The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks

Abstract

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.

Quality-Based and Energy-Efficient Data Communication for the Internet of Things Networks

“…Similarly, the same situation can be considered between sensor nodes and CH; in principle, it depends on the network architecture or topology. Interested readers can refer to Dias et al [22] to study the impact of data prediction schemes on the reduction of the number of transmissions in WSN.…”

“…Our AM-DR model is based on the assumption that the required energy for radio transmissions in WSNs is some order of magnitude higher than the energy required for local processing at sensor nodes (as discussed and shown in [22,29,30,31]). We consider that each cluster member (i.e.…”

Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

“…The prediction-based methods are able to guarantee the timely data acquisition, as well as the suppression of data transmission. Theoretically, high accuracy forecasts can reduce the amount of transmissions by 30–80% (varying with application cases) with satisfactory accuracy [31,32]. However, it is required for the operation mode to build a model and to transmit the model parameters among the network.…”

An adaptive method for data reduction in the Internet of Things

“…There are several existing work on prediction-based methods and solutions [5,6]. Some of these approaches such as [7] and [8] assume that sensor observations are highly correlated.…”

“…To address this problem, dual prediction schemes have been proposed. Dual prediction minimises the number of data transmissions between sensor and sink nodes at the finest level such that sensor nodes transmit only a set of their sensed values without affecting the quality of the actual measurement values [6]. For example, Jain et al [8] propose a dual prediction scheme using Kalman filters for reducing the number of transmissions.…”