HMMR: Hidden Markov Model Prediction-Based Routing in Opportunistic IoT Scenario

Abstract

Opportunistic networks are the networks having dynamic connections which are ad hoc in nature and are not fixed because of their dynamic movement. Predicting connection between two nodes is difficult in such scenarios and it depends on various parameters such as speed of the connection, number of nodes transmitting simultaneously, number of messages and time to live (TTL) of the message generated by a node for a given time interval. Opportunistic network are being used widely due to the development in IoT and 5G, messages generated has to be delivered effectively in such environment becomes very critical. Therefore, in this paper, Hidden Markov Model Routing (HMMR) algorithm is proposed which uses Hidden Markov technique to find the most efficient route for the transmission of the message. The proposed algorithm uses emission probability and transition probability to predict the success rate of the route while predicting routing path. The proposed HMMR algorithm results have been compared with similar algorithms in order to evaluate its efficiency. The proposed algorithm can be used in health care application in order to monitor patients suffering from Cardio-Vascular disease.