Citation
Satheeskumaran, S., Zhang, Y.-D. and Pelusi, D. (2021), "Guest editorial", International Journal of Pervasive Computing and Communications, Vol. 17 No. 5, pp. 445-446. https://doi.org/10.1108/IJPCC-11-2021-216
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited
Intelligent edge and 5G technologies for pervasive computing and communications
Smartphones, sensors, wearable devices, smart home products and connected devices have become a part of our daily lives. Mobile and pervasive computing are essential to deal with the rapid increase in the usage of embedded devices and the Internet of Things. 5G mobile networks are expected to revolutionize the entire world in the near future, and there are a lot of challenges and opportunities in 5G implementation. 5G mobile communication is fast and energy-efficient, which aims to support the billions of connected devices. Edge devices provide a lot of new opportunities in pervasive mobile computing applications. Intelligent edge computing has become popular in recent years due to increased bandwidth, low latency and smart decision-making capability. Integrating edge analytics with 5G mobile networks is critical for reliability and speed.
The special issue focuses on applying intelligent edge analytics and 5G technologies to pervasive computing and communication systems. The first paper, titled “A non-linear mathematical model-based routing protocol for WBAN based Healthcare Systems,” focuses on reducing energy consumption and maximizing the data transmission rate in WBANs. The second paper, “Hyperparameter Tuning of AdaBoost Algorithm for Social Spammer Identification,” proposes a hybrid modified whale optimization algorithm for spam profile detection. The proposed method reduces the server load by excluding complex features and retaining only lightweight features. The third paper, “Lifetime Ratio Improvement Technique Using Special Fixed Sensing Points in Wireless Sensor Networks,” aims to improve the lifetime ratio of wireless sensor networks by maintaining the battery level at the desired point for better network health. The fourth paper, “Heal Nodes Specification Improvement using Modified CHEF method for Group based Detection Point Network”, attempts to resolve the network lifetime problems during the communication of detection points over a period of time. It was developed to increase the lifetime ratio, throughput, residual energy and number of alive nodes.
In the fifth paper “Industrial IoT Enabled Fuzzy Logic Based Flame Image Processing for Rotary Kiln Control”, a fuzzy logic rule-based analysis is proposed to measure temperature using a burning flame image in which it considers red, green and blue (RGB) magnitude planes. In the sixth paper, “IoT Based Lung Cancer Detection Using Machine Learning and Cuckoo Search Optimization,” IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. The next paper, “Safety Driven Intelligent Autonomous Vehicles for Smart Cities using IoT” focuses on developing IoT-based intelligent and safe autonomous vehicles for deployment in smart cities. In the eighth paper, “RNN Based Multispectral Satellite Image Processing for Remote Sensing Applications”, a deep learning-based automated method is presented for classifying multispectral images. In the next paper, “Enhanced RSA key encryption application for metering data in Smart Grid”, the authentication of the smart metre data has been proposed with enhanced RSA key encryption using an efficient way of generating large prime numbers. This efficient generation of prime numbers can be successfully applied to the smart metre systems, thereby increasing the strength and speed of the key encryption. The final paper, “Design of Low Power SRAM Based Ubiquitous Sensors for Wireless Body Area Networks,” focuses on developing smart Ubiquitous Sensors for deployment in wireless body area networks to improve digital healthcare services.
About the authors
S. Satheeskumaran received PhD degree in Information and Communication Engineering in 2016. Currently, he serves as a professor in the Department of Electronics and Communication Engineering, Anurag Group of Institutions, Hyderabad, India. He is a guest editor for Inderscience and Springer journals and served as a volume editor for Springer conference proceedings. He has research and teaching experience of more than 18 years, and his research interests include Internet of Things, machine learning, biomedical signal processing and artificial intelligence-based health care applications. He received research grants from government and private funding agencies and was recipient of the best faculty award and emerging researcher award for his contribution towards teaching and research. He has published more than 50 research papers in SCI/Scopus journals and reputed conferences. He is a reviewer for IEEE, Elsevier, Springer, Taylor & Francis, Wiley and IGI Global journals and reviewed more than 200 research papers. He also served as keynote speaker, advisory committee member and session chair for IEEE/Springer international conferences.
Yu-Dong Zhang received his PhD degree from Southeast University in 2010. He worked as postdoc from 2010 to 2012 at Columbia University, USA, and as an assistant research scientist from 2012 to 2013 at the Research Foundation of Mental Hygiene, USA. He served as a full professor from 2013 to 2017 in Nanjing Normal University, where he was the director and founder of Advanced Medical Image Processing Group in NJNU. Now he serves as professor in the Department of Informatics, University of Leicester, UK form June/2017. He was included in “Most Cited Chinese researchers (Computer Science)” from 2014 to 2017. He published over 160 papers, and 16 were included in “ESI Highly Cited Papers” and two were included in “ESI Hot Papers”. His citation reached 9,286 in Google Scholar and 4,943 in Web of Science. He is the fellow of IET and a senior member of IEEE and ACM. He is the editor of Scientific Reports, IEEE Transactions on Circuits and Systems for Video Technology, etc. He served as the guest editor of Neural Networks, IEEE Transactions on Intelligent Transportation Systems, etc. He has conducted many successful academic grants and industrial projects.
Danilo Pelusi received a PhD degree in Computational Astrophysics from the University of Teramo, Italy. Now he serves as an associate professor at the Faculty of Communication Sciences, University of Teramo. He is an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, International Journal of Machine Learning and Cybernetics (Springer) and Array (Elsevier). He served as Guest Editor for Elsevier, Springer and Inderscience journals, program member of many conferences and editorial board member of many journals. He is a reviewer for many reputed journals such as IEEE Transactions on Fuzzy Systems and Neural Networks and Machine Leaning, and conferences such as IEEE Congress on Evolutionary Computation. His research interests include fuzzy Logic, neural networks, information theory, machine learning and evolutionary algorithms.