IoT devices usually produce large volume of data in low semantic levels. Also, they constantly communicate to the sink node. Since IoT devices are resource-constrained in terms of battery, memory and processing power, the usage of advanced machine learning techniques to support real-time decision making is unfeasible. So a challenge is to provide machine learning techniques tailored to the resource-constrained environment of IoT either by adapting existing techniques or developing new ones. In this paper, we propose an heuristic for adapting data prediction and data fusion techniques to preprocess data to avoid unnecessary communication between sensor devices and sink node. Also, in this work we compare (i) linear estimation; (ii) Weightless Neural Networks; and (iii) Moving Average Convergence Divergence in the aforementioned context. The main contribution is the proposal of an heuristics to mitigate unnecessary communications while avoiding data accuracy loss by combining data fusion and data prediction techniques. As implementation of the heuristics we present in this work, we introduce two algorithms: Dione and Delfos. Dione as a centralized approach and Delfos as a decentralized approach. We executed experiments to assess the performance of Dione and Delfos.

中文翻译：

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基于多传感器预测的物联网启发式算法

物联网设备通常会在低语义级别产生大量数据。此外，它们不断地与接收器节点通信。由于物联网设备在电池、内存和处理能力方面受到资源限制，因此使用先进的机器学习技术来支持实时决策是不可行的。因此，一个挑战是通过调整现有技术或开发新技术来提供适合物联网资源受限环境的机器学习技术。在本文中，我们提出了一种启发式方法，用于适应数据预测和数据融合技术来预处理数据，以避免传感器设备和汇节点之间不必要的通信。此外，在这项工作中，我们比较了（i）线性估计；(ii) 失重神经网络；(iii) 上述情况下的移动平均收敛散度。主要贡献是提出了一种启发式方法，通过结合数据融合和数据预测技术来减少不必要的通信，同时避免数据准确性损失。作为我们在这项工作中提出的启发式算法的实现，我们引入了两种算法：Dione 和 Delfos。Dione 作为集中式方法，而 Delfos 作为分散式方法。我们进行了实验来评估 Dione 和 Delfos 的性能。