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Cost efficiency optimization for industrial automation

  • Hafiz Husnain Raza Sherazi
  • , Luigi Alfredo Grieco
  • , Gennaro Boggia
  • , Muhammad A. Imran
  • Polytechnic University of Bari
  • University of Glasgow

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

This chapter aims at shedding light on low power technologies as the key enablers for industrial automation. It discusses the different types of costs involved in harsh industrial environments, with a focus on the damage penalty and battery replenishment costs for the sensor motes deployed across the production line. It analyses the reciprocal relationship and trade-off analysis for these costs to suggest some optimization strategies for ultimate production efficiency. The chapter presents an overview of the aforementioned low energy networking protocols and highlights the key technologies and the results of long range wide area networks evaluation. Energy harvested from renewable energy sources, available in the industrial environment, can dramatically play its role to improve both performance and production efficiency in industry 4.0. Harvesting sources available in the industry are considered to demonstrate the huge cost savings and the great impact of the availability of this surplus energy on the industrial cost optimization process.

Original languageEnglish
Title of host publicationWireless Automation as an Enabler for the Next Industrial Revolution
Publisherwiley
Pages141-171
Number of pages31
ISBN (Electronic)9781119552635
ISBN (Print)9781119552611
DOIs
StatePublished - 27 Dec 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Cost efficiency optimization
  • Industrial automation
  • Industry 4.0
  • Long range wide area networks
  • Sensor nodes
  • Surplus energy

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