A Cognitive Approach to Improve the Quality of Service in NB-IoT


  • Madiraju. Sirisha
  • P. Abdul Khayum
  • Joseph Rajiv Kantheti


Narrow Band Internet of Things (NB-IoT), a 3GPP Release-13 proposed technology is well known for its low power consumption and wide area coverage. These are the most urging requirements of current scenarios of industrial, research and also social. To enhance the coverage of NB-IoT, redundant data is transmitted. This redundant data transmission improves the area  coverage in NB-IoT when compared with Long Term Evolution (LTE).  But a large number of repetitions of the data leads to a reduction in the throughput and a raise in the delay. The battery lifetime of the IoT devices gets reduced and the cost of maintenance increases. In this paper, an efficient routing (Q-AODV routing algorithm) using Reinforcement algorithm is suggested to avoid repetition of data for an extent. A Q-learning algorithm is used for decision making in an Ad hoc On demand Distant Vector (AODV) routing algorithm. This improves the throughput and decreases the delay. Simulation of a network with  Q-AODV routing algorithm is performed and compared with the traditional AODV routing algorithm.