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Smart cities aim to use technology and data to efficiently utilize city resources, improving residents’ quality of life and advancing sustainability. Efficient management of these resources, with the Internet of Things (IoT) playing a crucial role, is essential for enhancing smart cities’ functionality and sustainability. As the number of users grows, providing efficient and timely services becomes increasingly challenging and intelligent management of resources is key to delivering effective IoT services. This paper introduces a novel approach, the Ensemble Fog Layered Service Management (EFLSM) model, designed to manage and optimize resource allocation and usage within a smart city’s fog computing layer. This model addresses challenges such as unnecessary application migrations, and efficient context sharing and fog nodes migration issues. The proposed EFLSM model operates in three stages: Firstly, it assigns service requests to the appropriate fog nodes based upon the available computing resources and context history through a multi-channel queuing model; secondly, it ensures efficient context sharing and migration to address context instance shortages; and thirdly, it guides service request migration to select fog nodes using ensemble machine learning predictions. Experimental results from a hardware test-bed illustrate the deployment of the EFLSM model and demonstrate its superior performance over existing methods like SMCQ, greedy strategies, and random allocation.

Background: In today’s world it is highly difficult to manage the smart things and fulfill the communication needs without the Internet as it provides ultimate means for human to human (H2H) communication. The ‘things’ could be entities or devices that contribute for the communication. But to enhance and improve such smart communication among the things that involves nonhuman intervention, there is a need to add few more smart capabilities to the Internet. As the world of Internet is on its way of transformation into a new smart world called ‘Internet of Things’ (IoT) where the things should possess the ability of sensing, communication and control to let the things exchange information without complete intervention of humans to provide advanced and qualitative services, which is possible with the help of protocols. Objective: This paper discusses how the transition could be started and being progressed in wired and wireless systems, and how it changes the traditional means of communication among different devices and humans into a smart way. Methods: Transition here refers to how isolated things are being intermingled with each other to generate a smart protocol communication. Results and Conclusion: The aim of such transition is to improve the efficiency, flexibility, adaptability and interoperability. This paper also explores various factors that contribute to IoT.

Energy conservation is a major challenge in the Internet of Things (IoT) as the number of resource‐constrained devices is connected to the network. Routing plays a vital role in IoT to extend the lifespan of the network. Routing protocol for low‐power and lossy networks (RPL) is a standard routing protocol in IoT. The parent selection is a crucial role in the routing process to exchange the data. In RPL, the researchers have introduced a single metric, composite metric, and multiobjective optimization algorithm for parent selection. However, the improper parent selection causes the packet losses, congestion among the network nodes, depletes more energy, and increases the convergence time. To overcome these issues, this article proposes energy efficient optimal parent selection in RPL (EEOPS‐RPL) using firefly optimization algorithm to extend the lifespan of the IoT network. In EEOPS‐RPL, each node in the network is considered to be firefly and also calculates the current location of firefly, attraction of the fireflies, random function, velocity, and the global best values in the network. Residual energy and expected transmission count are attractiveness parameters and distance is a movement parameter to choose the optimum parent in the destination‐oriented directed acyclic graph for data transmission. The simulation is conducted using COOJA. The EEOPS‐RPL provides better performance in comparison to the efficient parent selection for RPL (EPC‐RPL) and the E‐RPL. The EEOPS‐RPL improves the packet transmission ratio and lifespan of the network by 2% to 5%, and 5% to 10%, respectively, compared with EPC‐RPL and E‐RPL. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time. In EEOPS‐RPL, each participant node applies the firefly algorithm over the parent node information such as distance, residual energy, and expected transmission count through DODAG information object control message to pick the best parent node in the DODAG. The firefly algorithm provides the fast convergence that able to choose the optimal parent quickly. Thus, it avoids the packet loss during the route establishment in network. Thus, EEOPS‐RPL extends the lifespan of the network and reduces the convergence time.