scholarly journals Big Data Reduction and Optimization in Sensor Monitoring Network

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bin He ◽  
Yonggang Li
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Data Communication ◽  
Monitoring Network ◽  
Temporal Correlation ◽  
Network Size ◽  
Sensor Nodes ◽  
Underground Tunnel ◽  
Efficient Data ◽  
Subway Tunnels

Wireless sensor networks (WSNs) are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.

scholarly journals A Lossless Compression Approach Based on Delta Encoding and T-RLE in WSNs

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Abdeldjalil Saidani ◽  
Xiang Jianwen ◽  
Deloula Mansouri
Keyword(s):  
Data Compression ◽  
Power Saving ◽  
Data Communication ◽  
Lossless Compression ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Limited Capacity ◽  
Wireless Sensor Nodes ◽  
Scope Project ◽  
Capacity Data

The sending/receiving of data (data communication) is the most power consuming in wireless sensor networks (WSN) since the sensor nodes are depending on batteries not generally rechargeable characterized by limited capacity. Data compression is among the techniques that can help to reduce the amount of the exchanged data between wireless sensor nodes resulting in power saving. Nevertheless, there is a lack of effective methods to improve the efficiency of data compression algorithms and to increase nodes’ energy efficiency. In this paper, we proposed a novel lossless compression approach based on delta encoding and two occurrences character solving (T-RLE) algorithms. T-RLE is an optimization of the RLE algorithm, which aims to improve the compression ratio. This method will lead to less storage cost and less bandwidth to transmit the data, which positively affects the sensor nodes’ lifetime and the network lifetime in general. We used real deployment data (temperature and humidity) from the sensor scope project to evaluate the performance of our approach. The results showed a significant improvement compared with some traditional algorithms.

scholarly journals Stream-Based Lossless Data Compression Applying Adaptive Entropy Coding for Hardware-Based Implementation

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 159 ◽  
Author(s):  
Shinichi Yamagiwa ◽  
Eisaku Hayakawa ◽  
Koichi Marumo
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Data Stream ◽  
High Speed ◽  
Data Communication ◽  
Streaming Data ◽  
Entropy Coding ◽  
Big Data Applications ◽  
Lossless Data Compression ◽  
Compressed Data

Toward strong demand for very high-speed I/O for processors, physical performance growth of hardware I/O speed was drastically increased in this decade. However, the recent Big Data applications still demand the larger I/O bandwidth and the lower latency for the speed. Because the current I/O performance does not improve so drastically, it is the time to consider another way to increase it. To overcome this challenge, we focus on lossless data compression technology to decrease the amount of data itself in the data communication path. The recent Big Data applications treat data stream that flows continuously and never allow stalling processing due to the high speed. Therefore, an elegant hardware-based data compression technology is demanded. This paper proposes a novel lossless data compression, called ASE coding. It encodes streaming data by applying the entropy coding approach. ASE coding instantly assigns the fewest bits to the corresponding compressed data according to the number of occupied entries in a look-up table. This paper describes the detailed mechanism of ASE coding. Furthermore, the paper demonstrates performance evaluations to promise that ASE coding adaptively shrinks streaming data and also works on a small amount of hardware resources without stalling or buffering any part of data stream.

A New Rockburst Experiment Data Compression Storage Algorithm based on Big Data Technology

2019 ◽  
pp. 561-573
Author(s):  
Yu Zhang ◽  
Yan-Ge Wang ◽  
Yan-Ping Bai ◽  
Yong-Zhen Li ◽  
Zhao-Yong Lv ◽  
...  
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Experiment Data ◽  
Big Data Technology

scholarly journals A Remote Monitoring System of Logistics Carrier Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 4
Author(s):  
Wang Weidong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Remote Monitoring System ◽  
Monitoring Center

To improve the efficiency of the remote monitoring system for logistics transportation, we proposed a remote monitoring system based on wireless sensor network and GPRS communication. The system can collect information from the wireless sensor network and transmit the information to the ZigBee interpreter. The monitoring system mainly includes the following parts: Car terminal, GPRS transmission network and monitoring center. Car terminal mainly consists by the Zigbee microcontroller and peripherals, wireless sensor nodes, RFID reader, GPRS wireless communication module composed of a micro-wireless monitoring network. The information collected by the sensor communicates through the GPRS and the monitoring center on the network coordinator, sends the collected information to the monitoring center, and the monitoring center realizes the information of the logistics vehicle in real time. The system has high applicability, meets the design requirements in the real-time acquisition and information transmission of the information of the logistics transport vehicles and goods, and realizes the function of remote monitoring.

Energy Efficient Data Compression in Cloud based IoT

2021 ◽  
pp. 1-1
Author(s):  
Halah Mohammed Al-Kadhim ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Data Compression ◽  
Energy Efficient ◽  
Efficient Data

Big Data Compression in Smart Grids via Optimal Singular Value Decomposition

2020 ◽  
Author(s):  
Seyed Naser Hashemipour ◽  
Jamshid Aghaei ◽  
Abdullah Kavousi-fard ◽  
Taher Niknam ◽  
Ladan Salimi ◽  
...  
Keyword(s):  
Big Data ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Smart Grids ◽  
Singular Value ◽  
Value Decomposition

Technology Research on the Fault Diagnosis of Wireless Sensor Network System

2013 ◽  
Vol 846-847 ◽  
pp. 442-445
Author(s):  
Chun Lin He
Keyword(s):  
Fault Diagnosis ◽  
Sensor Network ◽  
Simulation Experiment ◽  
Temporal Correlation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network System ◽  
Technology Research ◽  
Spatio Temporal ◽  
Network Fault

The fault diagnosis technology have emerged and developed rapidly with the development of wireless sensor networks and requirements of applications improve. This paper describes two commonly used sensor network fault modeling. What is more, in order to solve this problem that sensor nodes are vulnerable and therefore produce wrong data, the paper proposes a distributed fault detecting algorithm based on spatio-temporal correlation among data of adjacent nodes. The simulation experiment shows that the algorithm can efficiently detect errors in the network and very few errors are introduced.

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