Rest, ice, compression, and elevation (RICE) is an established combination to prevent swelling but hardly able to stimulate lymphatic resorption. Recently, an epicutaneously applied negative pressure suction apparatus (LymphaTouch®) has been introduced to stimulate lymphatic flow. While postoperative recovery, soft tissue, and osseous healing as well as functional outcome are probably linked to the amount of postoperative swelling, estimates on this relative to prevention (RICE) or prevention + stimulated resorption  will be initiated on the morning of the 2nd postoperative day and repeated every 24 h until the time of discharge from the hospital. Outcome measures are (i) the relative amount and the time course of the postoperative swelling, (ii) the demand for analgesic therapy (type and amount) together with the perception of pain, (iii) the rate of complications, and (iv) mobility of the ankle joint and the recovery of walking abilities during a 12-weeks follow-up period. Serum and urine samples taken prior to sugery and during postoperative recovery will allow to evaluate the ratio of naturally occurring stable calcium isotopes (δ44/42Ca) as a marker of skeletal calcium accrual.This is a single-center, evaluator-blinded randomized pilot trial to investigate postoperative swelling in adults requiring surgical fixation of a closed unilateral ankle fracture. A total of 50 patients will be recruited and randomly assigned to RICE or RICEThe protocol was approved by the institutional Ethics Committee  in accordance with the Declaration of Helsinki . The results of this study will be actively disseminated through scientific publications and conference presentations.DRKS00023739. Registered on 14 December 2020DRKS,  Ankle fractures account for approximately 9% of all fractures, and occur preferentially either in physically active adults or as an osteoporotic fracture in the elderly –4. Any i+) after surgical fixation of a closed unilateral ankle fracture. We already established contact-less photooptical 3D-scanning as a reliable and highly standardized method for the determination of ankle volume [+ concept are pain, postoperative complications, mobility of the joint, and walking abilities. Quantitative pulse echography  will allow for scoring of bone mineral density . The ratio of naturally occurring stable calcium isotopes (δ44/42Ca) in the serum and urine has been introduced as a marker of bone mineral balance [44/42Ca will change according to the recruitment of calcium into the bone, i.e., allows to monitor the process of fracture healing during postoperative recovery and the follow-up period relative to baseline values prior to surgery.Very recently, an epicutaneously applied negative pressure suction apparatus  has been introduced and successfully been used for the reduction of pre- and postoperative swelling in patients undergoing unilateral surgery on the upper and lower extremity , 13. It e volume . Additio balance , 16. ThuThis monocentric, evaluator-blinded randomized controlled pilot-trial aims (i) to obtain an estimate on the standard deviation of the normalized ankle volume during postoperative recovery and (ii) to assess the feasibility of the proposed randomized allocation to the treatments and the suggested outcome measures. The latter include the demand for analgesics, the perception of pain, the rate of complications, the mobility of the joint, walking abilities, and skeletal calcium accrual together with bone mineral density. In summary, the results may broaden the knowledge on the interference between soft tissue swelling, functional outcome, and bone metabolism.This is a single-center evaluator-blinded randomized controlled pilot trial with two parallel groups and a 1:1 allocation ratio of patients receiving surgical fixation of an unilateral ankle fracture. The study is going to be conducted at the Department of Traumatology, Hand and Reconstructive Surgery at Rostock University Medical Center. Starting from the 2nd postoperative day, there will be daily interventions for up to 10 days . Repeated measures of outcome will be performed at baseline (prior to surgery), during the intervention period, and during the 12-week follow-up period. The study will be performed according to the ethical standards of the Institutional Ethics Committee  and in accordance with the Declaration of Helsinki.inclusion criteria are (i) a BMI of at least 16 kg/m2 and at a maximum 35 kg/m2, (ii) willingness to be randomized, and (iii) the ability to give written informed consent. Patients with open uni- or bilateral fractures, a history of secondary lymphedema due to tumoral disease during the preceding 5 years, dietary patterns other than omnivore or lacto-ovo-vegetarian , and/or being incapable of adherence to follow-up examinations are excluded. Furthermore, pregnant women are not eligible. Eligible patients will receive detailed information on the study by trained physicians of our outpatient facility where preoperative assessment and preoperative necessary preparations for the surgical procedure take place when the indication for surgery has been secured. Prior to enrollment, all patients have to read and to sign the informed consent.Adults (≥ 18 years of age) requiring surgical fixation of a closed unilateral ankle fracture (any fracture type scheduled for surgery) are eligible. Additional A researcher neither involved in clinical care or the day-to-day running of the trial will handle the randomization. Patients who gave written informed consent are assigned to either one of the treatment arms according to the results of a list-based block randomization procedure . Only thThis pilot study is designed to obtain an estimate on the standard deviation of the normalized ankle volume during postoperative recovery and to assess the feasibility of both, the proposed randomized allocation to the treatment arms, and the suggested outcome measures. According to the recommended sample size for feasibility studies, we aim to enroll 25 participants per arm  –21. This+, the device-based negative pressure therapy will be initiated on the morning of the 2nd postoperative day by a trained physician/medical student (last year of education) and repeated every 24 h until discharge from the hospital. The first treatment will be right after the wound dressing has been changed to a transparent and sterile film dressing . This wound dressing will be applied to all study patients and allow for constant monitoring of the wound and peri-wound area while eliminating the need to remove the dressing every time before the objective evaluation of the edema by 3D scanning and to ensure blinding of the staff with respect to the treatment arm. In all patients scheduled for RICE+, negative pressure therapy (30 min per session) will be applied by a trained physician/medical student in a standardized manner, i.e., starting at the supraclavicular fossa and following the lymphatic vessels down to the affected ankle. We use the same sequence as in manual lymphatic drainage, i.e., to increase lymph kinetic in normal lymphatics by mild mechanical stimuli as this augments the drainage across the lymphatic watershed. When starting manual lymphatic treatment at the inflow of the lymph into the venous system, edema fluid will pass through the already dilated area with successive decongestion of the lymphatic trunk [Patients randomized to either treatment group will receive identical postoperative care, i.e., prescription of analgesics and the classical RICE concept to stimulate resorption of the postoperative swelling. The latter will be initiated right after surgery and continued until discharge from the hospital. Patients are advised to strictly adhere to the RICE concept in order to minimize variability inherent to this type of therapy. In particular, a positioning splint will be placed under the knee and the operated lower leg to elevate the ankle above heart level and ice packs will be provided for cooling during daytime according to the individual preference of the patient. The staff on the ward will not be notified on the treatment arm but asked to record the number of ice packs per day as a surrogate measure of both, the individual preference for cooling and adherence to the baseline therapy. Mobilization during the postoperative period and walking will be allowed with crutches and a lower leg orthosis under partial weight-bearing with 20 kg according to a personal training on a scale under supervision of a physiotherapist and without notification of the physiotherapist on the treatment arm. In patients allocated to RICEic trunk , 22, 23.Patients of either treatment arm will be instructed not to comment on the mode of physical therapy, and the evaluator will have access neither to the ward during the times of treatment nor to the randomization list.44/42Ca) in the serum and urine as a marker of bone mineral balance during fracture healing.All measures will be taken at baseline and at pre-specified time points during the treatment and follow-up period Fig. . Non-con+. To ensure objective evaluation, a contact-less 3D optical scanning procedure  will be used to determine the volume of both ankles. To account for the interindividual variability of the ankle volume, per patient, the volume of the fractured ankle will be normalized to the one determined at the healthy site. The scan will cover an area ranging from the planta pedis up to one-third of the lower leg. The latter is defined as the distance between the medial knee joint space and the medial ankle. The upper border of the area to be scanned will be marked with permanent felt-tip pens. Proprietary software of the scanner  will be used to quantify ankle volumes. To minimize the effects of wound dressing, in all patients, a sterile and transparent film dressing  will be applied on the 2nd postoperative day .The primary objective of the study is to investigate the amount of postoperative swelling relative to the application of RICE or RICESecondary objectives are the need for analgesics (type and amount of the prescribed ones and those beyond) during post-operative recovery and the individual perception of pain. The information will be gathered by chart review, interview, and utilization of a numeric analog scale (NAS), respectively. The latter is scaled from zero  to ten (maximum pain) and the patient is asked to rank the perceived pain accordingly.Clinically common goniometers will be used to gain knowledge on the mobility (ROM) of the affected and contralateral ankle joint (dorsi- and plantarflexion) at the time of discharge from the hospital and during follow-up according to the neutral zero method.These investigations are paralleled by an assessment of the functional outcome, i.e., the walking abilities. For this purpose, pressure-sensitive insoles  providing time-resolved information on normal ground reaction forces will be used. The patients are provided with the system at the time of discharge and during follow-up visits and will use it with or without assistive devices, as appropriate. Every session will start with a familiarization period followed by a recording of time-resolved normal ground reaction forces during 3 min straight level walking at a self-selected speed. This approach will provide information on functional recovery, i.e., cadence, gait velocity, and the factor of imbalance as a surrogate marker for recurrence of gait symmetry during fracture healing.44Ca and 42Ca have been established as markers of bone mineral balance. Both isotopes are provided by and absorbed from the diet and the rate of sequestering into different body compartments is intimately linked to the atomic mass. Thus, chemical transport reactions result in an enrichment of 42Ca in the respective compartment of the body, while the isotopically heavier 44Ca is excreted [44/42Caserum ratio reflects bone mineral balance, i.e., the ratio is higher under conditions of mineral accrual by the skeleton compared to a situation when bone resorption exceeds bone formation. The same holds true for the isotope ratios in urine and feces [Recently, the naturally occurring stable (non-radioactive) Ca isotopes excreted , 25–27. nd feces . To monind feces .Quantitative pulse echography  is used to assess bone mineral density the day before surgery, at the time of discharge, and the final visit. In particular, the cortical thickness of the healthy tibia is measured and translated into an estimate of bone mineral density  via the in-build proprietary algorithm , 29.This is a single-center evaluator blinded randomized controlled pilot study, and the device-based application of negative pressure therapy is deemed to be of rather low risk. The same holds true for the proposed outcome measures, i.e., chart review, questionnaires, and touchless 3D optical scanning of the ankle and lower leg. However, all complications  are to be recorded, and participants will be encouraged to report any discomfort related to either therapy or outcome measures. Discontinuation or modification of the treatment will be an individual decision of the responsible physician. There will be no special criterion for stopping the trial except for severe postoperative complications making a surgical reintervention necessary.Clinical and anthropometric data including a history of disease, time to surgery, current medication, staged or single-step surgery, comorbidities, and the results of routine assessments  at baseline will be gathered by chart review and interview. Furthermore, postoperative complications  are recorded in both treatment groups. At the time of enrollment, patients will receive a unique identification code (ID), and all documents and hard copies generated for the purpose of this trial will be linked to this identifier. The underlying key will be stored in a password-protected file accessible only to the principal investigators . Of note, the evaluator will be unblinded only after the last patient has finalized the study examinations. Regardless of this, access to the data will be limited to the researchers involved in this trial.+ will be asked by the one in charge for the application of the device-based negative pressure therapy to rate this additional treatment on a scale ranging from 1 (very good) to 5 (no effect). Proceeding to the main trial will be deemed feasible if (a) at least two-thirds of the patients allocated to RICE+ will retrospectively judge this additional treatment positive and (b) at least two-thirds of all patients accept the repetitive contact-less 3D optical scanning procedure.Data will be analyzed using the SPSS statistical package 26.0  and Sigma Plot V10  for graphical presentation. The normal distribution of continuous variables will be evaluated by the Kolmogorov-Smirnov test, descriptive summary measures will be calculated. Categorical variables are summarized as numbers and percentages. For comparison of the categorical variables, the chi-square test or Fisher’s exact test will be used, as appropriate. Repeated measure analysis will allow for longitudinal examination of the treatment effects and differences between the study groups. Data from all randomized participants will be included . No missing data imputation will be performed, and in view of the rather small number of participants per treatment arm, neither subgroup or adjusted analyses are planned. During the study, patients and personnel are encouraged to mention all problems related to the study investigations and especially to the acceptance of the contact-less 3D optical scanning procedure. At the time of discharge, patients randomized to RICE+) with respect to the resorption of lymphedema related to the surgical fixation of an isolated ankle fracture. Although standardization of the components of RICE is hard to achieve even in a hospital setting, we record the number of ice packages per day not only as a marker of the individual preference for cooling but as a surrogate marker of adherence to the baseline therapy, as well. To objectively assess the effects of either therapeutic approach, we decided to perform touchless 3D optical scanning as this turned out to be a highly reliable technique for quantifying the volume of the foot and ankle region [+. The blinding of the evaluator and even the staff involved in surgery and postoperative care and the fact that the person in charge for the device-based application of negative pressure therapy will show up anyway are expected to minimize the risk of a bias in standard care. Further, the dynamics of the ratio of naturally occurring calcium isotopes in relation to the postoperative development of lymphedema, the manifestation of eventual postoperative complications as infection or non-union, and bone mineral density will permit insight into the mechanisms of interaction between perioperative swelling and uneventful bone remineralization or prolonged demineralization in case of complications in the postoperative period. In summary, the results of the proposed pilot study will enable us to decide whether it is worth comparing the effects of RICE and RICE+ in a sufficiently powered multicenter trial and which of the outcome markers are best suited for this purpose. Without saying, this decision will consider the feedback of patients on either therapeutic approach as well as comments from the staff.The study is designed to assess the effects of the classical RICE concept and the additional application of a device-based negative pressure therapy  in accordance with the Declaration of Helsinki . The results of this study will be actively disseminated through scientific publications and conference presentations.DRKS Identifier 00023739 version 1, posted on December 3, 2020. Recruitment is scheduled from June 2021 to June 2022.https://www.dimdi.de/dynamic/de/weitere-fachdienste/deutsches-register-klinischer-studien/studienregistrierung/).Any changes to the protocol will be presented for approval to the university’s ethics committee. Subsequently, the revised version of the protocol will be deposited at the DRKS - German Clinical Trials Register (This trial involves the voluntary provision of blood and urine samples for additional biochemical analysis of markers related to bone metabolism and mineralization.


Self-healing sensors have the potential to increase the lifespan of existing sensing technologies, especially in soft robotic and wearable applications. Furthermore, they could bestow additional functionality to the sensing system because of their self-healing ability. This paper presents the design for a self-healing sensor that can be used for damage detection and localization in a continuous manner. The soft sensor can recover full functionality almost instantaneously at room temperature, making the healing process fully autonomous. The working principle of the sensor is based on the measurement of air pressure inside enclosed chambers, making the fabrication and the modeling of the sensors easy. We characterize the force sensing abilities of the proposed sensor and perform damage detection and localization over a one-dimensional and two-dimensional surface using multilateration techniques. The proposed solution is highly scalable, easy-to-build, cheap and even applicable for multi-damage detection. Self-healing elastomeric polymers can provide improved performances and novel functionalities to existing soft robotic and wearable systems ,2,3. TheSoft robotic sensors are vital for obtaining information about internal physical states in a non-intrusive manner ,12. TypiDamage detection abilities are vital for monitoring the integrity of the surrounding system and for safer interactions. Conventional methods have used specialized tools for damage detection and localization. They include techniques, such as visual inspection, X-ray radiography, ultrasonics, etc. ,32,33,34Continuous localization capabilities can be obtained by extending the proposed system by measuring the pressure signals inside the enclosed chamber through different pathways. Since pressure signals travel at the speed of sound in the medium, we can employ multilateration techniques for localizing the source of the pressure signal based on the time-of-flight of the pressure signal. As the pressure signals are generated by the contact or damage event itself, an additional energy source is not required for localization, unlike typical multilateration systems. Due to the impedance mismatch between the fluidic chamber and the surrounding self-healing material, the localization sensor can be scaled to any complex geometry.This work presents a soft sensor design based on encapsulated fluid bodies that allows us to develop self-healing sensors with only the non-conductive supramolecular SH polymers. This allows us to obtain almost instantaneous self-healing even at room temperature. Due to the compliance of the material and its biocompatible nature, these sensors are well suited for soft robotic applications. We investigate the self-healing characteristics of the material experimentally and the sensing properties using a finite element model. Finally, we demonstrate the applicability of the sensor for damage detection and localization in a one-dimensional and two-dimensional sensing surface. This work is the first to demonstrate a soft self-healing sensor that can detect and localize damage on a continuous surface.The self-healing supramolecular elastomer was obtained in a process described previously from 2 to amino-4-hydroxy-6-methyl-pyrimidine, 4,4,-methylenebis(cyclohexyl isocyanate) and poly(tetramethylene oxide) (Mn = 1000) , resultiOnce the SH material is formed into films, the sensors can be developed using compression molding techniques a. The inThe working principle of the sensor is based on the transmission of external forces through pressure waves inside the enclosed fludic chamber a. These For measuring the pressure inside the chamber, we use the MPXH6400A Absolute, Integrated Pressure Sensor. The analog pressure values from the sensor are read using a National Instruments (NI) USB 6212 data acquisition system. The analog signals are then sampled at 200 KHz with a 16-bit resolution. For measuring the applied forces, we use the ATI Nano43 6-axis force sensor. The analog signals from the force sensors are amplified using the NI FTIFPS1 amplifier and then read by the NI USB-6212 ADC. The data from the data acquisition system is read through the serial port and processed in MATLAB. The indentation probe is controlled using a UR5 robotic manipulator.finddelay is used for measuring the lag between the two pressure signals.All the data processing of the pressure signals is conducted on MATLAB. For characterizing the sensor properties, the sensor signals are read through the serial port on-demand. For detecting and localizing damage, the sensor signals are read continuously at 200 KHz for a total duration of 10 s. The raw signals are then filtered using a bandpass filter (using MATLAB bandpass function) with a passband frequency range of 150–1000 Hz for both the sensor morphologies. The steepness of the filter was set at 0.8, and the stopband attenuation was set to 50 dB. The filtered signal is further smoothened using a simple thresholding method to remove low decibel noise. The MATLAB function imregionalmin and bwskel are then used to trace the thinnest connected pathway in the binary image. This pathway in the image space is then calibrated with respect to the real system by using the total length of the fluidic tube and its corresponding pixel length.For obtaining the pathway of the 2D sensor morphology, we use some standard image processing tools. First, a picture of the sensor is taken, and the pathway is manually traced on picture. A binary image of this is obtained after converting the picture to an HSV colormap. The MATLAB functions We study the capabilities of our proposed sensing system using three experimental scenarios. In the first scenario, we characterize the performance of the single-output fluidic sensor and investigate its potential as a force sensor. In the second and third experimental scenarios, we investigate and study the performance of the multi-output fluidic sensor for damage detection and localization in a one-dimensional and two-dimensional surface, respectively.The single-output hemispherical sensor schematically represented in Finally, the resilience of the sensor to damage is studied. For this, we apply constant and impulsive indentation to the sensor. Impulsive forces are used to ensure that the enclosure is tightly sealed after damage. The results from this test are shown in L is the total length of the air cavity between the ends of the two pressure sensors, x is the distance of contact from the centre of the air cavity, and The damage detection and localization capabilities of the multi-output self-healing sensor is investigated along one dimension first. The sensory system and its schematic is shown in Measuring the time-of-arrival difference requires the identification of discernible features in the signal for the particular sampling frequency. As the sampling frequency decreases, the features must lie at a higher frequency spectrum for accuracy. Hence, normal contacts are difficult to be localized. However, damage events have high-frequency components that are characteristic of their internal geometry. This makes the detection and localization easier even at a relatively low sampling frequency of 200 KHz. For our sampling rate, the highest localization resolution we can obtain is 0.165 cm. The raw pressure signals and its spectrogram are shown in As mentioned before, due to the high impedance among different mediums, the path taken by the pressure signal is almost always along the fluidic chamber, even if the chamber is not straight. This allows us to easily scale the proposed system to arbitrarily complex surfaces with the same number of output pressure sensors. This article presents the design and fabrication of a self-healing soft fluidic sensor. The sensor works on the principle of information transfer from the physical stimuli via pressure waves traveling in an enclosed fluidic chamber. This allows us to obtain tactile information remotely and unobtrusively. More importantly, this enables us to fabricate these soft sensors without the addition of any functional materials. By using self-healing supramolecular polymers as the enclosing matrix, we obtain exemplary self-healing properties characterized by instantaneous functional recovery at ambient conditions without external inputs. We perform extensive characterization of the soft sensor and its application as a force sensor. Furthermore, using finite element analysis, we demonstrate how the chamber morphology can be designed to obtain higher sensitivity to applied forces. By leveraging acoustic characteristics of damage and the relatively slow speed of pressure waves, we finally show how our sensing principle can be used to even detect and localize damage in a continuous manner indefinitely. To the best of our knowledge, this is the first demonstration of such a use of a soft sensor. We demonstrate highly reliable damage detection and accurate localization abilities on our modified multi-output sensor morphologies using commonly available hardware. The detection and localization performance can be improved and conducted in real-time by executing all the computation using electronic components. This would also facilitate highly scalable contact localization abilities using just two output pressure sensors. The ability to detect and localize damage repeatedly could be an important tool for soft robots and soft wearable devices for monitoring structural integrity and as a feedback mechanism to adapt and react to damage-causing actions. These systems can also be devised for structural health monitoring as a replacement to conventional Non-Destructive Testing techniques due to low compliance, ease of manufacturing and deployment. The self-healing capabilities also increase the life-span of the sensors.


The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data. Maritime transportation is recognized for its central role in the global supply chain considering it accounts for 90% of international trade by volume and 70% by value . The UniMaximizing the potential of maritime big data is essential for predicting vessel positions. The automatic identification system (AIS) is a self-reporting message system on board a vessel that records its position and condition . Each reThe objective of this study is to achieve a 24 h interval ship position prediction for MSA. Previous studies focused mainly on short- and medium-term prediction, ranging from 30 min to 10 h intervals as the AIS data were dense  but in a limited period or timespan. Naturally, a vessel position prediction with long time intervals  requires data with a long timespan, given that state-of-the-art ML algorithms such as deep learning require large amounts of data to make accurate predictions for longer intervals [The methods for forecasting vessel positions can be classified into three categories: trajectory-based, point-based, and motion-based methods. Overall, studies concerning ship position prediction are summarized in In the trajectory-based prediction method, clusters of vessel trajectories must be initially created and classified based on shipping routes from historical voyage patterns in the examined area, at times focusing solely on particular ships . A ship In the point-based method, the examined area is first transformed into non-overlapping cells or grids. The position of a ship inside the area is converted into a grid reference and defined by the occupied cells. The movement of these occupied cells indicates the movement of ships after a given time. A trained ML model then calculates the prediction of the next position of the ship. Duca et al. 2017) employed a kNN classifier for ship route prediction  employed. Zhou etIn the motion-based method, the subsequent vessel position is estimated using either motion functions or a trained ML model where historical information is used as input. Most researchers have used this method for short- and medium-term predictions within a small area  and particular trajectories. Juraszek et al. 2020) used an extended Kalman filter for fast short-term prediction in the distributed processing system Apache Flink . Mao et 20 used aAs long-term vessel position predictions have not been performed yet, determining a suitable method is vital for achieving long-term prediction. Consequently, the observed location (and dataset) must be considerably larger than the ship distance intervals to accommodate and capture their long-term movements; a small observation area limits long-term movement. The point-based method inevitably involves computing the entirety of cell information in each computation cycle, and thus the observed area cannot be too large because the computational costs would be exorbitant. Some researchers used CNNs by converting vessel positions from AIS data into a gridded ocean floor model as the input. Meanwhile, utilization of the trajectory-based method is expected to result in high accuracy for any time-interval threshold and area size; however, this method is not sufficiently flexible for adaptation to factors other than the observed variable. It also involves arduous work on route clustering and classification and trajectory reconstructions, in addition to anomaly detection owing to the partially incomplete and noisy AIS data. Furthermore, although we can create a database for millions of historical trajectories, ship trajectories are not fixed but quickly adapt to the weather and other conditions; ship routes evolve owing to various factors, such as climate change and the emergence of new ports and new routes . In contTherefore, from the perspective of long-term position prediction on large international open waters without the consent of any restricted trajectory, a more general and updatable model with generalization to data variation and robustness to noise needs to be constructed that incorporates a fast prediction generation for predicting the long-term position of a vessel. In this study, a motion-based method was used to develop a generalized deep learning model for long-term vessel position prediction to overcome these challenges.We processed AIS data from exactEarth , consideThe dataset comprises nine years of archived AIS messages of global capesize bulk carriers, from July 2010 to December 2018. This type of vessel exhibits proper records with less irregularity compared to those of smaller vessel types and more diverse shipping routes than containers and tankers . The datt between less than 1 h and up to 48 h, averaging 24-h intervals; the time-interval distribution is depicted in Daily archived AIS messages have random time intervals ∆Ship specification data from IHS SeaWeb’s fleet data  were utiAll datasets were collected independently from each source and stored in a single SQL relational database for easy access and management. An IMO number identifies every vessel, each of which is related to another piece of information in the dynamic and static data. When a model needs to be generated, a specified chunk of the AIS dataset is retrieved from the relational database and subsequently processed into a DL model. All scripts from preprocessing to postprocessing were written in Python.In previous studies, such as in Zhang et al. (2020), trajectories with missing records over one day and lasting less than one day were excluded from the training and testing process . Howeverx interval condition as t are relatively large with uneven distribution, the interpolation causes a considerable error in the aligned positions [An end-to-end DL model was developed to supersede a vessel position prediction model that requires arduous preprocessing steps  as the time intervals of the AIS data vary considerably. A trajectory reconstruction becomes impractical as the time intervals and their variation grow. The asynchronous nature of AIS messages with time intervals of less than 3 min allows the application of a trajectory reconstruction from a short-interval to a long-interval dataset without a significant error. This is because the AIS points can be aligned using a simple method of sampling the nearest point in the time dimension that satisfies the ariables . Specifiariables . Then, iositions .In this study, the dataset has ∆t values ranging between almost zero and 48 h (in the absence of outliers), indicating that although the dataset consists of daily AIS messages (averaging 24 h), their exact time interval can be longer than 24 h see . The larBased on the nature of the AIS dataset while achieving the objective , performing a trajectory reconstruction is unattainable. Therefore, in this study, DL with the regular feed forward network (MLP) was employed to solve the long-term position prediction. Previous studies have also attempted to use this basic architecture but have been limited to shallow networks with limited time intervals and area sizes. We aimed for a larger-scale and longer timespan of the AIS dataset, and more importantly, developed a more general and updatable model from a dataset with a long-time-interval distribution, which has not been achieved thus far. By leveraging the advantages of deep learning systems supported by extensive fleet data and computing power, the model was developed to learn robustness to noise from extensive data with unrestricted trajectories in international open waters and to realize generalization to data variation of time intervals, vessel activities, statuses, and locations.A straightforward method that takes advantage of the capacity provided by deep learning systems with sufficient data and computing power is proposed to produce long-term ship position prediction. Fast result generation and generalization from the trained model make the proposed method suitable for practical use. The flowchart in A set of AIS messages is first retrieved from the SQL database, given a boundary condition of location and time. The specified input and output features are then extracted from the dataset with vessel states at the current time step Input features of After retrieving the input and target, all input features Two sets of standardized input and target features, A DL model refers to the process of training and testing multilayered NNs that can learn complex structures and achieve high levels of abstraction . BuildinA hold-out validation split was conducted to prevent information leakage from the test data. AIS data from July 2010 to December 2017 were randomly shuffled into a training set, and the dataset was disrupted with data from other vessels and timelines. Meanwhile, a dev set was randomly sampled from half of the 2018 data. The remainder was retained as a test set that had never been used for model prototyping. The split was designed to reflect the dev/test set as the recent AIS data. The dev set was used to validate the training set while having the same distribution as the test set. The randomly shuffled data combined with mini-batch training ensured rapid convergence of the gradient descent with minimal disturbances. Moreover, this combination prevented deep networks from conspiring to memorize the chronological sequences of vessel locations.During the prototyping process, the most salient input features were selected by evaluating permutation feature importance (PFI) based on Fischer et al. (2018) . After aThe permuted data The PFI evaluation was conducted using the dev set at the prototyping stage to determine the extent to which the model relied on each input feature to generate predictions . All irrThe model was constructed as a DL model to solve regression problems. Compared with the classification problem that treats portions of the covered area as classes, regression problems can achieve a higher location resolution and, therefore, higher prediction accuracy . In thisThe proposed model uses a deep network with six layers: five hidden layers with a sigmoid activation function {64, 64, 32, 32, 16} and a one-unit linear output layer. The mean absolute error (MAE) was employed as the loss function since it is less sensitive to outliers than the root mean square error (RMSE), and mini-batch gradient descent with a mini-batch size of 64 was used for training. We used an adaptive learning rate method involving the adaptive moment estimation optimization algorithm (Adam), an update to the RMSProp with momentum . The leaTwo separate models were trained for each standardized target feature The metric scores for the combined results  were defined as the mean distance error (MDE) and mean angular error (MaE). The distance error is the haversine distance between the prediction and target (true position) in kilometers, signifying the extent to which the result (prediction) deviates from the true position (target). MDE can be a single evaluation metric for the long-term prediction of vessel location. However, in cases where the distance between previous positions and target positions is remarkably diverse , another evaluation metric is required.The MaE as the second evaluation metric was calculated based on the three known points of the base, target, and predicted positions. The angular error between the target and the prediction is a spherical triangle that can be solved using the law of haversines . BecauseA non-ML baseline and a sanity check were used as model-building guidelines to conduct error analysis on bias and variance, gaining confidence in the overall performance of the DL models. The baseline replicates a human-level performance as a proxy for the Bayes optimal error in the classification problem .The predicted positions were defined as the next timestep positions averaging 24-h intervals, and consequently, a conventional formula for the dead reckoning position  of the ship could not be adopted as the baseline. The dead reckoning calculation is too simple for two distant points; while it is accurate in the Euclidian space, it fails to map the Earth as a great circle . An equation for the geodesic on a spherical surface, namely the great circle equation, is more accurate for planning routes .Therefore, the geodesic calculation was established as a baseline model, computed based on the shortest distance on a spherical earth. Given a starting position . Compared to all previous studies, the boundaries were relatively larger, adequately capturing the continuous long-term movements of the vessels. The dataset was split into a training set and dev-test sets according to the hold-out validation split described in The MAE, as the performance evaluation metric of the model, is shown in Interestingly, the DL model performance on the training set and dev set surpassed the geodesic calculation as the baseline model. The model displayed minimal bias, fitting the training and dev sets satisfactorily. In practice, the sole focus of a regression model is not the deviation between the training loss and dev loss , but rather its prediction performance, that is, the dev loss. Nevertheless, the variance in the model was insignificant, indicating that the model generalized very well to the dev set without overfitting.Moreover, hyperparameters sensitivity of the DL model was carried out while also applying regularization techniques to confirm further that the model generalized very well to the dev set. All hyperparameters combinations were permutated except the structure of hidden layers and their units to see the effect on prediction performance from hyperparameters’ influence. This permutation was repeated for other models with regularization: 20% and 40% dropout on all hidden layers and a model with batch normalization.Based on these error and sensitivity analyses, we determined that the proposed deep learning model achieved an optimal error with generalization to the dev set and was ready for the evaluation on the unseen test set resembling a practical scenario.The performance evaluation metrics of the DL and baseline models are presented in The DL model adequately outperformed the baseline model and almost doubled its performance based on the metric scores. Given that the distance between the current position of a vessel and its position the next day was approximately 486 km, the geodesic calculation might be accurate for long route planning, yielding an average distance error of 40 km. However, the DL model generated more accurate and precise predictions with a slight mean distance error of approximately 25 km and a mean angular error of 1.8°. If we assume that the evaluation metric  ,48. The In the second experiment, 12 DL models were built for six open oceans and six maritime chokepoints with the same model properties to assess the influence of the respective areas. Open waters of the North Pacific Ocean (NPO), South Pacific Ocean (SPO), North Atlantic Ocean (NAO), South Atlantic Ocean (SAO), Indian Ocean (IO), and Philippine Sea (PS) were selected considering that vessels generally sail across these deep oceans without stopping. According to Rodrigue (2020), the primary maritime shipping chokepoints were selected to represent long-term vessel behavior near the ports and congested (high-traffic) waters, namely Gibraltar Strait (GS), South Africa Coast (SAC), Bab al-Mandab Strait (BMS), Strait of Hurmuz (HS), Laccadive Sea (LS), and Malacca Strait (MS) . AltogetMDE scores. The improvement scores were notably higher in the chokepoint areas than in the open ocean areas, scoring more than a 50% improvement over the baseline model for almost all the chokepoint areas. The geodesic calculation appears accurate enough to predict moving vessels. However, it fails to predict vessel behavior near the ports and high-traffic areas . With no information regarding vessel status, historical trajectory, or destination, conventional calculations would fail.The performance evaluation metrics of the models for each examined area are listed in MaE unsuitable as a metric score. The angle between the prediction and the target no longer corresponds to the accuracy of the prediction because the distance interval between two consecutive positions is extremely short. Accordingly, for the evaluation of the chokepoint areas, the MDE alone suffices as a single metric score.Both the DL model and geodesic calculation showed more accurate predictions for the ocean areas than the chokepoint areas, but not in the average distance intervals between the current position and the next position, indicating that more vessels have shorter distance intervals in the chokepoint areas. Almost all vessels were moving in the ocean areas, whereas in the chokepoint areas, many vessels were idle for berthing or resting at anchorages. These activities resulted in an extremely short distance interval between two consecutive AIS messages at any time interval. Many vessel activities have very low distance intervals in the chokepoint areas but not in the ocean areas. Consequently, the average distance intervals in the chokepoint areas would indicate lower performance evaluation metrics than the ocean areas. The irregularity of idle vessels and activities other than sailing with minimal distance intervals renders the In the ocean areas, as most data have distance intervals between 400 and 600 km, the DL models generated continuous accurate predictions with a distance error below 40 km for 80% of the population over the entire test set. For the same 80% population, the geodesic calculation generated a distance error below 60 km with a more diverse distribution, where its standard deviation was twice as high as that of the DL models.In the chokepoint areas, as most data have distance intervals distributed between 400 km and near zero, the DL models constantly generated accurate predictions with a distance error below 65 km for 80% of the population. In contrast, the geodesic calculation generated projections with distance errors below 100 km for only 65% of the population. Its projections resulted in grave errors when the distance intervals were broad, but the errors became less severe when the distance intervals were near zero. Overall, the geodesic calculation fails to make accurate projections for the complex movements of vessels in chokepoint areas.Deep learning systems can learn from large datasets to effectively improve their performance to generate accurate predictions by discovering complex variations across all input features . AccordiBased on the results for both the chokepoint and ocean areas, the speed component of the vessel  area encompasses half of South East Asian waters, including the most congested maritime primary chokepoints and three secondary chokepoints in Indonesian waters . The proThe dataset size affects deep learning performance, as a small amount of training data may degrade the performance . We recrIn the third experiment, a DL model was built for the Malacca Strait (MS) area, and a sample location was selected in which the number of ships was counted each day consecutively, resembling a simulation of practical application. In the MS area, ships traveled approximately 417 km daily on average see . Thus, tFor the application, uninterrupted AIS data for a period of time were required as the test set. Consequently, the previous hold-out validation split cannot be applied because the test and dev sets are equivalently derived from shuffled 2018 data. In addition, the training and dev sets have to be generated in the same manner as the models in the previous experiment to produce a generalized DL model similar to the previous experiment.Therefore, a different scheme for the hold-out validation split was developed. Randomly shuffled AIS data from July 2010 to September 2017 were used as the training set, while randomly shuffled data between October 2017 and September 2018 were used as the dev set. The last three-month data were not randomized and were retained chronologically as the test set. The predictions were processed sequentially, day by day, rather than instantaneously. Subsequently, the number of ships was counted at the selected locations every day.The fitted plots of the overall results from the DL models and geodesic calculations were compared to the actual data, as shown in Moreover, Detailed ship position predictions inside the sample location are shown in The MLP model shows a remarkable performance compared to the ensemble-based ML models and conventional approach from the dataset with the long-time-interval distribution. End-to-end means that the approach does not need a trajectory reconstruction and other preprocessing/postprocessing steps for making a prediction.To further clarify the robustness of end-to-end DL-based models, we built two DL-based models: Deep RNN and LSTM. These sequence-based DL architectures were built with the same input, output, and validation splits to the MLP model so that their performances are broadly comparable . The input does not go through an interpolation or trajectory reconstruction to ensure an equivalent model. It already carries information regarding time interval The Indian Ocean and Malacca Straits areas similar to the second experiment, representing the simple and complex movement of ships on the ocean and chokepoint areas, respectively, were selected as a definitive test for the DL-based models. The overall prediction methodology of these two recurrent models is similar to the second experiment. The models were also set to generalize on the data at which the training set was shuffled. The number of steps was considered one-time steps: the input only contains information at time step The performance of the DL-based models is presented in The patterns observed result from the small sequences (time steps), at which the regular feed-forward network is sufficient for this task. This is confirmed as the application of the LSTM model did not deliver better performance than the RNN model since LSTMs are much more capable to handle long sequential data than simple RNNs with limited short-term memory. The results correspond to those of Gers et al. (2002) who tested RNNs and LSTMs for time-series predictions, but the results were poor as simple multilayer perceptrons (MLPs) often outperformed LSTMs when applied to the same data .https://gitlab.com/algorithms-for-threat-detection/2019/atd2019; accessed date 19 September 2021). All AIS training and challenge data with different properties were combined and used to construct a dataset with varying time-interval distribution. A small dataset with short-time intervals near the ports was used to further analyze the robustness of the end-to-end DL models. The dataset was retrieved from the ATD2019 challenge dataset . This limited sequence allows the feed-forward networks to deliver optimum performance but would not maximize the potential of the sequence-based recurrent networks. Moreover, on the dataset with varying time-interval distribution and without trajectory reconstruction, the feed-forward networks generated predictions as accurate as those of the LSTM with faster training times and less trainable parameters.This study employed DL models with experimentally defined properties using exactEarth AIS daily data. In the first experiment, a model for the Indian Ocean area was examined, and in the subsequent experiment, 12 models were investigated on open oceans and maritime chokepoints. In the last experiment, a selected sample location within the Malacca Strait area was examined, resembling a simulation of practical application.The results demonstrated that predictions with an average time interval of 24 h were possible, confirming that the straightforward motion-based method can generate an accurate long-term prediction of the vessel position. The DL models generated more accurate predictions than the geodesic calculation as the baseline model in all areas. Predictions in the open ocean areas yielded higher accuracy than the chokepoint areas. However, compared to the geodesic calculation, the improvement scores were higher in the chokepoint areas than in the ocean areas because the geodesic calculation failed to predict vessel behavior near the ports and congested waters. The DL model can predict the complex movement of ships near ports and congested routes, whereas conventional calculations fail, with no information regarding vessel status, historical trajectory, or destination. The prediction accuracy was directly proportional to the amount of training data. Furthermore, the last experiment demonstrated that the DL model appears to have a sense of the dimension of the geographic coordinate system that can be or is often passed, wherein the chokepoint areas rely more on the input features of the current latitude and longitude of the vessels. Moreover, on the dataset with varying and uneven time-interval distribution and without a trajectory reconstruction, the proposed MLP model generated predictions as accurate as those of the LSTM with faster training times.This paper is the first study to explore long-term ship position prediction using an AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide, providing a valuable benchmark for future studies. The model properties may be enhanced by adding new essential input features regarding the vessel status, destination, or historical trajectory (or longer time steps) without a trajectory definition. In our future studies, we intend to improve the model performance by utilizing these techniques and incorporating other types of vessels, thereby increasing the complexity to the model. In addition, we will attempt a longer time interval for long-term prediction.


Edge computing is a product of the evolution of IoT and the development of cloud computing technology, providing computing, storage, network, and other infrastructure close to users. Compared with the centralized deployment model of traditional cloud computing, edge computing solves the problems of extended communication time and high convergence traffic, providing better support for low latency and high bandwidth services. With the increasing amount of data generated by users and devices in IoT, security and privacy issues in the edge computing environment have become concerns. Blockchain, a security technology developed rapidly in recent years, has been adopted by many industries, such as finance and insurance. With the edge computing capability, deploying blockchain platforms/applications on edge computing platforms can provide security services for network edge environments. Although there are already solutions for integrating edge computing with blockchain in many IoT application scenarios, they slightly lack scalability, portability, and heterogeneous data processing. In this paper, we propose a trusted edge platform to integrate the edge computing framework and blockchain network for building an edge security environment. The proposed platform aims to preserve the data privacy of the edge computing client. The design based on the microservice architecture makes the platform lighter. To improve the portability of the platform, we introduce the Edgex Foundry framework and design an edge application module on the platform to improve the business capability of Edgex. Simultaneously, we designed a series of well-defined security authentication microservices. These microservices use the Hyperledger Fabric blockchain network to build a reliable security mechanism in the edge environment. Finally, we build an edge computing network using different hardware devices and deploy the trusted edge platform on multiple network nodes. The usability of the proposed platform is demonstrated by testing the round-trip time (RTT) of several important workflows. The experimental results demonstrate that the platform can meet the availability requirements in real-world usage scenarios. Nowadays, the Internet of Things (IoT) is playing a prominent role in the real world by supporting operations and communications autonomously, thereby facilitating and advancing new services that are widely used in everyday life . With thTo solve these problems, edge computing is presented. Edge computing is a new paradigm, and it calls for technologies to enable computation to be done at the edge of the network close to data sources . In ordeBlockchain can be defined as a distributed decentralized, shared tamper-resistant database. It is maintained, shared, replicated, and synchronized by multiple participants in a Peer-to-Peer (P2P) network. It can facilitate to establish a secure, trusted, and decentralized intelligent system to solve the security and privacy problems in edge computing . Edge coTherefore, it has become an inevitable trend to integrate edge computing frameworks and blockchain networks in the edge environment . BlockchIn this paper, we aim to integrate an edge computing framework and blockchain network to provide edge computing clients with a secure authentication mechanism. We implement a trusted edge platform based on microservice architecture, access Edgex Foundry framework to meet the needs of edge computing, and enhance the ability of Edgex through the edge application module. The interaction process with blockchain is abstracted into multiple microservices to form a security authentication module. The security authentication microservices are responsible for authentication and access control. Design the API gateway to parse external requests. Then it routes the request to the endpoints of the internal security authentication microservice and Egdex microservice. The significant contributions of this paper are lists as follows:The trusted edge platform is seen as an intermediary between the Hyperledger Fabric blockchain network and Edgex Foundry framework. It provides a private network environment that allows for private data exchange among the microservices. The platform supports collaboration among multiple edge computing nodes, and a collaborative process is designed for this purpose.The trusted edge platform is organized in a microservice architecture with excellent scalability. The platform uses Docker containers to build services. The introduction of the EdgeX Foundry framework enhances the ability of the platform to handle heterogeneous data.The proposed platform access to the Hyperledger Fabric blockchain network introduces reliable security mechanisms for the edge environment. The identity information of both the terminal and the user is stored on the blockchain. External requests need to be authenticated and access controlled before they can be processed by internal services.The rest of the paper is structured as follows. In this section, we will survey the latest frameworks of integrated blockchain and edge computing Systems. The authors of  propose In , a blockThe authors proposed a video surveillance system in  that useIn order to solve the problem of edge computing security in IIoT, the authors of  develop In , the autIn  and 18]18], the Based on the design idea of microservice architecture, we propose a trusted edge platform based on a blockchain network in an edge computing environment. As shown in The terminal is a device that connects the physical world and cyberspace. They can be classified into smart devices and resource-constrained devices based on their computing power. Smart devices are capable of performing various functions such as initial data processing, encryption, and transmission, while resource-constrained devices are only capable of sensing and collecting environmental data. In the edge computing environment, we abstract the terminals into two types of devices: acquisition devices and execution devices. The terminals are connected to the proximate edge computing servers through multiple communication protocols, such as NB-IoT, LoRa, WiFi, and NFC. The terminal senses the changes in the environment through the RF module or sensor module at the front end, and after calculation, makes decisions on the required response.Edgex Foundry, an open-source edge computing framework, connects to various IoT end devices through different protocols, manages them and collects data from them, and exports the data to applications at the edge or in the cloud for further processing ,20,21,22The blockchain networks build secure, trusted, decentralized intelligent systems in an edge environment to solve security and privacy problems in edge computing. Multiple edge nodes can construct a blockchain network. Part of the terminal devices is limited to their computing power. Thus, they are not parts of a blockchain network. The identity information of all these terminals is stored in the blockchain network. After the blockchain verifies the identity information, authorized terminal entities can read the blockchain data but can only write by the edge server. It contributes to reducing the excessive exposure of data. The edge servers can edit smart contracts to create access control policies specific to their needs. In the edge computing environment, device identity information, access control information, and access control policy are stored by a decentralized blockchain network, which can effectively prevent data from being tampered with.Hyperledger Fabric is a federated chain platform that allows multiple parties to participate, develop, deploy and run blockchain applications . HyperleIn order to integrate the capabilities of the edge computing framework and the blockchain network more efficiently, we propose a trusted edge platform based on microservice architecture. This platform has the characteristics of light-weight, ease of deployment, and high security. The proposed platform consists of three types of microservices, as illustrated in The edge application extends edge-side business capabilities to close the loop on local data by combining with the Edgex Foundry framework. The edge application module allows users to define operational functions to meet terminal requirements and run complex models such as neural networks. Edge computing resources are fully dispatched through the collaboration of several microservices as described below:(1) Application Process: The Application Process Service responds to requests for tasks from terminals. The service consists of the following three components: Task Scheduler, Task Queue, and Task Manager. The task scheduler accepts task requests and generates an ID for each task to be executed. The ID is used as a key to store the task name and source data in the database. Thereafter, the ID is inserted into the task queue by the scheduler. The scheduler can also edit the scheduling rules to execute some specific tasks at regular intervals. The task queue manages the ID of all pending tasks, and the data in the task queue can only be accessed by the task scheduler and the task manager. The task manager gets the task ID from the task queue, retrieves the task information in the database based on the ID, and executes them. The manager has an overload handling policy, a thread selection policy and, an idle policy to control the consumption of platform resources by tasks.(2) Task Registration: Task registration service provides a task registration process for terminals and platforms. The registration service first calls the authentication service in the security authentication module to distinguish the service level. The registration service maintains a task registration table that records information about each user’s registered tasks. The collaborative process service can access this table to get information about the required tasks for building collaborative processes.(3) Collaborative Process: This service is used to organize multiple edge computing nodes to achieve collaborative work. The Edgex Foundry platform provides good interoperability locally, but for multi-node collaboration, it requires the participation of a third-party platform to organize the services. The collaboration process service can be free from the third party, and several edge nodes can organize the service by themselves. Multiple edge nodes elect a leader node through the Raft algorithm. The leader node has the control authority of the whole collaborative process and can create Hyperledger Fabric channels and edit smart contract templates. The leader node integrates the edits made to the smart contract by the member nodes and verifies their validity. After the leader node deploys the smart contract, the nodes in the channel work according to the constraints of the smart contract, enabling collaboration among multiple nodes.(4) Database: Database is used to store applications and provides caching services for them during their work. The database has access rights and opens up different add, delete, and check functions to users with different identities.The security authentication decoupled the security functions into multiple microservices and deployed them on Docker containers. These decentralized security microservices work as a service cluster to offer a scalable, flexible, and light-weight data sharing and access control mechanism for the edge computing system. The key service components and operations are introduced below:(1) Registration Service: In the proposed edge platform, before accessing Edgex Foundry services, all terminal entities must submit a registration request to the registration microservice. The registration service performs the SHA-256 algorithm to generate InfoHash, a hash of identity information used as authentication credentials when authenticating. The Hyperledger Fabric blockchain creates a new peer node. It binds the peer node to the terminal, which means that the peer and the terminal share the public-private key pair. peer node invokes a smart contract to save the InfoHash in the blockchain and generates and returns the VID which uniquely identifies the terminal’s identity data. The terminal stores the public-private key pair and the VID locally.(2) Identity Authentication: The terminal submits the identity data identifier (VID), the identity plaintext data (Info), and the digital signature of the private key ) to the identity authentication service. The identity authentication service obtains the corresponding InfoHash data through the smart contract based on the VID. The identity authentication service calculates whether the Hash (Info) is equal to the InfoHash to ensure that the Info provided by the terminal is consistent and complete with the stored identity data. The identity authentication service ensures that the terminal has the corresponding private key by verifying the digital signature of the terminal. After authenticating the identity data of thr terminal, the authentication service invokes the smart contract to record the authentication process. After completing the above work, the authentication service returns the authentication result.(3) Access Control: In order to successfully access a service or resource at a service provider on a trusted edge platform, the terminal needs to send an access permission request to the access control service to obtain a permission token. The access control service runs an authorization policy to evaluate the access request based on the identity information of visitor. If the access request is authorized, the access control service invokes a smart contract to update the evaluation result on the blockchain and returns a querying token. When the service provider receives a request from an endpoint, the service provider determines whether to accept the service by whether the request carries a token. After that, the authorization data on the blockchain is queried by the token to determine whether the service is provided to the terminal.(4) Security Management: In the Security Authentication module, the Security Management microservice acts as a data and security service manager, managing the registered data and access control policies. The policies for access control are maintained and updated by the security management service. When a device is registered, the security management service sets responsive permissions for each endpoint based on a predefined permission policy. The edge server administrator can modify the information of registered devices through the security management service, while ordinary users can only modify the information of their own registered terminals. The accessed privileges of the terminal can be modified by the administrator and its own registered users, while the access privileges can only be modified by the administrator.The API gateway extends the device access layer of the Edgex Foundry framework to add security control mechanisms while meeting terminal access needs. The API gateway provides an HTTP listener to receiving requests from clients. There are several essential components, such as the controller, which is used by the API gateway to parse the requests it receives; the handler, which provides business logic by calling the internal microservices of the platform; the configuration, where the configuration information of APIs is stored; and logs, which records the key historical data of API gateway.The key to the proposed platform design is how to take advantage of the edge computing platform and blockchain. Edgex Foundry has strong interoperability and is responsible for data collection, integration, and heterogeneous processing in the whole system. Hyperledger Fabric deployed using microservices architecture has better compatibility with the proposed platform. The following will elaborate on how the platform integrates edge computing and blockchain.After system initialization, the user initiates a terminal device registration request to the API gateway. The request is forwarded to the registration microservice through the processor of the API gateway. The registration microservice hashes the terminal identity information transmitted by the user and obtains a virtual ID (VID) as proof of identity authentication. The registration service calls the Hyperledger Fabric service to create peer nodes and extract their public-private key pairs. When the peer node completes smart contract deployment, the registration service initiates a transaction process to store the InfoHash into the blockchain. The virtual ID (VID) used to query the InfoHash is returned when the transaction is completed. The registration service returns the public-private key pair and VID to the terminals. After that, the handler invokes the device service API of Edgex to register the necessary information and data communication configuration of the terminal. When accessing the authentication microservice, the terminal needs to submit the identity data identifier (VID), identity data (Info), and the terminal private key digital signature ). The identity service obtains the corresponding InfoHash identity data through a smart contract based on the VID. After hashing Info and comparing whether Hash (Info) is equal to InfoHash, the authentication service can determine that the Info provided by the terminal is consistent and complete with the stored identity data. The identity authentication service ensures that the terminal has the corresponding private key by verifying the digital signature and proves that the identity data belongs to the terminal. After completing the above operation, the authentication service invokes a smart contract to record this authentication service on the blockchain. Finally, the authentication service returns the authentication result. The terminal needs to determine its access rights before requesting services or obtaining resources. The API Gateway detects whether the endpoint request contains a token, and if there is no token, then the access control microservice needs to be invoked. The API gateway sends the terminal information and the information of the accessed service to the Access Control microservice. First, the access control service obtains the latest authorization policy from the security management microservice. After that, it runs the authorization policy and evaluates whether the visitor meets the authorization conditions. If the assessment is passed, the access control service invokes a smart contract to update the assessment result to the blockchain. After the smart contract is executed, it returns a token used to query the verification result. Finally, the access control microservice returns the token data. Before the terminal officially works after registration, users need to deploy application services on the Edge Application module. The user accesses the interface exposed by the API gateway and transmits the information about the target task. The Handler component simply processes the task information and forwards data to the task scheduler. The scheduler temporarily stores the task data in the database and generates a unique ID to identify the task. The task scheduler inserts the task ID into the end of the task queue. The task manager takes out the task ID from the task queue, then extracts the task information from the database and loads the task resources. After waiting for a while, the task starts to execute, and Edgex Foundry will export the data to the task data interface. In order to increase the interaction between edge computing nodes, the Trusted Edge Platform provides collaborative process microservices to help multiple nodes establish cooperative relationships. The Channel mechanism of Hyperledger Fabric has data isolation, and multiple nodes can join the same Channel. The cluster of nodes elects the leader node based on the draft algorithm. After the leader node of the cluster is elected, the leader is responsible for organizing the members to write, verify and deploy the smart contract. The cluster then works according to the smart contract. We build the edge computing network with two different performance hardware devices. A desktop with a 2.3 GHz Intel Core i7 (8-core) CPU and 16 GB RAM memory capacity meets our needs due to the power of the edge server. The low power consumption and the compact size and portability make Raspberry Pi a prime candidate for edge processing. We deployed our edge computing environment using a Raspberry Pi 4 with a 1.7 GHz Cortex-A72 CPU and 4 GB of RAM. On the software side, we used Docker and Docker Compose to simplify the operation of the application. The details of the hardware and software specifications are presented in To evaluate the usability of the proposed platform, a physical network environment is carried out on a physical network environment, which includes three desktops and two Raspberry Pis. Each device is deployed with an edge computing and blockchain environment. We use IoT end devices simulated by node-red. Edgex Foundry is deployed based on the Geneva version. Hyperledger Fabric version number is 2.1.0. Hyperledger Fabric blockchain network consists of five Fabric organizations. There are seven Orders randomly deployed on five physical devices in the whole network. Each organization initially has only one peer node. We use Docker Swarm to provide an overlay network layer for containers across hosts to enable containers to communicate with each other. A Schematic diagram of the experimental equipment organization scheme is shown in To measure the general cost incurred by the proposed platform in terms of edge device processing time, we selected two desktops and a Raspberry Pi for a multi-node interoperability test. The Raspberry Pi acts as the data generator, capturing images through a RaspiCam camera connected to the Edgex Foundry platform. One desktop acts as the data processing side, invoking the image recognition model to judge the images. On the command execution side, the other desktop responds to the image recognition result and sends the command to the simulation terminal through Edgex Foundry. The experimental design is shown in To evaluate the usability of the designed platform in a real-world environment, we performed Round-Trip Time (RTT) tests on the system workflows in Comparative experiments on multi-node collaboration tasks are conducted in a stable network environment. The experiments measured the time required to complete the task flow at different acquisition frequencies. The acquisition frequency of the camera gradually increases from one frame per second to 10 frames per second. The usability of the proposed platform when collaborating among multiple nodes is judged by comparing the two sets of test data. The experimental data statistics are shown below.The author of  proposesThe data in In this paper, we propose a trusted edge platform for integrating edge computing and blockchain, which uses a microservice architecture to address privacy security challenges in edge environments through blockchain-based authentication and access control mechanisms. A conceptual validation prototype is constructed in a physical edge computing network environment for verifying the feasibility of the proposed platform. In the edge environment where terminal, edge computing, and blockchain participate simultaneously, the trusted edge platform acts as a hub to connect three parties. The API gateway handles external access requests and routes them to internal microservices. The security authentication module is connected to the Hyperledger Fabric blockchain network and is responsible for user/terminal identity authentication and access control. The edge application module is used to improve the intelligence of the Edgex Foundry framework on which different types of applications can be deployed. With the features of Hyperledger Fabric blockchain, we designed a multi-node inter-collaboration process on the platform. Extensive experimental studies have been conducted with satisfactory results. It verifies that the trusted edge platform can effectively authenticate and control access policies in real-time in the edge environment. The intelligent applications on the platform can promptly respond to requests from terminals. This work shows that the proposed trusted edge platform is a promising approach. It can provide portable, scalable, and fine-grained security mechanisms for integrating edge computing and blockchain. It can also improve the coverage of blockchain networks in edge computing environments.While the reported work meets practical applications, there is still a long way to provide a complete decentralized and light-weight security solution to integrate edge computing and blockchain. Our subsequent work aims to focus on the research of light-weight consensus algorithms for blockchain.


Filtering facepiece respirators (FFR) with anti-odor properties are used to reduce odor nuisance occurring both in everyday life and at workplaces. Unfortunately, there are no standardized methods to measure the efficiency of odor reduction of such personal protective devices. This paper aims to determine whether olfactometric-based methods, commonly used in environmental studies, can be employed for this purpose. The proposed procedure is based on the detection of n-butanol by study participants, and it consists of three subsequent stages: (i) defining the individual levels of odor sensitivity of each study participant; (ii) determining THE odor detection level while using FFRs with varying anti-odor properties; and (iii) completing a questionnaire concerning the subjective perceptions of study participants. As a measure of odor reduction efficiency, a coefficient W, defined as a quotient of the degree of odor reduction by the FFR, and the individual odor sensitivity of the subject, was proposed. The experimental results showed the ability of our measure to differentiate the effectiveness of odor reduction of tested FFRs. This indicates that it can be potentially employed as the assessment tool to confirm the effectiveness of such respiratory protective devices as a control measure mitigating the adverse effects of malodors on workers’ health, cognition, and behavior. Odor pollution has become a significant socio-environmental and public health issue. The sources of malodor include both natural and man-made activities. Those found at workplaces are usually anthropogenic and arise mainly from activities of chemical, petrochemical ,2, pharmIndependent of their origin, malodors can elicit negative responses in the human body, cognition, and behavior due to toxicological effects, innate aversion, conditioning or stress responses, and reactions to non-odorous components such as endotoxins ,18. MaloTo mitigate the adverse effects of malodor exposure on the health and wellbeing of workers and neighboring household occupants, industrial facilities implement odor control measures ,30. The Only limited research is available on the methods that could be used to assess the odor adsorption efficiency of such FFRs ,33,34. TAt the same time, odor interactions and deodorization efficiency assessment methods are very well established in environmental studies ,37,38,39Our research aimed to determine whether olfactometric methods, generally used for environmental odor assessment, can be employed to determine the odor reduction efficiency of FFRs with anti-odor properties. For this study, we modified one of the methods used to test the olfactory ability of odor inspection staff. In addition, our method included the assessment of practical aspects related to the use of FFRs, such as leak-tightness, comfort, and subjective perception of odor reduction. We used this method to evaluate the effectiveness of three FFRs with different anti-odor properties. The experimental results were then analyzed in terms of the ability to differentiate the effectiveness of odor reduction of these FFRs.Three types of filtering facepiece respirators (FFRs) were used for the study: two FFRs with anti-odor properties containing an activated carbon layer and a reference FFR without the activated carbon layer. Anti-odor FFRs were selected to differ significantly in sorption properties (the same amount of activated carbon but with a much different surface area). The design characteristics of the FFRs are shown in The study was performed on a group of 21 volunteers. Before the tests, subjects filled out a short questionnaire about gender, age, occupation, and health status. In addition, they were asked not to eat or drink in the half hour directly preceding the planned tests. The characteristics of the study group are shown in The study group was heterogeneous in age (24–60 years), gender, and occupation. Among the group, only one person was a smoker; two reported to have been unwell in the week before (rhinitis), while one reported symptoms of the common cold.The proposed methodology for assessing odor reduction by using FFRs included three stages, shown schematically in First, the participants were informed about the aim of the study, and all of the assessment stages were discussed. Next, the participants were instructed on how to put on and check the fit of the FFR. Individual odor sensitivity levels were determined in the first stage of the assessment  for which they detected n-butanol odor and indicate whether they could detect the odor to a palpable degree  or made a random choice . The round ended when the subject correctly detected (D) two pens of successive dilutions.Each round’s individual odor sensitivity level was determined as the number of the first of two consecutive correct detects. The arithmetic mean of the results obtained in rounds 1 and 2 constituted the result.The individual odor sensitivity level determination method was modified to determine the odor reduction efficiency of the FFRs. The same measuring tools (Odor Pen Test Kit) and a procedure that combined the ascending concentration method with the 3-AFC method were used . First, The test procedure did not require a warm-up round, as the participants were already familiar with the practical aspects of the experiment. Thus, the study was performed in two consecutive rounds, separated by a 5-min break. The pens were presented in triplicates (two blind and one containing n-butanol) according to decreasing dilution order. The order of pen presentation was determined by randomly selecting one of the three specially designed results sheets .i is the individual odor sensitivity level of the specified study participant, and FFRSLi is the odor detection level obtained for the specified participant while using FFR.The odor reduction degree (ORD) was defined as the difference between individual odor sensitivity levels (ISL) of participants and the odor detection level obtained with the selected FFR (FFRSL). As a measure of odor reduction efficiency (W) of a given FFR, a value defined as follows was calculated:The final stage of the evaluation included filling out the questionnaire, which set out what factors affect odor reduction efficiency and how the participants perceive this for a given type of FFR. The questionnaire was composed of four questions regarding the leak-tightness of the facepiece, donning and fitting difficulty, and subjective perceptions of odor reduction (subjective odor reduction efficiency). The answers were ranked using a five-level Linkert’s scale  . Additiop < 0.05), mean values were compared using Tukey’s post hoc procedure at the significance level of 0.05.Statistical analyses were performed using STATISTICA 13.1 software . Descriptive statistics for all variables of interest were calculated. One-way analysis of variance (ANOVA) at the significance level of 0.05 was performed to identify statistical differences between parameters describing odor reduction efficiency of the FFRs. When statistical differences were detected . Part of this reduction can be undoubtedly a result of the test odor provision method (some of the test odor dissipates before it can be aspirated trough the mask), it may also stem from the difference in pressure between the ambient air and the breathing zone, where the so-called ‘dead space’ with limited air exchange occurs. No differences were found between MB 10VC and 3M 9915 FFRs for this parameter . This means that the degree of odor reduction is not a good measure of actual odor reduction, as it does not differentiate between different types of FFRs. Instead, we found statistically significant differences while comparing the odor reduction efficiencies W for tested FFRs . For the MB 10VC, it is was higher than the reference by 17%, while for 3M 9915, by 36%. Thus, the results indicate that such a coefficient constitutes a reasonable measure differentiating the efficiency of FFRs with anti-odor properties. They also serve as a premise to continue working in this area to define the criteria for assessing odor reduction efficiency for this personal protective device using olfactometric methods.The questionnaire results, in which answers were based on a Likert scale, are presented in There were no instances of head harness elastic band snapping or facepiece sliding. None of the subjects indicated that the reference FFR emits any pungent or unpleasant odors. In contrast, there was one such indication for the MB 10VC FFR and three for the 3M 9915 one. Most study participants pointed to the low leak-tightness of FFRs, which would significantly influence the degree to which such devices limit malodors at a workplace. The comfort of using the FFRs depended on the type of equipment used and the facial features of the participant. The donning and fitting difficulty was assessed as low to average, depending on whether or not the participants were well acquainted with such devices. Considerable individual variability in the subjective assessment of odor reduction of the FFRs was observed. The summary of the comparative analysis of the results is shown in Comparative analysis indicated no statistically significant differences in the subjective assessment of the leak-tightness and donning and fitting difficulty between individual FFR types. In terms of the comfort of use, the differences were demonstrated only for the reference variant and 3M 9915 FFR. Regarding the subjective degree of odor reduction, the 3M 9915 FFR was rated the best, while the reference FFR was the worst. There were also statistically significant differences between individual equipment types, consistent with the results obtained based on the olfactometric method.Proper control of workplace exposures to malodors can reduce the risk of adverse health effects among employees. In situations where organizational and technical control measures are insufficient or not feasible from an economic point of view, FFRs with anti-odor properties provide an easy option to mitigate the risks. Depending on the region of the world, different legal regulations ensure the effectiveness of FFRs before they are placed on the market. For example, in the United States, respiratory protective devices are certified following the requirements described in Title 42 of the Code of Federal Regulations part 84. At the same time, in the European Union, RPDs should bear Conformitè Europëenne (CE) marking, which generally certifies its compliance with the relevant European standards. Different national requirements are in force in other countries. None of the globally existing normative documents considers methods for determining and approving the ability of a dust mask to capture odorous compounds. Thus, many brands are available for purchase that have not been assessed in terms of odor reduction efficiency; therefore, their effectiveness remains unclear.This study aimed to verify whether a simple olfactory-based method can be utilized for assessing odor reduction efficiency. The proposed research methodology included three consecutive stages . The coefficient W of odor reduction efficiency, defined as the quotient of the degree of odor reduction by the FFR and the individual odor sensitivity level of the participant, expressed as a percentage, was proposed as a measure of odor reduction. Based on the analysis of the test results, it was found that this coefficient is a good measure to differentiate the odor reduction efficiency of FFRs with anti-odor properties.Further work in developing olfactometric-based methods is needed to determine the criteria for assessing the odor reduction efficiency of such types of FFRs. In particular, better means of test odor provision ensuring uniform aspiration conditions while testing panelists with and without FFR  should be developed. Moreover, a broader choice of test odors should be considered. In our study, n-butanol was used because it is commonly utilized for odor testing and is an excellent substance to challenge the human olfactory system without unnecessary risk to the test participant. Unfortunately, it can be insufficient to test FFRs ability to remove a large variety of odors occurring at different types of workplaces. Therefore, it would be advisable to use other test substances with different odor characteristics and chemical properties as an alternative to n-butanol  to better represent the conditions of actual use of this type of FFRs. Additionally, comparing the olfactometric method with functional tests in a natural work environment would also be necessary to validate the method and determine the results’ repeatability and reproducibility.