Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73 the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.
@cite_7 , the authors propose an approach to answering queries in s without fetching the data directly from sensor nodes. The Principal Component Analysis method was used to analyze historical data and select only the sensor nodes that measured most of the variance observed in the environment. This technique reduced the workload of the sensor nodes and reduced up to $50 in real testbeds. However, the authors do not define how the environment's evolution would be addressed. For example, if the temperature varies more often during the day, it would be necessary more measurements from more sensor nodes during these hours to build datasets that reliably describe the environment.
@cite_7
2067757953
In wireless sensor networks (WSN), a query is commonly used for collecting periodical data from the objects under monitoring. Amount of sensory data drawn across WSNs by a query can significantly impact WSN’s power consumption and its lifetime, since WSNs are battery operated. We present a novel methodology to construct an optimal query containing fewer sensory attributes as compared to a standard query, thereby reducing the sensory traffic in WSN. Our methodology employees a statistical technique, principal component analysis on historical traces of sensory data to automatically identify important attributes among the correlated ones. The optimal query containing reduced set of sensory attributes, guarantees at least 25 reduction in energy consumption of WSN with respect to a standard query. Furthermore, from reduced set of data reported by optimal query, the methodology synthesizes complete set of sensory data at a base station (reporting unit with surplus power supply). We validated the effectiveness of our methodology with real world sensor data. The result shows that our methodology can synthesize complete set of sensory data analogues to standard query with 93 accuracy.