A Decentralized Technology Model Incorporating Reinforcement Learning for Protected and Energy-Conserving Infrastructures

In Wireless Sensor Networks, nodes are deployed randomly throughout the network and these nodes work without human intervention. Sensor devices can be moni- tored remotely by authorized persons only but sometimes hackers attack to destroy or steal data from the network. Sensor nodes are connected in such a way that they can broadcast data in the network according to the routing algorithm in an appropri- ate manner. WSN itself has many limitations such as sensor devices present in the network having less storage capacity and low battery backup. Security and power constraints are the main concerns in wireless sensor networks. Sensor nodes can be deployed in inaccessible areas to study its various factors. WSNs are widely used systems throughout the world nowadays. For instance, no- table WSN applications are military, healthcare, environmental, traffic control, ed- ucation, and home care. In healthcare, nodes are deployed on the body of the patient so that a doctor can monitor and diagnose the illness. The defense forces utilize sen- sor devices to control and monitor the suspected activities in the territory. In WSNs, data nodes themselves are the routers where transmission is done in multi-hops to reach the data packet to its final destination and these sensor nodes carry vulnerable data that can be attacked and destroyed by the wrongdoers. Data transmitted by the sensor nodes is all in the air and prone to attacks. Furthermore, sensor nodes can fail at any point of transmission in the network because of their low energy constraint.