Abstract
© 2015 IEEE.Wireless sensor networks usually have a massive number of randomly deployed sensor nodes that perform sensing and transmitting data to a base station. This can be a cause of sensor redundancy and data duplication. Sensor scheduling is a solution to reducing the enormous amount of the data load by selecting certain potential sensors to perform the tasks. Meanwhile, the quality of connectivity and coverage is also assured. This paper proposes a sensor scheduling method, called 4-Sqr, which uses a virtual square partition that is composed of consecutive square cells. Based on coordinates upon a monitored area, sensors learn their position on the virtual partition themselves; these are divided into groups of target areas, depending on the sensors' geographical locations. They are then ready for the node selection phase. In order to distribute energy consumption equally, the sensors with the highest residual energy within the same group usually have more chance of being active than the others. Compared to other existing methods, the proposed method is outstanding in many aspects such as the quality of connected coverage, the chance of being selected and the network's lifetime.