Wireless sensor networks are used to monitor targets in a specific area, the use of thousands of sensors in monitoring simultaneously causes the energy of the sensors to be depleted faster, so we suggest dividing the sensors into groups so that they are in an active position and fulfill the monitoring of all targets and the rest of the sensors are in sleep mode called these groups With groups of non-separate covers, so that the sensor can participate in more than one group or cover, and its energy is fully utilized, unlike groups of separate covers, in which one sensor participates in only one cover, we do this division using two methods and then compare between them The first method is linear programming issued by precise methods And the assembly issued by machine learning after dividing the sensors into groups that we schedule so that these covers are arranged and determine the time for each cover and when to use it using two methods, one of which is machine learning and the other is an algorithm that depends on the population. We use the Anaconda program and Python language in programming and for linear programming we use cplex program.
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