Journal of Computer Science

The Effect of Packet Redundancy Elimination Technique in Sensor Networks

Deepa Priya and Sharmini Enoch

DOI : 10.3844/jcssp.2018.740.746

Journal of Computer Science

Volume 14, Issue 6

Pages 740-746


A lot of sensor nodes which are able to sense, process and communicate with external base station with the data obtained from external environment belong to a class of adhoc networks called wireless sensor networks. The main challenge in sensor network is to increase the life time of the network thereby reducing duplicate by detecting the redundant information. While the communication energy-efficiency is necessary to increase the lifetime of the sensor network, an important implementation is to reduce duplicate packets which are considered as a serious issue in these networks. Here the network is hierarchical. The nodes which are intermediate collects information from the source node. These intermediate nodes have their own as well as additional information about the sensed data .This causes redundancy. The redundancy propagates further to the nodes above the network. The main purpose is to detect the packet level redundancy and then to eliminate it. As the nodes are continuously transferring data from sender to receiver and also the sensor nodes consume lot of energy for transmission the data redundancy must be avoided sometimes in order to avoid the loss of needed packets for some critical applications. The packet redundancy elimination hashing algorithm by Rabin Karp is to focus on the identification and elimination of the packet level redundancy with less energy consumption thereby receiving the data with reduction of duplicity. The performance analysis is on energy level of the network and on packet delivery. The level of energy possessed by the nodes in varying time period is noted. The energy levels for existing and proposed methods are compared. The comparison is made with time and energy level of the nodes. The bandwidth of the network is also compared and improved bandwidth is up to 65% in sensor networks when compared with conventional networks.


© 2018 Deepa Priya and Sharmini Enoch. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.