DATA AGGREGATION & FUSION MODELS FOR WIRELESS SENSOR NETWORK

Abstract

Wireless Sensor Networks (WSNs) is a network which formed with maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area , for example temperature , pressure, environmental monitoring, water level ,industrial monitoring, health care and military application. Wireless sensor network poses two unique characteristics in comparison of traditional wireless communication system i. The limited battery power of source and ii. The redundant data, which are correlated among different sensor nodes. These two are associated with energy consumption and data traffic control. Data aggregation is a technique of gathering data from sensor nodes, eliminating redundant measurements and transmission of extracted information in an efficient way. Data fusion is also a process of combining of data from multiple sources such that the communication overhead of sending individual sensor readings to base station is reduced. The research in this thesis aims designing a model for data aggregation & fusion based on clustering, dynamic positioning and energy efficient routing for wireless sensor network. Firstly, thesis proposed a probabilistic clustering technique for data aggregation considering differential and temporal factors for a node and cluster along with relative positions of selected cluster heads. Simulation results show that proposed technique obtains considerable improvements and allows a large stable network lifetime compared to the state of the art techniques. Secondly, to reduce the energy consumption and redundancy,a cluster based aggregation technique using particle swarm optimization technique with discrete search space is proposed. This approach aims to minimize the intra cluster communication energy and energy loss due to cluster head and base station communication. The performance of proposed approach is compared to clustering techniques and results shows that proposed technique greatly improves over lifetime of the network. Thirdly, a dynamic positioning based aggregation approach is proposed to reduce the redundancy and maximization of coverage area using modified artificial bee colony algorithm. In proposed approach, we have incorporated a hybrid search for balancing the exploration and exploitation. The results shows that the modifications carried out leads to enhance the lifetime and communication cost as compared to the conventional artificial bee colony algorithm. Finally, routing based in-network data aggregation approach is proposed exploiting principles of swarm intelligence in ant colony optimization for routing and fuzzy rules to evaluate goodness of a path. Routing based data aggregation aims to maximize the number of collected data packets, while minimizing energy consumption and data gathering delay. Data collection delay should be minimum as it is key to data freshness. Due to resource constraint in wireless sensor network, minimum energy consumption can play an important role in the field of its application.