Implementing a Reliable, Fault Tolerance and Secure Framework in the Wireless Sensor-actuator Networks for Events Reporting

Abstract

Abstract The emergence of embedded systems, mobile, and the presence of sensors in all areas of human life without the presence of humans, makes it easy to do things, and to reduce the computational complexity. Wireless sensor networks are one of the types of distributed systems in recent years has been of interest to many researchers and have been used in many places, such as the military areas, banks, airports and other places, for use in protection systems. Wireless sensor networks consisting of tens, hundreds or even thousands of self-directed sensors that are wirelessly at a distance from each other and embedded in the environment, so that are associated with each other and perform the task of finding events and gather information from the environment and transmits the information to a monitoring center. Wireless sensor networks with the introduction of powerful or even mobile Actuators have improved their existing wireless sensor networks. An actuator interacts with the environment according to information received from the sensors and processing information. In order to have reliable operation for the activation, it looks critical to design a reliable, secure, and fault tolerance report for sensors to alert activation of peripheral events. In this paper, we use machine learning techniques such as clustering and Bayesian rules to represent a reliable, fault tolerance and secure framework to report events in the wireless sensor-actuator networks, which are enable to optimal collect data received from the environment and report it to the actuators.