Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment . Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information.  Despite recent progress in computer vision with the introduction of deep learning, tracking in a cluttered environment remains a challenging task due to various situations such as illumination changes, color camouflage, and the presence of other distractions in the scene.One of the recent trends in deep learning, attention models, are inspired by the visual perception and cognition system in humans , which hIllumination changes and color camouflage are two other challenges for tracking algorithms. The emergence of depth sensors opened new windows to solve these challenges due to their stability in illumination changes and not being sensitive to the presence of shadow and a similar color profile. These features make depth information a suitable option to complement RGB information. In this paper, a RGBD based tracking algorithm has been introduced. In the proposed methods, a convolutional neural network (CNN) has been utilized to extract two types of feature: Spatial attention features, which select the part of the scene where the target is presented; and appearance features—local features related to the target being tracked. We take advantage of the attention model to select the region of interest (ROI) and extract features. Different models are proposed to select the ROI based on both depth and RGB. In addition, a model is implemented using only depth as the resource for tracking, which takes advantage of the same structure. Our work extended the existing work proposed by Kosiorek et al. , which cThe remainder of the paper is divided into four sections. In Establishing a tracking algorithm using RGB video streams has been the subject of much research. A tracking algorithm usually consists of two different components: A local component, which consists of the features extracted from the target being tracked; and global features, which determine the probability of the target’s location. Using a convolutional neural network (CNN) to extract the features of the target being tracked is a very common and effective method. This approach mainly focuses on object detection and the local features of the target to be employed for tracking purposes ,6,7. Qi Recurrent neural networks (RNN) are also well studied to provide the spatial temporal features to estimate the location of the target in the scene (focusing on the second component of tracking). For example, Yang et al.  fed inpuVisual attention method recently gained popularity and are being utilized to extract the local and global features in a tracking task ,11,12,13There are shown in many situations where only RGB information fails to address accurate tracking in the state-of-the-art algorithms ,17. One However, adding depth introduces some additional challenges, such as missing points, noise, and outliers. For instance, in the approach explained by Nguyen et al. , first tThough there have been multiple attempts in using RGB and depth together, using the robustness of depth data by itself has also shown tremendous results. Even in the absence of RGB, depth features are still very distinguishable, making them an important feature which can complement existing visual tracking models. Additionally, using depth by itself has a lot of merits for cases where privacy is important and when one needs to reconstruct the shape and posture of people in the environment. For many applications, providing a tracking method based on only depth can protect the privacy of the target and hence this can be more desirable. Several studies have focused on using only depth data to analyze its strength: Chen et al.  employedIn this paper, we modified the HART structure proposed by Kosiorek et al.  to develThe structure of the tracking module for RGB input is shown in Spatial Attention: The spatial attention mechanism applied here was similar to that of Kosiorek et al. [k et al. . Two matThe glimpse can then be defined as Appearance Attention: This module converts the attention glimpse  . The forThe output of both streams was then combined in the MLP module (Equation (2)).State estimation: In the proposed method, LSTM was used for state estimation. It was used as working memory, hence protecting it from the sudden changes brought about by occlusions and appearance changes. Equations (3)–(6) describe the state updates where The purpose of the tracking part was to increase the tracking success rate as well as the intersection over union (IoU). The negative log of IoU was utilized to train the LSTM network, similar to the approach introduced by Yu et al. .The appearance attention was employed to improve the tracking by skipping pixels which do not belong to the target; hence, we define the glimpse mask as 1 where the attention box overlapped the bounding box and zero wherever else. The attention loss function was defined as the cross-entropy of the bounding box and attention box, similar to the approach adopted by Kosiorek, et al. . The spatial attention determines the target in the scene; thus, the bounding box and the attention box should overlap to make sure that the target is located on the attention box. Meanwhile, the attention box should be as small as possible, hence we minimized the glimpse size while increasing its overlap with the bounding box. Based on intersection over union (IoU), we limited the number of hyper parameters by automatically learning loss weighting for the spatial attention and appearance attention. To add depth information to complement the RGB information, we have proposed three different structures. In the following sections, more details are provided for each of these methods.To add depth information in this method, first we separated the RGB channels and transformed them into two channel spaces. Given the device’s three color channels , the chromaticity coordinates r, g and b can be defied as: In this model, the depth map and RGB information were fed to an extended CNN using one layer of one-by-one convolution layer, whose output was then fed into the object detection model. The structure is shown in This model allows the feature extraction CNN to learn features that contain information from all four channels available. Also, since the number of learnable parameters is not significantly increased, a simpler architecture can be obtained. In the last RGBD proposed model, we employed two distinct parallel CNN to extract features from the depth channel and RGB channels, respectively c. The exUntil now, three methods have been discussed to utilize depth data along with RGB information. In this section, a tracking model was discussed to use only depth data for tracking. To extract features of depth information, four convolution layers were utilized, and similar to the previous method, the extracted features were fed into LSTM to track the target through time. The structure of this model is depicted in In this section, we compare and discuss the four approaches proposed, along with the method proposed in . The Pri dataset  were selAn example tracking scenario and the predicted bounding box with each model is demonstrated in The Princeton RGBD tracking dataset  providesi” ri = ed below :(8)R=1N∑ follows :(9)TypeThe average success rate for our human tracking experiment, tested on the benchmark, is shown in Using the Princeton dataset benchmark, we were able to reduce type I error from 0.80 in the HART model to 0.73 in the RGBD-combined approach, and reduce it further to 0.62 in the RGBD parallel approach .We define precision of the tracking as follows:Hence, the average precision for each sequence, similar to Equation (8), is as follows:We captured real-world data using a Kinect v2.0 sensor to evaluate the proposed approaches compared to the one proposed by Kosiorek et al. . The empIn this paper, inspired by the RGB-based tracking model , we prop


The deployment of small unmanned aircraft systems (UAS) to collect routine in situ vertical profiles of the thermodynamic and kinematic state of the atmosphere in conjunction with other weather observations could significantly improve weather forecasting skill and resolution. High-resolution vertical measurements of pressure, temperature, humidity, wind speed and wind direction are critical to the understanding of atmospheric boundary layer processes integral to air–surface  exchanges of energy, momentum, and moisture; how these are affected by climate variability; and how they impact weather forecasts and air quality simulations. We explore the potential value of collecting coordinated atmospheric profiles at fixed surface observing sites at designated times using instrumented UAS. We refer to such a network of autonomous weather UAS designed for atmospheric profiling and capable of operating in most weather conditions as a 3D Mesonet. We outline some of the fundamental and high-impact science questions and sampling needs driving the development of the 3D Mesonet and offer an overview of the general concept of operations. Preliminary measurements from profiling UAS are presented and we discuss how measurements from an operational network could be realized to better characterize the atmospheric boundary layer, improve weather forecasts, and help to identify threats of severe weather. Dramatic, high-impact weather events, such as severe thunderstorms with hail and wind, tornadoes, excessive rainfall and flooding, tropical storms, ice storms, heavy snowstorms, and blizzards have an impact of billions of dollars per year on the economy of the United States ,2. AdditRegarding atmospheric observations, a long-desired component to U.S. operational observing systems is the ability to measure vertical profiles of wind, temperature, and moisture in the lower troposphere at high spatial and temporal resolution. These so-called sounding or profiling data can be used to assess regions of thermal stratification and the degree of atmospheric static and dynamic stability, which play a role in convection initiation and maintenance of storms entering an area. This need is reflected in several recent studies, some of which provide explicit recommendations to collect more observations within the atmospheric boundary layer (ABL) in general, with a focus on vertical sampling (profiling) in particular ,6,7,8,9.Overall, processes in the ABL can vary dramatically over a single diurnal cycle, as depicted in The ABL generally provides the moisture, instability, low-level wind shear, and forcing necessary for the formation of severe storms with attendant tornadoes, hail, lightning, and hazardous winds. Forecasters regularly look at moisture advection and moisture gradient patterns when considering the potential for convection initiation ,11.WithiVertical shear in the near-ground layer can also play a significant role in severe weather formation and is an important criterion when distinguishing tornadic from nontornadic supercells. Traditional observational data, such as those provided by wind profiling radars do not offer adequate vertical resolution to resolve the shear . The moTo address the need for more measurements in the ABL, advance several key recommendations listed in the latest NASA Strategic Plan , and fulAnother emerging technology which could have a dramatic impact on observations of the lower atmosphere is unmanned aircraft systems (UAS). It has been suggested that such profiling could be achieved by small UAS assuming that autonomous flights extend at least through the depth of the boundary layer . As outlIn recent decades, numerous states have established mesonet networks to aid decision making across various sectors ranging from emergency management to agriculture to weather forecasting to transportation ,24. In gIn Mesonets can provide valuable information on the spatiotemporal structure and development of events such as the one depicted in 3D Mesonet. This framework would allow us to fill data gaps in the ABL that conventional instrumentation cannot easily or feasibly provide and facilitate the detection of complex mesoscale features embedded in weather systems. As we have noted, the New York Mesonet has implemented ground-based remote sensing platforms to obtain profiling data at select sites. However, we see WxUAS as being a less expensive alternative to this approach, albeit with several logistical challenges that need to be addressed.Here, we explore the prospects of extending the mesonet concept beyond the surface by including the capacity to sample the vertical structure of the Earth’s atmosphere through the use of WxUAS. That is, 2D surface observations from tower based sensors would be complemented by profiling measurements within the ABL and lower free troposphere from instrumented unmanned aerial vehicles (UAVs) launched from a network of ground stations capable of supporting these operations. As a point of clarification, we will use the terms UAV or WxUAV when referring to actual aircraft and UAS or WxUAS in the context of the complete system, including the aircraft, ground station, and other components associated with operations. The WxUAS would add a third spatial dimension to the sampling strategy of conventional mesonets and, as such, the proposed concept is referred to here as a In the following sections, we begin with a general overview of the 3D Mesonet concept and its potential impact on weather forecasting. Then, we discuss the development, calibration, and validation of a WxUAV designed for the needs of the 3D Mesonet along with other components needed for operations and risk mitigation. Next, we present the data visualization system and some preliminary results that highlight the unique capabilities the WxUAS has at profiling the ABL. Finally, we will outline the anticipated trajectory of the system along with future work that would be needed to transform the 3D Mesonet concept from a fundamental research question to an operational system.There has been rapid development in WxUAS technology and its integration into meteorological research for atmospheric boundary layer studies ,31,32. TThere are very few rotary-wing WxUAS commercially available which offer all measurement and telemetry capabilities envisioned for autonomous operations. One exception is the collection of Meteodrones developed by Meteomatics, AG in Switzerland. This line of WxUAS has been specifically engineered to autonomously collect and report atmospheric profiles of pressure, temperature, humidity, wind speed, and wind direction. Additionally, Meteomatics has recently introduced a charging box, which can be used with the Meteodrones. The University of Oklahoma (OU) CopterSonde, which is briefly described in Our concept of a 3D Mesonet consists of several fundamental components: (1) a network of tower-based surface observing stations distributed over a designated surface area; (2) a potentially less spatially dense yet complementary network of ground stations from which small WxUAVs can be launched, recovered, and reused; (3) the ability to operate unattended and with minimal human interaction in diverse weather conditions; (4) a method of monitoring the airspace in which the WxUAVs are operating and mitigating the risk of potential air collisions between WxUAVs and manned aircraft and between WxUAVs and other UAVs (deconfliction); (5) a robust and reliable methodology for communicating operational critical commands between the WxUAV and the ground station; and (6) a robust and reliable method of communicating data from all sensors to a physical and/or cloud-based command center. Since the focus is on profile data, the WxUAV would execute a vertical ascent and descent. The proposed modular system will allow for customization, upgrade, and in-field replacement of sensor packages as desired. Such a system would facilitate off-site maintenance and calibration and would provide the ability to add new sensors as they are developed, or as new requirements are identified. The small WxUAV must be capable of handling the weight of all sensor packages and have lighting, communication, and aircraft avoidance systems necessary to meet existing or future FAA regulations. The system must be able to operate unattended or with remote pilots at such time that FAA regulations allow.Regulations regarding access to and operations in the National Airspace System (NAS) are continuously evolving, so it is difficult to anticipate the level of risk mitigation required to satisfy the FAA. We propose to designate a cylindrical “geofence” within the NAS, with the center of the cylinder corresponding to the vertical flight path of the WxUAV . If an aTo facilitate unattended and autonomous operations, several other components are required for the 3D Mesonet station. For example, some form of charging pad is needed to charge the batteries in the WxUAV. A housing or shelter is also necessary to protect the WxUAV from the elements between flights. It will also be necessary to conduct diagnostics checks of the WxUAS before, during, and after flights. These functions will be facilitated through a ground control station, which will serve as the central hub for the WxUAS. It will manage communications and control as well as video monitoring of the WxUAV between and during flights. The ground control station and other elements of a 3D Mesonet station are provided below. A conceptual illustration is provided in A prototype 3D Mesonet WxUAS station is being developed, constructed, and tested at OU under the direction of the university’s Center for Autonomous Sensing and Sampling (CASS). CASS is an interdisciplinary center with the primary focus of helping to develop a better understanding of the environment in which we live through the assistance of autonomous systems. The development site for the prototype is the OU Kessler Atmospheric and Ecological Field Station (KAEFS), which is located about 28 km south-southwest of the main OU campus. KAEFS hosts a wide variety of research thrusts and instrumentation. Among these is one of towers in the Oklahoma Mesonet—the Washington (WASH) site. CASS conducts much of its WxUAS research at KAEFS and the prototype is co-located with the WASH Mesonet tower. As we discuss below, several of the requisite components of a 3D Mesonet WxUAS station are already being tested and integrated at KAEFS.Before building out a fully functioning 3D Mesonet, there are several questions that can be explored regarding how the individual WxUAS stations should be deployed and operated and what potential impact one expects. For example, what would be the optimal horizontal spacing of the WxUAS? To what height should the WxUAV ascend? At what frequency should the WxUAV be operated? There is still much work to be done, but we have completed a preliminary study to examine the potential improvement that a WxUAS network could have on storm-scale NWP using an Observation System Simulation Experiment (OSSE) approach. An OSSE was performed over the state of Oklahoma in which it was assumed that a WxUAV could be launched from 110 Oklahoma Mesonet  stationsRegarding the question of maximum WxUAV altitude, four experiments were run comparing no WxUAV flights and flights up to 400 ft, 1 km, 2 km, and 3 km AGL. In the test case storm initiation was impacted by a dry line extending through Oklahoma and progressing eastward. Initial results clearly show an improved boundary layer structure and subsequent convective initiation location and timing when WxUAV data are used. Considering network density, experiments were also conducted involving simulated WxUAS stations located at 110, 75, 50, 25, and 10 Mesonet sites. Simulated WxUAV flights went up to a height of 1-km AGL. It was found that, while a WxUAS network consisting of 110 3D Mesonet stations produced the best results, the convective initiation forecast was improved with only 75 stations. It is worth noting that results from the study suggest that even a 25-station network could provide benefit to boundary layer analyses and short term convective forecasts. In summary, although sensitivities to the quality of the moisture analysis are noted, the results suggest that a real-world deployment of automated WxUAVs could have a positive impact on atmospheric analyses and short-term NWP of convective initiation. Again, complete results can be found in Moore  and a foThe general concept of operations for the 3D Mesonet places certain design criteria on the WxUAV to be used. Flights should be conducted autonomously or semi-autonomously with minimal human interactions. Moreover, it should consist of a vertical take-off and landing (VTOL) aircraft for the sake of docking and charging. This can be achieved using a rotary-wing aircraft or a hybrid vehicle capable of operating in a rotary-wing mode for take off and landing and then transitioning into a fixed-wing aircraft as its primary mode for data collection. Here, we present developments of a rotary-wing VTOL vehicle, known as the CopterSonde. It should be noted that the WxUAV described below is still undergoing modifications. Therefore, it should be taken as representative of the aircraft to be used as part of the 3D Mesonet. However, we are also exploring hybrid vehicles, which are under development within CASS and will be presented in forthcoming publications.The CopterSonde series rotary-wing WxUAV was developed in-house by a CASS team of engineers and meteorologists to be both robust and optimized for atmospheric sampling. True to its name, the platforms are able to collect vertical profiles of the atmosphere like a traditional radiosonde. In addition, these platforms provide highly resolved data (<10 m) in the lower atmosphere that are not easily achievable using conventional methods. The CopterSonde  is basedAll CopterSonde platforms utilize open-source technologies to enable autonomous operation of the vehicle. The first of these is a Pixhawk 2.1 flight controller (ProfiCNC), which is comprised of a rugged and vibrationally isolated microcontroller, a single central processing unit that executes the flight control system and also allows for communication with a large variety of sensors over a wide range of communication protocols  When paired with the Here+ GPS module (ProfiCNC), the Pixhawk is able to utilize Real Time Kinematic (RTK) GPS, which can reduce the uncertainty of the location of the UAS to less than 1 m in both the horizontal and vertical if properly set-up and calibrated. The microcontroller runs the ArduPilot autopilot software suite, which is composed of navigation software on-board the vehicle. The autopilot comes with a ground station software which is used to transmit commands to the UAV and monitor its status over telemetry link. Both software packages are open-source for any developer, which allows to create highly customizable environments/configurations and the addition of code that enables for other sensor integration and special features, such as to dictate the sampling speed of the sensors, flight path of the vehicle, ascent rate, and much more.sufficient temporal and vertical resolution. For reference, we present in The design of the CopterSonde has been driven by meteorological sampling needs coupled with the overall concept of operations and the logistical requirements of unattended operations. Meteorological sampling needs include both the types of measurements to be acquired and the accuracy of those measurements. For the 3D Mesonet concept, we are primarily interested in characterizing the thermodynamic and kinematic state of the lower atmosphere, namely, atmospheric pressure, temperature, and humidity along with the wind speed and direction with The CopterSonde is equipped with three Innovative Sensor Technology HYT 271 humidity sensors, three InterMet Bead Thermistors, and a MS5611 Barometer to measure pressure. The temperature and humidity sensors are located on the front of the vehicle in the 3D printed scoop designed to protect the sensors from solar radiation and serve as the front half of the platform’s protective shell . A ducteWind speed and direction is estimated as follows. The autopilot system has been configured such that the CopterSonde can orient into the direction of the incoming wind. Since the pitch, roll and yaw of the vehicle are measured, wind direction is found from the yaw angle . The winAs a whole, the ground control station (GCS) for a single 3D Mesonet unit serves as the primary information conduit between the WxUAV and the rest of the world and is used to coordinate the functionality of the various mission-critical components required for the WxUAS operations. A schematic representation of how the GCS interacts with these components is depicted in To promote safe operations of the WxUAV in the NAS as part of the 3D Mesonet, we are developing radar technology that supports beyond-visual-line-of-sight (BVLOS) WxUAS operations within a specified (geofenced) airspace volume. Namely, the specific goal is to demonstrate the utility of deploying a low-cost, readily manufactured ground-based detect-and-avoid (GBDAA) radar at the 3D Mesonet. The radar has been designed to detect manned aircraft. A prototyple of the so-called GeoFence Radar has been built in the team’s lab at the Advanced Radar Research Center (ARRC) at the University of Oklahoma. The GeoFence Radar is capable of detecting aircraft that enter into the geofenced volume. It has been tested by flying aircraft from the university’s Department of Aviation Studies in specific patterns to confirm radar detections. The radar’s primary specifications are: 5.6 GHz operating frequency, 10–20 MHz of operating bandwidth, 6.3 kW peak transmit power, maximum duty cycle = 10%, range = 5 km, probability of detection within range = 99.9%. As such, the radar scans continuously, aiming for a high probability of detecting targets entering a 5 km range. Power supplies, a processing unit, an antenna, and radio frequency components for both transmitting and receiving signals have been designed for the radar. A picture of the radar deployed at a test site in Oklahoma (KAEFS) is provided in To detect targets, we implemented an algorithm derived from a traditional Constant False-Alarm Rate (CFAR) detection algorithm. This algorithm uses radar data to estimate random noise levels and distinguish targets from noise. While operating, the GeoFence Radar generates a stream of measurements, each pointed in the current direction of the radar as it spins. Once the radar completes a full circle, the receiver has assembled a Plan Position Indicator (PPI). The second component of the program is our CFAR implementation, which plots detections on an image of the PPI. We were successful in running the program in real time, ideally allowing for immediate visualization of aircraft within the radar’s effective range. The program also saves data from completed PPIs and periodically stores the resulting visualization for later review. We conducted several flight tests. The aircraft target used was a single-engine, four-seater Piper Warrior III, a common aircraft which served as a type specimen for the light aircraft GeoFence Radar was designed to detect. This test consisted of a series of maneuvers that aimed to test the capabilities of the GeoFence Radar. We developed a flight plan which ensured that the radar could view its target at many different altitudes and ranges.On 26 February 2018, the flight plan began with the plane approaching the location of the radar from Max Westheimer Airport. The the Piper Warrior III airplane then proceeded to complete two figure-eight patterns of one-mile radius loops. The plane then ascended to 1500 ft (460 m), completed two passes over the radar, and ascended to 5000 ft (1520 m). The final maneuver of the plan was to complete a loop four miles (6.4 km) in radius before returning to the airport. Specific detections are shown in The ADS-B system is widely used surveillance technology for tracking the location, speed, and heading of aircraft. An ADS-B equipped aircraft determines its position using satellites and on-board calculations are made to estimate other navigational information. The information is broadcast using ADS-B out. Air traffic controllers and other pilots can also monitor the tracking information of other ADS-B equipped aircraft using ADS-B in. This helps to deconflict the NAS and allow individual pilots to exercise self separation. The incorporation of ADS-B technology is part of the FAA Next Generation Air Transportation System (NextGen). All aircraft operating in controlled airspace in the US will be required to have ADS-B out capability by 2020.Although there are several commercially available ADS-B hardware/software packages that allow aircraft monitoring, we have opted to develop our own system of registering and visualizing flight tracks using data from an ADS-B receiver. This provides the versatility to not only track an aircraft but also predict its future location based on past trajectory data. The forecast algorithm is still in development, but when completed, should provide additional warning of incoming aircraft and assist in the decision process to abort a WxUAV flight or instigate evasive maneuvers. The hardware we have selected to receive ADS-B signals is the PingStation from uAvionix. This receiver is a dual band networkable receiver with Power over Ethernet (POE). The PingStation detects ADS-B equipped aircraft within a 240-km (150-mile) radius. The PingStation is robust enough to be used in harsh environmental conditions and small enough to be used as a mobile asset for roaming operations. It uses an Ethernet connection and communicates using JSON UDP. Specifications for the PingStation are provided in In the current configuration of our flight tracking software, data from aircraft are obtained using the PingStation and then sent to a processing computer by configuring Dynamic Host Configuration Protocol (DHCP) connection between the receiver and processing computer. A visualization software was developed in Python v3.6 and utilizes Mapbox, which provides custom online maps for websites and applications. This has allowed us to build our aircraft visualization applications, which are flexible and portable. Data received from the PingStation are used to view air traffic in a web browser, which updates air traffic on the map every two seconds. A more detailed discussion can be found in Kanneganti et al. . An examUsing the current and planned architecture, the work load of data processing, distribution, and visualization can be achieved on the GCS, in the cloud, and on remote servers. We provide an example of the data flow from the WxUAV (CopterSonde) to remote users in A critical role of the data processing architecture will be that of monitoring and managing flight operations. It was mentioned earlier that information from the GeoFence Radar and the ADS-B system will be used to deconflict the air space; however, it was not mentioned how this will be accomplished. For expediency and safety, the processing will be largely conducted on the WxUAV and GCS. That is, the GCS will monitor the data streams from the risk-mitigation instruments, e.g., GeoFence Radar and ADS-B system, then assess whether or not a flight can be initiated or should be aborted. If the WxUAV is in flight, then the GCS will communicate with the WxUAV and plan evasive maneuvers to avoid in-air collision. This command-and-control interface is still in development.The data flow depicted in The success of the 3D Mesonet will not only depend on our ability to provide raw data to models but also to render data visualization products. The data display should be versatile and able to manage data streams from several WxAUVs and other data feeds. For this task, we can share computation burden with our cloud-based computing resources. Remote users may opt to visualize data streams differently, depending on their particular interests. Thus, the data visualization software should be versatile and configurable.Here, we provide examples of measurements collected using a single or a few WxUAS. Logistically, it is challenging to sample the atmosphere at multiple, spatially separated locations because of personnel issues. This is why it is important to move towards unattended operations. However, even data collected at a few sites provides valuable information pertaining to the thermodynamic and kinematic state of the ABL. Data from the campaigns discussed below are still being evaluated, but the results provided reveal the value of using WxUAS to sample the ABL. There have been some reports in the literature of using combined rotary-wing and fixed-wing WxUAS to obtain coordinated measurements in the ABL ,37,46. WThe first robust field testing of the CopterSonde in approximately its current configuration occurred in February 2018. A team of researchers from five universities descended on a remote island on the Finnish side of the Bay of Bothnia to study the stable Arctic atmospheric boundary layer. The University of Oklahoma CASS team was invited to join the Finnish Meteorological Institute (FMI), The Geophysical Institute of the University of Bergen (UiB), University of Tübingen, and the University of Applied Science Ostwestfalen-Lippe (OWL) for ISOBAR, or Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer. The four-week sampling combined more conventional ground-based instrumentation such as sodars, lidar, flux stations, and sonic anemometers with small WxUAS to characterize the stable boundary layer in Arctic-like conditions. In addition to completing the scientific goals of characterizing the stable boundary layer, the CASS team achieved many firsts in operations using the CopterSonde, such as flying at night and beyond visual line of sight, and setting a new altitude record by climbing to 1800 m (5900 ft) AGL. The CASS group participated in several intensive operational periods in conjunction with the other teams to collect continuous boundary layer profiles over the course of 16–24 h. This provided a means to observe the diurnal evolution of the stable boundary layer that would have been impossible by one crew alone.In July 2018, the CopterSonde was deployed to the San Luis Valley in south-central Colorado for the Lower Atmospheric Process Studies at Elevation—a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). LAPSE-RATE was organized in conjunction with the International Society for Atmospheric Research using Remotely-piloted Aircraft (ISARRA) meeting in Boulder, CO. During the six-day field campaign (14–19 July 2018), over 100 participants from 13 institutions participated in LAPSE-RATE. Collectively, 35 WxUAS were deployed to collect data over 260 flight hours distributed across 1287 flights. The scientific objectives can be broadly characterized as studying environmental conditions leading up to convective initiation, studying drainage flows originating from the surrounding mountains, and characterizing conditions in the ABL during the morning transition period. Another major objective of the campaign was to validate measurements taken from platforms flown by the various universities and industry partners .During the LAPSE-RATE, researchers from the University of Oklahoma were positioned at two locations within the valley: the Moffat, CO Consolidated School and the Saguache, CO Municipal Airport. Data from one of the flights at the Moffat School is shown in Numerous deployments have also taken place just south of the University of Oklahoma campus at KAEFS. An example of data collected during a complicated morning transition at KAEFS is shown in For some deployments at KAEFS, the Collaborative Lower Atmospheric Mobile Profiling System (CLAMPS ) was co-The combination of CopterSonde and CLAMPS data helps shed light on features present in the 10 m temperature from the Washington, OK Mesonet site. After sunset, the expected radiational cooling at the surface was present from 00:00 UTC to 04:00 UTC. After 0400 UTC, warming and an increase in wind speed (not shown) was observed at the surface. Around this time, the DL observed a robust low-level jet (LLJ) form. The strength of the winds in the LLJ limited the max height of the UAS, thus observations were limited to heights below the LLJ core. However, the CopterSonde observed what was likely downward mixing of warm air and momentum, which caused the warming and wind speed increase at the surface. This would be unobservable without the WxUAS and DL since the entire event occurred between the typical radiosonde launches.As we have noted, there is a clear need to improve our understanding of the Earth’s ABL, a view that appears frequently in several national studies ,5,6,7,8.The continuing deployment of small WxUAS to collect in situ measurements of the atmospheric state in conjunction with surface conditions will significantly expand weather observation capabilities. Having the ability to deploy networks of WxUAS could significantly enhance the safety of individuals and support commerce through improved observations and short-term forecasts of the weather and other environmental variables in the lower atmosphere. In particular, observations from a 3D Mesonet could play a critical role in helping to address several pressing scientific questions, including several outlined in the most recent National Academies’ Decadal Survey “Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space” . Examinimeans to an end, with the end being a better understanding of of the Earth’s atmosphere and its processes. Therefore, a holistic approach should be envisioned that involves complementary observational data from a variety of sources. Additionally, carefully constructed modeling approaches should be adopted to best utilize the observational data. This has been our guiding philosophy when developing the 3D Mesonet concept.Clearly a novel approach for ABL monitoring such as WxUAS must be developed and tested to fill this data gap. Moreover, we recognize that WxUAS technology is not a panacea. Rather, it is simply a As outlined in this discussion, we have been able to conceptualize, develop, and successfully test many of the individual components needed to deploy a 3D Mesonet station; however, there is still much to be done before completing a fully autonomous prototype system. For example, we must identify a suitable charging station for the WxUAV based on several design considerations. The WxUAV must be able to execute a precision landing on the charging station. There must be some mechanism for aligning the aircraft with an inductive or direct connection charging port or to physically swap the battery of the WxUAV. The charging station and the WxUAV should be protected from the elements when not in use. There are commercially available rotary-wing UAS, which offer autonomous charging capability; however, we must find or build one suitable to our particular aircraft and sensor suite.Another critical consideration when designing a 3D Mesonet prototype is that of system integration. For example, the GeoFence Radar must have software in place to reliably detect and track manned aircraft. Furthermore, when it has been determined that the aircraft has entered into geofenced air space, appropriate deconfliction actions must be taken, which could involve a variety of actions depending on conditions: delay launch, return to launch area, create separation between the manned aircraft and the WxUAV, and so forth. Similar actions could be taken if a manned aircraft is detected through its ADS-B out signal. Creating system software to allow for safe and reliable operations will require considerable development and testing.With these considerations in mind, we continue to work towards developing a prototype of a 3D Mesonet WxUAS station, which can operate unattended. In addition to the hardware, software, sensor, and system engineering challenges, we realize that there are regulatory hurdles to overcome before a 3D Mesonet can be implemented. As previously mentioned, several risk mitigation factors are already developed or in development. Additionally, we must consider the ability of the system to operate in a range of environmental conditions, including: extreme temperatures; strong winds and turbulence; and icing. The current limitations of the system are being explored. Hardening the WxUAS against adverse weather conditions is a topic of ongoing research. During field campaigns, the CopterSonde itself has been operated in a temperature range of −25° to 30°, in winds up to 25 m sAs WxUAS technology continues to mature and our capacity to make robust and accurate observations of the lower atmosphere grows, we must correspondingly match this development within the realm of atmospheric modeling. Once data are available from WxUAS deployments, these observations can be assimilated into NWP models along with all other available weather data to determine the extent of improvement to the model forecasts and the longevity of the impact with a focus on high impact weather events depending on the season and location. These types of modeling studies are known as Observing Simulation Experiments (OSEs) ,49. The At this stage, the 3D Mesonet concept is only a dream, but from dreams, reality can happen. Such a network would carry us closer to filling the atmospheric data gap that exists in the ABL and help us to better understand complex surface–atmosphere interactions and energy exchanges. Admittedly, as we move forward, we must also be open to other complementary and evolving sampling technologies to collect these data. Currently, however, WxUAS offer much potential. The needs of society are placing ever increasing demands on forecasters to improve the granularity and fidelity of weather predictions. This in turn requires networks of atmospheric observing networks along with the ability to integrate the observations efficiently into NWP models. There is work to be done to get there. We feel that realizing the dream of a 3D Mesonet can play a critical role in reaching these goals.


Vehicular ad hoc networks (VANETs) provide information and entertainment to drivers for safe and enjoyable driving. Wireless Access in Vehicular Environments (WAVE) is designed for VANETs to provide services efficiently. In particular, infotainment services are crucial to leverage market penetration and deployment costs of the WAVE standard. However, a low presence of infrastructure results in a shadow zone on the road and a link disconnection. The link disconnection is an obstacle to providing safety and infotainment services and becomes an obstacle to the deployment of the WAVE standard. In this paper, we propose a cooperative communication protocol to reduce performance degradation due to frequent link disconnection in the road environment. The proposed protocol provides contention-free data delivery by the coordination of roadside units (RSUs) and can provide the network QoS. The proposed protocol is shown to enhance throughput and delay through the simulation. Due to the recent rapid development of electronic and communication technologies, vehicles are expected to evolve into a vehicle ad-hoc network (VANET) that can provide drivers with safety, entertainment, and convenience. A vehicle-to-infrastructure (V2I) communication can provide commercial services such as real-time traffic information, digital maps, movies, and music through the connection with the external Internet to the vehicle. A vehicle-to-vehicle (V2V) communication can provide the driver safety information service such as collision avoidance and accident alert to the vehicle.However, to provide various intelligent transport system (ITS) services, unlike a conventional mobile ad hoc network (MANET), it is required a vehicle network technology suitable for high speed and frequent movement of a vehicle. Various countries such as the US, Europe, and Japan are pursuing national-level projects to build infrastructures and striving to establish standards.A dedicated short-range communication (DSRC) standard, which is widely used for road traffic information service and electronic toll collection (ETC) service, was developed to exchange information between the roadside unit (RSU) and on-board unit (OBU) in a short range. The DSRC has a transmission rate of up to 1 Mbps at a maximum speed of 160 km/h and a maximum communication range of about 100 m. However, as the ITS has been recently developed, the amount of information to be exchanged between the RSU and the OBU will increase, and a communication system with a higher transmission rate and a more extended communication range is required. Furthermore, the need for communication between the two vehicles has emerged.The American Society for Testing and Materials (ASTM), a DSRC specification task group, has decided to explore the next generation DSRC technology to meet the requirements of the ITS while preserving the advantages of the legacy DSRC .As a result of reviewing various communication technologies, the ASTM pointed out the wireless local area network (LAN) technology which is widely used in daily life and has secured stability and marketability. Based on an IEEE 802.11a standard, the ASTM defines a new specification called IEEE 802.11p that satisfies poor power supply noise and in-vehicle communication environment . In the The IEEE 802.11p standard defines the MAC layer and PHY layer considering a vehicle communication environment in IEEE 802.11 which is a conventional wireless LAN standard. In the legacy IEEE 802.11 standard, an inter-node communication is possible after the completion of scanning, authentication, and association procedures. However, the IEEE 802.11p standard defines an outside context of BSS (OCB) for omitting these procedures and enabling communication. IEEE 1609 is a standard of the upper protocol layer of the WAVE standard and includes IEEE 1609.2, IEEE 1609.3, IEEE 1609.4, and IEEE 1609.12 ,6,7. IEEWAVE can support a data rate up to 27 Mbps in vehicles with a speed of up to 200 km/h. In consideration of natures of a vehicle network, WAVE standard adopts a WAVE basic service set (WBSS) concept and has a different technical feature from the existing IEEE 802.11 standard . The WAVThe WAVE communication system provides not only communication between the OBU and RSU, but also communication between two OBUs. The OBU is mounted in the vehicle and provides services to the driver and the passenger. The RSU is installed on the roadside and provides with the function of connecting to the external network. Therefore, the OBU can be connected to the external network through the RSU. Many studies related to WAVE have been performed ,15,16,17In this paper, we propose a cooperative communication protocol to reduce performance degradation due to frequent link disconnection in the road environment. The proposed scheme is compatible with the WAVE standard and can provide the quality of service (QoS) required by the network by reducing the delay time. The key features of our proposed protocol include the following. First, we model the path maintenance expectation time (PMET) to predict the link expiration time. Second, when a relay node is selected, a relay node with a longer PMET is selected. The proposed scheme can reduce the link disconnection between devices and improve the network performance. Lastly, since only the reserved devices communicate in the reserved resources, collision by other devices does not occur, and there is no competition for the data transmission. Thus, the proposed scheme is not subject to interference from other devices and can improve network performance. In addition, the proposed scheme is less affected by the network environment.The WAVE PHY layer is defined in the IEEE 802.11p standard [The IEEE 802.11p standard may have security issues due to the lack of authentication procedures in existing wireless Internet standards for fast communication setup. The IEEE 1609 standard is further defined to address this problem. Currently, the IEEE 802.11p standard defines the PHY layer and MAC layer. The MAC layer and the upper layers are defined in the IEEE 1609 standard, and it is divided into four detailed definitions. The IEEE 1609.12 standard specifies allocations of WAVE identifiers defined in the IEEE 1609 series of standards. The IEEE 1609.2 standard provides security services for the WAVE networking stack and for applications that are intended to run over the stack. The IEEE 1609.3 standard specifies the functions associated with the LLC, network, and transport layers of the OSI model and calls them WAVE networking services. The IEEE 1609.4 standard provides enhancements to the IEEE 802.11p MAC to support multi-channel operations.The IEEE 1609.4 standard divides the wireless channel into a CCH and an SCH and allocates different frequency channels to each interval. The WAVE standard uses one CCH and six SCHs. The CCH is dedicated to system control messages, and the SCH is used to exchange service data packets. The CCH interval and the SCH interval are fixed as 50 ms, respectively. The OBU receives WAVE control packets in the CCH interval and transmits or receives service data frames. The channel access in the CCH interval and the SCH interval uses the IEEE 802.11e enhanced distributed channel access (EDCA) mechanism. The WBSS, which is configured to communicate using WAVE standard, consists of a provider that starts WBSS and a user that subscribes to WBSS. The provider periodically broadcasts a WAVE service announcement (WSA) message, which is a beacon frame containing network parameters such as WBSS identifiers required to join WBSS, service channels to be used by WBSS, and timing information for synchronization. When the user receives the WSA message, it can subscribe to the WBSS by switching the channel to the service channel used by the WBSS at the next SCH interval. The CCHInterval indicates the length of the CCH interval. During this interval, the management message for the service advertisement and the data message of the high priority application are transmitted. The SCHInterval indicates the length of the SCH interval, and a general data service message is transmitted during this interval.In , the autIn , the autIn , the autTherefore, we propose a new cooperative communication scheme that can be applied to the WAVE standard. The proposed scheme can improve the reliability and connectivity of the WAVE communication system and can provide the network QoS.In this paper, we assume that the received signal amplitude in the vehicular networks follows the Rayleigh PDF. The Rayleigh distribution is frequently used to model multi-path fading with no direct line-of-sight (LOS) path. In vehicular networks, as the separation between source and destination devices increases, the LOS component may be lost, and the PDF of the received signal amplitude follows the Rayleigh distribution ,44,45.In ITS applications using a WAVE system, all WAVE devices periodically broadcast basic safety message (BSM) messages. BSM messages are messages defined in SAE J2735 to increase the safety of the vehicle in operation and frequently broadcast from all vehicles . Each veAs shown in The existing cooperative communication schemes have selected the relay node considering the data rate on the wireless link ,48. HoweR is the transmission distance of OBU, and iD is the distance between itself and the i-th OBU. iV is the moving speed vector of the i-th OBU, and oV is the moving speed vector of itself.The OBU that does not receive the WSA can receive the BSM message from the neighboring OBUs in the CCH interval. After receiving the BSM, it calculates the PMET between the neighboring OBU and itself, the equation for the PMET is as follows.PMET, the OBU selects three adjacent OBUs with the longest PMET and transmits a Coop request message. After calculating the WSMP Version field shows the version of the WAVE protocol, PSID (Provider Service Identifier) is a numerical field used by the IEEE1609 standard to identify a particular application. To get access to the WAVE service, an application should be registered with its unique PSID. The WAVE provider devices use PSID in its announcement messages to indicate that it provides a certain application. WSMP header extension field defines the channel that is used for communication. The PMET field indicates the calculated PMET value. The Data Rate field is set to the data rate that the recipient device recommends to the source device. OBUs receiving the Coop Request message transmit a Coop Relay message to the RSU, and The Type field indicates whether the transmitted message is a request or a relay. The PMETSR field indicates the PMET between the OBU requesting the cooperative communication and the relay candidate OBU, and the PMETRD field indicates the PMET between the relay candidate OBU and the RSU. The DRSR field indicates the data rate between the OBU requesting the cooperative communication and the relay candidate OBU, and the DRRD field indicates the data rate between the relay candidate OBU and the RSU.txP is the transmit power, a is the path loss exponent and K is a constant associated with the path loss model. K has given by [TG and RG, respectively, represent the transmit and receive antenna gains. C is the speed of light, and cf is the carrier frequency. In this paper, we assume that the antennas are omni-directional (TG = RG = 1), and the carrier frequency cf = 5.9 GHz. Let xR denote x Mbps data rate. The probability that rate xR is achievable is calculated as follows:xψ is the minimum required SNR threshold to support x Mbps data rates.RSU receiving Coop Relay message has to calculate the total transmission time to transmit WSA message from source to destination. Considering the Rayleigh fading model, the received SNR has exponential distribution given by :(2)f(γ)=given by ,50,51: of the relay candidates and selects the OBU with the lowest RDP as the relay node. The RDP is calculated as follows.The The RSU announces the type of service applications to OBUs using the Service Info field. Using the Channel Info field, the RSU also announces the number of the channel in which it provides the service. The WAVE Routing Advertisement element provides information about infrastructure internetwork connectivity, allowing receiving devices to be configured to participate on the advertised IPv6 network.The Coop Info Element includes a WAVE Element ID, Length, Index, Path Info, and Resource Info fields. The value of the WAVE Element ID is selected one of the reserved values so that the proposed Coop Info Element can be distinguished. The Length field indicates the length in octets of Index, Path Info, Resource Info fields. The index field contains the number of relay paths included in the Coop Info Element. The Dest Addr field indicates the MAC address of the device requesting the cooperative communication and the Relay Addr field contains the MAC address of the OBU selected as the relay node. The Src Addr field is set to the MAC address of the RSU. The PMETSR field indicates the PMET between the RSU and the relay node, and the PMETRD field indicates the PMET between the relay node and the destination node. The Channel Number field indicates the SCH channel selected by the RSU for relay communication. The Start of Time field indicates the beginning of the resource allocated for the relay, which specifies the universal coordinated time (UTC) second of GPS. The Beacon Length field is the interval to relay the WSA message to the destination node. The Upper Link Length field is the interval that the RSU has allocated to communicate with the relay node. The remaining allocated resources are the intervals allocated for the relay node and the destination node to communicate. The End of Time field indicates the end of the resource allocated for relay communication and specifies the UTC second. The RSU that receives the Coop Relay message from the relay candidate nodes calculates the RDP using the information in the received message and selects the OBU with the lowest RDP as the relay node. The RSU allocates resources for the cooperative communication in the SCH Interval and broadcasts the WSA including the information on the relay path and the allocated resources. The relay node receiving the WSA message generates a Coop Response message using the information included in the WSA. The relay node sets the Type field Response and sets the Path Info and Resource Info fields to the values of the Path Info and Resource Info fields contained in the received WSA. The relay node transmits the Coop Response message to the destination device. The OBU receiving the Coop Response message checks the relay node selected from the Path Info field and ascertains the SCH channel and the allocated resource to perform cooperative communication from the Resource Info field. The relay node forwards the received WSA message to the destination node. Devices on the relay path exchange data frames in the allocated Relay interval. In In If the relay node does not receive data or response from the destination node for a certain period, it performs reservation resource release procedure. If cooperation communication is no longer needed, the destination node also performs a resource reservation release procedure. To release the reserved resource, the destination node sends a Coop Release message to the relay node. To terminate the relay communication, the relay node sets the Type field to Release and includes the information of the routing path and resources used for the cooperative communication to the Coop Release message. When the relay node receives the Coop Release message from the destination node, it forwards the Coop Release message to the RSU. After receiving the Coop Release message, the RSU broadcasts the WSA message except for the corresponding information at the next Sync Interval. The relay node that receives the WSA message no longer participates in the cooperative communication, and the destination node that receives the Coop Release message also no longer performs the cooperative communication. For evaluation purposes, we compare our proposed scheme with the WAVE standard and overhearing-driven counter-based delayed forwarding (OCBDF) scheme in WAVE standard . PerformA radio module implements the 802.11p PHY and MAC model of OMNET++, and parameters are set according to the recommended values in [As shown in In this simulation, we fixed the distance between RSUs to 1000 m. In In In In In this paper, we have proposed a cooperative communication protocol to improve the link connectivity in the WAVE standard. In particular, we have studied the limitations in the WAVE standard for the congestion and the shadow zone between neighboring RSUs and have proposed the cooperative communication scheme to address those limitations. The proposed scheme has been demonstrated to notably improve the network performance even when a low presence of infrastructure results in shadow zones between areas of coverage. The proposed scheme also has been designed according to the intricacies and special characteristics of the WAVE standard.The proposed scheme provides better performance than the existing algorithm. However, when service applications provided by the RSU increase, the number of SCHs for service applications increases, and the available resources for the relay communication are also reduced. As service applications provided to automobiles increase, the use of the proposed algorithm becomes more difficult. However, since the proposed algorithm can transfer data without the contention in reserved resources, collisions by access of multiple OBUs do not occur. Therefore, the proposed algorithm can provide a high throughput, a high delivery success ratio, and a low delay regardless of the number or speed of the vehicle. In the future, we will introduce cognitive radio algorithms and develop a relay communication protocol that operates in an environment where various service applications are provided. We will also develop OBU and RSU to perform field tests for the proposed algorithm.Our simulation results show that the WAVE standard may extend the area of coverage using multi-hop communications, and the proposed scheme can reduce performance degradation due to frequent link disconnection caused by frequent vehicle speed changes. The proposed scheme can also provide the QoS since OBUs can transmit data frames without the contention. In the proposed scheme, the RSU can choose the best relay based on different parameters, such as relay reliability and link duration.


The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage. Battery-free sensors gradually realize the vision of perpetual and maintenance-free sensing applications . The eneSmall supercapacitors are attractive for mobile systems because they charge and boot the battery-less sensor node in a matter of seconds whenever energy becomes available. This choice, however, prevents the sensor node from performing energy-intensive operations, such as communicating with a remote base station. Large capacitors, on the other hand, may take such a long time to charge, that the node may leave a high energy area long before the capacitor gets sufficient charge to start-up the node, let alone perform any useful task. Furthermore, as the node leaves the high energy area, the partial charge in the large supercapacitor dissipates rapidly without being used. Surprisingly, the design trade-offs in selecting an optimal storage capacity and the impact of mobility on the charging performance of intermittently-powered battery-less systems have not been described in the literature.The supercapacitors differ from traditional batteries by low energy density and high self-discharge current: Once fully charged, the supercapacitors discharge at a much higher rate than a traditional battery. Prior work focused on supercapacitor characterization , modelliIn this paper, we propose a novel dual capacitor energy storage platform that dynamically selects an appropriate energy storage based on the amount of ambient energy. The dynamic switching is not trivial since it requires tracking and predicting the amount of available energy in the environment. We show that the hybrid approach allows meeting the otherwise contradictory requirements by avoiding the design trade-offs present in traditional single-capacitor designs. We define a new performance metric, which we call usable harvested energy, as the amount of energy directly available to applications and show that it can be a fraction of total energy harvested by the node due to supercapacitor self-discharge current and residual charge. Finally, we evaluate the proposed platform through extensive simulations and a hardware prototype based on the PowerCast RF energy harvesting development kit . To the The rest of the paper is structured as follows. Our system consists of mobile Radio Frequency (RF) energy harvesting sensor nodes deployed over a large geographical area and powered by dedicated RF power transmitters deployed at strategic locations. As the maximum transmit power of RF transmitters is limited by regulations, the charging range is small relative to distance between RF power transmitters, and the resulting charging network is assumed to be sparse. Whenever a sensor node approaches an RF power transmitter, it starts charging and boots if a sufficient amount of energy is harvested. As the node leaves an area with an RF power transmitter, it keeps operating until it uses up the available energy stored in the capacitor. Each RF charging station modulates a charging signal to embed additional information, such as station ID, which enables the mobile nodes to distinguish between different stations and therefore locations.R is the distance between a charging station and a sensor node and The amount of harvested energy depends on the distance to the charging station, transmit power and the amount of time the sensor spends near the charging station. The available wireless RF power on a receiving antenna of a sensor node is given by the Friis equation:It is important to note that not all of the stored energy is actually usable because the boost converter requires a minimum voltage, As the amount of harvested energy depends on the environment, there is no guarantee that the capacitor voltage will be charged to t is modelled as:As the node boots, the most energy-efficient way to utilise the energy is to consume it straight away to avoid energy loss due to capacitor leak current Last but not least, the task size is an important factor in the design of the energy harvesting system. Even the most basic applications require the ability to combine light tasks, such as sensing or computation, with large tasks, such as communicating with other nodes or the base station. These tasks are often separated in time with light tasks dominating most of the time and larger tasks executed occasionally to upload sensed data or reprogram the node firmware. The light tasks may benefit from a small storage to quickly start up the node and execute a task. On the other hand, energy-intensive tasks require a larger storage, which again comes into conflict with the ability to boot quickly and charging efficiency.To summarise, the design of a battery-less energy harvesting systems is driven by a number of trade-offs and sometimes contradicting requirements. The energy storage size is the key design parameter, which has implications on the charging problem, keeping the charge and maximum task size. In this paper, we argue that these challenges can be met by a hybrid storage solution, which combines several capacitors of various sizes.The proposed RF energy harvesting sensor platform contains a hybrid storage containing two supercapacitors of various sizes. In mobile applications, where a node may have limited time passing through the RF charging station, it is critical that the sensor is able to charge its capacitor fully before it leaves the area. The small capacitor is used to boot quickly and perform at least a minimal task, such as sensing or sending a beacon. The large capacitor accumulates energy in high energy areas for energy-intensive tasks, such as communication with the base station. The proposed platform also incorporates a switching hardware, an energy prediction based on the Kalman filter and a switching algorithm, as described in the following subsections.The proposed platform consists of an energy harvesting sensor node, two capacitors and a switching circuit. The switching circuit consists of one bi-stable relay, two bipolar junction transistors (BJT) and two resistors, as shown in The key to efficient dual capacitor operation is an intelligent switching algorithm that dynamically switches between small and large capacitors depending on the situation. The amount of energy harvested by the node near an RF charging station largely depends on collocation time In human- and animal-centric applications, the collocation time and the node mobility in general are well known to exhibit strong statistical patterns ,9, whichThe main idea of the proposed switching algorithm is to learn the amount of harvestable energy in each location based on the average time the node spends near a charging station. This is based on the observation that human mobility is not random and exhibits strong statistical patterns. Consider for example a wearable application, where a mobile node charges from RF charging stations located throughout a university campus. When a person enters his or her office, there is a high probability that he or she will stay in that location for a longer period of time, A. The The proposed algorithm works as follows. When a sensor node approaches a charging station, it starts sampling an RF signal strength at fixed time intervals t, t, a is a state transition model, h is a measurement matrix and Since collocation time is random and may change every time a user visits a certain location, we use the Kalman filter to predict the future collocation time. The Kalman filter is computationally lightweight and requires minimum processing power and storage , which m−1)+w(t)z(t)=hx, which can harvest energy from six frequency bands and store energy in a built-in 50-mF capacitor or an external capacitor, connected through an extension port; see The XLP 16-bit development kit  used as the sensor node consists of a PIC24F16K102 microcontroller, the temperature sensor MCP9700 and a MiWi MRF24J40MA short-range low power transceiver. The MCP9700 is a low power thermistor operating at 6 The goal of the first experiment was to demonstrate the performance of a simple RF energy harvesting application, the impact of capacitor size and the distance on the charging time of a sensor node running a simple application, where a sensor node simply boots and transmits a packet to a base station. The interval between packets is taken as the charging time of the supercapacitor. The upper and lower threshold voltages were set to 1.23 V and 1.02 V, respectively. The goal of the second experiment was to demonstrate the lifetime of a node running a simple application, which wakes up to sense temperature and then sleeps every 5 s. In this experiment, the harvester board with a 50-mF capacitor is first charged to 1.25 V by a 33-dBm 866-MHz source and then connected to a sensor node with the lower threshold voltage set to 1.02 V. The experiment has been repeated with a 200-mF capacitor. The voltage across the In the final experiment, we demonstrate the practical feasibility of the hybrid dual-capacitor configuration for a simple sensing and data collection application, which senses a temperature reading, saves it to a flash storage and transmits wirelessly to the base station. To evaluate the dynamic switching storage, a sensor node was periodically moved from a distance of 3 to 1 m from the RF charging station to emulate low and high energy areas, respectively. The RSSI threshold was set to 2 dBm representing a distance of approximately 2 m, while the sojourn time near the charging station has been set to 40 s. In practice, these parameters are application dependent and can be tuned depending on the mobility of the node and transmit power of charging stations. The sensor node was configured to use the small capacitor at the initial boot. Whenever the node sensed that RSSI as higher than threshold value, it estimated the collocation time and saved it into a flash memory after each visit. After the third visit, the node was able to predict correctly that the collocation time within the high energy area would be sufficient to switch to a large capacitor, and after completing the switch, the node successfully sent the collected data to the base station. When moved out of the high energy area, the node waited until the large capacitor voltage dropped below 1.045 V to switch back to the small capacitor and resume the sensing task. The experiment has been repeated in a single fixed-capacitor configuration of either 50 mF and 200 mF by varying the sojourn time between 40 s and 10 s. With a 40-s sojourn time, both small and large capacitors were able to boot the node; however, only the large one was able to support the transmit operation. When the sojourn time was reduced to 10 s to mimic environments with lower energy, the node has never been able to boot on a large capacitor, but booted successfully on a small one.Supercapacitors have become an attractive option for energy storage due to high power density, fast charge time and virtually unlimited charge/discharge cycles. The main limitations of supercapacitors are low energy density and relatively high self-discharge current compared to conventional chemical batteries. As the classical conventional supercapacitor circuit model mostly fails to explain its behaviour,  proposedTask scheduling: The resulting models have been used by researchers for optimizing task scheduling. The work in  adapted The work in  describeBattery-free energy harvesting sensors provide a promising solution for a maintenance-free and perpetual sensor operation. We have proposed a novel approach for hybrid energy storage, which is able to adapt and reconfigure energy storage depending on the environment and resolve an important trade-off between fast start-up time and amount of stored energy. The evaluation based on simulations and the hardware implementation on the PowerCast RF energy harvesting kit have demonstrated that the hybrid platform can operate in environments where fixed capacitor nodes can fail. In particular, the proposed hybrid approach showed up to 40% and 80% performance improvement over a fixed capacitor design in terms of usable harvested energy and the sensor coverage, respectively. We have demonstrated that a traditional single fixed-capacitor design is not suitable for certain RF energy harvesting applications in sparse mobile networks. While we have used a dual-capacitor configuration as an example of a hybrid storage concept, extending the proposed scheme to three or more capacitors may provide applications with more fine-grained control over energy storage. As a typical sensor node has a limited number of output pins for addressing individual capacitors, this would require modifications to the switching circuit and would lead to higher switching cost due to the increased number of relays and other components. Investigating the design trade-offs for various numbers of energy storage devices is a potential future work.