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Fog Computing paradigm that provisions low-latency computing services at the edge network, is a bonanza for supply chain computing resources in Internet of Things (IoT) applications. In different scenarios such as smart homes/healthcare systems, multiple IoT applications are distributed simultaneously in cloud and fog nodes to provide different IoT-based services. In addition, each program requires resources and has its desired quality of service (QoS) which should be met. One of the key challenges in fog computing environment is how to efficiently allocate resources to IoT applications because inefficient resource allocation leads to burdening providers high costs and it lowers down the delivered QoS to users. Since the majority of IoT applications are time-sensitive, the low delay and near physically allocated resources improve the amount of delivered QoS. Therefore, the resource clustering algorithms with the lowest distance error rate and the lowest delay as a consequence are favorable. The aim is to reduce clustering errors and improve the overall performance of the system. This paper formulates resource allocation to IoT applications in heterogeneous 4-layered fog platforms to an optimization problem. To solve this problem, a fusion approach incorporating a genetic algorithm (GA) and the k-means clustering approach is presented. Firstly, it utilizes the k-means approach and Jaccard measurement to cluster fog nodes with a minimum clustering rate. Then, the resources of fog clusters are allocated to IoT devices with the minimum error rate by incorporating GA algorithm. This selection of processing nodes in a fog layer helps to minimize latency and allows applications to access resources simultaneously. The simulation results in extensive scenarios prove the superiority of the proposed algorithm against other successful meta-heuristic approaches in terms of the objective function and lowest error/delay rate. [ABSTRACT FROM AUTHOR]

Traditional k-means clustering algorithm is sensitive to the choice of initial cluster centers and leads to local optimal results. k-means++ is a hybrid k-means clustering algorithm which specifies the procedure to initialize the cluster centers before proceeding with the standard k-means algorithm. Inspired by nature, some contemporary optimization techniques such as Cuckoo, Bat and Krill Herd algorithms etc., are used for optimization as they mimic the swarming behaviour and allows to cooperatively move towards an optimal objective within a reasonable time. In this paper, these nature-inspired techniques are used for optimizing k-means++ clustering algorithm to enhance clustering quality and generate new hybrids of unprecedented performance. The results of the evaluation experiments on the integration of nature-inspired optimization methods with k-means++ algorithm are reported. [ABSTRACT FROM AUTHOR]