Mobile Edge and Real-Time Data-Driven Innovations
Jari Collin
Adjunct Professor at Aalto University and CTO at Telia Finland
Search for more papers by this authorJari Collin
Adjunct Professor at Aalto University and CTO at Telia Finland
Search for more papers by this authorSummary
Industry 4.0 will transform asset-intensive industries by digitalizing processes and systems across the entire value chain. This transformation will have a major impact on the operational technology (OT) environment, where it is crucial that enterprises can manage the collection, integration, and analysis of all types of data from multiple systems, sensors, and assets. Edge computing creates a basis for new data-driven innovations and use cases that also fulfill the key industrial requirements on data sovereignty, cybersecurity, resilience, and latency. However, enterprises need a good strategy for decentralizing computing power to keep pace with the growing demand for real-time data analysis and automation. The drastic increase in data collection, processing, and analysis imposes a heavy workload on the infrastructure and creates the need for the future mobile edge-driven innovations and the strategic planning enabling these actions for a successful digital transformation.
References
- T. Qiu , J. Chi , X. Zhou , Z. Ning , M. Atiquzzaman and D. Wu , “ Edge computing in industrial internet of things: Architecture, advances and challenges ,” IEEE Communications Surveys & Tutorials , vol. 22 , no. 4 , pp. 2462 – 2488 , 2020 .
- T. Tran , A. Hajisami , P. Pandey and D. Pompili , “ Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges ,” IEEE Communications Magazine , vol. 55 , no. 4 , pp. 54 – 61 , 2017 .
- IDC , “ Spending Guide Forecasts Double-Digit Growth for Investments in Edge Computing ,” 2022 .
-
Y. Chen
,
N. Zhang
,
Z. Wu
and
S. Shen
,
Energy Efficient Computation Offloading in Mobile Edge Computing, Wireless Networks
,
Switzerland
:
Springer Nature
,
2022
.
10.1007/978-3-031-16822-2 Google Scholar
- N. Hassan , K. Yau and C. Wu , “ Edge computing in 5G: A review ,” IEEE Access , vol. 7 , pp. 127276 – 127289 , 2019 .
- W. Yu , F. Liang , X. He , W. Hatcher , C. Lu , J. Lin and X. Yang , “ A survey on the edge computing for the Internet of Things ,” IEEE Access , vol. 6 , pp. 6900 – 6919 , 2017 .
- World Economic Forum , “ Digital Transformation Initiative: Mining and Metals Industry – White Paper ,” World Economic Forum , 2017 .
- World Economic Forum , “ The Impact of 5G: Creating New Value Across Industries and Society – White Paper ,” World Economic Forum , 2020 .
- W. Dai , H. Nishi , V. Vyatkin , V. Huang , Y. Shi and X. Guan , “ Industrial edge computing: Enabling embedded intelligence ,” IEEE Industrial Electronics Magazine , vol. 13 , no. 4 , pp. 48 – 56 , 2019 .
- A. Abdellatif , A. Mohamed , C. Chiasserini , M. Tlili and A. Erbad , “ Edge computing for smart health: Context-aware approaches, opportunities, and challenges ,” IEEE Network , vol. 33 , no. 3 , pp. 196 – 203 , 2019 .
-
F. S. Giust
,
V. Sciancalepore
,
D. Sabella
,
M. C. Filippou
,
S. Mangiante
,
W. Featherstone
and
D. Munaretto
, “
Multi-access edge computing: The driver behind the wheel of 5G-connected cars
,”
IEEE Communications Standards Magazine
, vol.
2
, no.
3
, pp.
66
–
73
,
2018
.
10.1109/MCOMSTD.2018.1800013 Google Scholar
- M. Uddin , M. Ayaz , A. Mansour , E. Aggoune , Z. Sharif and I. Razzak , “ Cloud-connected flying edge computing for smart agriculture ,” Peer-to-Peer Networking and Applications , vol. 14 , no. 6 , pp. 3405 – 3415 , 2021 .
- J. Collin and A. Saarelainen , Teollinen Internet , Helsinki : Talentum Pro , 2016 .