TY - GEN
T1 - Evaluating Urban Environments for the Integration of Cutting-Edge Technologies Enhances Smart Cities' Evolution
AU - Andreou, Andreas
AU - Mavromoustakis, Constandinos X.
AU - Batalla, Jordi Mongay
AU - Markakis, Evangelos
AU - Mastorakis, George
AU - Chatzimisios, Periklis
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The endeavours to interpret the acquired data are combined with the efforts to strengthen the smart city's multidimensional framework. As the name implies, smart cities are built atop more intelligent data. However, it is a significant challenge because Big Data needs to be evaluated to provide interpretation for a posterior evolution of the current technology. Therefore, using the right building blocks is vital, aligned with clear and convincing guidelines on best practices. To achieve a scale of evaluation, we need standards. The intertwining development drivers need to define how we see and measure the world around us and how this Big Data in the era of IoT informs the decision-making processes. Hence, we are introducing an evaluation model for Big Data obtained from the assessment of Quality of Service (QoS) and Quality of Experience (QoE) delivery in an urban environment. Using the Best-Worst Method (BWM) combined with the orientation of Intuitionistic fuzzy sets. We obtained intuitive preference information based on various criteria. Thus, by prioritizing these end-user predilections and transmitting them into adaptable technological improvements, we achieved a significant step toward sustainable Smart Cities.
AB - The endeavours to interpret the acquired data are combined with the efforts to strengthen the smart city's multidimensional framework. As the name implies, smart cities are built atop more intelligent data. However, it is a significant challenge because Big Data needs to be evaluated to provide interpretation for a posterior evolution of the current technology. Therefore, using the right building blocks is vital, aligned with clear and convincing guidelines on best practices. To achieve a scale of evaluation, we need standards. The intertwining development drivers need to define how we see and measure the world around us and how this Big Data in the era of IoT informs the decision-making processes. Hence, we are introducing an evaluation model for Big Data obtained from the assessment of Quality of Service (QoS) and Quality of Experience (QoE) delivery in an urban environment. Using the Best-Worst Method (BWM) combined with the orientation of Intuitionistic fuzzy sets. We obtained intuitive preference information based on various criteria. Thus, by prioritizing these end-user predilections and transmitting them into adaptable technological improvements, we achieved a significant step toward sustainable Smart Cities.
KW - Best-Worst method
KW - Big Data
KW - intuitionistic fuzzy sets
KW - QoE
KW - QoS
KW - Smart City
UR - http://www.scopus.com/inward/record.url?scp=85178294069&partnerID=8YFLogxK
U2 - 10.1109/ICC45041.2023.10278752
DO - 10.1109/ICC45041.2023.10278752
M3 - Conference contribution
AN - SCOPUS:85178294069
T3 - IEEE International Conference on Communications
SP - 3327
EP - 3332
BT - ICC 2023 - IEEE International Conference on Communications
A2 - Zorzi, Michele
A2 - Tao, Meixia
A2 - Saad, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Communications, ICC 2023
Y2 - 28 May 2023 through 1 June 2023
ER -