The Impact of Sensor Networks' Packet Redundancy Elimination Technique

This chapter highlights the technique using Rabin Karp hashing algorithm is to take full advantage of the duration of network with redundancy removal by increasing reliability, reducing delay and reducing energy consumption. A network which consists of a number of bulky sensor nodes that are distributed over a region in an adhoc fashion is known as Wireless Sensor Networks (WSN’s). Information from the source node is collected by the intermediary nodes. These intermediary nodes have information about the detected data that is both unique to them and supplementary.The result is redundancy. Further nodes above the network receive the redundancy. The fundamental goal is to identify and then get rid of any packet level redundancy. Using In-network storage is followed as the existing way of avoiding redundancy is by the application of Data Centric Storage Schemes. This scheme is suitableto data that is neither very matured and existing data’s and those are also considered to be query nor present at the sensor node which is used for measure. Proficient data access is done in network storage by target nodes and sensor nodes. The goal of Rabin Karp's packet redundancy elimination hashing method is to reduce energy usage while identifying and eliminating packet level redundancy, which will result in less duplicate data being received. The network energy level and packet delivery are the focus of the performance analysis. 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. In order to reduce the amount of time required for its execution, the advanced technique focuses on obtaining a superior time complexity. Additionally, the method can be enhanced in the future by choosing the cluster head nodes based on a fuzzy algorithm to reduce packet redundancy and incorporate videos into real-time simulation for data aggregation and transmission.