2 research outputs found
On the importance and feasibility of forecasting data in sensors
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.Comment: 30 pages and 12 figures. This paper has been submitted to the
Transactions on Mobile Computing journa
The Impact of Dual Prediction Schemes on the Reduction of the Number of Transmissions in Sensor Networks
Future Internet of Things (IoT) applications will require that billions of
wireless devices transmit data to the cloud frequently. However, the wireless
medium access is pointed as a problem for the next generations of wireless
networks; hence, the number of data transmissions in Wireless Sensor Networks
(WSNs) can quickly become a bottleneck, disrupting the exponential growth in
the number of interconnected devices, sensors, and amount of produced data.
Therefore, keeping a low number of data transmissions is critical to
incorporate new sensor nodes and measure a great variety of parameters in
future generations of WSNs. Thanks to the high accuracy and low complexity of
state-of-the-art forecasting algorithms, Dual Prediction Schemes (DPSs) are
potential candidates to optimize the data transmissions in WSNs at the finest
level because they facilitate for sensor nodes to avoid unnecessary
transmissions without affecting the quality of their measurements. In this
work, we present a sensor network model that uses statistical theorems to
describe the expected impact of DPSs and data aggregation in WSNs. We aim to
provide a foundation for future works by characterizing the theoretical gains
of processing data in sensors and conditioning its transmission to the
predictions' accuracy. Our simulation results show that the number of
transmissions can be reduced by almost 98% in the sensor nodes with the highest
workload. We also detail the impact of predicting and aggregating transmissions
according to the parameters that can be observed in common scenarios, such as
sensor nodes' transmission ranges, the correlation between measurements of
different sensors, and the period between two consecutive measurements in a
sensor.Comment: 30 pages, 8 figure