Machine learning based hybrid model for energy efficient secured transmission in wireless sensor networks

Abstract

Wireless Sensor Networks becomes robust while the nodes possessing the self-configuring capability by which they can be either keeping themselves as a part of the network or they can also simply leave. The transmission of Data is achieved through the inter communication among the nodes participated in the network. For most participative nodes, there is a possibility of energy depletion and thus the network dies despite the other nodes having optimal energy for being operative. Bring decentralized, another classical challenge for WSNs is the threat of intrusion detection which may arise DoS attacks that depleted the energy as well. Major works in the literature focuses either attempts to address the Intrusion detection to fix DoS attacks or keep the optimal energy level by introducing energy preserving strategy and dynamic clustering strategy so that the network lifetime could be extended. This work deals with the hybrid model in which the nodes are clustered together to form the Connected Dominating Set so that the data transmission could be augmented. The objective of the system is to include only nodes with adequate energy that can ensure the validity of the network by marking them as CDS node. The CDS probability of a node depends on foresight of the energy of the participant nodes till the transmission. Packet distribution is done based on the CDS of the node and thus the inappropriate network breakdown that keeps same node waking so often could be avoided. Thereby, retransmission has been avoided due to packet drops, which also conserved the energy of the network. From the simulation results, it is understood that the technique decreases the loss of packets and deferment. The simulation results demonstrated that the proposed model has improved performance metrics such as goodput, throughput, packet dropping ratio. Also, it is seen that the network lifetime has been significantly increased to 50% meantime packet dropping ratio has been reduced to 50% with the energy consumption little lower than 3% when compared with the preliminary enhanced energy-dependent constraint DoS (denial-of-service) detection model and thus the network seems fault-proof and extended reliable.

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Correspondence to N. M. Saravana Kumar.

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Saravana Kumar, N.M., Suryaprabha, E. & Hariprasath, K. Machine learning based hybrid model for energy efficient secured transmission in wireless sensor networks. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02946-y

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Keywords

  • Machine learning
  • Security
  • Energy efficient
  • detection
  • Hybrid