Self-Healing Neural Model for Stabilization Against Failures Over Networked UAVs

Unmanned aerial vehicles (UAVs) allow formation of wide range ad hoc networks. These ad hoc formations with unmanned vehicles provide coverage of vast areas of applications involving mission dependent activities. Such networks can solve various issues related to civilian and military activities. One of the main applications of these networks is continuous surveillance. Surveillance by multiple nodes in ad hoc mode is directly dependent upon the continuous data sharing, cooperative decision making and stabilized network formation. Failures in network can hinder the performance and can decrease its operability. It is difficult to aloof network from discrete failures. Therefore, stabilized model is required which can provide stability to the whole network. For this, a self-healing neural model is developed which is capable of handling uncertain failures. It also provides provision for recovery of nodes from failure to stabilized state.