Płaczek Bartłomiej
Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland.
Sensors (Basel). 2023 Oct 15;23(20):8478. doi: 10.3390/s23208478.
Sensor networks can provide valuable real-time data for various IoT applications. However, the amount of sensed and transmitted data should be kept at a low level due to the limitations imposed by network bandwidth, data storage, processing capabilities, and finite energy resources. In this paper, a new method is introduced that uses the predicted intervals of possible sensor readings to efficiently suppress unnecessary transmissions and decrease the amount of data samples collected by a sensor node. In the proposed method, the intervals of possible sensor readings are determined with a multi-agent system, where each agent independently explores a historical dataset and evaluates the similarity between past and current sensor readings to make predictions. Based on the predicted intervals, it is determined whether the real sensed data can be useful for a given IoT application and when the next data sample should be transmitted. The prediction algorithm is executed by the IoT gateway or in the cloud. The presented method is applicable to IoT sensor networks that utilize low-end devices with limited processing power, memory, and energy resources. During the experiments, the advantages of the introduced method were demonstrated by considering the criteria of prediction interval width, coverage probability, and transmission reduction. The experimental results confirm that the introduced method improves the accuracy of prediction intervals and achieves a higher rate of transmission reduction compared with state-of-the-art prediction methods.
传感器网络可为各种物联网应用提供有价值的实时数据。然而，由于网络带宽、数据存储、处理能力和有限能源资源的限制，传感和传输的数据量应保持在较低水平。本文介绍了一种新方法，该方法利用可能的传感器读数的预测区间来有效抑制不必要的传输，并减少传感器节点收集的数据样本量。在所提出的方法中，通过多智能体系统确定可能的传感器读数区间，其中每个智能体独立探索历史数据集，并评估过去和当前传感器读数之间的相似度以进行预测。基于预测区间，确定实际传感数据是否对给定的物联网应用有用以及何时应传输下一个数据样本。预测算法由物联网网关或在云端执行。所提出的方法适用于利用处理能力、内存和能源资源有限的低端设备的物联网传感器网络。在实验过程中，通过考虑预测区间宽度、覆盖概率和传输减少等标准，证明了所介绍方法的优势。实验结果证实，与现有预测方法相比，所介绍的方法提高了预测区间的准确性，并实现了更高的传输减少率。
