A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related to power consumption and system reliability. To address the challenges of latency, energy consumption, and fault tolerance in these environments, this paper proposes a latency-aware, fault-tolerant framework for resource scheduling and data management, referred to as the FORD framework, for smart cities in fog environments. This framework is designed to meet the demands of time-sensitive applications, such as those in smart transportation systems. The FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments, leveraging resources from both fog and cloud environments. Through simulation-based executions, tasks are allocated to the nearest available nodes with minimum latency. In the event of execution failure, a fault-tolerant mechanism is employed to ensure the successful completion of tasks. Upon successful execution, data is efficiently stored in the cloud data center, ensuring data integrity and reliability within the smart city ecosystem.