Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

Wireless Sensor Networks (WSNs) generate huge amounts of data and the number of physical sensor nodes used are increasing each day. However, to store and process the raw generated data, it is very crucial to use high-performance computing resources such as, distributed clusters, mainframe or Cloud Computing. In this context, a novel architecture based on WSN with cloud computing to support the big data processing and storing capabilities at different levels. The aim behind proposed architecture is to build a new virtual sensor Network (VSN) design based on Named Data Networking (NDN) paradigm response for routing sensed data between virtual sensors Network. The performance of the current architecture has been compared with different internal and external IP-based internet protocols. A primary evaluation of the proposed architecture tends to confirm its suitability to be adopted as a potential concurrent for WSN integration with the Cloud.