Abstract
Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.
Highlights
Wireless sensor networks (WSNs) consist of several embedded devices, known as sensor nodes, which are used to measure environmental phenomena in real time and send data back to workstations through a wireless component
Empty status (ES): This mode is often activated at night or in bad weather, when there is no energy acquired from the solar cell and the battery fully powers the sensor node
The harvested energy is intermittent in practice, and it may not even be sufficient to constantly satisfy the demand of outdoor applications
Summary
Wireless sensor networks (WSNs) consist of several embedded devices, known as sensor nodes, which are used to measure environmental phenomena in real time and send data back to workstations through a wireless component. A sensor node is typically powered by a limited-capacity lithium battery, which supplies the circuitry by providing the current required to sustain each component of the node. Shadow detection and movement prediction methods are required to avoid this risk. To control the energy consumption and achieve a longer lifetime, we built a model to describe the energy status of the sensor node and used suitable methods for harvesting prediction, shadow detection, task scheduling, and routing optimization. The primary contributions of this report can be described as follows: (1) a piecewise least squares curve fitting with extended Kalman filter is introduced for harvested energy prediction on a sensor node to improve its task schedule;.
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