Parallel Algorithm for Wireless Data Compression and Encryption

As the wireless network has limited bandwidth and insecure shared media, the data compression and encryption are very useful for the broadcasting transportation of big data in IoT (Internet of Things). However, the traditional techniques of compression and encryption are neither competent nor efficient. In order to solve this problem, this paper presents a combined parallel algorithm named “CZ algorithm” which can compress and encrypt the big data efficiently. CZ algorithm uses a parallel pipeline, mixes the coding of compression and encryption, and supports the data window up to 1 TB (or larger). Moreover, CZ algorithm can encrypt the big data as a chaotic cryptosystem which will not decrease the compression speed. Meanwhile, a shareware named “ComZip” is developed based on CZ algorithm. The experiment results show that ComZip in 64 b system can get better compression ratio than WinRAR and 7-zip, and it can be faster than 7-zip in the big data compression. In addition, ComZip encrypts the big data without extra consumption of computing resources.

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Block-Split Array Coding Algorithm for Long-Stream Data Compression

Journal of Sensors ◽

10.1155/2020/5726527 ◽

2020 ◽

Vol 2020 ◽

pp. 1-22

Author(s):

Qin Jiancheng ◽

Lu Yiqin ◽

Zhong Yu

Keyword(s):

Big Data ◽

Data Compression ◽

Compression Ratio ◽

Large Scale ◽

Industrial Revolution ◽

Parallel Implementation ◽

Small Data ◽

Stream Data ◽

Network Bandwidth ◽

General Data

With the advent of IR (Industrial Revolution) 4.0, the spread of sensors in IoT (Internet of Things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. Hence, the study of big data compression is valuable in the field of sensors. A problem is how to compress the long-stream data efficiently with the finite memory of a sensor. To maintain the performance, traditional techniques of compression have to treat the data streams on a small and incompetent scale, which will reduce the compression ratio. To solve this problem, this paper proposes a block-split coding algorithm named “CZ-Array algorithm,” and implements it in the shareware named “ComZip.” CZ-Array can use a relatively small data window to cover a configurable large scale, which benefits the compression ratio. It is fast with the time complexity O(N) and fits the big data compression. The experiment results indicate that ComZip with CZ-Array can obtain a better compression ratio than gzip, lz4, bzip2, and p7zip in the multiple stream data compression, and it also has a competent speed among these general data compression software. Besides, CZ-Array is concise and fits the hardware parallel implementation of sensors.

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Big Data Compression Technology Based on Internet of Vehicles

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v15i01.9773 ◽

2019 ◽

Vol 15 (01) ◽

pp. 85

Author(s):

Guohua Xiong

Keyword(s):

Big Data ◽

Data Compression ◽

Compression Ratio ◽

High Efficiency ◽

Large Data ◽

Internet Of Vehicles ◽

Fixed Threshold ◽

Data Volume ◽

Networking Technology ◽

Vehicle Networking

To ensure the high efficiency of the development of car networking technology, large data compression technology based on car networking was studied. First, RFID technology and vehicle networking, big data technology in vehicle networking, RFID path data compression technology in the Internet of vehicles were introduced. Then, RFID path data compression verification experiments were performed. The results showed that when the data volume was relatively small, there was no obvious change in the compression ratio under the fixed threshold and the threshold change. However, when the amount of data gradually increased, the compression ratio under the condition of changing the threshold was slightly higher than the fixed threshold. Therefore, RFID path big data processing is feasible, and compression technology is efficient.

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Performance evaluation of arithmetic coding data compression for internet of things applications

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v13.i2.pp591-597 ◽

2019 ◽

Vol 13 (2) ◽

pp. 591

Author(s):

Nor Asilah Khairi ◽

Asral Bahari Jambek ◽

Rizalafande Che Ismail

Keyword(s):

Internet Of Things ◽

Data Compression ◽

Compression Ratio ◽

Arithmetic Coding ◽

Font Size ◽

Sea Level Pressure ◽

Memory Space ◽

Stride Interval ◽

Real World Datasets ◽

Iot Devices

<span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span><table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0in 5.4pt 0in 5.4pt;" width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0in 5.4pt 0in 5.4pt; height: 63.4pt;" valign="top" width="387"><p class="MsoNormal" style="margin-top: 6.0pt; text-align: justify;"><span style="font-size: 9.0pt; color: black; mso-bidi-font-style: italic;">Wireless Sensor Network (WSN) is known for its autonomous sensors, where it has been found to be useful during the development of Internet of Things (IoT) devices. However, WSN is also known for its limited energy supply and memory space, as it carries small-sized batteries and memory space. Hence, a data compression approach has been introduced in this paper with the purpose of solving this particular issue. This paper focused on the performance of the Arithmetic Coding algorithm. Temperature (Temp), Sea-Level Pressure (Pressure), stride interval (Stride), and heart rate (BPM) were chosen as the dataset in this project. Based on the results, the compression ratio of Temp, Pressure, Stride, and BPM were 0.428, 0.255, 0.217, and 0.159 respectively. From this analysis, BPM produced the best compression ratio. Undeniably, the Arithmetic Coding algorithm is one of the best methods to compress real-world datasets. Hence, by using this approach, it can reduce the usage of energy and memory space.</span></p></td></tr></tbody></table>

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Financial Big Data Based on Internet of Things and Wireless Network Communication

Wireless Communications and Mobile Computing ◽

10.1155/2021/8944618 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Lingqi Xue

Keyword(s):

Big Data ◽

Internet Of Things ◽

Wireless Communication ◽

Wireless Network ◽

Social Utility ◽

Network Communication ◽

The Internet ◽

The Social ◽

Internet Of Things Technology ◽

The Internet Of Things

With the advent of the era of big data, Internet of things technology and wireless communication technology have been in a state of rapid development. Opportunities and challenges in all walks of life are being subverted. Financial management, as the foundation of corporate governance, is important for improving economic efficiency and achieving sustainable business development which plays an important role. In order to realize the management and classification of financial big data, better identify the financial data of different enterprises, strengthen the safe storage of financial information, and provide early warning for the security issues involved, this article is based on the Internet of things and wireless communication networks. In the method part, this article introduces the framework of the Internet of things, Bluetooth, and infrared data transmission in wireless network communication and the principles of financial big data. The algorithm introduces a single-user MIMO system, free space propagation, and spectrum and energy efficiency. The analysis part analyzes the spectrum efficiency of different algorithms, social utility, average number of retransmissions, comprehensive scores of competitiveness in various fields of the Internet of things, and the significance of financial indicators. By comparing the data, it can be seen that the algorithm in this paper is superior to the two algorithms of IAN-CoMP and IA-CoMP. When the number of users is 100, the social utility of the algorithm in this paper is 4.45, while IAN-CoMP is 3.43 and IA-CoMP is 3.67. When the number of users increases to 700, the social utility of the algorithm in this paper is 28.34. The other two algorithms are, respectively, 24.45 and 25.99, and we know that the social utility of the algorithm in this paper is the best. Through comprehensive analysis, it is concluded that the financial big data model based on the Internet of things and wireless network communication in this paper can better realize data management and collection, so as to meet the needs of information developers.

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