Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Secure cluster based data aggregation in wireless sensor networks

Abstract: Data aggregation is the best technique for energy conservation in Wireless Sensor Networks (WSN). Because of the open deployment, sensors are vulnerable for security threats. In this paper we address the data aggregation and security issues together. In our approach, we modify our Energy efficient Cluster Based Data Aggregation (ECBDA)[1] scheme to provide secure data transmission. Since, sensors nodes are low powered in nature, it is not viable to apply standard cryptography methods. Cluster head performs data aggregation and Bayesian fusion algorithm to enable security. Trust is the directional relationship between two sensor nodes. By checking the trustworthiness of a node, we can enable secure communication. Bayesian fusion algorithm calculates the trust probability of a sensor based on the behavior of the node. The simulation results show that our approach effectively detects the untrustworthy nodes with minimum energy consumption.

On the Effects of Aggregation on Reliability in Sensor Networks

Abstract: Data collected in a sensor network is transported hop-by-hop to a sink for further analysis. The quality of the analysis depends on the amount of data reaching the sink. Hence, data transport reliability influences the quality of the analysis. Data aggregation is a common method used in sensor networks to reduce the amount of messages transported. By aggregating, the data contained in several messages is fused into one single message. Therefore, data aggregation significantly influences the overall data transport reliability observed at the sink. This influence is analyzed and described analytically and by experiment within this paper. Furthermore it is shown how the influence of data aggregation on data transport reliability can be controlled for a particular class of data gathering application

Energy Efficient for Data Aggregation in Wireless Sensor Networks

Abstract: The wireless sensor networks is the decentralized and self configuring type of network in which senor nodes can sense information and pass it to base station. Due to decentralized nature and far deployment energy consumption is the major issues of wireless sensor networks. To reduce energy consumption of wireless sensor hierarchal clustering is the efficient type of clustering technique. In this scheme whole network will be divided into fixed size clusters and cluster heads are selected in each cluster. The cluster heads are selected on the basis of energy and distance from base station. The sensor node which has least distance from the base station and has maximum energy is selected as cluster head. The cluster heads can communicate with each other and data will be transmitted to base station. In this research work, WEMER protocol is implemented and improved to increase lifetime of wireless sensor networks. In the WEMER protocol, whole network is divided into clusters and cluster heads are selected in each cluster. The leader nodes are also selected in the network which take data from the cluster heads and pass it to base station. In the improvement of WEMER protocol. Gateway nodes are deployed in network to increase lifetime of WSN. In the proposed improvement gateway nodes are deployed near to base station which takes data from the leader nodes. The leader nodes take data from the cluster head. The proposed WEMER protocol and WEMER protocol are implemented in MATLAB. The simulation results shows that proposed WEMER protocol has less number of dead nodes, high number of alive nodes, send more number of packets and more remaining energy consumption. CHAPTER

Energy-efficient data acquisition using a distributed and self-organizing scheduling algorithm for wireless sensor networks

Abstract: Wireless sensor networks are often densely deployed for environmental monitoring applications. Collecting raw data from these networks can lead to excessive energy consumption. Thus using the spatial and temporal correlations that exist between adjacent nodes we appoint a few as representative nodes that perform in-network aggregation. This reduces the total number of transmissions. Our distributed scheduling algorithm autonomously assigns a particular node to perform aggregation and reassigns schedules when network topology changes. These topology changes are detected using cross-layer information from the underlying MAC layer. We also present theoretical performance estimates and upper bounds of our algorithm and evaluate it by implementing the algorithm on actual sensor nodes, demonstrating an energy-saving of up to 80% compared to raw data collection.