EconPapers    
Economics at your fingertips  
 

Near-Edge Computing For Industry 5.0

Megha Sharma (), Abhinav Tomar () and Abhishek Hazra
Additional contact information
Megha Sharma: Netaji Subhas University of Technology Delhi
Abhinav Tomar: Netaji Subhas University of Technology Delhi
Abhishek Hazra: Indian Institute of Information Technology Sricity

A chapter in Industry 5.0, 2025, pp 505-528 from Springer

Abstract: Abstract Industry 5.0 is a new chapter in the history of the industrial revolution, moving away from Industry 4.0’s automation-centric approach and towards a more human-centric paradigm. Industry 5.0 aims to balance machine precision and human inventiveness, encouraging human-machine collaboration. By maximising resource utilisation and minimising environmental effects, this change promotes sustainability initiatives and enables increased customisation in manufacturing. Near-edge computing, which decentralises data processing by bringing it closer to the data source, greatly reduces latency and improves response times in crucial industrial operations, a major enabler of this shift. Near-edge computing plays a vital role in realising the vision of Industry 5.0. In contrast to conventional cloud-based architectures, near-edge computing guarantees real-time decision-making. This chapter explores the function of near-edge computing. It emphasises how it can provide faster feedback loops, increase productivity, and promote human-machine collaboration in dynamic industrial environments. We look at real-world use scenarios to show how near-edge computing might help achieve Industry 5.0’s main objectives: energy-efficient production and real-time monitoring in smart factories. The chapter thoroughly reviews this developing topic and discusses possible research difficulties, future trends, and ethical considerations surrounding integrating near-edge technology in human-centric industrial systems.

Keywords: Near-edge computing; Industry 5.0; IoT; Predictive maintenance; Low latency; Autonomous system (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-87837-4_21

Ordering information: This item can be ordered from
http://www.springer.com/9783031878374

DOI: 10.1007/978-3-031-87837-4_21

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-07-21
Handle: RePEc:spr:sprchp:978-3-031-87837-4_21