Analysis of Privacy Preservation Techniques in IoT

2021 ◽  
pp. 1343-1351
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

Analysis of Privacy Preservation Techniques in IoT

2019 ◽  
Vol 10 (3) ◽  
pp. 27-33
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

TSCache

2021 ◽  
Vol 14 (13) ◽  
pp. 3253-3266
Author(s):  
Jian Liu ◽  
Kefei Wang ◽  
Feng Chen
Keyword(s):  
Time Series ◽  
High Speed ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Efficient System ◽  
Indexing Structure ◽  
System Solution ◽  
Challenges And Opportunities ◽  
Cache System

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

scholarly journals IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2615
Author(s):  
Xinyao Sun ◽  
Aaron Zimmer ◽  
Subhayan Mukherjee ◽  
Parwant Ghuman ◽  
Irene Cheng
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Optimization Problems ◽  
Ground Deformation ◽  
Series Data ◽  
Two Stage ◽  
Real Time Processing ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Model

Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

scholarly journals Security Problems in Iot Model and Privacy Preserving in Various Iot Devices

2019 ◽  
Vol 8 (4) ◽  
pp. 4418-4421
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Publishing ◽  
Series Data ◽  
Personal Privacy ◽  
Privacy Threats ◽  
Security Concerns ◽  
Iot Devices

It is instead required for IoT units to equip along with the potential to withstand security and privacy threats when fulfilling the intended useful criteria and services. To obtain these targets, there are many brand-new problems for the IoT to apply personal privacy-preserving records manipulation. Initially, data professionals need to have to process privacysensitive data to remove the counted on details without particular privacy enclosure. Within this paper, our company temporarily showed the kinds of security concerns in IoT style, personal privacy-preserving in different IoT devices as well as additional challenges and also method for privacy-preserving time-series data publishing

scholarly journals Dynamic predict in-hospital mortality risk in intensive care unit with a new deep learning of artificial intelligence

2020 ◽  
Author(s):  
Yu-wen Chen ◽  
Yu-jie Li ◽  
Zhi-yong Yang ◽  
Kun-hua Zhong ◽  
Li-ge Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Deep Learning ◽  
Mortality Risk ◽  
Time Series Data ◽  
Risk Model ◽  
Series Data ◽  
Physiological Variables ◽  
Prediction Of Mortality ◽  
Mimic Iii

Abstract Background Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods Finally, 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results The Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837(0.824–0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (< 50%). Conclusions Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.

Low Cost Modular Telemetry for Coastal Time-Series Data

2002 ◽  
Author(s):  
Daniel E. Frye ◽  
W. R. Geyer ◽  
Bradford Butman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data

Automatic Linear Disturbance Footprint Extraction Based on Dense Time-Series Landsat Imagery

2014 ◽  
Author(s):  
Zhaohua Chen ◽  
Bill Jefferies ◽  
Paul Adlakha ◽  
Bahram Salehi ◽  
Des Power
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Oil And Gas ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Series Data ◽  
Linear Features ◽  
Seismic Lines

Linear disturbances from the construction of pipelines, roads and seismic lines for oil and gas extraction and mining have caused landscape changes in Western Canada; however these linear features are not well recorded. Inventory maps of pipelines, seismic lines and temporary access routes created by resource exploration are essential to understanding the processes causing ecological changes in order to coordinate resource development, emergency response and wildlife management. Mapping these linear disturbances traditionally relies on manual digitizing from very high resolution remote sensing data, which usually limits results to small operational area. Extending mapping to large areas is challenging due to complexity of image processing and high logistical costs. With increased availability of low cost satellite data, more frequent and regular observations are available and offer potential solutions for extracting information on linear disturbances. This paper proposes a novel approach to incorporate spectral, geometric and temporal information for detecting linear features based on time series data analysis of regularly acquired, and low cost satellite data. This approach involves two steps: multi-scale directional line detection and line updating based on time series analysis. This automatic method can effectively extract very narrow linear features, including seismic lines, roads and pipelines. The proposed method has been tested over three sites in Alberta, Canada by detecting linear disturbances occurring over the period of 1984–2013 using Landsat imagery. It is expected that extracted linear features would be used to facilitate preparation of baseline maps and up-to-date information needed for environmental assessment, especially in extended remote areas.

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