Traffic reduction technologies and data aggregation control to minimize latency in IoT systems

Improved priority-based data aggregation congestion control protocol

Modern Physics Letters B ◽

10.1142/s0217984920500293 ◽

2019 ◽

Vol 34 (02) ◽

pp. 2050029

Author(s):

Prabhdeep Singh ◽

Anuj Kumar Gupta ◽

Ravinder Singh

Keyword(s):

Network Lifetime ◽

Data Aggregation ◽

Energy Efficient ◽

Sensor Nodes ◽

Control Protocol ◽

Wide Range ◽

Token Bucket ◽

Aggregation Control ◽

Computing Approach ◽

Energy Efficient Protocols

With advancement in multimedia applications, Wireless sensor networks (WSNs) are becoming popular due to their inherent characteristics and wide range of applications. However, WSNs contain very small sensor nodes, these nodes are battery constrained. Also, the batteries of these sensor nodes are not either replaceable or rechargeable. Therefore, many energy efficient protocols have been implemented so far to improve the network lifetime. However, the data aggregation at sink may suffer from data flooding issue, which reduces the network lifetime of WSNs. For handling this issue, in this paper, an effective data aggregation approach is designed. We have designed a priority-based data aggregation control protocol, which considers token bucket, Lempel–Ziv–Welch (LZW) compression and a hybrid of ant colony optimization and particle swarm optimization-based soft computing approach. Extensive experiments reveal that the presented protocol provides better network lifetime in contrast to the existing energy efficient protocols.

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HDAC High-Dimensional Data Aggregation Control Algorithm for Big Data in Wireless Sensor Networks

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2017100105 ◽

2017 ◽

Vol 12 (4) ◽

pp. 72-86 ◽

Cited By ~ 3

Author(s):

Zeyu Sun ◽

Xiaohui Ji

Keyword(s):

Big Data ◽

Data Aggregation ◽

Control Algorithm ◽

Dimensional Space ◽

High Dimensional Data ◽

High Dimensional ◽

High Dimensional Space ◽

Significant Difference ◽

Low Dimensional ◽

Aggregation Control

The process of high-dimensional data is a hot research area in data mining technology. Due to sparsity of the high-dimensional data, there is significant difference between the high-dimensional space and the low-dimensional space, especially in terms of the data process. Many sophisticated algorithms of low-dimensional space cannot achieve the expected effect, even cannot be used in the high-dimensional space. Thus, this paper proposes a High-dimensional Data Aggregation Control Algorithm for Big Data (HDAC). The algorithm uses information to eliminate the dimension not matching with the specified requirements. Then it uses the principal components method to analyze the rest dimension. Thus, the simplest method is used to reduce the calculation of dimensionality reduction as can as it possible. In the process of data aggregation, the self-adaptive data aggregation mechanism is used to reduce the phenomenon of network delay. Finally, the simulation shows that the algorithm can improve the performance of node energy-consumption, rate of the data post-back and the data delay.

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HDAC High-Dimensional Data Aggregation Control Algorithm for Big Data in Wireless Sensor Networks

Sensor Technology ◽

10.4018/978-1-7998-2454-1.ch014 ◽

2020 ◽

pp. 286-300

Author(s):

Zeyu Sun ◽

Xiaohui Ji

Keyword(s):

Big Data ◽

Data Aggregation ◽

Control Algorithm ◽

Dimensional Space ◽

High Dimensional Data ◽

High Dimensional ◽

High Dimensional Space ◽

Components Method ◽

Low Dimensional ◽

Aggregation Control

The process of high-dimensional data is a hot research area in data mining technology. Due to sparsity of the high-dimensional data, there is significant difference between the high-dimensional space and the low-dimensional space, especially in terms of the data process. Many sophisticated algorithms of low-dimensional space cannot achieve the expected effect, even cannot be used in the high-dimensional space. Thus, this paper proposes a High-dimensional Data Aggregation Control Algorithm for Big Data (HDAC). The algorithm uses information to eliminate the dimension not matching with the specified requirements. Then it uses the principal components method to analyze the rest dimension. Thus, the simplest method is used to reduce the calculation of dimensionality reduction as can as it possible. In the process of data aggregation, the self-adaptive data aggregation mechanism is used to reduce the phenomenon of network delay. Finally, the simulation shows that the algorithm can improve the performance of node energy-consumption, rate of the data post-back and the data delay.

Download Full-text