NCCLA: new caledonian crow learning algorithm based cluster head selection for Internet of Things in smart cities

Recent advancements in sensing and networking technology, such as the Internet of Things (IoT), provide a low-cost monitoring system that serves as the backbone of the smart city. In recent years, various real-time applications have developed to monitor and control physical objects using smart sensors. The use of smart sensors generates a massive amount of data in terms of text, numeric, image and video formats, etc. In IoT, transmitting an enormous amount of data consume more energy. So, clustering is the right option for collecting and exchanging data over the network. Many researchers have used the optimization algorithms to choose cluster head (CH) in the cluster. During the CH rotation, many of the optimization algorithms take more time to converge. To address this problem, this paper proposes a new caledonian crow learning algorithm (NCCLA) to identify the appropriate CH in the cluster. Generally, it has two processes, namely cluster formation and CH selection. The Euclidean distance is used to create a cluster within the network. The NCCLA algorithm is proposed to find the best CH on the cluster. Simulation is conducted on MATLAB R2019a. The efficacy of the proposed NCCLA algorithm is compared to the fuzzy clustering particle swarm optimization (FCPSO) and the Improved Fruit Fly Optimization Algorithm (IFFOA). The performance of NCCLA increases the network lifespan by 10% and increases the data packet delivery by 10%. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.