IMPLEMENTATION OF DTN ROUTINGIN IOT

Abstract

Routing strategies in DTN is basically to choose a suitable node to carry forward the message copies for the successful transfer of message to the destination node. The routing decision, whether to transfer the data to the node encountered or not can be taken as a multilabel classification problem. We have used five multilabel classification techniques for finding the optimum technique for this task on Zebranet UTM-1 data for PRoPHET Routing and Epidemic Routing. The techniques used are Ensemble chain classifiers (ECC), CC(Chain classifiers), BR(Binary relevance), Label Power-set problem transformation and OneVsRest. We have used 7 classifiers as the base learners like XGBoost, AdaBoost, Random Forest, Naive Bayes , Decision Tree, k-NN and MLP. The library used for parameter optimization of the classifiers are hyperopt library and GridSearchCV. Ensemble chain technique with XGBoost classifier on PRoPHET routing data outperformed all the techniques for the PRoPHET and epidemic routing with an accuracy score of 96.1% and Jaccard score of 92.08%.