You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: This paper is aimed at saving energy in information collection in wireless sensor networks. Our method is to keep sensor nodes from transmitting redundant information, which can be predicted by the sink node. By utilizing the ARIMA model for prediction, we propose an energy efficient information collection scheme. Since the ARIMA model is capable of modeling a wide variety of complex time series by simply adjusting parameters, we believe it can be used in various applications without requiring extra human labor in model selection case by case. The effectiveness of our proposed method is demonstrated with a real application based on the MICA2 platform

[2]: The longevity of wireless sensor network (WSN) deployments is often crucial for real-time monitoring applications. Minimizing energy consumption by utilizing intelligent information processing is one of the main ways to prolong the lifetime of a network deployment. Data streams from the sensors need to be processed within the resource constraints of the sensing platforms to reduce the energy consumption associated with packet transmission. In this paper we carried out both simulation and real-world implementation of light-weight adaptive models to achieve a prolonged WSN lifetime. Specifically, we propose a Naive model that incurs virtually no cost with low memory footprint to realize this goal. Our results show that, despite its minimal complexity, the Naive model is robust when compared with other well-known algorithms used for prediction in WSNs. We show that our approach achieves up to 96 communication reduction, within 0.2 degrees error bound with no significant loss in accuracy and it is comparable in performance to the more complex algorithms like Exponential Smoothing (ETS).

[3]: Data reduction strategy is one of the schemes employed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application of WSN are compared with well-known prediction algorithms such as ARIMA, LMS and WMA models. We implemented a real-world deployment using 32bit mote-class device. Overall, up to 96 transmission reduction is achieved using our Naive method, while still able to maintain a considerable level of accuracy at 0.5 °C error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.
The works above inspired our model for data transmissions. However, different from our proposal, they focused only on data aggregation and did not evaluate the effects of DPS in WSN . Concerning the evaluation of DPS , were the first authors to introduce statistical methods to choose which prediction model better fits a certain environment [1] . They created a formula to estimate the Prediction Cost ( @math ), which considers the percentage of transmitted measurements ( @math ) and the user desired level of accuracy ( @math ) [1] . More recently, an extended formula was designed and implemented in real sensor nodes to compare the savings using several prediction methods, such as the , ES and ARIMA methods [2] [3] . The new formula is more generic than the original proposed and also considers the prediction models' memory footprint ( @math ) as a significant computational cost for sensor nodes: