Bibliometric insights and conceptual grid framework for load balancing in fog computing for smart city

The proliferation of smart cities has increased significantly in recent years, driven by rising public, engineering and research interest, and new technologies which are changing urban life. Although technology has been enhanced, cloud computing in smart cities still faces issues such as latency, mobility, and limited bandwidth. Fog computing addresses these challenges by bringing computation closer to the users. The research is a bibliometric analysis of the last 12 years related to fog computing and Load Balancing (LB) algorithms in the sphere of smart cities, covering 1676 articles in the Scopus database from 2012 to 2025. Publication types, subject areas, growth trends, countries and authors, sources of funding and keywords are reviewed. The results emphasise the importance of Fog Computing (FC) and LB across various smart city areas, including traffic management, healthcare, parking systems, smart homes, mobile devices, and infrastructure monitoring. Based on the trends and research gaps revealed by the bibliometric analysis, a conceptual framework is presented for fog-enabled smart grid environments. The framework integrates LSTM-based load prediction, reliability-aware resource allocation, and fault-tolerant mechanisms to support adaptive load balancing under dynamic operating conditions. For an initial evaluation, the proposed framework was compared with two baseline approaches, namely Least Loaded (LL) and Round Robin (RR). Preliminary simulation results indicate improvements in response time, energy consumption, fault recovery, and deadline satisfaction. Furthermore, complexity analysis shows that the framework operates with linear time complexity, making it suitable for resource-constrained fog computing environments.