Quality Based Clustering of Node using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection in Healthcare Application

Abstract

Big data recently has gained tremendous importance in the way information is being disseminated. Transaction based data, unstructured data streaming to and fro from social media, increasing amounts of sensor and machine-to-machine data and many such examples rely on big data in conjunction with cloud computing. It is desirable to create wireless networks on-the-fly as per the demand or a given situation. In such a scenario reliable transmission of big data over mobile Ad-Hoc networks plays a key role in military healthcare applications. Limitations like congestion, Delay, Energy Consumption and Packet Loss Rate constraint pose a challenge for such systems. The most essential problem of Hybrid Mobile Ad-hoc Networks (H-MANET) is to select a suitable and secure path that balances the load through the Internet gateways. Also, the selection of gateway and overload through the network may cause packet losses and Delay (DL). Therefore, load-balancing between different gateways is required for achieving better performance. As a result, steady load balancing technique was employed that selects the gateways based on the Fuzzy Logic (FL) system and enhances the network efficiency. However, the Energy Consumption (EC) was high since gateways were selected directly from the number of nodes. Hence in this article, a novel Node Quality-based Clustering Algorithm (NQCA) using Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS) is proposed. In this algorithm, NQCA is performed based on the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm separates the total network into number of clusters and the Cluster Head (CH) for each cluster is elected on the basis of the node priority, transmission range and node neighborhood fidelity. Moreover, the clustering quality is estimated according to the different parameters like node degree, EC, DL, etc, which are also utilized for estimating the combined weight value by using the FL system. Then, the combined weight values are optimized by using Genetic Algorithm (GA) to pick the most optimal weight value that selects both optimal CH and gateway. Conversely, the convergence time of GA and the error due to parameter tuning during optimization are high. Hence, a NQCA using Fuzzy-Fruit Fly optimization for Cluster Head and Gateway Selection (FFFCHGS) is proposed. In this algorithm, improved Fruit Fly (FF) algorithm is proposed instead of GA to select the most optimal CH and gateway. Finally, a performance effectiveness of the FFFCHGS algorithm is evaluated through the simulation outcomes in terms of EC, Packet Loss Rate (PLR), etc.