Improving Quality of Data: IoT Data Aggregation Using Device to Device Communications

Sanyal, Sunny; Zhang, Puning

doi:10.1109/access.2018.2878640

Cited by 39 publications

(27 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1, i f P ij is not missing 0, i f P ij is missing (5) Next, compute the root mean square error (RMSE) between the P and P', which is given in Equation (6) below.…”

Section: Incremental Space-time Model (Istm)mentioning

confidence: 99%

“…The primary objectives of IoT sensor networks include (i) sensing the critical information from the external physical environment, (ii) the sampling of internal system signals, and (iii) obtaining meaningful information from sensor data to perform decision-making [4,5]. It is to be noted that IoT-enabled applications involve a wireless sensor network (WSN).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

227

View full text Add to dashboard Cite

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

show abstract

“…1, i f P ij is not missing 0, i f P ij is missing (5) Next, compute the root mean square error (RMSE) between the P and P', which is given in Equation (6) below.…”

Section: Incremental Space-time Model (Istm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

227

View full text Add to dashboard Cite

show abstract

“…The reasons why we ignore the importance of ''the performance of de-duplication'' are: (1) the previous papers (AE, RAM, etc) use CDC to reduce hard disk usage and the resulting chunks are stored on disk directly and perhaps permanently, thus the resulting chunks should be equal to or as many times as disk sector to reduce fragmentation on the hard disk. (2) We use CDC only to find different parts between two similar files. During incremental synchronization, it is necessary to find out the difference between two files stored in different places by means of network communication.…”

Section: B Motivationmentioning

confidence: 99%

Function of Content Defined Chunking Algorithms in Incremental Synchronization

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Data chunking algorithms divide data into several small data chunks in a certain way, thus transforming the operation of data into the one of multiple small data chunks. Data chunking algorithms have been widely used in duplicate data detection, parallel computing and other fields, but it is seldom used in data incremental synchronization. Aiming at the characteristics of incremental data synchronization, this paper proposes a novel data chunking algorithm. By dividing two data that need synchronization into small data chunks, comparing the contents of these small data chunks, different ones are the incremental data that need to be found. The new algorithm determines to set a cut-point based on the number of 1 contained in the binary format of all bytes in an interval. Thus it improves the resistance against the byte shifting problem at the expense of the chunk size stability, which makes it more suitable for the incremental data synchronization. Comparing this algorithm with several known classical or state of art algorithms, experiments show that the incremental data found by this algorithm can be reduced by 32%∼57% compared to the others with same changes between two data. The experimental results based on real-world datasets show that PCI improves the calculation speed of classic Rsync algorithm up to 70%, however, with a drawback of increasing the Transmission compression rate up to 11.8%. INDEX TERMS Data synchronization, chunking algorithm, data backup, increment.

show abstract

“…Finally, we examine the scalability [25] of the proposed scheme for small as well as a large number of devices. The energy efficiency (η ) of the system with n number of IoMT devices [26] can be computed as:…”

Section: Performance Evaluationmentioning

confidence: 99%

A Federated Filtering Framework for Internet of Medical Things

Sanyal

Wang

Nour

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

Self Cite

View full text Add to dashboard Cite

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (T olF ) and the local filtering parameter (δ); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95% of the communication cost while maintaining reasonable data privacy and low latency.

show abstract

Improving Quality of Data: IoT Data Aggregation Using Device to Device Communications

Cited by 39 publications

References 25 publications

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Function of Content Defined Chunking Algorithms in Incremental Synchronization

A Federated Filtering Framework for Internet of Medical Things

Contact Info

Product

Resources

About