Primary-secondary immune response adaptation for wireless sensor network

Biological Immune Systems have intelligent capabilities of detecting foreign bodies which attack our body. Moreover they have inherent insightful capabilities to remember them, when they hit the body again. Primary response is the initial response instantiated by the body to the attack and secondary response is the response hence forth. Secondary response is naturally faster because of its characteristic of remembering the cure of the attack. Similar type of perspicacious nature can be adapted in removal of fraudulent nodes in wireless sensor network. The work proposes a novel algorithm for the detection and removal of the fraudulent nodes. It first detects the fraudulent nodes by machine learning module and then removes these nodes by immune-inspired module. Eventually if the same type of malicious nature is seen again, analogy of secondary response of immune system is instigated in sensor network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy and robustness of the proposed model.