Abstract
In wireless sensor networks, it is important to use the right number of sensors to optimize the network and consider the key design and cost. Due to the limited power of sensors, important issues include how to control the state of the sensor through an automatic control algorithm and how to power-save and efficiently distribute work. However, sensor nodes are usually deployed in dangerous or inaccessible locations. Therefore, it is difficult and impractical to supply power to sensors through humans. In this study, we propose a high reliability control algorithm with fast convergence and strong self-organization ability called the sensor activity control algorithm (SACA), which can efficiently control the number of sensors in the active state and extend their use time. In the next round, SACA considers the relationship between the total number of active sensors and the target value and determines the state of the sensor. The data transmission technology of random access is used between the sensor and the base station. Therefore, the sensor in the sleep state does not need to receive the feedback packet from the base station. The sensor can achieve true dormancy and power-saving effects. The experimental results show that SACA has fast convergence, strong self-organization capabilities, and power-saving advantages.
Highlights
Wireless sensor networks (WSNs) are critical technologies of the Internet of Things, and they are mainly composed of base stations and hundreds or thousands of sensors
We propose a new method called the sensor activity control algorithm (SACA)
The idea of SACA is to use the relationship between the number of active sensors and the target value to decide whether to change the state of sensors
Summary
Wireless sensor networks (WSNs) are critical technologies of the Internet of Things, and they are mainly composed of base stations and hundreds or thousands of sensors. Controlling the state of the sensor through an automatic control algorithm and maximizing the function of the sensor, power saving, and efficient distribution of the mission have become important issues. Efficiently controlling the number of sensors in the active state improves their working strategy and efficiency and extends their lifetime [1–4]. The number of sensors in the active state should be controlled according to the application requirements. The number of sensors required to monitor the temperature of a reservoir or air pollution in the environment is small. For applications that require higher accuracy, such as military and disaster area monitoring, the number of sensors can be increased to thousands
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