel quality, adaptive error control mechanisms can be implemented for underground nodes. In this paper, two error control mechanisms, adaptive-rate forward error control and adaptive transmit power control, are considered for WUSNs. The results indicate that compared to ARQ, adaptive FEC code can increase the ranges of soil moisture values within which the network is reliable by reducing the bit error rate. In addition, adaptive transmit power control can improve energy efficiency when a wide range of transmit power levels is available. Our evaluations show that to achieve 60m communication distance in practical soil settings, the output power of the transmitter should to be adjusted in a range of 0dBm to 25dBm to improve the energy efficiency of the underground nodes.

Recent developments in wireless underground communication have enabled the realization of underground sensor network applications. To this end, it is desirable to provide a sustainable operation for wireless underground sensor networks (WUSNs) with extended lifetimes as maintenance is significantly costly. One promising method towards sustainable operation is to harvest energy underground based on the vibration sources in the environment. However, to the best of our knowledge, underground vibration energy harvesting has not been investigated before. In this paper, the feasibility of vibration energy harvesting for WUSNs is investigated. First, an analytical framework is developed to model the maximum harvestable power by a piezoelectric energy harvester at a certain depth underground, due to an above-ground vibration source. Then, field experiments are conducted to measure the vibration in an agricultural testbed and evaluated the harvestable output power. The results from this study illustrate the feasibility of vibration energy harvesting as a promising approach to be considered for the future underground sensor networks.

Wireless underground sensor networks (WUSNs) consist of sensors that are buried in and communicate through soil. The channel quality of WUSNs is strongly impacted by environmental parameters such soil moisture. Thus, the communication range of the nodes and the network connectivity vary over time. To address the challenges in underground communication, above ground nodes are deployed to maintain connectivity. In this paper, the connectivity of WUSNs under varying environmental conditions is captured by modeling the cluster size distribution under sub-critical conditions and through a novel aboveground communication coverage model for underground clusters. The resulting connectivity model is utilized to analyze two communication schemes: transmit power control and environment-aware routing, which maintain connectivity while reducing energy consumption. It is shown that transmit power control can maintain network connectivity under all soil moisture values at the cost of energy consumption. Utilizing relays based on soil moisture levels can decrease this energy consumption. A composite of both approaches is also considered to analyze the tradeoff between connectivity and energy consumption.

This paper presents CraneTracker, a novel sensor platform for monitoring migratory birds. The platform is designed to monitor Whooping Cranes, an endangered species that conducts an annual migration of 4,000 km between southern Texas and north-central Canada. CraneTracker includes a rich set of sensors, a multi-modal radio, and power control circuitry for sustainable, continental-scale information delivery during migration. The need for large-scale connectivity motivates the use of cellular technology in low-cost sensor platforms augmented by a low-power transceiver for ad-hoc connectivity. This platform leads to a new class of cellular sensor networks (CSNs) for time-critical and mobile sensing applications. The CraneTracker is evaluated via field tests on Wild Turkeys, Siberian Cranes, and an on-going alpha deployment with wild Sandhill Cranes. Experimental evaluations demonstrate the potential of energy-harvesting CSNs for wildlife monitoring in large geographical areas, and reveal important insights into the movements and behaviors of migratory animals. In addition to benefiting ecological research, the developed platform is expected to extend the application domain of sensor networks and enable future research applications.

The Whooping Crane is an endangered species native to North America and there are approximately 575 in existence. There have been recent efforts to provide ecologists with a tool to study the multifaceted behavior of the endangered species. Like many species, cranes display distinctly identifiable movements while being threatened, acting territorial, migrating, or preening. The preliminary experiments described in this poster provide evidence that sensor data presented by a novel sensing platform, the CraneTracker, can be used to identify crane behaviors on-board. With the ability to identify these behaviors, ecologists will have a more granular insight on what occurs during a crane's life on a daily basis.

In this demo, a novel underground communication system and an online underground sensor network testbed is demonstrated. The underground communication system, developed in the Cyber-physical Networking (CPN) Laboratory at the University of Nebraska-Lincoln, includes an underground antenna that is tailored to mitigate the adverse effects of soil on underground communication. An online connection is established with the CPN underground sensor network testbed that is located at Clay Center, Nebraska. The underground sensor network testbed consists of a network of underground communication systems equipped with soil moisture sensors and a mobile data harvesting unit equipped with cellular communication capabilities. Real-time soil moisture data delivery from Nebraska to Korea is demonstrated.

Wireless Underground Sensor Networks (WUSNs) allow for continuous field monitoring without interfering with aboveground activities, such as plowing or football games. Due to the increased path loss in soil, it is challenging to ensure that a large-scale underground network is connected while still being cost effective in terms of deployment and maintenance. In this paper, a practical WUSN architecture is developed, consisting of mobile nodes that harvest data from stationary underground nodes. To this end, the impacts of packet size and error control schemes on network performance are investigated through field experiments. By developing a better understanding of the wireless channel and the reliability between underground and aboveground nodes, a family of mobile WUSN protocols is developed and evaluated in terms of packet delivery success, delay tolerance and network lifetime.

Emerging applications of wireless sensor networks (WSNs) require real-time quality-of-service (QoS) guarantees to be provided by the network. Due to the nondeterministic impacts of the wireless channel and queuing mechanisms, probabilistic analysis of QoS is essential. One important metric of QoS in WSNs is the probability distribution of the end-to-end delay. Compared to other widely used delay performance metrics such as the mean delay, delay variance, and worst-case delay, the delay distribution can be used to obtain the probability to meet a specific deadline for QoS-based communication in WSNs. To investigate the end-to-end delay distribution, in this paper, a comprehensive cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework is generic and can be parameterized for a wide variety of MAC protocols and routing protocols. Case studies with the CSMA/CA MAC protocol and an anycast protocol are conducted to illustrate how the developed framework can analytically predict the distribution of the end-to-end delay. Extensive test-bed experiments and simulations are performed to validate the accuracy of the framework for both deterministic and random deployments. Moreover, the effects of various network parameters on the distribution of end-to-end delay are investigated through the developed framework. To the best of our knowledge, this is the first work that provides a generic, probabilistic cross-layer analysis of end-to-end delay in WSNs.

Wireless Underground Sensor Networks (WUSNs) are an emerging type of wireless sensor networks (WSNs), where sensor nodes are located under the ground and communicate through soil. The major challenge in the development of efficient communication protocols for WUSNs is the characterization of the underground channel. So far, none of the existing models fully capture all the components of electromagnetic signal propagation in the soil medium. In this paper, three major components that influence underground communication are identified: direct, reflected, and lateral waves, where the latter has not been analyzed for WUSNs so far. Accordingly, a closed- form three-wave (3W) channel model is developed based on EM propagation principles of signals through soil. The 3W channel model is shown to agree well with both underground testbed experiments and EM analysis based on Maxwell's equations, which cannot be represented in closed-form.

A dual-network Cyber-physical Networking (CPN) testbed developed at the University of Nebraska-Lincoln is described. The CPN testbed consists of two geographically disparate wireless sensor networks connected by a traditional TCP/IP network and enables peer-to-peer communication between each sensor in the network. The functionality of the testbed is enhanced by a range of software tools that support remote programming and network monitoring, and real-time visualization of sensor data. The resulting architecture supports easy deployment and evaluation of applications for both traditional and interconnected wireless sensor networks. To demonstrate the features of this testbed, a novel ping application was developed and deployed as a proof-of-concept application for peer-to-peer communication in geographically separated wireless sensor networks. Experimental evaluations of the ping application yield insight into the communications overhead that can be expected in future applications of peer-to-peer interconnected sensor networks.

Sandstorms are serious natural disasters, which are commonly seen in the Middle East, Northern Africa, and Northern China.In these regions, sandstorms have caused massive damages to the natural environment, national economy, and human health. To avoid such damages, it is necessary to effectively monitor the origin and development of sandstorms. To this end, wireless sensor networks (WSNs) can be deployed in the regions where sandstorms generally originate so that sensor nodes can collaboratively perform sandstorm monitoring and rapidly convey the observations to remote administration center. Despite the potential advantages, the deployment of WSNs in the vicinity of sandstorms faces many unique challenges, such as the temporally buried sensors and increased path loss during sandstorms. Consequently, the WSNs may experience frequent disconnections during the sandstorms. This further leads to dynamically changing topology. In this paper, a topology analysis of the WSNs for sandstorm monitoring is performed. Four types of channels a sensor can utilize during sandstorms are analyzed, which include air-to-air channel, air-to-sand channel, sand-to-air channel, and sand-to-sand channel. Based on the channel model solutions, a percolation-based connectivity analysis is performed. It is shown that if the sensors are buried in low depth, allowing sensor to use multiple types of channels improves network connectivity. Accordingly, much smaller sensor density is required compared to the case, where only terrestrial air channels are used. Through this topology analysis a WSN architecture can be deployed for very efficient sandstorm monitoring.

Emerging applications of wireless sensor networks (WSNs) require real-time event detection to be provided by the network. In a typical event monitoring WSN, multiple reports are generated by several nodes when a physical event occurs, and are then forwarded through multi-hop communication to a sink that detects the event. To improve the event detection reliability, usually timely delivery of a certain number of packets is required. Traditional timing analysis of WSNs are, however, either focused on individual packets or traffic flows from individual nodes. In this paper, a spatio-temporal fluid model is developed to capture the delay characteristics of event detection in large-scale WSNs. More specifically, the distribution of delay in event detection from multiple reports is modeled. Accordingly, metrics such as mean delay and soft delay bounds are analyzed for different network parameters. Motivated by the fact that queue build up in WSNs with low-rate traffic is negligible, a lower-complexity model is also developed. Testbed experiments and simulations are used to validate the accuracy of both approaches. The resulting framework can be utilized to analyze the effects of network and protocol parameters on event detection delay to realize real-time operation in WSNs. To the best of our knowledge, this is the first approach that provides a transient analysis of event detection delay when multiple reports via multi-hop communication are needed.

Wireless embedded smart cameras provide flexibility in camera deployment in terms of the locations and number of the cameras. However, these battery-powered embedded vision sensors have very limited energy, memory, and processing power. Energy consumption and latency are two major concerns in wireless embedded camera networks. In multi-camera tracking applications, the amount of data exchanged between cameras has an effect on the tracking accuracy, the energy consumption of the camera nodes and the latency. In this paper, we provide a detailed quantitative analysis of the accuracy-latency-energy tradeoff for overlapping and non-overlapping camera setups when different-sized data packets are transferred in a wireless manner. The experiments have been performed with an actual wireless embedded smart camera network employing CITRIC motes, and performing tracking of objects.

In Wireless multimedia sensor networks (WMSNs), two graphs, communication network graph and vision graph, can be established. The camera nodes connected in the vision graph share overlapped field of views (FOVs) and they depend on the densely deployed relay nodes in the communication network graph to communicate with each other. Given a uniformly deployed camera sensor network with relay nodes, the problem is to find the number of hops for the vision-graph- neighbor-searching messages to construct the vision graph in an energy efficient way. In this paper, mathematical models are developed to analyze the FOV overlap of the camera nodes and the multi-hop communications in two dimensional topologies, which are utilized to analyze the optimal hop number. In addition, simulations are conducted to verify our models.

Energy and bandwidth are limited resources in wireless sensor networks, and communication consumes significant amount of energy. When wireless vision sensors are used to capture and transfer image and video data, the problems of limited energy and bandwidth become even more pronounced. Thus, message traffic should be decreased to reduce the communication cost. In many applications, the interest is to detect composite and semantically higher-level events based on information from multiple sensors. Rather than sending all the information to the sinks and performing composite event detection at the sinks or control-center, it is much more efficient to push the detection of semantically high-level events within the network, and perform composite event detection in a peer-to-peer and energy-efficient manner across embedded smart cameras. In this paper, three different operation scenarios are analyzed for a wireless vision sensor network. A detailed quantitative comparison of these operation scenarios are presented in terms of energy consumption and latency. This quantitative analysis provides the motivation for, and emphasizes (1) the importance of performing high-level local processing and decision making at the embedded sensor level and (2) need for peer-to-peer communication solutions for wireless multimedia sensor networks.

Wireless Underground Sensor Networks (WUSNs) consist of wirelessly connected underground sensor nodes that communicate untethered through soil. WUSNs have the potential to impact a wide variety of novel applications including intelligent irrigation, environment monitoring, border patrol, and assisted navigation. Although its deployment is mainly based on underground sensor nodes, a WUSN still requires aboveground devices for data retrieval, management, and relay functionalities. Therefore, the characterization of the bi-directional communication between a buried node and an aboveground device is essential for the realization of WUSNs. In this work, empirical evaluations of underground-to- aboveground (UG2AG) and aboveground-to-underground (AG2UG) communication are presented. More specifically, testbed experiments have been conducted with commodity sensor motes in a real-life agricultural field. The results highlight the asymmetry between UG2AG and AG2UG communication with distinct behaviors for different burial depths. To combat the adverse effects of the change in wavelength in soil, an ultra wideband antenna scheme is deployed, which increases the communication range by more than 350% compared to the original antennas. The results also reveal that a 21% increase in the soil moisture decreases the communication range by more than 70%. To the best of our knowledge, this is the first empirical study that highlights the effects of the antenna design, burial depth, and soil moisture on both UG2AG and AG2UG communication performance. These results have a significant impact on the development of multi-hop networking protocols for WUSNs.

Developing applications for wireless sensor networks (WSNs) can provide many challenges. Environmental conditions have a large impact on the behavior of an application, but it may not be feasible to replicate the conditions of the deployment environment while creating the application. Furthermore, long-term deployment of monitoring applications require extensive pre-deployment analysis of such applications since the sensors cannot be accessed after their deployment. Through a combination of simulation and software engineering practices, it is possible to rigorously test and validate the software for WSNs. In this paper, several methods for simulating distributed mobile WSNs and testing the software are provided. These methods are used in the development of a WSN that was deployed to track Whooping Cranes during their year long migration.

Limited energy resources in wireless sensor networks (WSNs) call for a comprehensive cross-layer analysis of energy consumption in a multi-hop network. In this paper, we provide a stochastic analysis of the energy consumption in a random network environment. Accordingly, a comprehensive cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework accurately predicts the distribution of energy consumption for nodes in WSNs during a given time period. We show that when the time duration is long, the energy consumption asymptotically approaches a Normal distribution. Using the distribution of energy consumption, the distribution of node lifetime is also investigated. With the help of this probabilistic model, a case study with an anycast protocol is conducted to show how the developed framework can analytically predict the distribution of energy consumption and lifetime. Comprehensive simulations and testbed experiments are provided to validate the developed model. The cross-layer framework is also used to identify relationships between the distribution of energy consumption and network parameters, such as network density, duty cycle, and traffic rate. To the best of our knowledge, this is the first work to investigate probabilistic distribution of energy consumption in WSNs.

Anycasting has been proposed recently as an efficient communication method for asynchronous duty-cycled wireless sensor networks. However, the interdependencies between end-to-end communication cost and the anycasting design parameters have not been systematically studied. In this paper, a statistical end-to-end cost model is presented to capture the end-to-end latency and energy consumption of anycasting operation under a realistic wireless channel model. By exploring the relationship between the end-to-end cost efficiency and the forwarding decision dependent anycasting design parameters, two anycasting forwarding metrics are proposed for fully distributed forwarding decision. By exploring the relationship among the preamble length, the size of the forwarding set and the achievable end-to-end cost efficiency, a series of preamble length control guidelines are proposed for low and extremely low duty-cycled WSNs. According to our analytical results and simulation validation, the proposed forwarding metrics help reduce the end-to-end latency and energy consumption by about 55% for anycasting with moderate preamble length, compared with the existing heuristic forwarding metrics. The proposed preamble length control guidelines help reduce, by more than half, the end-to-end energy and latency costs in low and extremely-low duty-cycled WSNs.

Severe energy constraints of battery-powered sensor nodes necessitate energy-efficient communication in Wireless Sensor Networks (WSNs). However, the vast majority of the existing solutions are based on the classical layered protocol approach, which leads to significant overhead. It is much more efficient to have a unified scheme, which blends common protocol layer functionalities into a cross-layer module. In this paper, a cross-layer protocol (XLP) is introduced, which achieves congestion control, routing, and medium access control in a cross-layer fashion. The design principle of XLP is based on the cross-layer concept of initiative determination, which enables receiver-based contention, initiative-based forwarding, local congestion control, and distributed duty cycle operation to realize efficient and reliable communication in WSNs. The initiative determination requires simple comparisons against thresholds, and thus, is very simple to implement, even on computationally constrained devices. To the best of our knowledge, XLP is the first protocol that integrates functionalities of all layers from PHY to transport into a cross-layer protocol. A cross-layer analytical framework is developed to investigate the performance of the XLP. Moreover, in a cross-layer simulation platform, the state-of-the-art layered and cross-layer protocols have been implemented along with XLP for performance evaluations. XLP significantly improves the communication performance and outperforms the traditional layered protocol architectures in terms of both network performance and implementation complexity.

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. However, designing real-time scheduling and communication solutions for these networks is challenging since the characteristics of QoS metrics in WSNs are not well known yet. Due to the nature of wireless connectivity, it is infeasible to satisfy worst-case QoS requirements in WSNs. Instead, probabilistic QoS guarantees should be provided, which requires the definition of probabilistic QoS metrics. To provide an analytical tool for the development of real-time solutions, in this paper, the distribution of end-to-end delay in multi-hop WSNs is investigated. Accordingly, a comprehensive and accurate cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework captures the heterogeneity in WSNs in terms of channel quality, transmit power, queue length, and communication protocols. A case study with the TinyOS CSMA/CA MAC protocol is conducted to show how the developed framework can analytically predict the distribution of end-to-end delay. Testbed experiments are provided to validate the developed model. The cross-layer framework can be used to identify the relationships between network parameters and the distribution of end-to-end delay and accordingly, to design real-time solutions for WSNs. Our ongoing work suggests that this framework can be easily extended to model additional QoS metrics such as energy consumption distribution. To the best of our knowledge, this is the first work to investigate probabilistic QoS guarantees in WSNs.

In this paper, a cross-layer solution for packet size optimization in wireless sensor networks (WSN) is introduced such that the effects of multi-hop routing, the broadcast nature of the physical wireless channel, and the effects of error control techniques are captured. A key result of this paper is that contrary to the conventional wireless networks, in wireless sensor networks, longer packets reduce the collision probability. Consequently, an optimization solution is formalized by using three different objective functions, i.e., packet throughput, energy consumption, and resource utilization. Furthermore, the effects of end-to-end latency and reliability constraints are investigated that may be required by a particular application. As a result, a generic, cross-layer optimization framework is developed to determine the optimal packet size in WSN. This framework is further extended to determine the optimal packet size in underwater and underground sensor networks. From this framework, the optimal packet sizes under various network parameters are determined.

Supporting end-to-end real-time communication is important for wireless sensor networks (WSNs) to acomplish the collaborative sensing tasks with specific timing constraints. However, without considering the unique constraints for WSNs, many existing real-time communication protocols prove to be infeasible for low-cost WSNs. In this paper, we propose a novel real-time communication scheme (RCS) to provide service-differentiated soft real-time guarantees for end-to-end communication in WSNs. We use hop-based geographic grouping to enable location awareness for sensor nodes with extremely low control overhead.We use dynamic forwarding with load-balanced receiver contention to provide a light-weight, yet efficient, routing technique, which can be easily adapted for duty cycle design. We use polling contention period based real-time MAC support to improve the service-differentiation granularity with better bandwidth utilization. The performance evaluation shows that our scheme can achieve low end-to-end latency, high on-time delivery ratio, fine services-differentiation granularity with load-balance for real-time traffic in unsynchronized low-cost WSNs.

Wireless Sensor Networks (WSN) are comprised of densely deployed sensor nodes collaboratively observing and communicating extracted information about a physical phenomenon. Dense deployment of sensor nodes makes the sensor observations highly correlated in the space domain. In addition, consecutive samples obtained by a sensor node are also temporally correlated for the applications involving the observation of the variation of a physical phenomenon. Based on the physical characteristics and dispersion pattern over the area, the phenomenon to be observed can be modeled as point source or field source. Clearly, understanding the spatio-temporal correlation characteristics of the point and field sources brings potential advantages to be exploited in the design of efficient communication protocols. In this paper, a theoretical analysis of spatio-temporal correlation in WSN is carried out. The objective of this analysis is to capture the spatio-temporal characteristics of point and field sources in WSN. First, the model for point and field sources are developed and their spatio-temporal characteristics are analytically derived along with the distortion functions. Based on the theoretical analysis, numerical simulations are performed. This analytical work provides tools for finding the feasible operating region in terms of spatial and temporal resolution for a specific distortion constraint considering spatio-temporal correlation, signal properties, and network variables in WSN.

Severe energy constraints and hence the low power communication requirements amplify the significance of the energy efficient and preferably cross-layer error control mechanisms in wireless sensor networks (WSN). In this paper, a cross-layer methodology for the analysis of error control schemes in WSNs is presented such that the effects of multi-hop routing and the broadcast nature of the wireless channel are investigated. More specifically, the cross-layer effects of routing, medium access and physical layers are considered. This analysis enables a comprehensive comparison of forward error correction (FEC) and automatic repeat request (ARQ) in WSNs. FEC schemes improve the error resiliency compared to ARQ. In a multi-hop network, this improvement can be exploited by reducing the transmit power (transmit power control) or by constructing longer hops (hop length extension), which can be achieved through channel-aware routing protocols. The results of our analysis reveal that for certain FEC codes, the hop length extension decreases both the energy consumption and the end-to-end latency subject to a target PER compared to ARQ. Thus, FEC codes can be regarded as an important candidate for delay sensitive traffic in WSNs. On the other hand, transmit power control results in significant savings in energy consumption at the cost of increased latency. Moreover, the cases where ARQ outperforms FEC codes are indicated for various end-to-end distance and target PER values