Telemetry Data Collection Research Articles

Enhancing Reliability in Distributed Systems: A Comprehensive Approach to Telemetry and Monitoring

This comprehensive article explores the implementation of telemetry and monitoring in distributed systems, addressing the challenges and opportunities in managing complex, scalable architectures. The article begins by examining the fundamental concepts of telemetry, including data collection methodologies, transmission protocols, and analysis techniques. It then provides an in-depth comparison of popular telemetry tools such as AWS CloudWatch and Prometheus, discussing their features, capabilities, and integration challenges. The article delves into effective monitoring strategies, emphasizing the importance of key performance indicators (KPIs) in distributed environments and the implementation of comprehensive solutions including real-time dashboards, proactive alert systems, and automated remediation techniques. Furthermore, the study investigates emerging trends in AI-driven monitoring and predictive analytics, highlighting their potential to revolutionize system observability. The article also addresses critical challenges facing the field, including scalability concerns in large-scale systems, privacy, and security considerations in telemetry data collection, and the need for balance between monitoring overhead and system performance. By synthesizing current practices with future directions, this article provides valuable insights for practitioners and researchers alike, contributing to the ongoing evolution of telemetry and monitoring practices in the rapidly advancing landscape of distributed computing.

Design and implementation of terminal power information collection and control system based on low-code platform

Abstract As the number of power consumption collection terminals continues to increase, the business needs for remote signaling and telemetry data collection through interface services continue to expand. Different professional collection front-end openness levels and open standards are not unified, and each professional terminal can only be managed in a decentralized manner. As a result, the current power consumption information data collection capabilities are scattered. In general, active data collection and sensing in enterprises must rely on various collection services to carry out applications. The lack of unified operation and management mechanisms and tools leads to difficulties in the development of interfaces and the management of terminal equipment. A set of power consumption information collection service integration systems developed based on a low-code platform is proposed. In view of the different communication protocols of power terminals and the need to develop different interfaces, etc., the command issuance and message encapsulation of different power collection protocols is carried out, to provide customized collection services and improve unified access and management capabilities of terminals.

Optimizing in-band network telemetry problem

In-band network telemetry is a powerful framework for network monitoring. It allows the collection of telemetry data in real-time and provides network-wide visibility. However, depending on the routing of network flows and which telemetry data are collected, the network-wide visibility and the performance of monitoring applications may decrease. In this paper, we present the in-band network telemetry problem and extend the existing mathematical optimization models of the problem by proposing a new model that computes the routing of network flows. Results show that the new model outperforms existing models in term of network coverage and monitoring applications performance. The results of this work can be useful for network managers and enterprises to gain real-time insights into network performance.

Vnode: Low-Overhead Transparent Tracing of Node.js-Based Microservice Architectures

Tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With Node.js emerging as a leading development environment recognized for its rapidly growing ecosystem, there is a pressing need for innovative performance debugging approaches that reduce the telemetry data collection efforts and the overhead incurred by the environment’s instrumentation. In response, we introduce a new approach designed for transparent tracing and performance debugging of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent Tracing and Context Reconstruction (ITTCR) technique. ITTCR is adept at correlating internal metrics from various distributed trace files to reconstruct the intricate execution contexts of microservices operating in a Node.js environment. Our method achieves transparency by directly instrumenting the Node.js virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. Compared to other methods, our approach incurs an overhead of approximately 5% on the system for the trace collection infrastructure while exhibiting minimal utilization of system resources during analysis execution. Experiments demonstrate that our technique scales well with very large trace files containing huge numbers of events and performs analyses in very acceptable timeframes.

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.

Imputation Approaches for Animal Movement Modeling

The analysis of telemetry data is common in animal ecological studies. While the collection of telemetry data for individual animals has improved dramatically, the methods to properly account for inherent uncertainties (e.g., measurement error, dependence, barriers to movement) have lagged behind. Still, many new statistical approaches have been developed to infer unknown quantities affecting animal movement or predict movement based on telemetry data. Hierarchical statistical models are useful to account for some of the aforementioned uncertainties, as well as provide population-level inference, but they often come with an increased computational burden. For certain types of statistical models, it is straightforward to provide inference if the latent true animal trajectory is known, but challenging otherwise. In these cases, approaches related to multiple imputation have been employed to account for the uncertainty associated with our knowledge of the latent trajectory. Despite the increasing use of imputation approaches for modeling animal movement, the general sensitivity and accuracy of these methods have not been explored in detail. We provide an introduction to animal movement modeling and describe how imputation approaches may be helpful for certain types of models. We also assess the performance of imputation approaches in two simulation studies. Our simulation studies suggests that inference for model parameters directly related to the location of an individual may be more accurate than inference for parameters associated with higher-order processes such as velocity or acceleration. Finally, we apply these methods to analyze a telemetry data set involving northern fur seals (Callorhinus ursinus) in the Bering Sea. Supplementary materials accompanying this paper appear online.

Telemetric assessment of social and single housing: Evaluation of electrocardiographic intervals in jacketed cynomolgus monkeys

IntroductionProactive efforts to socially house laboratory animals are a contemporary, important focus for enhancing animal welfare. Jacketing cynomolgus monkeys has been traditionally considered an exclusionary criterion for social housing based on unsubstantiated concerns that study conduct or telemetry equipment might be compromised. Our objective was to evaluate the effects of jacketing naïve, adolescent cynomolgus monkeys in different single and social housing types based on parallel comparisons of heart rate. MethodsEight naive cynomolgus monkeys were randomized into pairs and ECG data were collected for 24h from each animal in each housing condition using a crossover design. Caging paradigms consisted of standard individual, standard pair, quaternary pair (4 linked cages), and European-style pair housing in non-sequential order varied by pair to control for possible time bias. Dosing and blood collection procedures were performed to characterize any effects of housing on ECG data during study conduct. ResultsThere was no increase in the incidence of equipment damage in pair vs. individually housed animals. Further, animals in all 4 housing paradigms showed similar acclimation assessed as heart rate (mean 139–154 beats per minute), and maintained similar diurnal rhythms, with an expected slowing of the heart rate at night (aggregate lights out HR 110±4bpm compared to daytime 146±7bpm). DiscussionThis study demonstrates the effects of different social access and housing types on the study—naïve cynomolgus monkeys during jacketed cardiovascular telemetry data collection in a repeat-dose toxicology study design. There were no discernible effects of social housing on baseline ECG parameters collected via jacketed telemetry, and all animals maintained expected diurnal rhythms in all housing settings tested. These data demonstrate that cynomolgus monkeys can be socially housed during data collection as a standard practice, consistent with global efforts to improve study animal welfare.

Space use by endangered giant pandas

Studies on animal space use can reveal insights into how animals interact with one another and their environment. Research on the space use patterns of the endangered giant panda (Ailuropoda melanoleuca) in China has nevertheless lagged behind that of many other species, as a government moratorium prevented telemetry data collection on pandas from 1995 to 2006. We studied 5 giant pandas using GPS telemetry and estimated home ranges, core areas, and space use using model-based approaches. Home range 95% area was 6 km2 for the single male studied and averaged 4.4 ± 1.2 (± SD) km2 for the 4 females. Pandas occupied several small core areas that they revisited after time lags of up to several months. Pandas also displayed significant space use interactions, especially among the male and 2 different females across several weeks during a fall season, a time of year not previously thought to involve extensive inter-panda interaction.

Using dynamic Brownian bridge movement modelling to measure temporal patterns of habitat selection

Accurately describing animal space use is vital to understanding how wildlife use habitat. Improvements in GPS technology continue to facilitate collection of telemetry data at high spatial and temporal resolutions. Application of the recently introduced dynamic Brownian bridge movement model (dBBMM) to such data is promising as the method explicitly incorporates the behavioural heterogeneity of a movement path into the estimated utilization distribution (UD). Utilization distributions defining space use are normally estimated for time-scales ranging from weeks to months, obscuring much of the fine-scale information available from high-volume GPS data sets. By accounting for movement heterogeneity, the dBBMM provides a rigorous, behaviourally based estimate of space use between each set of relocations. Focusing on UDs generated between individual sets of locations allows us to quantify fine-scale circadian variation in habitat use. We used the dBBMM to estimate UDs bounding individual time steps for three terrestrial species with different life histories to illustrate how the method can be used to identify fine-scale variations in habitat use. We also demonstrate how dBBMMs can be used to characterize circadian patterns of habitat selection and link fine-scale patterns of habitat use to behaviour. We observed circadian patterns of habitat use that varied seasonally for a white-tailed deer (Odocoileus virginianus) and coyote (Canis latrans). We found seasonal patterns in selection by the white-tailed deer and were able to link use of conifer forests and agricultural fields to behavioural state of the coyote. Additionally, we were able to quantify the date in which a Rio Grande wild turkey (Meleagris gallopavo intermedia) initiated laying as well as when during the day, she was most likely to visit the nest site to deposit eggs. The ability to quantify circadian patterns of habitat use may have important implications for research and management of wildlife. Additionally, the ability to link such patterns to behaviour may aid in the development of mechanistic models of habitat selection.

Measures of Space use and Association of Two Unrelated Male Red Wolves in a Shared Area

The United States Fish and Wildlife Service (USFWS) listed the red wolf (Canis rufus) as an endangered species in 1967 and began a recovery program in 1973 (USFWS 1989). By that time, however, the red wolf had already been extirpated from most of its range (McCarley and Carley 1979). Only 1 wild population of red wolves currently exists, reintroduced in 1987 to the Alligator River National Wildlife Refuge (ARNWR) in northeastern North Carolina, U.S.A. (USFWS 1989). Most research on the red wolf has addressed its status as a species (e.g., Wayne and Jenks 1991; Wilson et al. 2000; Kyle et al. 2006; Murray and Waits 2007). Ecological studies have been limited by small sample sizes or duration (Mauney 2005; Hinton 2006; Chadwick et al. 2010; Hinton and Chamberlain 2010) or by VHF telemetry data collection that is infrequent and limited to daytime observations (Phillips et al. 2003). Although GPS and VHF radio collars are used to monitor the population, intra-group spatial relationships are largely undocumented. Red wolves form groups consisting of the breeding pair and their sexually immature offspring (Phillips et al. 2003) and offspring from previous years have been observed as helpers at the den (Sparkman et al. 2010). Unrelated wolves of the same gender have not been previously reported in the same group. In this study, 2 unrelated male red wolves using a shared area were monitored 5 times per day for 12 months using GPS collars. The purpose was to determine if the non-breeding male was a member of the group composed of the breeding male and his mate, an uncollared female. We hypothesized that association between the males would be high if the non-breeder was a member of the group, whereas there would be no significant association if the nonbreeder was simply using the same area. If the unrelated non-breeder was a group member, this represents a unique pack composition that has not been studied in this species. Methods