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Making Cities Smarter-Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications,
Abbas Shah Syed1, Daniel Sierra-Sosa2, Anup Kumar1
1Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40208, USA.
Sensors (Basel, Switzerland)
|June 24, 2022
Summary
This review explores combinatorial optimization methods for smart cities, focusing on Internet of Things (IoT) applications. It maps popular algorithms like genetic algorithms and particle swarm optimization to smart city challenges, offering a research starting point.
Area of Science:
- Computer Science
- Urban Planning
- Engineering
Background:
- Smart city development aims to optimize urban resource management amidst growing populations.
- While Artificial Intelligence (AI) is widely studied, combinatorial optimization in IoT-enabled smart cities remains under-explored.
Purpose of the Study:
- To review and map combinatorial optimization methods and their applications within Internet of Things (IoT) smart cities.
- To consolidate research on optimization techniques for smart city resource management.
Main Methods:
- The review covers five popular optimization methods: ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization, and differential evolution.
- It maps these algorithms to identified smart city applications, detailing algorithms, objectives, formulations, and constraints.
Main Results:
- A comprehensive overview of computational optimization applications in IoT smart cities is provided.
- Specifics on algorithm usage, objectives, formulations, constraints, and data setups for each application are discussed.
Conclusions:
- This review offers a consolidated starting point for researchers in smart city application optimization.
- It highlights the potential of combinatorial optimization techniques for enhancing smart city resource management.
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