Rajesh Kaluri¹, Dharmendra Singh Rajput¹, Qin Xin^2,*, Kuruva Lakshmanna¹, Sweta Bhattacharya¹, Thippa Reddy Gadekallu¹, Praveen Kumar Reddy Maddikunta¹

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 453-469, 2021, DOI:10.32604/iasc.2021.014369 - 18 January 2021

Abstract Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from… More >

Ahmad F. Subahi^*

Computer Systems Science and Engineering, Vol.36, No.1, pp. 13-39, 2021, DOI:10.32604/csse.2021.014376 - 23 December 2020

Abstract This work presents the design of an Internet of Things (IoT) edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places. A wireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design. A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design. A Neo4j graph database is used as a target implementation generated from the proposed transformational system… More >

Yangyang Li¹, Xue Wang², Weiwei Fang^2,*, Feng Xue², Hao Jin¹, Yi Zhang¹, Xianwei Li³

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 493-508, 2019, DOI:10.32604/cmc.2019.05178

Abstract With the recent proliferation of Internet-of-Things (IoT), enormous amount of data are produced by wireless sensors and connected devices at the edge of network. Conventional cloud computing raises serious concerns on communication latency, bandwidth cost, and data privacy. To address these issues, edge computing has been introduced as a new paradigm that allows computation and analysis to be performed in close proximity with data sources. In this paper, we study how to conduct regression analysis when the training samples are kept private at source devices. Specifically, we consider the lasso regression model that has been More >