scholarly journals A Novel Software Architecture Solution with a Focus on Long-Term IoT Device Security Support

2021 ◽  
Vol 11 (11) ◽  
pp. 4955
Author(s):  
Ivica Dodig ◽  
Davor Cafuta ◽  
Tin Kramberger ◽  
Ivan Cesar
Keyword(s):  
Software Architecture ◽  
Big Data Analysis ◽  
Smart Device ◽  
Software Components ◽  
Flow Sensors ◽  
Device Model ◽  
Measurement Results ◽  
Calibration Device ◽  
Iot Devices

This paper presents a solution for upgrading a previous device model to an Industry 4.0 smart device, with the goal of maintaining high compatibility. A novel IoT architecture is presented that satisfies the characteristics of a smart device. We analysed existing IoT architectures and proposed a new architecture to achieve long-term security and usability. To ensure long-term security, we eliminated the possibility of device configuration outside the immediate vicinity of the device with a dedicated protocol. The security concepts of the existing architectures were also analysed and further modified. To improve compatibility with previous device models, we propose a new method to collect data from sensors by introducing a multithreaded microcontroller. We propose additional software components to ensure factory programming, maintenance, and cloud Big Data analysis. Based on our experiments, we adapted the algorithm to increase the accuracy of the temperature and flow sensors by using a temperature calibration device and known flow cycles. Measurement results are presented to confirm the successful upgrade. We designed a hardware architecture to ensure compatibility with previous and future device models. Issues with previous sensors encountered during the upgrade were discussed and resolved. A novel software architecture based on security for long-term IoT devices is proposed.

scholarly journals Spectrum Based Power Management for Congested IoT Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2681
Author(s):  
Kedir Mamo Besher ◽  
Juan Ivan Nieto-Hipolito ◽  
Raymundo Buenrostro-Mariscal ◽  
Mohammed Zamshed Ali
Keyword(s):  
Power Management ◽  
Network Performance ◽  
Channel Allocation ◽  
Battery Life ◽  
Packet Routing ◽  
Data Packets ◽  
Increasing Demand ◽  
Iot Devices ◽  
Set Up

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

scholarly journals Atmospheric CO2, CH4, and CO with CRDS technique at the Izaña Global GAW station: instrumental tests, developments and first measurement results

2017 ◽  
Author(s):  
Angel J. Gomez-Pelaez ◽  
Ramon Ramos ◽  
Emilio Cuevas ◽  
Vanessa Gomez-Trueba ◽  
Enrique Reyes
Keyword(s):  
Drift Rate ◽  
Inlet Pressure ◽  
Response Functions ◽  
Measurement Results ◽  
Ambient Measurements ◽  
Acceptance Tests ◽  
Term Maintenance

Abstract. At the end of 2015, a CO2/CH4/CO Cavity Ring-Down Spectrometer (CRDS) was installed at the Izaña Global Atmosphere Watch station (Tenerife, Spain) to improve the Izaña Greenhouse gases GAW measurement programme, and to guarantee the renewal of the instrumentation and the long-term maintenance of this programme. We present the results of the CRDS acceptance tests, the processing of raw data applied through novel numerical codes, and the response functions used. Also, the calibration results, the implemented water vapour correction, the target gas injection statistics, the ambient measurements performed from December 2015 to July 2017, and their comparison with other continuous in situ measurements are described. The agreement with other in situ continuous measurements is good most of the time for CO2 and CH4, but for CO is just outside the GAW 2-ppb objective. It seems the disagreement is not produced by significant drifts in the CRDS CO WMO tertiary standards. The main novelties are: 1) determination of a slight CO2 correction that takes into account changes in the inlet pressure/flow rate; 2) detailed justification of the use of virtual tanks to monitor the response function changes in time; 3) drift rate determination for the pressure and temperature sensors located inside the CRDS cavity; 4) novelties in the determination of the H2O correction for CO; and 5) determination and discussion of the origin of the CRDS-flow inlet pressure and H2O dependences.

Establishment of Mathematical Model in Evaluation of Uncertainty in Indication Error Measurement Results

2014 ◽  
Vol 651-653 ◽  
pp. 428-431
Author(s):  
Xia Zhao
Keyword(s):  
Mathematical Model ◽  
Electrical Measuring ◽  
Electrical Measuring Instrument ◽  
Error Measurement ◽  
Measuring Instrument ◽  
Uncertainty In Measurement ◽  
Measurement Result ◽  
Three Phase ◽  
Measurement Results ◽  
Calibration Device

Taking Model DK-52 (Grade 0.05) multi-function three-phase electrical measuring instrument calibration device as an example, the paper describes how to evaluate the uncertainty in measurement result of indication errors in mathematical model.

Spending Less Than 100$ on Real-Time Sewer Flow Measurements

2021 ◽  
Author(s):  
Robert Meier ◽  
Franz Tscheikner-Gratl ◽  
Christos Makropoulos
Keyword(s):  
Water Level ◽  
Industrial Revolution ◽  
New Technologies ◽  
Surface Velocity ◽  
Prediction Algorithm ◽  
Raspberry Pi ◽  
Laboratory Setting ◽  
Flow Sensors ◽  
Flow Measurements

<p>As more and more computational power becomes available at increasingly affordable prices, the last years have seen a veritable explosion in the number of sensors and interconnected devices. This evolution is well known and often referred to as the 4th industrial revolution, or the IoT. The water sector, albeit often conservative in adopting new technologies, will profit from this continued digitalisation in various ways.</p><p>In this work we focus on the vision of covering entire sewer systems by tightly knit sensor networks which can process the generated amount of data simultaneously. Given the large number of sensors required, the only possibility to implement such a network is keeping costs as low as possible for the individual devices or use already existing sensors in multiple ways (e.g., traffic cameras helping in flood detection).</p><p>Using hardware of the Raspberry Pi ecosystem, currently retailing at less than 100$, we collected continuous video footage of an artificial open channel in a laboratory setting and used a deep neural network to extract the water level and surface velocity. The measurement accuracy of the prediction algorithm was then compared to conventional flow sensors to assess the practicality of this approach. Preliminary results in a laboratory setting indicate a sufficient prediction accuracy of the water level for engineering uses but further work is needed to verify this in a long-term field study.</p><p>After this initial stage, deploying the sensor in a real-world setting as part of the B-WaterSmart project is planned. Apart from verifying the results under real conditions, we will then be able to assess the long-term behaviour of this approach. This includes an evaluation of the maintenance effort. As the sensor is not in direct contact with the sewage, the typical need for frequent cleaning should be greatly reduced, which in turn is expected to further lower the costs.</p><p>We argue that if such a cheap sensor can ultimately be established as a viable alternative to more conventional flow sensors, the vision of sewer networks covered entirely by sensors, could become more attainable in practice.</p>

scholarly journals All-Printed Human Activity Monitoring and Energy Harvesting Device for Internet of Thing Applications

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1197 ◽  
Author(s):  
Shawkat Ali ◽  
Saleem Khan ◽  
Amine Bermak
Keyword(s):  
Energy Harvesting ◽  
Human Activity ◽  
Indium Tin Oxide ◽  
Activity Monitoring ◽  
The Self ◽  
Smart Device ◽  
Bridge Rectifier ◽  
Self Powered ◽  
Iot Devices ◽  
Human Activity Detection

A self-powered device for human activity monitoring and energy harvesting for Internet of Things (IoT) devices is proposed. The self-powered device utilizes flexible Nano-generators (NGs), flexible diodes and off-the-shelf capacitors. During footsteps the NGs generate an AC voltage then it is converted into DC using rectifiers and the DC power is stored in a capacitor for powering the IoT devices. Polydimethylsiloxane (PDMS) and zinc stannate (ZnSnO3) composite is utilized for the NG active layer, indium tin oxide (ITO) and aluminum (Al) are used as the bottom and top electrodes, respectively. Four diodes are fabricated on the bottom electrode of the NG and connected in bridge rectifier configuration. A generated voltage of 18 Vpeak was achieved with a human footstep. The self-powered smart device also showed excellent robustness and stable energy scavenger from human footsteps. As an application we demonstrate human activity detection and energy harvesting for IoT devices.

scholarly journals Design of Automated Robotic System for Draping Prepreg Composite Fabrics

Robotica ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 72-87 ◽  
Author(s):  
L.-P. Ellekilde ◽  
J. Wilm ◽  
O. W. Nielsen ◽  
C. Krogh ◽  
E. Kristiansen ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
System Design ◽  
Software Architecture ◽  
System Integration ◽  
Integrated System ◽  
Software Components ◽  
Proof Of Concept ◽  
Design And Implementation ◽  
Linear Actuators ◽  
Composite Fabrics

SUMMARYThis paper presents a novel solution for precision draping of prepreg composite fabrics onto double curved molds. Our contributions relate to system design, including hardware and software components, and to system integration. On the hardware side, design and implementation of a drape tool with up to 120 suction cups positioned individually by linear actuators are described. On the software side, design and implementation of the software architecture are presented, along with necessary algorithms within sensor technologies and mathematical modeling. The essential system’s components were verified individually, and the entire integrated system was successfully validated in the Proof-of-Concept experiments, performed on an experimental physical model of the system.

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

2015 ◽  
Vol 87 (3) ◽  
pp. 815-835 ◽  
Author(s):  
Pavel Baltiiski ◽  
Ilia Iliev ◽  
Boian Kehaiov ◽  
Vladimir Poulkov ◽  
Todor Cooklev
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Access Networks ◽  
Spectrum Monitoring ◽  
Radio Access Networks ◽  
Radio Access

scholarly journals Heart Rate Variability and Mild Traumatic Brain Injury: Case Study and Review of Literature

Neurology ◽  
2019 ◽  
Vol 93 (14 Supplement 1) ◽  
pp. S2.2-S2
Author(s):  
Harrison Seltzer ◽  
Karim Elghawy ◽  
Robert Baker
Keyword(s):  
Traumatic Brain Injury ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Training Session ◽  
The United States ◽  
Smart Device ◽  
Cross Country

ObjectiveUse biofeedback measures to manage a patient's long term recovery from concussion.BackgroundSports-related mild traumatic brain injury (MTBI) is estimated to affect 3.8 million people in the United States. Identifying quantitative measures of recovery has become a point of interest in treatment. Heart Rate Variability (HRV), the average fluctuation in the interval between heartbeats, shows promise as a noninvasive biomarker.Design/MethodsCase report following cardiovascular recovery of a 15 year old cross country runner 4 months post-injury. Average heart rate and maximum heart rate per training session were collected from the patient's smart device.ResultsA 15-year-old Caucasian male cross-country runner hit the back of his head during a soccer game suffering an MTBI. The patient rested from the activity for 1 week then returned to training. Two months after the injury the patient complained of persistent shortness of breath, fatigue, and increased heart rate while running. According to the patient, his average BPM while running prior to the injury was in the 160s. The patient's smart device post-concussion reports a spike into the 180s. 3 months post-concussion the patient was instructed to keep his heart rate below 170 during training. In the following month, the patient's condition improved gradually with a return to baseline activity.ConclusionsHRV is a promising point of investigation for the management of post-concussive symptoms. Further research is necessary to elucidate the long term effects of concussion on heart rate variability.

scholarly journals The robotized stand for the diagnosing compression refrigerating machines

2018 ◽  
Vol 224 ◽  
pp. 02088
Author(s):  
Sergey Petrosov ◽  
Michael Lemeshko ◽  
Alexander Kozhemyachenko
Keyword(s):  
Energy Efficiency ◽  
Technical Diagnostics ◽  
Experimental Conditions ◽  
Automatic Mode ◽  
Simultaneous Evaluation ◽  
Measurement Results ◽  
Research Problems ◽  
Reducing Costs ◽  
Long Term Studies

The process of creation and improvement of small refrigerating machines, the way of increasing their energy efficiency and reducing costs should be provided with reliable means and effective technologies of technical diagnosing these machines. One of the methods for achieving this goal is the creation of robotic research complexes. The authors, using the example of the automated research stand, show a possible variant of solving research problems when the researcher needs to make a measurement program, including a program of varying experimental conditions. Long-term tests with recording measurement results are performed in automatic mode without the operator-researcher’s participation. The robotic stand usually replaces the research operator, and ensures the effective results of long-term studies as they can be carried out more qualitatively and deeply, with simultaneous evaluation of results and adjustments of the measurement plans. The scheme and description of the developed stand are given. The significant advantages of a robotic stand are shown in comparison with the known analogous means of technical diagnostics of small refrigerating machines.

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