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Innovations in Device-to-Device Communication for Mechanical Tool Optimization
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Author(s): P. Sreenivas (Department of Mechanical Engineering, K.S.R.M. College of Engineering, Kadapa, India), Divakar Harekal (Department of Information Science and Engineering, Jyothy Institute of Technology, Bengaluru, Iran), T. K. S. Rathish Babu (Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India), Sudheer Kumar Battula (Department of Mechanical Engineering, Lakireddy Balireddy College of Engineering, Krishna District, India)and J. Ramya (Easwari Engineering College, Chennai, India)
Copyright: 2025
Pages: 22
Source title:
Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials
Source Author(s)/Editor(s): Kamalakanta Muduli (Papua New Guinea University of Technology, Papua New Guinea), Bikash Ranjan Moharana (Papua New Guinea University of Technology, Papua New Guinea), Steve Korakan Ales (Papua New Guinea University of Technology, Papua New Guinea)and Dillip Kumar Biswal (Aryan Institute of Engineering and Technology, Bhubaneswar, India)
DOI: 10.4018/979-8-3693-7974-5.ch022
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Abstract
This chapter explores the emerging innovations in Device-to-Device (D2D) communication aimed at optimizing the performance and efficiency of mechanical tools. D2D communication allows mechanical tools and devices to directly exchange data without relying on centralized networks, enabling faster, more reliable interactions. The chapter delves into various technologies such as 5G, Internet of Things (IoT), and edge computing, highlighting their role in enhancing real-time communication, predictive maintenance, and autonomous tool operations. Additionally, it examines the challenges and solutions related to data security, energy efficiency, and interoperability in D2D networks. Case studies and applications in industries such as manufacturing, automotive, and construction are presented to illustrate the practical benefits of these innovations. The chapter concludes by discussing future trends, including the integration of artificial intelligence and machine learning in D2D systems, which promises to further revolutionize mechanical tool optimization.
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