Hydrologic Calculator: An Educational Interface for Hydrological Processes Analysis

Hydrology, which deals with the study of water, is one of the fundamental courses to the undergraduate program of many disciplines: civil engineering, agricultural engineering, earth sciences, environmental sciences, geography, etc.  This course covers various events of the hydrological cycle, namely, rainfall, runoff, hydrograph, infiltration, evapotranspiration, and flood routing. These events involve a large number of techniques and methods for the analysis, which are time-consuming.  To enhance the learning, this study presents a tool called ‘Hydrologic Calculator’, an educational interface with eight modules for analyzing the various hydrological related events. In addition, ‘Help’ module in ‘Hydrologic Calculator’ provides a thorough understanding of the theory and methodology adopted for solving the different hydrological problems. Hydrologic Calculator includes a graphical user interface, which helps in input data preparation and output display in both graphical and tabular forms. Besides, it also provides detailed results in log (.txt) format. All the eight modules of the software were tested using the available published data. The validation results obtained using Hydrologic Calculator were in good agreement with the respective results given in the source. Thus, Hydrologic Calculator can be used as a professional computer tool for teaching and analyzing different hydrological processes.

SPA: A sense-predict-actuate TDMA latency reduction scheme in networked quadrotors

In this paper, we propose the use of a Long Short-Term Memory (LSTM) based server-side sequence prediction algorithm to ease network data-load caused by rapid polling of multiple sensors onboard aerial robotic platforms, which are wirelessly tethered to a remote server for control and coordination. Our scheme reduces the network access time latencies between these platforms and the remote server hosting the control and scheduling mechanisms. Reduction in the TDMA-based access time is achieved by reducing the actual amount of data transmitted over the network, using partial transmission of actual sensor data over the network and server-side sequence prediction of the voluntarily missed sensor values. Our scheme allows the TDMA control of an increased number of networked platforms without change of infrastructure or the network characteristics

A survey of unmanned aerial sensing solutions in precision agriculture

We attribute the gain in popularity of Unmanned Aerial Vehicles (UAV), Platforms and Systems (UAS) to its ease of operation, versatility, and risk-free piloting. The primary UAV application domain has expanded from recreational and military flights to include scientific surveys and agriculture. The popularity of UAVs in scientific data gathering and applications, especially the use of small multi-rotor UAVs is quite widespread. These portable multi-rotor UAVs are portable, low-cost, highly maneuverable, and easy to handle. These features make such UAVs attractive to scientists and researchers worldwide. There has been a sudden spurt of UAV use in niche domains such as agriculture. Agriculturalists are choosing UAV-based field operations and remote sensing over the time-tested satellite-based ones, especially for local-scale and high spatiotemporal resolution imagery. In this survey, we explore various UAV application areas, types, sensors, research domains, and deployment architectures. We provide comparisons between various UAV types, sensing technologies (UAV, WSN, satellites), UAV architectures, and their utility in precision agriculture. Finally, we outline the challenges and the future scope of such UAV-based solutions for precision agriculture

Distributed aerial processing for IoT-based edge UAV swarms in smart farming

This work addresses the challenges of a decentralized and heterogeneous Unmanned Aerial Vehicle (UAV) swarm deployment – some fitted with multimedia sensors, while others armed with scalar sensors – in resource-constrained and challenging environments, typically associated with farming. Subsequently, we also address the resulting problem of sensing and processing resource-intensive data aerially within the Edge swarm in the fastest and most efficient manner possible. The heterogeneous nature of the Edge swarm results in under-utilization of the available computation resources due to unequal data generation within its members. To address this, we propose a Nash bargaining-based weighted intra-Edge processing offload scheme to mitigate the problem of heavy processing in some of the swarm members. We do this by distributing the data to be processed to all the swarm members. Real-life hardware tuned simulation of a large UAV swarm shows that by increasing the number of UAVs in the swarm, our scheme achieves better scalability and reduced processing delays for intensive processing tasks. Additionally, in comparison to regular star and mesh topologies, our scheme achieves an increase in collective available network processing speeds by 100% for only 25% of the number of UAVs in a star topology

Blind entity identification for agricultural IoT deployments

Integration of various technologies to an Internet of Things (IoT) framework share the common goals of a consistent and structured data format that can be applied to any device, given the vast application scope of IoT. Additional goals include minimizing channel traffic and system energy consumption. In this paper, we propose to dismiss the requirement of certain seemingly crucial identifier fields from packets arriving through various sensor nodes in an agricultural IoT deployment. The proposed approach reduces packet size, thereby reducing channel traffic and energy consumption, as well as retaining the capability of identifying these originating nodes. We propose a method of a blind agricultural IoT node and sensor identification, which can be sourced and operated from a master node as well as a remote server. Additionally, this scheme has the capability of detecting the radio link quality between the master and slave nodes in a rudimentary form, as well as identifying the sensor nodes. We successfully trained and tested various multilayer perceptron-based models for blind identification, in real-time, using our implemented agricultural IoT implementation. The effect of changes in learning rate and momentum of the optimizer on the accuracy of classification is also studied. The projected cumulative energy savings across the network architecture, of our scheme, in conjunction with TCP/IP header compression techniques, are substantial. For a 100 node deployment using a combination of the proposed blind identification reduced sampling strategies over regular IPv4-based TCP/IP connection, an estimated annual saving of ≈99% is projected

Modelling the dynamics of evapotranspiration using Variable Infiltration Capacity model and regionally calibrated Hargreaves approach

Computation of reference evapotranspiration (ETO) at different spatiotemporal scales is constrained by limited in situ meteorological data availability which, in turn, led to alternate ET estimation methods using the commonly available meteorological data of maximum and minimum temperatures. In this study, the Hargreaves–Samani model and the water budget approach of the Variable Infiltration Capacity (VIC-3L) land surface model was evaluated for grid-scale actual ET estimation using the benchmark FAO-56 Penman–Monteith (PM) equation and crop coefficient relationships. These approaches were field-tested in the Kangsabati River basin in eastern India, a tropical monsoon-type climate region with dominant paddy land uses. The results revealed that the VIC model could estimate the grid-scale ET reasonably well; however, the corresponding estimates by the Hargreaves method were highly overestimated. To enhance the field applicability of the Hargreaves method for data-scarce regions, this method was coupled with a genetic algorithm-based bias correction approach that improved the Nash–Sutcliffe efficiency significantly. Hence, this study reveals that there is a need for regional-scale standardization of the Hargreaves ET estimates using the FAO-56 PM, lysimeter data or the VIC-3L model

Evaluation of variable-Infiltration capacity model and MODIS-Terra satellite-derived grid-scale evapotranspiration estimates in a river basin with tropical monsoon-type climatology

With the limited availability of meteorological variables in many remote areas, estimation of evapotranspiration (ET) at different spatiotemporal scales for efficient irrigation water management and hydrometeorological studies is becoming a challenging task. Hence, in this study, indirect ET estimation methods, such as moderate resolution imaging spectroradiometer (MODIS) satellite-based remote-sensing techniques and the water-budget approach built into the semidistributed variable infiltration capacity (VIC-3L) land-surface model are evaluated using the Penman-Monteith (PM) equation approach suggested in the literature together with a crop coefficient approach. To answer the research question of whether regional or local controls of a river basin with tropical monsoon-type climatology affect the accuracy of the VIC and MODIS-based ET estimates, these methodologies are applied in the Kangsabati River Basin in eastern India at 25 × 25 km resolutions attributed with dominant paddy land uses. The results reveal that the VIC-estimated ET values are reasonably matched with the PM-based ET estimates with the Nash-Sutcliffe efficiency (NSE) of 54.14-71.94%; however, the corresponding MODIS-ET values are highly underestimated with a periodic shift that may be attributed to the cloud cover and leaf shadowing effects. To enhance the field applicability of the satellite-based MODIS-ET products, these estimates are standardized by using a genetic-algorithm-based transformation that improves the NSE from -390.83 to 99.57%. Hence, this study reveals that there is the need of a regional-scale standardization of the MODIS-ET products using the PM or lysimeter data or possible modification of the MOD16A2 algorithm built-into the MODIS for generalization. Conversely, the satisfactory grid-scale ET estimates by the VIC model show that this model could be reliably used for the world's river basins; however, at smaller temporal scales, the estimates could be slightly inconsistent

ECoR: Energy-Aware Collaborative Routing for Task Offload in Sustainable UAV Swarms

In this work, we propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. We divide the proposed scheme into two parts -- routing path discovery and routing path selection. The scheme selects the most optimal path between a source and destination from a massive set of all possible paths, based on the maximization of residual energy of UAVs along a selected path. This routing path selection ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Actual readings from our small-scale UAV swarm testbed are utilized to emulate a large-scale scenario and analyze the behavior of our proposed scheme. Upon comparison of the ECoR scheme with broadcast-based routing and the shortest path based routing, we observe better sustainability regarding the longevity of the UAV lifetimes in the swarm, optimized individual UAV, as well as reduced collective path-based energy consumption, all the while having comparable transmission delays to the shortest path based scheme

Identification of Suitable Hydrological Models for Streamflow Assessment in the Kangsabati River Basin, India, by Using Different Model Selection Scores

The increasing demand for water in developing countries, like India, requires efficient water management and resource allocation. This is crucial to accurately assess and predict hydrological processes such as streamflow, drought, and flood. However, simulations of these hydrologic processes from various hydrological models differ in their accuracy. By analyzing different characteristics of hydrological models, selection scores can be used to select the best model for the intended purpose based on their inherit strengths (i.e., some models are better for streamflow prediction). In this study, 13 different criteria were used for the model selection scores including temporal and spatial resolutions, and processes involved. Thereafter, based on different scores, we selected two different hydrological models for streamflow prediction in the Kangsabati River Basin (KRB) in eastern India, namely (1) Génie Rural à 4 paramètres Journalier (GR4J), a conceptual model, and (2) Variable Infiltration Capacity (VIC), a semi-distributed model. The models were calibrated against the daily observed streamflow at upper KRB (Reservoir) and lower KRB (Mohanpur) from 2000 to 2006 and validated during the period from 2008 to 2010. Despite the differences in model structure and data used, both models simulated streamflow at a daily time scale with Nash–Sutcliffe coefficient of 0.71–0.82 for the VIC model and 0.63–0.71 for the GR4J. Due to the simpler structure, parsimonious nature, fewer parameters, and reasonable accuracy, the results suggest that a conceptual rainfall—runoff model like GR4J can be used in data-deficient conditions