TY - GEN
T1 - User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization
AU - Tiausas, Francis
AU - Talusan, Jose Paolo
AU - Ishimaki, Yu
AU - Yamana, Hayato
AU - Yamaguchi, Hirozumi
AU - Bhattacharjee, Shameek
AU - Dubey, Abhishek
AU - Yasumoto, Keiichi
AU - Das, Sajal K.
N1 - Funding Information:
This work was supported by R&D for Trustworthy Networking for Smart and Connected Communities, Commissioned Research of National Institute of Information and
Funding Information:
Communications Technology (NICT) and National Science Foundation through award numbers 1647015, 1818901, CNS-1818942, SaTC-2030624, SaTC-2030611. REFERENCES
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.
AB - The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.
KW - Distributed route planning
KW - Edge computing
KW - Multi-objective Optimization
KW - NSGA-II
KW - Smart cities
UR - http://www.scopus.com/inward/record.url?scp=85117580078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117580078&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP52413.2021.00031
DO - 10.1109/SMARTCOMP52413.2021.00031
M3 - Conference contribution
AN - SCOPUS:85117580078
T3 - Proceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021
SP - 77
EP - 82
BT - Proceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Smart Computing, SMARTCOMP 2021
Y2 - 23 August 2021 through 27 August 2021
ER -
