BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data

Introduction Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects. Methods BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s $$\kappa$$ κ . Results Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid. Conclusion As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.

Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

A Novel Loss Tolerant Data Transmission Schemes for Airborne Telemetry System of a Long Range Aerospace Vehicle

The on-board telemetry system of an aerospace vehicle sends the vehicle performance parameters to the ground receiving station at all instances of its trajectory. During the course of its trajectory, the communication channel of a long range vehicle, experiences various phenomena such as plume attenuation, stage separation, manoeuvring of a vehicle and RF blackout, causing loss of valuable telemetry data. The loss of communication link is inevitable due to these harsh conditions even when using the space diversity of ground receiving systems. Conventional telemetry systems do not provide redundant data for long range aerospace vehicles. This research work proposes an innovative delay data transmission, frame switchover and multiple frames data transmission schemes to improve the availability of telemetry data at ground receiving stations. The proposed innovative schemes are modelled using VHDL and extensive simulations have been performed to validate the results. The functionally simulated net list has been synthesised with 130 nm ACTEL flash based FPGA and verified on telemetry hardware.

Towards data sharing economy on Internet of Things: a semantic for telemetry data

Internet of Things (IoT) provides data processing and machine learning techniques with access to physical world data through sensors, namely telemetry data. Acquiring sensor data through IoT faces challenges such as connectivity and proper measurement requiring domain-specific knowledge, that results in data quality problems. Data sharing is one solution to this. In this work, we propose IoT Telemetry Data Hub (IoT TeleHub), a general framework and semantic for telemetry data collection and sharing. The framework is principled on abstraction, layering of elements, and extensibility and openness. We showed that while the framework is defined specifically for telemetry data, it is general enough to be mapped to existing IoT platforms with various use cases. Our framework also considers the machine-readable and machine-understandable notion in regard to resource-constrained IoT devices. We also present IoThingsHub, an IoT platform for real-time data sharing based on the proposed framework. The platform demonstrated that the framework could be implemented with existing technologies such as HTTP, MQTT, SQL, NoSQL.

Evaluation of Frequencies for the IoT Telemetry in Smart Agriculture

The IoT is becoming a widely known technology for the gathering of telemetry data, while mostly the concept of Smart cities is usually seen as the most challenging area for implementation. The different situations can be found in the smart agriculture concept, where different requirements and especially conditions exist. The purpose of this paper is to make an overview of IoT frequency bands available, with special focus on the situation in the EU, their theoretical usability and, using experimental measurements of typical background noise in different bands and calculations of transmission reliability on expected distance, estimate the practical usability of those technologies in the smart agriculture, compared to the smart city’s requirements. Most of the IoT installations outside 5G systems are in the 900 MHz band, but is this well-suitable for smart agriculture?

Binary classification of small satellites telemetry data based on deep learning approach

The paper presents solutions to the actual problem of intelligent analysis of telemetry data from small satellites in order to detect its technical states. Neural network models based on modern deep learning architectures have been developed and investigated to solve the problem of binary classification of telemetry data. It makes possible to determine the normal and abnormal state of the small satellites or some of its subsystems. For the computer analysis, the data of the functioning of the small satellites navigation subsystem were used: a time series with a dimension of 121690 × 9. A comparative analysis was carried out of fully connected, onedimensional convolution and recurrent (GRU, LSTM) neural networks. We analyzed hybrid neural network models of various depths, which are sequential combinations of all three types of layers, including using the technology of adding residual connections of the ResNet family. Achieved results were compared with results of widespread neural network models AlexNet, LeNet, Inception, Xception, MobileNet, ResNet, and Yolo, modified for time series classification. The best result, in terms of classification accuracy at the stages of training, validation and testing, and the execution time of one training and validation epoch, were obtained by the developed hybrid neural network models of three types of layers: one-dimensional convolution, recurrent GRU and fully connected classification layers, using the technology of adding residual connections. In this case, the input data were normalized. The obtained classification accuracy at the training, validation and testing stages was 0.9821, 0.9665, 0.9690, respectively. The execution time of one learning and validation epoch was twelve seconds. At the same time, the modified Inception model showed the best alternative result in terms of accuracy: 0.9818, 0.9694, 0.9675. The execution time of one training and validation epoch was twenty seven seconds. That is, there was no increase in the classification accuracy when adapting the well-known neural network models used for image analysis. But the training and validation time in the case of the best Inception model increased by more than two times. Thus, proposed and analyzed hybrid neural network model showed the highest accuracy and minimum training and validation time in solving the considered problem according to compared with a number of developed and widely known and used deep neural network models.

Benford’s Law for Telemetry Data of Wildlife

Benford’s law (BL) specifies the expected digit distributions of data in social sciences, such as demographic or financial data. We focused on the first-digit distribution and hypothesized that it would apply to data on locations of animals freely moving in a natural habitat. We believe that animal movement in natural habitats may differ with respect to BL from movement in more restricted areas (e.g., game preserve). To verify the BL-hypothesis for natural habitats, during 2015–2018, we collected telemetry data of twenty individuals of wild red deer from an alpine region of Austria. For each animal, we recorded the distances between successive position records. Collecting these data for each animal in weekly logbooks resulted in 1132 samples of size 65 on average. The weekly logbook data displayed a BL-like distribution of the leading digits. However, the data did not follow BL perfectly; for 9% (99) of the 1132 weekly logbooks, the chi-square test refuted the BL-hypothesis. A Monte Carlo simulation confirmed that this deviation from BL could not be explained by spurious tests, where a deviation from BL occurred by chance.