A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors.

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing.

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness.

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces.

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.

IoT Device Integration and Payment via an Autonomic Blockchain-Based Service for IoT Device Sharing.

The Internet of Things (IoT) incorporates billions of IoT devices (e.g., sensors, cameras, wearables, smart phones, as well as other internet-connected machines in homes, vehicles, and industrial plants), and the number of such connected IoT devices is currently growing rapidly. This paper proposes a novel Autonomic Global IoT Device Discovery and Integration Service (which we refer to as aGIDDI) that permits IoT applications to find IoT devices that are owned and managed by other parties in IoT (which we refer to as IoT device providers), integrate them, and pay for using their data observations. aGIDDI incorporates a suite of interacting sub-services supporting IoT device description, query, integration, payment (via a pay-as-you-go payment model), and access control that utilise a special-purpose blockchain to manage all information needed for IoT applications to find, pay and use the IoT devices they need. The paper describes aGIDDI's novel protocol that allows any IoT application to discover and automatically integrate and pay for IoT devices and their data that are provided by other parties. The paper also presents aGIDDI's architecture and proof-of-concept implementation, as well as an experimental evaluation of the performance and scalability of aGIDDI in variety of IoT device integration and payment scenarios.

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms.

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

Digital Twins Supporting Efficient Digital Industrial Transformation.

Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

Evaluation of a Josephson Arbitrary Waveform Synthesizer at Low Voltages for the Calibration of Lock-In Amplifiers.

We have evaluated a Josephson arbitrary waveform synthesizer (JAWS) in the voltage range from 1 µV to 1 mV at frequencies from 60 to 1000 Hz for the use in the calibration of lock-in amplifiers. The uncertainty contribution from the JAWS system is 45 nV for 1 mV at 1000 Hz and k = 2.0. We anticipate that the JAWS will help extend the lower voltage and frequency range of ac voltage metrology and improve the uncertainties by one order of magnitude compared to conventional techniques.

Patient dose in brain perfusion imaging using an 80-slice CT system.

BACKGROUND AND PURPOSE: Brain CT Perfusion (CTP) is an X-ray imaging technique for the assessment of brain tissue perfusion, which can be used in several different entities. The aim of this study is the evaluation of the radiation dose to patients during a comprehensive brain CT prescription protocol (CPP) consisting of an unenhanced brain CT, a brain CT angiography and a CTP scan. MATERIALS AND METHODS: Eighteen patients were studied using an 80-slice CT system, with an iterative reconstruction algorithm. The volume Computed Tomography Dose Index (CTDIvol) and dose length product (DLP) were recorded from the dose report of the system. The calculation of effective dose (ED) was accomplished using the DLP values. RESULTS: For the CTP examinations, the CTDIvol ranged from 116.0 to 134.8mGy, with the mean value 119.5mGy. The DLP ranged from 463.9 to 539.2mGy·cm, with the mean value 478mGy·cm. For the CPP, the total ED ranged from 3.31 to 5.07mSv, with the mean value 4.37mSv. CONCLUSIONS: These values are lower than the values reported in corresponding studies, including studies utilizing CT systems with more slices.

AC Voltage Measurements to 120 V With a Josephson Arbitrary Waveform Synthesizer and an Inductive Voltage Divider.

We have developed a system to extend, by a factor of up to 1000, the voltage range over which Josephson arbitrary waveform synthesizers (JAWS) can be used in ac voltage metrology. The system is based on a precision inductive voltage divider, with a lock-in amplifier as the detector. Using a JAWS with a maximum output voltage of 250 mV (root mean square), we have made accurate voltage measurements up to 120 V at 60 Hz with expanded uncertainties (k = 2) of no more than 1.5 µV/V and demonstrate that the system can operate up to 1 kHz. We anticipate that our JAWS-based system will improve uncertainties in ac voltage metrology by one order of magnitude compared to techniques based on thermal voltage converters.

Josephson Arbitrary Waveform Synthesizer as a Reference Standard for the Measurement of the Phase of Harmonics in Distorted Waveforms.

We have used the Josephson arbitrary waveform synthesizer (JAWS) to provide traceability for the phase of the harmonics, relative to their fundamental frequency, of a distorted waveform. For distorted waveforms with rms values from 0.154 to 0.2 V and harmonic magnitudes from 5% to 40% of the fundamental, our system can generate odd harmonics up to the 39th with best phase uncertainties from 0.001° to 0.010° (k = 2.0), depending on the harmonic number and harmonic magnitude. We anticipate that the ability of the JAWS to generate distorted waveforms with the lowest possible uncertainty in the magnitude, and phase spectra will make it a unique tool for low-frequency spectrum analysis.

Hemodynamic Impact of the C-Pulse Cardiac Support Device: A One-Dimensional Arterial Model Study.

The C-Pulse is a novel extra-aortic counter-pulsation device to unload the heart in patients with heart failure. Its impact on overall hemodynamics, however, is not fully understood. In this study, the function of the C-Pulse heart assist system is implemented in a one-dimensional (1-D) model of the arterial tree, and central and peripheral pressure and flow waveforms with the C-Pulse turned on and off were simulated. The results were studied using wave intensity analysis and compared with in vivo data measured non-invasively in three patients with heart failure and with invasive data measured in a large animal (pig). In all cases the activation of the C-Pulse was discernible by the presence of a diastolic augmentation in the pressure and flow waveforms. Activation of the device initiates a forward traveling compression wave, whereas a forward traveling expansion wave is associated to the device relaxation, with waves exerting an action in the coronary and the carotid vascular beds. We also found that the stiffness of the arterial tree is an important determinant of action of the device. In settings with reduced arterial compliance, the same level of aortic compression demands higher values of external pressure, leading to stronger hemodynamic effects and enhanced perfusion. We conclude that the 1-D model may be used as an efficient tool for predicting the hemodynamic impact of the C-Pulse system in the entire arterial tree, complementing in vivo observations.

Plasma Urotensin II levels in children and adolescents with chronic kidney disease: a single-centre study.

BACKGROUND: Increased plasma Urotensin II (UII) levels have been found in adults with renal diseases. Studies in children are scarce. The objective of the study is to estimate plasma UII levels in subjects with chronic kidney disease (CKD) stages 3 to 5 and renal transplant recipients (RTR). In addition, the correlation of UII with anthropometric features and biochemical parameters was assessed. METHODS: Fifty-four subjects, aged 3 to 20 years old, 23 with CKD, 13 with end-stage kidney disease (ESKD) undergoing hemodialysis (HD) and 18 RTR were enrolled. A detailed clinical evaluation was performed. Biochemical parameters of renal and liver function were measured. Plasma UII levels were measured in all patients and in 117 healthy controls, using a high sensitive enzyme immunoassay (EIA) kit. All data were analyzed using STATA™ (Version 10.1). RESULTS: Median UII and mean log-transformed UII levels were significantly higher in CKD and RTR patients compared to healthy subjects (p < 0.001). HD patients had higher but not statistically significant UII and log-UII levels than controls. UII levels increased significantly at the end of the HD session and were higher than controls and in line to those of other patients. The geometric scores of UII in HD (before dialysis), CKD and RTR patients increased respectively by 42, 136 and 164% in comparison with controls. Metabolic acidosis was associated with statistical significant change in log-UII levels (p = 0.001). Patients with metabolic acidosis had an increase in UII concentration by 76% compared to those without acidosis. CONCLUSIONS: Children and adolescents with CKD, particularly those who are not on HD and RTR, have significantly higher levels of UII than healthy subjects. UII levels increase significantly at the end of the HD session. The presence of metabolic acidosis affects significantly plasma UII levels.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

Systemic vascular effects of acute electrical baroreflex stimulation.

We intended to determine if acute baroreflex activation therapy (BAT) increases venous capacitance and aortic conductance. BAT is effective in resistant hypertension, but its effect on the systemic vasculature is poorly understood. Left ventricular (LV) and aortic pressures and subdiaphragmatic aortic and caval flows (ultrasonic) were measured in six anesthetized dogs. Changes in abdominal blood volume (Vabdominal) were estimated as the integrated difference in abdominal aortic inflow and caval outflow. An electrode was implanted on the right carotid sinus. Data were measured during control and BAT. Next, sodium nitroprusside (SNP) was infused and BAT was subsequently added. Finally, angiotensin II (ANG II) was infused, and three increased BAT currents were added. We found that BAT decreased mean aortic pressure (PAo) by 22.5 ± 1.3 mmHg (P < 0.001) and increased aortic conductance by 16.2 ± 4.9% (P < 0.01) and Vabdominal at a rate of 2.2 ± 0.6 ml·kg(-1)·min(-1) (P < 0.01). SNP decreased PAo by 17.4 ± 0.7 mmHg (P < 0.001) and increased Vabdominal at a rate of 2.2 ± 0.7 ml·kg(-1)·min(-1) (P < 0.05). During the SNP infusion, BAT decreased PAo further, by 26.0 ± 2.1 mmHg (P < 0.001). ANG II increased PAo by 40.4 ± 3.5 mmHg (P = 0.001). When an increased BAT current was added, PAo decreased to baseline (P < 0.01) while aortic conductance increased from 62.3 ± 5.2% to 80.2 ± 3.3% (P < 0.05) of control. Vabdominal increased at a rate of 1.8 ± 0.9 ml·kg(-1)·min(-1) (P < 0.01), reversing the ANG II effects. In conclusion, BAT increases arterial conductance, decreases PAo, and increases venous capacitance even in the presence of powerful vasoactive drugs. Increasing venous capacitance may be an important effect of BAT in hypertension.

Measurement of ice nucleation-active bacteria on plants and in precipitation by quantitative PCR.

Ice nucleation-active (INA) bacteria may function as high-temperature ice-nucleating particles (INP) in clouds, but their effective contribution to atmospheric processes, i.e., their potential to trigger glaciation and precipitation, remains uncertain. We know little about their abundance on natural vegetation, factors that trigger their release, or persistence of their ice nucleation activity once airborne. To facilitate these investigations, we developed two quantitative PCR (qPCR) tests of the ina gene to directly count INA bacteria in environmental samples. Each of two primer pairs amplified most alleles of the ina gene and, taken together, they should amplify all known alleles. To aid primer design, we collected many new INA isolates. Alignment of their partial ina sequences revealed new and deeply branching clades, including sequences from Pseudomonas syringae pv. atropurpurea, Ps. viridiflava, Pantoea agglomerans, Xanthomonas campestris, and possibly Ps. putida, Ps. auricularis, and Ps. poae. qPCR of leaf washings recorded ∼10(8) ina genes g(-1) fresh weight of foliage on cereals and 10(5) to 10(7) g(-1) on broadleaf crops. Much lower populations were found on most naturally occurring vegetation. In fresh snow, ina genes from various INA bacteria were detected in about half the samples but at abundances that could have accounted for only a minor proportion of INP at -10°C (assuming one ina gene per INA bacterium). Despite this, an apparent biological source contributed an average of ∼85% of INP active at -10°C in snow samples. In contrast, a thunderstorm hail sample contained 0.3 INA bacteria per INP active at -10°C, suggesting a significant contribution to this sample.

A national registry of haemoglobinopathies in Greece: deducted demographics, trends in mortality and affected births.

Haemoglobinopathies are the most common hereditary disorders in Greece. Although there is a successful national prevention program, established 35 years ago, there is lack of an official registry and collection of epidemiological data for haemoglobinopathies. This paper reports the results of the first National Registry for Haemoglobinopathies in Greece (NRHG), recently organized by the Greek Society of Haematology. NRHG records all patients affected by thalassaemia major (TM), thalassaemia intermedia (TI), "H" Haemoglobinopathy (HH) and sickle cell disease (SCD). Moreover, data about the annual rate of new affected births along with deaths, between 2000 and 2010, are reported. A total of 4,506 patients are registered all over the country while the number of affected newborns was significantly decreased during the last 3 years. Main causes for still having affected births are: (1) lack of medical care due to financial reasons or low educational level; (2) unawareness of time limitations for prenatal diagnosis (PD); due either to obstetricians' malpractice or to delayed demand of medical care of couples at risk; and (3) religious, social or bioethical reasons. Cardiac and liver disorders consist main causes for deaths while life expectancy of patients lengthened after 2005 (p < 0.01). The NRHG of patients affected by haemoglobinopathies in Greece provides useful data about the haemoglobinopathies in the Greek population and confirms the efficacy of the National Thalassaemia Prevention Program on impressively decreasing the incidence of TM and sickle cell syndromes.

Th2 regulation of viral myocarditis in mice: different roles for TLR3 versus TRIF in progression to chronic disease.

Viral infections are able to induce autoimmune inflammation in the heart. Here, we investigated the role of virus-activated Toll-like receptor (TLR)3 and its adaptor TRIF on the development of autoimmune coxsackievirus B3 (CVB3) myocarditis in mice. Although TLR3- or TRIF-deficient mice developed similarly worse acute CVB3 myocarditis and viral replication compared to control mice, disease was significantly worse in TRIF compared to TLR3-deficient mice. Interestingly, TLR3-deficient mice developed an interleukin (IL)-4-dominant T helper (Th)2 response during acute CVB3 myocarditis with elevated markers of alternative activation, while TRIF-deficient mice elevated the Th2-associated cytokine IL-33. Treatment of TLR3-deficient mice with recombinant IL-33 improved heart function indicating that elevated IL-33 in the context of a classic Th2-driven response protects against autoimmune heart disease. We show for the first time that TLR3 versus TRIF deficiency results in different Th2 responses that uniquely influence the progression to chronic myocarditis.

Chronic baroreflex activation: a potential therapeutic approach to heart failure with preserved ejection fraction.

Heart failure with preserved ejection fraction (HFpEF) is a substantial public health issue, equal in magnitude to heart failure with reduced ejection fraction. Clinical outcomes of HFpEF patients are generally poor, related annual accrual of health care expenses amount to billions of dollars, and no therapy has been shown to be effective in randomized clinical trials. Baroreflex activation therapy (BAT) produced by stimulating the carotid sinuses using an implanted device (Rheos) is being studied for the treatment of hypertension, the primary comorbidity of HFpEF. Other potential benefits include regression of left ventricular hypertrophy, normalization of the sympathovagal balance, inhibition of the renin-angiotensin-aldosterone system, arterio- and venodilation, and preservation of renal function. This paper reviews the evidence suggesting that BAT may be a promising therapy for HFpEF and introduces the HOPE4HF trial (ClinicalTrials.gov Identifier: NCT00957073), a randomized outcomes trial designed to evaluate the clinical safety and efficacy of BAT in the HFpEF population.

Stabilization of human urine doping control samples: a current opinion.

Transportation of doping control urine samples from the collection sites to the World Anti-doping Agency (WADA) Accredited Laboratories is conducted under ambient temperatures. When sample delivery is not immediate, microbial contamination of urine, especially in summer, is a common phenomenon that may affect sample integrity and may result in misinterpretation of analytical data. Furthermore, the possibility of intentional contamination of sports samples during collection with proteolytic enzymes, masking the abuse of prohibited proteins such as erythropoietin (EPO) and peptide hormones, is a practice that has already been reported. Consequently, stabilization of urine samples with a suitable method in a way that protects samples' integrity is important. Currently, no stabilization method is applied in the sample collection equipment system in order to prevent degradation of urine compounds. The present work is an overview of a study, funded by WADA, on degradation and stabilization aspects of sports urine samples against the above threats of degradation. Extensive method development resulted in the creation of a mixture of chemical agents for the stabilization of urine. Evaluation of results demonstrated that the stabilization mixture could stabilize endogenous steroids, recombinant EPO, and human chorionic gonadotropin in almost the entire range of the experimental conditions tested.