Path Stability Prediction for Stable Routing using Markov Chain Model in MANETs

  • Authors

    • Calarany C
    • Manoharan R
    2018-11-27
    https://doi.org/10.14419/ijet.v7i4.19.22029
  • MANET, Path Stability, Energy, Markov chain, Active Interactive Rate, Path Reliability Factor.

  • The mobility factor and the energy level of a node during data transmission in MANET are considered to be the most important challenges amongst several other issues like link stability, security and QoS factors in the designing of routing protocols. Forecasting the path stability, leads to an effective and reliable communication between the nodes in a highly dynamic scenario. Subsequently, in most of the work metrics namely hop count and energy are considered for stable path selection and mobility is handled in few works. Therefore, in this paper a prediction model with bi-objective optimization using Active Interactive Neighbour Rate (AINR) and Energy (E) have been considered for stable path selection. Markov models are widely used for depicting random behaviour in several processes related with time series. However, this model has not been utilized for predicting the stable path in a highly mobile network, taking into consideration the issues of mobility and energy. Hence, in this work a new Path Stability Prediction model, using Markov chain namely PSPM has been proposed, using two metrics namely AINR and E. The proposed model is incorporated in a multipath routing protocol, which identifies the optimal path in terms of minimum AINR and minimum energy consumption, which has been evaluated using NS 2.35. From the simulation, it has been found that the new protocol shows enhancement in results than the existing protocols namely AODV and AOMDV, in terms of various parameters such as Packet Delivery Ratio, Throughput, End-to-End Delay, Energy consumption and Routing Overhead.

     

     

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    C, C., & R, M. (2018). Path Stability Prediction for Stable Routing using Markov Chain Model in MANETs. International Journal of Engineering & Technology, 7(4.19), 94-100. https://doi.org/10.14419/ijet.v7i4.19.22029