The first generation of wireless sensor nodes have constrained energy resources and computational power, which discourages applications to process any task other than measuring and transmitting towards a central server. However, nowadays, sensor networks tend to be incorporated into the Internet of Things and the hardware evolution may change the old strategy of avoiding data computation in the sensor nodes. In this paper, we show the importance of reducing the number of transmissions in sensor networks and present the use of forecasting methods as a way of doing it. Experiments using real sensor data show that state-of-the-art forecasting methods can be successfully implemented in the sensor nodes to keep the quality of their measurements and reduce up to 30 of their transmissions, lowering the channel utilization. We conclude that there is an old paradigm that is no longer the most beneficial, which is the strategy of always transmitting a measurement when it differs by more than a threshold from the last one transmitted. Adopting more complex forecasting methods in the sensor nodes is the alternative to significantly reduce the number of transmissions without compromising the quality of their measurements, and therefore support the exponential growth of the Internet of Things.
More recently, the mechanism presented in @cite_33 computes the forecasting models in the sensor nodes before transmitting them to the . That is, the sensor nodes became completely autonomous and independent of the data computation made in the . On the other hand, the choice of the method is still restricted by the sensor nodes' memory and processing power limitations, which also narrows the range of situations in which it can be successfully adopted.
@cite_33
2037141347
One of the most challenging goals of many wireless sensor network (WSN) deployments is the reduction of energy consumption to extend system lifetime. This paper considers a novel combination of techniques that address energy savings at the hardware and application levels: wake-up receivers and node-level power management, plus prediction-based data collection. Individually, each technique can achieve significant energy savings, but in combination, the results are impressive. This paper presents a case study of these techniques as applied in a road tunnel for light monitoring. Preliminary results show the potential for two orders of magnitude reduction in power consumption. This savings of 380 times allows the creation of an energetically sustainable system by considering integration with a simple, photovoltaic energy harvester.