基于多无线传感器网络数据的集中预测机制
A Centralized Mechanism to Make Predictions Based on Data From Multiple WSNs
AI总结：
本文提出了一种集中机制，通过多个无线传感器网络间的信息交换进行预测，并根据预测结果调整网络工作负载。通过具体用例展示了如何利用外部网络数据优化湿度测量网络，并通过仿真验证了在能耗与测量质量之间平衡的最优解。
摘要：
本文提出了一种方法，通过无线传感器网络（WSNs）间的信息交换进行预测，并根据预测结果调整WSN的工作负载。我们展示了利用其他WSN产生的信息（可能属于同一管理域或不同域）进行智能利用的可行性。通过一个具体用例，利用温度测量WSN的数据优化湿度测量WSN的操作。基于专门的性能评分，仿真结果表明该新方法能够找到能耗与测量质量之间的最佳平衡点。此外，我们还概述了需要克服的额外挑战，并提出了未来工作的指导性结论。
英文摘要：
In this work, we present a method that exploits a scenario with inter-Wireless Sensor Networks (WSNs) information exchange by making predictions and adapting the workload of a WSN according to their outcomes. We show the feasibility of an approach that intelligently utilizes information produced by other WSNs that may or not belong to the same administrative domain. To illustrate how the predictions using data from external WSNs can be utilized, a specific use-case is considered, where the operation of a WSN measuring relative humidity is optimized using the data obtained from a WSN measuring temperature. Based on a dedicated performance score, the simulation results show that this new approach can find the optimal operating point associated to the trade-off between energy consumption and quality of measurements. Moreover, we outline the additional challenges that need to be overcome, and draw conclusions to guide the future work in this field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
