The smart secured harvesting technique for embedded wireless sensor networking using network deep learning model

The well-established term wireless sensor network (WSN) refers to a new class of wireless systems, which are networks of distributed, self-organizing and fault-tolerant miniature electronic devices. Autonomic sources of nutrients. Intelligent nodes of such a network are capable of sending messages along the chain, providing a significant system coverage area with low transmitter power, and as a result, the high energy efficiency of the system becomes a potential component. To ensure high reliability and security of information transmitted in WSN, it is necessary to develop native radio protocols that are resistant to changes in the characteristics of the communication channel, radio congestion, interception and data imitation. In this paper the secured harvesting model was proposed for embedded wireless sensor networking. In this case, it is better to use spread spectrum technologies DSSS (direct number sequence) and FHSS (frequency hopping) methods. Regarding the means of accessing the data transmission medium, mutually exclusive requirements appear for the high energy efficiency of the system and the minimum time delay for data propagation in the WSN. Using CSMA/CA (Carrier Sense Multiple Media Access with Collision Avoidance) as the underlying mechanism has its drawback - network devices must be constantly listening over the air, which leads to increased power consumption.