The Industrial Internet of Things (IIoT) is transforming modern manufacturing environments by providing real-time data from interconnected devices, significantly enhancing information visibility and automation. Through a network of intelligent sensors integrated across the production system and equipment, it enables real-time monitoring and data collection. This continuous stream of sensor data contributes to industrial big data, facilitating more intelligent decision-making by providing significant insights into operational conditions. One of the major applications of this data is predictive maintenance, which uses machine learning models to identify early signs of equipment failure. This helps manufacturers to proactively schedule maintenance, reduce unexpected breakdowns and operational downtime, reduce maintenance costs, and extend the service life of equipment. In this work, a novel predictive maintenance framework is proposed using an enhanced version of Genghis Khan Shark Optimizer (CGKSO), a nature-inspired metaheuristic algorithm. Each CGKSO candidate represents a set of feature weights for a logistic classifier, and these weights evolve over successive iterations to maximize prediction accuracy. The model uses a sigmoid activation function to classify failure conditions, providing an efficient and accurate solution for equipment health monitoring in modern industries.

Load balancing in IoT-based Wireless Sensor Networks (WSNs) is essential for improving energy efficiency, reliability, and network lifetime, promoting the development of smart and sustainable cities through informed decision-making and resource optimization. This paper introduces a Workload Aware Clustering Technique (WLACT) to enhance energy efficiency and extend the network lifespan of IoT-based WSNs. WLACT focuses on overcoming challenges such as uneven workload distribution and complex scheme designs in existing clustering methods, highlighting the importance of load balancing, optimized data aggregation, and effective energy resource management in IoT-based heterogeneous WSNs. WLACT adapts Chicken Swarm Optimization (CSO) for efficient workload-aware clustering of WSNs, while also introducing the concept of average imbalanced workload parameter for clustered WSNs and utilizing it as an evaluation metric. By considering node heterogeneity and formulating an objective function to minimize workload imbalances among nodes during clustering, WLACT aims to achieve efficient energy resource utilization, improved reliability, and long-term operational support within smart city environments. A new cluster joining procedure for non-CHs based on multiple factors is also designed. Results reveal the superior performance of WLACT in terms of energy efficiency, workload balance, reliability, and network lifetime, making it a promising technique for sustainable smart city development.

The current Global Positioning System (GPS) has been heavily criticized by several parties (e.g., drivers and researchers) due to the delay in disseminating en-route data to drivers ahead of time. This issue resulted in poor rates of accuracy of data (i.e., traffic status), which eventually causes inefficient transportation. According to National Institute of Standards and Technology, the delay is caused mainly by the transmission medium. This paper presents a new distributed fog-based vehicles navigation system for efficient transportation. The proposed system uses closer-to-the-source data (aka drivers) nodes (i.e., fogs) as a new transmission medium for disseminate the road details to subscribed drivers who are connected to those nodes. A package delivery scenario has been implemented to show the impact of using fog nodes in reducing the navigation time and improving efficiency. Those fog nodes gather and broadcast real-time traffic data from/to drivers within the area they cover. In addition, a new road navigation simulator has been designed and developed to simulate the proposed system. The simulation results show superior performance of the new navigation system in terms of travel time and in-situ road data provisioning to drivers.