A scalable and robust key pre-distribution scheme with network coding for sensor data storage

2011 ◽  
Vol 55 (10) ◽  
pp. 2534-2544 ◽  
Author(s):  
Rongfei Zeng ◽  
Yixin Jiang ◽  
Chuang Lin ◽  
Yanfei Fan ◽  
Xuemin (Sherman) Shen
Keyword(s):  
Network Coding ◽  
Data Storage ◽  
Sensor Data ◽  
Distribution Scheme

scholarly journals A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment

2018 ◽  
Vol 8 (10) ◽  
pp. 1992 ◽  
Author(s):  
YiNa Jeong ◽  
SuRak Son ◽  
SangSik Lee ◽  
ByungKwan Lee
Keyword(s):  
Deep Learning ◽  
Data Storage ◽  
Good Condition ◽  
Sensor Data ◽  
Weather Data ◽  
Learning Models ◽  
Weather Information ◽  
Nutrient Environment ◽  
Total Crop ◽  
Crop Diagnosis

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops.

scholarly journals Rancang Bangun Sistem Pemantauan Kinerja Panel Surya Tipe Mono-Crystalline Silicon Berbasis IoT

Electrician ◽  
2019 ◽  
Vol 13 (3) ◽  
pp. 81-83
Author(s):  
Mahardika Yoga Darmawan ◽  
Mohamad Samsul Anrokhi ◽  
Ali Komarudin
Keyword(s):  
Standard Deviation ◽  
Time Delay ◽  
Monitoring System ◽  
Data Storage ◽  
Crystalline Silicon ◽  
Sensor Data ◽  
Current Sensor ◽  
Solar Panels ◽  
Voltage Sensors ◽  
Current Voltage

Abstrak — Panel surya dapat diketahui kinerjanya dengan cara mengukur parameter arus dan tegangan namun untuk mendapatkan informasi yang akurat dan berkelanjutan maka perlu adanya sistem pemantuan terhadap panel surya tersebut. Sistem pemantauan pada penelitian ini dibuat dengan berbasis ATMEGA328P, ESP 8266, sensor arus dan tegangan. Dapat disimpulkan bahwa untuk sistem pemantuan kinerja panel surya dalam pengujian memiliki galat sebesar 0.2% untuk sensor tegangan, 0.17 % untuk sensor arus. Untuk data dari sistem pemantuan menunjukkan nilai rata-rata tegangan dan arus sebesar 20.75 volt dan 2.81 ampere dengan standar deviasi 0.32 volt dan 0.14 ampere serta 15 detik untuk jeda pengiriman data ke platform penyimpan data. Kata kunci —Panel Surya, Arus, Tegangan, Pemantauan.     Abstract — Solar panels can be accessed by measuring the current and voltage parameters to obtain accurate and necessary information, so a system of monitoring for the solar panels is needed. The improvement system in this study was made based on ATMEGA328P, ESP 8266, current and voltage sensors. It can be concluded that the solar panel monitoring system in the test has an error of 0.2% for voltage sensor, 0.17 % for current sensor. Data from the monitoring system, the average voltage and current values ​​are 20.75 volts and 2.81 amperes with a standard deviation of 0.32 volts and 0.14 ampere then it takes 15 seconds for time delay in sending data to a data storage platform. Keywords— Solar Panels, Current, Voltage, Monitoring .

scholarly journals Feasibility study of using the RoboEarth cloud engine for rapid mapping and tracking with small unmanned aerial systems

2014 ◽  
Vol XL-1 ◽  
pp. 219-226 ◽  
Author(s):  
J. Li-Chee-Ming ◽  
C. Armenakis
Keyword(s):  
Data Storage ◽  
Large Data ◽  
Video Camera ◽  
Sensor Data ◽  
Single Frequency ◽  
Unmanned Aerial Systems ◽  
Indoor Environments ◽  
Processing Strategies ◽  
Outdoor Environments ◽  
Aerial Systems

This paper presents the ongoing development of a small unmanned aerial mapping system (sUAMS) that in the future will track its trajectory and perform 3D mapping in near-real time. As both mapping and tracking algorithms require powerful computational capabilities and large data storage facilities, we propose to use the RoboEarth Cloud Engine (RCE) to offload heavy computation and store data to secure computing environments in the cloud. While the RCE's capabilities have been demonstrated with terrestrial robots in indoor environments, this paper explores the feasibility of using the RCE in mapping and tracking applications in outdoor environments by small UAMS. <br><br> The experiments presented in this work assess the data processing strategies and evaluate the attainable tracking and mapping accuracies using the data obtained by the sUAMS. Testing was performed with an Aeryon Scout quadcopter. It flew over York University, up to approximately 40 metres above the ground. The quadcopter was equipped with a single-frequency GPS receiver providing positioning to about 3 meter accuracies, an AHRS (Attitude and Heading Reference System) estimating the attitude to about 3 degrees, and an FPV (First Person Viewing) camera. Video images captured from the onboard camera were processed using VisualSFM and SURE, which are being reformed as an Application-as-a-Service via the RCE. The 3D virtual building model of York University was used as a known environment to georeference the point cloud generated from the sUAMS' sensor data. The estimated position and orientation parameters of the video camera show increases in accuracy when compared to the sUAMS' autopilot solution, derived from the onboard GPS and AHRS. The paper presents the proposed approach and the results, along with their accuracies.

scholarly journals Crowdsourced Data

2021 ◽  
pp. 63-67
Author(s):  
Karel Charvát ◽  
Michal Kepka
Keyword(s):  
Data Storage ◽  
Mobile Applications ◽  
User Authentication ◽  
Volunteered Geographic Information ◽  
Scientific Work ◽  
Geographic Information ◽  
Geospatial Data ◽  
Sensor Data ◽  
Crowdsourced Data ◽  
Server System

AbstractCrowdsourcing together with Volunteered Geographic Information (VGI) are currently part of  a broader concept – Citizens Science. The methods provide information on existing geospatial data or is a part of data collection from geolocated devices. They enable opening parts of scientific work to the general public. DataBio Crowdsourcing Solution is a combination of the SensLog server platform and HSLayers web and mobile applications. SensLog is a server system for managing sensor data, volunteered geographic information and other geospatial data. Web and mobile applications are used to collect and visualize SensLog data. SensLog data model builds on the Observations & Measurements conceptual model from ISO 19156 and includes additional sections, e.g., for user authentication or volunteered geographic information (VGI) collection. It uses PostgreSQL database with PostGIS for data storage and several API endpoints.

Optimization of Threshold Values for Estimators Based on Single-Bit Quantized Sensors Using Genetic Algorithms

2002 ◽  
Author(s):  
Nishant Unnikrishnan ◽  
Ajay Mahajan ◽  
Antonios Mengoulis ◽  
R. Viswanathan
Keyword(s):  
Genetic Algorithms ◽  
Parameter Estimation ◽  
Data Storage ◽  
Dynamic Range ◽  
Mean Squared Error ◽  
High Reliability ◽  
Sensor Data ◽  
Threshold Values ◽  
Distributed Sensors ◽  
Range Resolution

The paper considers the problem of signal parameter estimation using a collection of distributed sensors called a sensor pack. Each sensor quantizes its data to one-bit information and sends it to a fusion processor for the estimation of the parameter. Estimation of a constant signal in additive noise is considered. Estimators are formulated based on one-bit sensor data and their mean squared error (MSE) performances are evaluated through simulation studies. It is shown that selecting certain threshold values for quantizing the sensor outputs can lower the MSE. Genetic algorithms are used to find the optimal threshold values for the sensors. Results from this study show that robust estimation of parameter is possible by using a moderately large number of one-bit quantized sensor data. This work has significance in applications that demand high reliability in sensor networks in spite of sensor failures, limited sensor dynamic range, resolution, bandwidth for data transmission or even data storage.

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