Machine Learning based Data Sensing Device Network with Optimized Group Formation and Group Head Selection

Abstract

Sensor devices for data sensing (referred to as DSDs) are used in use cases such as border control and vehicle tracking. The architecture of the Data Sensing Device Network (DSDN) is established by integrating numerous DSDs across a given region, forming multiple groups. Within each group, a specific DSD is designated to facilitate communication between independent groups. The multi attribute values are captured and these attribute values effect the selection of head DSDs. For each of DSD this value ranges between 0.1 to 1. The DSD which has the highest value of range will be treated as Group Head in LEACH. The attributes are namely distance, battery level for each DSD. From the source DSD to destination DSD the link formation will happen end to end by making use of DSDs and base station, generally the end-to-end link communication has larger hops. This will have a ripple effect on battery level for DSDs and can cause reduced lifetime.

The Energy based LEACH is modified on top of LEACH by computing the battery level for DSDs and picking DSD with highest battery level. The Energy based LEACH will have two DSDs in each group acting like head DSDs. Machine Learning Data Sensing Device Network (ML-DSDN) is proposed which will first create group DSDNs based on k means machine learning algorithm. ML-DSDN will find the head group DSD based on combination of random forest and SVM algorithm with set theory. The comparison is done of ML-DSDN with respect to ELEACH and LEACH method and it is proved that ML-DSDN performs better with respect to delay, link count, energy consumption, alive DSD count, dead DSD count, lifetime ratio, routing overhead.