Environmental Data Acquisition Research Articles

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration.

Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment

Environmental data acquisition tools are broadly used for climate monitoring and urban comfort assessment followed by data mining and sensing techniques for putting into evidence the relationship between environmental qualities of urban spaces and human well-being. Within this context, an environmental toolkit is a fundamental tool to evaluate transient outdoor comfort. This study explains the prototyping and validation of a mobile environmental sensor kit. The results show the prototype has reasonable accuracy despite its affordability with respect to industrial sensors.

Development of a large-scale computer-controlled ozone inhalation exposure system for rodents

Objective: Complete systems for laboratory-based inhalation toxicology studies are typically not commercially available; therefore, inhalation toxicologists utilize custom-made exposure systems. Here we report on the design, construction, testing, operation and maintenance of a newly developed in vivo rodent ozone inhalation exposure system.Materials and methods: Key design requirements for the system included large-capacity exposure chambers to facilitate studies with large sample sizes, automatic and precise control of chamber ozone concentrations, as well as automated data collection on airflow and environmental conditions. The exposure system contains two Hazelton H-1000 stainless steel and glass exposure chambers, each providing capacity for up to 180 mice or 96 rats. We developed an empirically tuned proportional-integral-derivative control loop that provides stable ozone concentrations throughout the exposure period (typically 3h), after a short ramp time (∼8 min), and across a tested concentration range of 0.2–2 ppm. Specific details on the combination of analog and digital input/output system for environmental data acquisition, control and safety systems are provided, and we outline the steps involved in maintenance and calibration of the system.Results: We show that the exposure system produces consistent ozone exposures both within and across experiments, as evidenced by low coefficients of variation in chamber ozone concentration and consistent biological responses (airway inflammation) in mice, respectively.Conclusion: Thus, we have created a large and robust ozone exposure system, facilitating future studies on the health effects of ozone in rodents.

Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar

Techniques for marine monitoring have been greatly evolved over the past decades, making the acquisition of environmental data safer, more reliable and more efficient. On the other hand, the marine renewable energy sector has introduced dissimilar ways of exploring the oceans. Marine energy is mostly harvested in murky and high energetic places where conventional data acquisition techniques are impractical. This new frontier on marine operations brings the need for finding new techniques for environmental data acquisition, processing and analysis. Modern sonar systems, operating at high frequencies, can acquire detailed images of the underwater environment. Variables such as occurrence, size, class and behavior of a variety of aquatic species of fish, birds, and mammals that coexist within marine energy sites can be monitored using imaging sonar systems. Although sonar images can provide high levels of detail, in most of the cases they are still difficult to decipher. In order to facilitate the classification of targets using sonar images, this study introduces a framework of extracting visual features of marine animals that would serve as unique signatures. The acoustic visibility measure (AVM) is here introduced as technique of identification and classification of targets by comparing the observed size with a standard value. This information can be used to instruct algorithms and protocols in order to automate the identification and classification of underwater targets using imaging sonar systems. Using image processing algorithms embedded in Proviwer4 and FIJI software, this study found that acoustic images can be effectively used to classify cod, harbour and grey seals, and orcas through their size, shape and swimming behavior. The sonar images showed that cod occurred as bright, 0.9 m long, ellipsoidal targets shoaling in groups. Harbour seals occurred as bright torpedo-like fast moving targets, whereas grey seals occurred as bulky-ellipsoidal targets with serpentine movements. Orca or larger marine mammals occurred with relatively low visibility on the acoustic images compared to their body size, which measured between 4 m and 7 m. This framework provide a new window of performing qualitative and quantitative observations of underwater targets, and with further improvements, this method can be useful for environmental studies within marine renewable energy farms and for other purposes.

Insights into the Short-Term Tidal Variability of Multibeam Backscatter from Field Experiments on Different Seafloor Types

Three experiments were conducted in the Belgian part of the North Sea to investigate short-term variation in seafloor backscatter strength (BS) obtained with multibeam echosounders (MBES). Measurements were acquired on predominantly gravelly (offshore) and sandy and muddy (nearshore) areas. Kongsberg EM3002 and EM2040 dual MBES were used to carry out repeated 300-kHz backscatter measurements over tidal cycles (~13 h). Measurements were analysed in complement to an array of ground-truth variables on sediment and current nature and dynamics. Seafloor and water-column sampling was used, as well as benthic landers equipped with different oceanographic sensors. Both angular response (AR) and mosaicked BS were derived. Results point at the high stability of the seafloor BS in the gravelly area (<0.5 dB variability at 45° incidence) and significant variability in the sandy and muddy areas with envelopes of variability >2 dB and 4 dB at 45° respectively. The high-frequency backscatter sensitivity and short-term variability are interpreted and discussed in the light of the available ground-truth data for the three experiments. The envelopes of variability differed considerably between areas and were driven either by external sources (not related to the seafloor sediment), or by intrinsic seafloor properties (typically for dynamic nearshore areas) or by a combination of both. More specifically, within the gravelly areas with a clear water mass, seafloor BS measurements where unambiguous and related directly to the water-sediment interface. Within the sandy nearshore area, the BS was shown to be strongly affected by roughness polarization processes, particularly due to along- and cross-shore current dynamics, which were responsible for the geometric reorganization of the morpho-sedimentary features. In the muddy nearshore area, the BS fluctuation was jointly driven by high-concentrated mud suspension dynamics, together with surficial substrate changes, as well as by water turbidity, increasing the transmission losses. Altogether, this shows that end-users and surveyors need to consider the complexity of the environment since its dynamics may have severe repercussions on the interpretation of BS maps and change-detection applications. Furthermore, the experimental observations revealed the sensitivity of high-frequency BS values to an array of specific configurations of the natural water-sediment interface which are of interest for monitoring applications elsewhere. This encourages the routine acquisition of different and concurrent environmental data together with MBES survey data. In view of promising advances in MBES absolute calibration allowing more straightforward data comparison, further investigations of the drivers of BS variability and sensitivity are required.

Integrating camera imagery, crowdsourcing, and deep learning to improve high-frequency automated monitoring of snow at continental-to-global scales.

Snow is important for local to global climate and surface hydrology, but spatial and temporal heterogeneity in the extent of snow cover make accurate, fine-scale mapping and monitoring of snow an enormous challenge. We took 184,453 daily near-surface images acquired by 133 automated cameras and processed them using crowdsourcing and deep learning to determine whether snow was present or absent in each image. We found that the crowdsourced data had an accuracy of 99.1% when compared with expert evaluation of the same imagery. We then used the image classification to train a deep convolutional neural network via transfer learning, with accuracies of 92% to 98%, depending on the image set and training method. The majority of neural network errors were due to snow that was present not being detected. We used the results of the neural networks to validate the presence or absence of snow inferred from the MODIS satellite sensor and obtained similar results to those from other validation studies. This method of using automated sensors, crowdsourcing, and deep learning in combination produced an accurate high temporal dataset of snow presence across a continent. It holds broad potential for real-time large-scale acquisition and processing of ecological and environmental data in support of monitoring, management, and research objectives.

A Collaborative UAV-WSN Network for Monitoring Large Areas.

Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value.

A template framework for environmental timeseries data acquisition

Environmental timeseries data variety is exploding in the Internet of Things era, making data reuse a very demanding task. Data acquisition and integration remains a laborious step of the environmental data lifecycle. Environmental data heterogeneity is a persistent issue, as data are becoming available through different protocols and stored under diverse, custom formats. In this work, we deal with syntactic heterogeneity in environmental timeseries data. Our approach is based on describing different dataset syntaxes using abstract representations, called templates. We designed and implemented EDAM (Environmental Data Acquisition Module), a template framework that facilitates timeseries data acquisition and integration. EDAM templates are written using programming language-agnostic semantics, and can be reused both for input and output, thus enabling data reuse via transformations across different formats. We demonstrate EDAM generality in seven case studies, which involve scraping online data, extracting observations from a relational database, or aggregating historical timeseries stored in local files. Case studies span different environmental sciences domains, including meteorology, agriculture, urban air quality and hydrology. We also demonstrate EDAM for data dissemination, as instructed by output templates. We identified several syntactic interoperability challenges though the case studies, that include managing with differences in formatting observables, temporal and spatial references, and metadata documentation, and addressed them with EDAM. EDAM implementation has been released under an open-source license.

From environmental data acquisition to assessment of gardeners' exposure: feedback in an urban context highly contaminated with metals.

Although growing vegetables in urban gardens has several benefits, some questions in relation with the safety of foods remain when the self-production is carried out on highly contaminated garden soils. To better assess the local population's exposure to Cd and Pb induced by the past activities of a lead smelter, a participatory program was initiated in 115 private kitchen gardens located in northern France to assist gardeners in understanding their soil environment. The challenge included contributing to the database of urban garden soils with the collection of a large number of samples: 1525 crops grouped into 12 types (leaf, fruiting, root, stem and bulbous vegetables, tubers, cabbages, leguminous plants, celeriac, fresh herbs, fruits, and berries), 708 topsoils, and 52 samples of self-produced compost. The main results were as follows: (i) topsoils were strongly contaminated by Cd and Pb compared to regional reference values; (ii) great variability in physicochemical parameters and metal concentrations in topsoils; (iii) the highest concentrations of Cd and Pb for celeriac and fresh herbs and the lowest for fruits and fruiting vegetables; (iv) a high percentage of vegetables that did not comply with the European foodstuff legislation; and (v) most self-produced compost samples were strongly contaminated. This study aimed to raise awareness and generate functional recommendations to reduce human exposure and to provide useful data that could be considered in other environmental contexts.

Environmental Data Acquisition, Elaboration and Integration: Preliminary Application to a Vulnerable Mountain Landscape and Village (Novalesa, NW Italy)

Abstract Climate conditions play a crucial role in the survival of mountain communities, whose survival already critically depends on socioeconomic factors. In the case of montane areas that are prone to natural hazards, such as alpine slope failure and debris flows, climatic factors exert a major influence that should be considered when creating appropriate sustainable scenarios. In fact, it has been shown that climate change alters the availability of ecosystem services (ES), thus increasing the risks of declining soil fertility and reduced water availability, as well as the loss of grassland, potential shifts in regulatory services (e.g., protection from natural hazards), and cultural services. This study offers a preliminary discussion on a case study of a region in the Italian Alps that is experiencing increased extreme precipitation and erosion, and where an isolated and historically resilient community directly depends on a natural resource economy. Preliminary results show that economic factors have influenced past population trends of the Novalesa community in the Piemonte Region in northwest Italy. However, the increasing number of rock fall and debris flow events, which are triggered by meteo-climatic factors, may further influence the livelihood and wellbeing of this community, and of other similar communities around the world. Therefore, environmental monitoring and data analysis will be important means of detecting trends in landscape and climate change and choosing appropriate planning options. Such analysis, in turn, would ensure the survival of about 10% of the global population, and would also represent a possibility for future economic development in critical areas prone to poverty conditions.

Design and implementation of wireless multimedia sensor network node based on FPGA and binocular vision

According to the actual demand of the wireless sensor network for multimedia information acquisition, the new multimedia wireless sensor node is designed and implemented with FPGA as the core and Zigbee wireless communication. On the multimedia nodes, sensors such as binocular camera and accelerometer are configured to realize real-time perception and acquisition of environmental information and image data, and the short distance wireless communication and detection ability is formed through Zigbee protocol. When the test distance is not more than 60 m, the measured data fluctuate around the theoretical curve, and the communication is reliable. When the distance of the node is within the 14 m, the data transmission has no error. In view of the fact that the mismatch rate of the traditional Census transform is high under the condition of accuracy and noise, an improved Census transform stereo matching algorithm is proposed, which is applied to the nodes of multimedia sensor network. The experimental results show that this scheme can realize the data transmission and processing of wireless sensor network nodes and has strong operation capability and visual matching precision while reducing power consumption.

Differences in acquisition of environmental data in strongly impacted marine sediments using gravity and vibro corers: The case-study of Augusta harbor (Eastern Sicily, Italy)

Sediment cores are used to reconstruct the chronological evolution of contamination in impacted areas while deep core levels may be considered as reference conditions for the assessment of environmental status. For this purpose, the collection of undisturbed cores is essential. Vibro and gravity corers are the most used devices for environmental research. In this study, chemical (Ba, Hg and PCBs) and grain size data obtained by means of gravity (SW-104) and vibro (Rossfelder®) corer from 3 stations of the heavily contaminated Augusta harbor (Sicily, Italy) were considered. Their vertical profiles were compared considering the different technical characteristics of corers, in order to highlight differences in data acquisition. Results showed that, for areas characterized by high sedimentation rates, the vibrocorer is the best choice for the higher penetration capacity. No significant differences were recognized for sediment compaction and potential downward drag of the contaminants.

Design Of Wireless Sensor Networks For IOT Application : A Challenges and survey

Internet of things (IOT) Can be defined in many ways it encompasses many aspects of life such as connected homes, connected cities, connected cars and roads, roads to devices that track an individual’s behaviour. It is expected that one trillion Internet-connected devices will be available with mobile phones as the eyes and ears of the applications connecting all of those connected things. IoT made it possible for billions objects to communicate over worldwide over a public, private internet protocol network In 2010. In 2010 -11 the number of everyday physical objects and devices connected to the Internet was around 12.5 billion. The essential idea of the Internet of Things (IoT) has been around for nearly two decades, and has attracted many researchers and industries because of its great estimated impact in improving our daily lives and society. When things like household appliances are connected to a network, they can work together in cooperation to provide the ideal service as a whole, not as a collection of independently working devices. This is useful for many of the real-world applications and services, and one would for example apply it to build a smart residence; windows can be closed automatically when the air conditioner is turned on, or can be opened for oxygen when the gas oven is turned on. The idea of IoT is especially valuable or persons with disabilities, as IoT technologies can support human activities at larger scale like building or society, as the devices can mutually cooperate to act as a total system. WSNs are integrated into the “Internet of Things”, where sensor nodes join the Internet dynamically, and use it to collaborate and accomplish their tasks. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. In this paper, it is proposed to implement a WSN platform that can be used for a range of long-term environmental monitoring for IoT applications. This paper
 presents functional design of WSN for IoT application. To provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges

Effects of seasonal variation and tidal regimes on macrobenthic invertebrates of the Warri River, Nigeria

An assessment of the effects of seasonal variation and tidal regimes on the distribution and density of macrobenthic invertebrates of the coastal Warri River, southern Nigeria was carried out from July 2014 to February 2015. Samples were collected from five longitudinal stations from headwater to mouth during high and low tidal regimes in the wet and dry seasons. A total of 50 taxa comprising 1664 individuals /m2 were collected. Macrobenthic invertebrates were significantly more abundant (P < 0.001) during the low tide than during the high tide and during the dry than in the wet season (P<0.001). In general, seasonal variation and tidal regimes siginificantly influenced macrobenthic community structure of the Warri River. Results are therefore more authentic and holistic when sampling in coastal tidal rivers takes into account, tidal and seasonal variations. It is therefore recommended that sampling for environmental baseline data acquisition in tidal rivers should be carried out to cover high and low tidal regimes and also the dry and wet seasons.Keywords: Macrobenthic invertebrates, tidal regimes, seasonal variation, taxa, density

Indoor Microclimate Quality (IMQ) certification in heritage and museum buildings: The case study of Vleeshuis museum in Antwerp

In order to detect possible downsides of building microclimate management or for identifying performance improvement options, it is fundamental to assess and certify building Indoor Microclimate Quality (IMQ).Considering that in heritage and museum buildings, the indoor microclimate should simultaneously ensure comfort of people and safety for the cultural heritage, its management takes on a multidimensional nature.Although in the literature IMQ certification methodologies for people already exist, these do not encompass the cultural heritage. We believe this integration, would be a valid management instrument for heritage buildings, historic houses and museums, especially if they are not equipped with full microclimate control systems.However, because environmental data acquisition activities have direct influence on the certification results, it is essential to evaluate them. These methodological aspects are here discussed on the basis of results from a microclimate monitoring in the main exhibition hall of Vleeshuis museum in Antwerp. Further, in accordance to the introduced methodological considerations, an IMQ certification model for building users and movable heritage is proposed.

Power and Channel Optimization for WiFi Networks Based on REM Data

The very high commercial exploitation of the WiFi based technologies in recent years and the absence of solutions for optimal WiFi orchestration, usually leads to suboptimal spectrum usage and user performances. The combination of WiFi based radio resource management (RRM) and the radio environmental maps (REMs) can provide an efficient solution for a Smart-WiFi technology, which improves the underlying spectrum usage as well as network performance. The REM facilitates efficient utilization of the radio environmental data, like device location, estimated channel models, real-time interference levels between the networks, WiFi channels occupancies etc. This information can be utilized for an intelligent and optimal RRM decision making in WiFi related scenarios. This paper proposes a novel REM based RRM approach for management and optimization of commercial WiFi devices that utilizes the available underlying radio environmental information. The paper demonstrates the proposed approach on a commercially available platform, conducting on-the-fly radio environmental data acquisition and optimized WiFi RRM allocation. The simulation analysis results also show that the proposed Smart-WiFi leverage noticeable performance gains for large scale scenarios, compared to conventional WiFi networks.

Widespread introgression in deep-sea hydrothermal vent mussels

BackgroundThe analysis of hybrid zones is crucial for gaining a mechanistic understanding of the process of speciation and the maintenance of species boundaries. Hybrid zones have been studied intensively in terrestrial and shallow-water ecosystems, but very little is known about their occurrence in deep-sea environments. Here we used diagnostic, single nucleotide polymorphisms in combination with one mitochondrial gene to re-examine prior hypotheses about a contact zone involving deep-sea hydrothermal vent mussels, Bathymodiolus azoricus and B. puteoserpentis, living along the Mid-Atlantic Ridge.ResultsAdmixture was found to be asymmetric with respect to the parental species, while introgression was more widespread geographically than previously recognized. Admixed individuals with a majority of alleles from one of the parental species were most frequent in habitats corresponding to that species. Mussels found at a geographically intermediate vent field constituted a genetically mixed population that showed no evidence for hybrid incompatibilities, a finding that does not support a previously inferred tension zone model.ConclusionsOur analyses indicate that B. azoricus and B. puteoserpentis hybridize introgressively across a large geographic area without evidence for general hybrid incompatibilities. While these findings shed new light onto the genetic structure of this hybrid zone, many aspects about its nature still remain obscure. Our study sets a baseline for further research that should primarily focus on the acquisition of additional mussel samples and environmental data, a detailed exploration of vent areas and hidden populations as well as genomic analyses in both mussel hosts and their bacterial symbionts.

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management.

Multicopter platform prototype for environmental monitoring

Due to the ecological devastation of the natural environment, there is a constant need for monitoring the environmental features changes. Airborne engineering is now rapidly developing, enabling the construction of small automatic flying vehicles, so called multicopters or flying drones. The main objective of this study is to design and manufacture the low cost and low weight quadcopter platform prototype for the purpose of the environmental monitoring and research. A multirotor concept was created, which assumed some executive functions i.e. hovering, environmental data acquisition or Global Positioning System movement. On the basis of these assumptions, platform components were chosen. They include: the frame, drives, electronics and software. Also the presentation of the environmental measurement capabilities, with the use of multicopters was done along with the discussion on application capabilities, advantages and disadvantages of Unmanned Aerial Vechicles for analytical agriculture. On the basis of the project, a prototype quadcopter platform was built. During the tests some corrections to specific components were made. As a result of the whole process, a platform that is able to accomplish the tasks was created. All components of the multicopter have been described, materials used, mounting and connecting them, as well as the presentation of the specific device problems. The manufacturing cost of the drone prototype was lower than EUR 500, weight of less than 750 g and the drone performed all assumed tasks, which was considered a success of the project. Scientific value of the paper includes the guidance of cheap and efficient setup along with the applications.

WiSE - a satellite-based system for remote monitoring

Recent technological advancements in sensors, processors and communications technology make it viable to perform digital acquisition of environmental data from remote locations. Declining costs and miniaturisation of electronics and sensors have enabled design of systems for intelligent remote monitoring. These advances pave the way for new tools to support field work by virtually extending researchers' reach to the field study area from the comfort of their offices. The Wireless Internet Sensing Environment project developed an architecture providing control and retrieval of data from networked sensors and cameras at a remote location using Internet backhaul. Satellite connectivity enabled this equipment to be deployed to remote locations to support an ecological application. This paper describes architecture and innovative design features for this challenging problem space, including motion event detection, power management and a method to upload collected data. © 2016 The Authors. International Journal of Satellite Communications and Networking published by John Wiley & Sons Ltd.