Mukesh Sahu

As the Internet of Things (IoT) maintains to increase the inexperienced control of the huge amounts of records generated becomes increasingly more crucial. One of the major issues is big energy intake associated with transmitting records throughout networks. Addressing this issue is vital for the sustainability and feasibility of IoT infrastructures, mainly in packages stressful actual-time records processing and assessment. This paper targets to introduce a completely unique, energy-green technique for IoT compression that minimizes strength intake at some stage in records transmission. By leveraging edge computing, that seeks the machine data closer to its supply, thereby decreasing transmission distances and related electricity costs. A three-layered framework is introduced to achieve lossless compression by capturing network packets of different data sizes. The framework comprises IoT layer, Edge layer and Cloud layer. The framework is carried out at the brink of the community, enhancing statistics, decreasing power consumption, and ensuring security from cyber threats. The results are evaluated using metrics affecting data compression such as Root Mean Squared Error (RMSE) and Peak Signal to Noise Ratio (PSNR). The experimental results show that the proposed compression approach achieves the lowest power consumption rate as 80%, 85%, 90% and 88% in case of image, sensor, financial and textual data types respectively. Furthermore, the proposed framework achieves the highest PSNR value (92.14) and the lowest RMSE value (0.6653) thereby validating the performance of the given IoT-based framework. It shows that the proposed approach is better than existing compression techniques and recent review studies.

The most efficient use of storage space is anticipated to be made possible by the adoption of one or more data compression techniques, which are expected to increase transmission bandwidth. The majority of IoT-based devices may store data on the cloud, which emphasizes the importance of making the best use of already-available storage. Due to these and other factors, data reduction is a crucial topic for IoT vision research. The main idea of our proposed work is to develop an IoT-based Smart healthcare management framework to achieve lossless data compression with the help of ontology. In this proposed work, a framework is divided into three modules namely pre-processing, ontology, and compression module. The sampling is done with the help of a healthcare analytics dataset taken from the Kaggle repository. The results are evaluated using metrics affecting data compression. The results obtained showed that the average compression ratio and average compression factor of the proposed work are 0.69 and 1.40 which is much better than existing recent works and compression algorithms. Furthermore, the proposed framework achieves highest compression percentage (31%), lesser time to compress original data (5.12 sec), highest peak signal to noise ratio value (91.18) and the lowest root mean squared error value (0.7853) thereby validating the performance of the given IoT-based framework. It shows that the proposed approach is better than existing compression techniques and recent review studies.