Power Load Distribution for Wireless Sensor and Actuator Networks in Smart Grid Buildings

This paper presents a design and analyzes the performance of an actuator operation scheduler for wireless sensor and actuator networks, aiming at efficiently managing power consumption and distributing peak load in smart grid buildings. To create a schedule within an acceptable response time, a genetic algorithm is designed, and the scheduler places the operations of activated tasks to appropriate time slots in the allocation table. For genetic operations, each schedule is encoded to an integer-valued vector, where each element represents either start time or binary allocation map of the associated task according to the task type. The fitness function evaluates the schedule quality by estimating the load of the peaking slot. Out-task model defines P-Penalty and N-Penalty to account for the extrapower load brought by the delayed start of task operation. The performance measurement results obtained from a prototype implementation reveal that our genetic scheduler reduces the peak load by up to 35.2% for the given parameter set compared with the Earliest scheduling scheme, intelligently compromising two conflicting requirements of even load distribution and small initiation delay.