Title: EdgeSmart: Hybrid Evolutionary Optimization for Edge-AI in Smart Cities
Authors:
Sayamuddin Ahmed Jilani
Department of Computer Science & Engineering, Maulana Abul Kalam Azad University, West Bengal, India.
email: 1075sam@gmail.com
Soumitra Kumar Mandal
Department of Electrical Engineering, National Institute of Technical Teachers Training and Research, Kolkata, West Bengal, India.
email: skmandal@nitttrkol.ac.in
Pages: 15-25
DOI: https://doi.org/10.55306/CJIESN.2025.010102
Abstract:

Efficient AI Processors for AI Processing in Smart Cities Using Genetic Algorithm Optimized Edge Networks introduces a novel system which aims to enhance the efficiency of distributed edge computing systems for time critical smart urban services. It employs GA to dynamically allocate and schedule task at edge nodes in a distributed way in order to balance the load and achieve low-latency. The system utilizes genetic searching methods to find configurations better than the optimal and saves the energy with powerful processing of the device. This optimization helps making decisions in a fast, context-guided manner, as required for smart city applications such as traffic control, health control and energy saving. Experimental results validate the superior of Edge Smart performance with respect to traditional edge-aware management, and evident improvements in processing speed, energy efficiency and system scalability are shown. These results show the capability of the framework as a viable solution to facilitate the deployment of edge intelligence in AI-based smart city infrastructures.
Key Words: AI, Computational Load Balancing, Decentralized Edge Networks, Edge Computing, Edge Intelligence. Energy Efficiency, Genetic Algorithms, Internet of Things (IoT), Latency Reduction, Optimization Techniques, Real-time Distributed Systems, Resource Optimization, Smart AI Processing, Scalability, Smart Cities, Task Scheduling.
Citation: S. A. Jilani et al., “EdgeSmart: Hybrid Evolutionary Optimization for Edge-AI in Smart Cities,” Ci-STEM Journal of Intelligent Engineering Systems and Networks, Vol. 1(1), pp. 15-25, 2025, doi: 10.55306/CJIESN.2025.010102