Aspect-Oriented Modeling of Spatial Data Interpolation for Estimating Missing Data in Internet of Things (IoT) Service Discovery

2019 ◽  
Vol 47 (6) ◽  
pp. 20180508
Author(s):  
Senthil Murugan Balakrishnan
Keyword(s):  
Missing Data ◽  
Internet Of Things ◽  
Spatial Data ◽  
Service Discovery ◽  
Data Interpolation ◽  
Aspect Oriented Modeling

scholarly journals Optimal Placement of Social Digital Twins in Edge IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6181
Author(s):  
Olga Chukhno ◽  
Nadezhda Chukhno ◽  
Giuseppe Araniti ◽  
Claudia Campolo ◽  
Antonio Iera ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Data Exchange ◽  
Service Discovery ◽  
Programming Model ◽  
Optimal Solution ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Digital Twins ◽  
The Social

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

scholarly journals Cloud Computing Storage Backup and Recovery Strategy Based on Secure IoT and Spark

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dajun Chang ◽  
Li Li ◽  
Ying Chang ◽  
Zhangquan Qiao
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Spatial Data ◽  
Loss Rate ◽  
Data Recovery ◽  
Computing Device ◽  
Data Packets ◽  
Recovery Strategies ◽  
Backup And Recovery ◽  
The Cost

Spatial data occupies a large proportion of the large amount of data that is constantly emerging, but a large amount of spatial data cannot be directly understood by people. Even a highly configured stand-alone computing device can hardly meet the needs of visualization processing. In order to protect the security of data and facilitate for users the search for data and recover by mistake, this paper conducts a research on cloud computing storage backup and recovery strategies based on the secure Internet of Things and Spark platform. In the method part, this article introduces the security Internet of Things, Spark, and cloud computing backup and recovery related content and proposes cluster analysis and Ullman two algorithms. In the experimental part, this article explains the experimental environment and experimental objects and designs an experiment for data recovery. In the analysis part, this article analyzes the challenge-response-verification framework, the number of data packets, the cost of calculation and communication, the choice of Spark method, the throughput of different platforms, and the iteration and cache analysis. The experimental results show that the loss rate of database 1 in the fourth node is 0.4%, 2.4%, 1.6%, and 3.2% and the loss rate of each node is less than 5%, indicating that the system can respond to applications.

scholarly journals Behaviour of spatial rainfall correlation For short distances

MAUSAM ◽  
2021 ◽  
Vol 43 (3) ◽  
pp. 269-272
Author(s):  
J. N. KANAUJIA ◽  
SURINDER KAUR ◽  
D. S. Upadhyay
Keyword(s):  
Theoretical Model ◽  
Missing Data ◽  
Network Design ◽  
Short Distance ◽  
Annual Rainfall ◽  
Parameter Distribution ◽  
Data Interpolation ◽  
Areal Rainfall ◽  
Transfer Of Information ◽  
Two Parameter

The correlation between two series of rainfall recorded at two stations which are at short distance, is usually found significant. This information has important applicability in the areas of data interpolation, network design, transfer of information in respect of missing data and deriving areal rainfall from point values. In this paper 70-year (1901-1970) annual rainfall data for about 1500 stations in India have been analysed. The distribution of correlation coefficient (r) for the stations located within a distance of 40 km were obtained. Attempt has been made to derive theoretical model of r. For this purpose two distributions, (1) a two parameter -distribution and (2) a two parameter bounded distribution, have been chosen as in both cases the variable ranges from 0 to 1.  

scholarly journals SPATIAL DATA INTERPOLATION

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Prof.RAE ZH Aliyev
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Spatial Distribution ◽  
Spatial Data ◽  
Movement Speed ◽  
Data Interpolation ◽  
Heavy Metal Concentrations ◽  
Groundwater Movement ◽  
Record Data ◽  
Inverse Distance

During the study and adjustment, techniques revealed our analysis of spatial data in vector format. The latter is best suited for the spatial analysis of discrete objects. However, when the spatial variable is represented as a field of scalar or vector greatness (for example, the spatial distribution of concentrations of heavy metal concentrations in soils or groundwater movement speed field). Convenient ways to record data is bitmap format. This approach is most often used for phenomena of processes that are characterized by considerable anisotropy. However, the characteristic feature of the method of inverse distance is the fact that the interpolated value in measured point is equal to the measured value. Key words: erosion, soil; heavy metals, extremum, spatial data, raster data anti-erosion measures

scholarly journals Missing Data Imputation in Internet of Things Gateways

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 425
Author(s):  
Cinthya M. França ◽  
Rodrigo S. Couto ◽  
Pedro B. Velloso
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Internet Of Things ◽  
Execution Time ◽  
Regression Models ◽  
Weather Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Imputation Methods ◽  
The Neural Network

In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.

scholarly journals A Comparative Analysis of Service Discovery Approaches for the Internet of Things

2017 ◽  
Vol 3 (1) ◽  
pp. 17 ◽  
Author(s):  
Meriem Aziez ◽  
Saber Benharzallah ◽  
Hammadi Bennoui
Keyword(s):  
Comparative Analysis ◽  
Internet Of Things ◽  
Service Discovery ◽  
The Internet ◽  
Service Description ◽  
Smart Healthcare ◽  
Healthcare It ◽  
Multiple Domains ◽  
The Internet Of Things ◽  
Significant Attention

Abstract—The Internet of Things (IOT) has gained a significant attention in the last years. It covers multiple domains and applications such as smart home, smart healthcare, IT transportation...etc. the highly dynamic nature of the IOT environment brings to the service discovery new challenges and requirements. As a result, discovering the desirable services has become very challenging. In this paper, we aim to address the IoT service discovery problem and investigate the existing solutions to tackle this problem in many aspects, therefore we present a full comparative analysis of the most representative (or outstanding) service discovery approaches in the literature over four perspectives: (1) the IoT service description model, (2) the mechanism of IoT service discovery, (3) the adopted architecture and (4) the context awareness.

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