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.
@cite_13: In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is opportunistically aggregated at intermediate nodes on a low-latency tree. In this paper, we explore and evaluate greedy aggregation, a novel approach that adjusts aggregation points to increase the amount of path sharing, reducing energy consumption. Our preliminary results suggest that, under investigated scenarios, greedy aggregation can achieve up to 45 energy savings over opportunistic aggregation in high-density networks without adversely impacting latency or robustness.
have already modeled the impact of data aggregation schemes in WSN @cite_13 . More specifically, they showed the impact of network density on data aggregation using directed diffusion with a greedy algorithm to construct trees. In their work, they proposed the in-network aggregation (along with the greedy tree) and compared the perfect aggregation (which saves the headers and merges the content in new packets with the same length) with the linear aggregation (which also saves the headers, but appends the content in larger payloads). According to simulation results, the greedy algorithm can reduce @math 43 in high-density networks. Finally, the authors concluded that, in high-density networks, more energy is saved by the greedy algorithm, and the delays are as good as using the opportunistic algorithm.