Big data collection and storage have become one of the most obvious challenge in this era. Indeed, much of that data is collected thanky to a huge number of connected devices in sensing-based IoT applications. Thus, in order to deal with data growth in such applications, researchers have focused on data reduction approach as an efficient solution for minimizing the amount of data collection and saving the limited sensor energy in such networks. Mainly, data reduction approach relies on various kinds of data processing techniques such that aggregation, compression, prediction, clustering, sensing frequency adaptation and spatial-temporal correlation. However, each of those techniques has its own advantages and disadvantages regarding sensor energy saving, data reduction ratio, data accuracy, complexity, etc. In this paper, we propose a hybrid data collection and energy saving mechanism, called All-in-One, for sensing-based IoT applications. The proposed mechanism takes advantages from existing data reduction techniques while optimizing various performance metrics. All-in-One relies on the cluster network architecture and works on three main phases: on-period, in-period and in-node. The first phase, e.g. on-period, allows each sensor node to search the similarity among its periodic collected data then to reduce its data transmission to the Cluster-Head (CH) by applying either data aggregation, compression or prediction technique. The second phase, e.g. in-period, allows each sensor to study the variation of the monitored condition then to reduce its data collection according to two techniques, on-off transmission or adapting sensing frequency. The last phase, e.g. in-node, is applied at the CH level and aims to remove the redundancy among data collected by neighboring nodes, based on in-network correlation or data clustering techniques, before sending the data to the sink. We conducted simulations on real sensor data in order to evaluate the efficiency of our mechanism, in terms of several performance metrics, compared to other exiting techniques.

大数据收集和存储已成为该时代最明显的挑战之一。确实，许多数据被大量收集到基于传感的物联网应用中的大量连接设备上。因此，为了应对此类应用中的数据增长，研究人员已将注意力集中在数据缩减方法上，将其作为将此类网络中的数据收集量降至最低并节省有限的传感器能​​量的有效解决方案。数据缩减方法主要依赖于各种数据处理技术，例如聚合，压缩，预测，聚类，感测频率适应性和时空相关性。但是，这些技术中的每一种在传感器节能，数据缩减率，数据准确性，复杂性等方面都有其优点和缺点。在本文中，我们针对基于传感的物联网应用提出了一种称为“多合一”的混合数据收集和节能机制。所提出的机制在优化各种性能指标的同时，充分利用了现有数据缩减技术的优势。多合一依赖于群集网络体系结构，并在三个主要阶段工作：on-period，in-period和in-node。第一阶段（例如，开启）允许每个传感器节点在其定期收集的数据之间搜索相似性，然后通过应用数据聚合，压缩或预测技术来减少其向簇头（CH）的数据传输。第二阶段（例如，在周期内）允许每个传感器研究所监视条件的变化，然后根据开/关传输或适应感测频率这两种技术来减少其数据收集。最后阶段，例如 节点内应用在CH级别，目的是在将数据发送到接收器之前，基于网络内相关性或数据聚类技术，消除相邻节点收集的数据之间的冗余。与其他现有技术相比，我们对真实的传感器数据进行了仿真，目的是根据几种性能指标来评估我们的机构的效率。