Design an efficient internet of things data compression for healthcare applications

Abstract

The internet of things (IoT) is an ecosystem of connected objects that are accessible and available through the internet. This "thing" in the IoT could be a sensor such as a heart monitor, temperature, and oxygen rate in the blood. These sensors produce huge amounts of information that lead to congestion and an effect on bandwidth in the IoT network. In this paper, the proposed system is based on the Zstandard compression algorithm to compress the sensor data to minimize the amount of data transmitted from the IoT level to the fog level and decrease network overloading. The proposed system was evaluated using compression ratio, throughput, and latency time for healthcare applications. The result showed better calculation through decreased response time and increased throughput for transmitted data compared with the case of non-compressed data. It showed the compression data ratio about 70% of orignial data, maximum number of IoT sensor reads as 100, throughput is 85.43 B/ms, and fog processing delay is 6.25 ms.

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System

“…Two of these works acknowledge the existence of these algorithms but employ a different approach [ 8 , 9 ]. One paper [ 10 ] refers to [ 11 ], presenting an approach that uses Lempel–Ziv–Welch as one of the compression steps. There is also a highly specialized paper, with a signal processing use case, that uses Lempel–Ziv–Welch to encode data [ 12 ].…”

“…The previous papers [ 10 , 11 , 14 , 15 , 16 , 17 , 18 , 19 ] on this topic have primarily focused on compression algorithms such as LZW and its variations (e.g., s-LZW) and have often employed hardware with substantial computing power, such as the Raspberry Pi 4B with 8 GB RAM. In contrast, this paper introduces, as the main contribution and novelty of our work, the following new elements: the evaluation of canonical Huffman, Lempel–Ziv 77, Lempel–Ziv 78, and Lempel–Ziv–Welch algorithms in terms of energy and time efficiency for compressing and transmitting text data and the evaluation of the Joint Photographic Experts Group algorithm in terms of energy and time efficiency for compressing and transmitting image data.…”

Performance evaluation of RYU controller under distributed denial of service attacks

“…Consequently, a controller attack in an SDN environment can have a much bigger impact than a comparable attack on a traditional environment. DoS and DDoS [12], [13] attacks have reportedly been undertaken against a number of SDN controllers [14], including FloodLight, NOX, and RYU [15]- [17], among others, according to past studies. To the best of our knowledge, no in-depth research has been conducted on DoS and DDoS attacks conducted via POX and RYU controllers.…”