A multi-Mobile Agent and optimal itinerary planning-based data aggregation in Wireless Sensor Networks

Abstract

Due to the energy constraints in Sensor Nodes (SN) and to increase energy efficiency in Data Aggregation (DA) approaches, Wireless Sensor nodes (WSN) requires a suitable network’s life cycle. In order to address the energy efficiency, a new system model is proposed for competent Data Aggregation (DA), and Data processing (DP) mechanism with the amalgamation of Mobile Agent (MA) in WSN intended towards collaborative signal systematized with information processing. Furthermore, with the expansion of Mobile Agent (MA) centred WSN, unique resource-efficient potentials for Data Aggregation (DA) and Data processing (DP) applications are identified. In the proposed system model, a single Mobile Agent (MA) is employed for data processing. Hence the MA-centred paradigm’s fundamental difficulty is determining the best route for agent traversal. Henceforth, using the Brownian Motion-Based Flower Pollination Algorithm (BMFPA), a new multi-MA-centred optimal itinerary planning strategy for performing data aggregation in WSN is proposed. Further, the proposed approach uses Fitness based Fuzzy C-Means (FFCM) for cluster creation and Cluster Head (CH) selection. Crossover Mutation based Firefly Algorithm (CM-FFA) is used. Moreover, the proposed model uses optimal itinerary planning-based Brownian Motion-Based Flower Pollination Algorithm (BMFPA) for MA migration and Data Gathering (DG) in WSN. Finally, after data gathering, the proposed model, the mobile Agent collects the information and communicates it to the base station or sink based on various applications scenarios. The system model’s investigation outcomes evidently exhibit that the proposed work competently performs well than prevailing algorithms for Data Gathering (DG) in WSN. Furthermore, the proposed model method can be fully utilized in a virtual wireless network’s scenario.