Identification of Malicious Node for Effective Top-k Query Processing in MANETS

In Mobile Ad-hoc networks, query processing is optimized using Top-k query processing. The accuracy of the results can be lowered if there exists malicious node. In our proposed system, we assume that malicious node perform Data Replacement Attack, in which the malicious node replaces necessary data sets with the false data sets. In our system malicious node identification method, the query issuing node receives the reply messages from the nodes; if a query-issuing node detects a DRA then it performs subsequent inquiries with the nodes which receive the information from the malicious node. In this way the query issuing node identifies the malicious node, and shares the information with the neighbouring nodes. Then the nodes share the information regarding the malicious node with the other nodes which are far away. Each node tends to identify the malicious node in the network, and then floods the information. Query issuing node performs grouping of the nodes based on the similarity of the information on malicious node detected by the nodes. Identification of malicious node is performed based on the results of malicious node identifications by these groups

METADATA CHALLENGE FOR QUERY PROCESSING OVER HETEROGENEOUS WIRELESS SENSOR NETWORK

ABSTRACT Wireless sensor networks become integral part of our life. These networks can be used for monitoring the data in various domain due to their flexibility and functionality. Query processing and optimization in the WSN is a very challenging task because of their energy and memory constraint. In this paper, first our focus is to review the different approaches that have significant impacts on the development o

Energy-efficient top-k query processing in wireless sensor networks

Technological advances have enabled the deployment of large-scale sensor networks for environmental monitoring and surveillance purposes. The large volume of data generated by sensors needs to be processed to respond to the users queries. However, efficient processing of queries in sensor networks poses great challenges due to the unique characteristics imposed on sensor networks including slow processing capability, limited storage, and energy-limited batteries, etc. Among various queries, top-k query is one of the fundamental operators in many applications of wireless sensor networks for phenomenon monitoring. In this paper we focus on evaluating top-k queries in an energy-efficient manner such that the network lifetime is maximized. To achieve that, we devise a scalable, filter-based localized evaluation algorithm for top-k query evaluation, which is able to filter out as many unlikely top-k results as possible within the network from transmission. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm on real datasets. The experimental results show that the proposed algorithm outperforms existing algorithms significantly in network lifetime prolongation

Energy-Efficient Top-k Query Processing in Wireless Sensor Networks

Technological advances have enabled the deployment of large-scale sensor networks for environmental monitoring and surveillance purposes. The large volume of data generated by sensors needs to be processed to respond to the users queries. However, efficient processing of queries in sensor networks poses great challenges due to the unique characteristics imposed on sensor networks including slow processing capability, limited storage, and energy-limited batteries, etc. Among various queries, top-k query is one of the fundamental operators in many applications of wireless sensor networks for phenomenon monitoring. In this paper we focus on evaluating top-k queries in an energy-efficient manner such that the network lifetime is maximized. To achieve that, we devise a scalable, filter-based localized evaluation algorithm for top-k query evaluation, which is able to filter out as many unlikely top-k results as possible within the network from transmission. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm on real datasets. The experimental results show that the proposed algorithm outperforms existing algorithms significantly in network lifetime prolongation