Abstract
Data Sensing Devices (DSD’s) have gained lot of traction for various use cases like border control, vehicle tracking. Data Sensing device network (DSDN) is shaped with the aid of combining lot of DSD’s across a random area. Like this multiple groups are formed. In each of the group the specific DSD is elected which is responsible for communication between two independent groups. Each of the group head has multiple attributes with first attribute based on distance, the second attribute based on remaining energy. These attributes will be input for the group head selection based on machine learning, The entire DSD’s inside a group are classified into HIGH, MEDIUM and LOW. The first priority will be given to HIGH followed by others for the primary group head selection. LEACH is a classical method used for transmission of chunks to the control center in a DSDN network. The selection of head DSD by LEACH will happen by making use of the random selection of DSD in each group using random probability selection mechanism. During the data chunk deliver the scanning process will happen from the initiator DSD to head DSD and from there the link is established with the base station (BS), the BS will then scan each group until the destination DSD is reached. The selection of head DSD by LEACH causes more holes in the DSDN because there are chances that the non-performer DSD can become a head DSD. Secondly for the transmission of chunks there is lot of back-and-forth propagation between the BS and the normal DSDs which reduces the battery level of the DSD by a large amount. The Energy based LEACH is modified on top of LEACH by measuring the energy of the DSDs and then selecting the group heads but suffers from multiple group head maintenance as well as more number of links. The proposed method will improve this by reducing the links used for end-to-end communication. In the proposed system the communication will happen based on initiator DSD, primary DSDs in different groups and then destination DSD which will avoid overhead compared to existing methods namely E-LEACH and LEACH. The proposed method is compared with LEACH and E-LEACH with respect to time taken, link count, energy consumption, residual energy measure, lifetime and overhead.
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More From: International Journal of Electrical and Electronics Research
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