Localization has long been considered as a crucial research problem for pervasive sensing systems, especially with the arrival of big data era. Various techniques have been proposed to improve the localization accuracy by leveraging common wireless signals, such as radio signal strength indication (RSSI), collected from sensors placed in pervasive environments. However, the measured signal value can be easily affected by noise caused by physical obstacles in such sensing environment, which in turn compromises the localization performance. Hence, we present a novel RSSI-based area localization scheme using deep neural network (DNN) to explore the underlying correlation between the RSSI data and the respective sensor placement to achieve a superior localization performance. Moreover, to cope with the sensor data loss issue that commonly occurs during wireless sensor network (WSN) operation, an algorithm is designed to reconstruct the missing data for respective sensors in order to preserve the performance of DNN localization model. The effectiveness of the proposed scheme is verified with a real-world WSN testbed deployed inside an office building. The results demonstrate that the proposed scheme provides satisfactory prediction accuracy in area localization for pervasive sensing systems, regardless of the data loss issue that occurs with the respective sensors.

长期以来，本地化一直被认为是普及感测系统的关键研究问题，尤其是在大数据时代到来之际。已经提出了各种技术来利用从放置在普适环境中的传感器收集的普通无线信号（例如，无线电信号强度指示（RSSI））来提高定位精度。然而，在这样的感测环境中，由物理障碍物引起的噪声容易影响所测量的信号值，从而损害了定位性能。因此，我们提出了一种使用深度神经网络（DNN）的新颖的基于RSSI的区域定位方案，以探索RSSI数据与各个传感器位置之间的潜在相关性，以实现出色的定位性能。此外，为了解决通常在无线传感器网络（WSN）操作期间发生的传感器数据丢失问题，设计了一种算法来重构各个传感器的丢失数据，以保留DNN定位模型的性能。提议的方案的有效性通过部署在办公大楼内的真实WSN测试平台进行了验证。结果表明，所提出的方案为普适传感系统提供了令人满意的区域定位预测精度，而与各个传感器所发生的数据丢失问题无关。提议的方案的有效性通过部署在办公大楼内的真实WSN测试平台进行了验证。结果表明，所提出的方案为普适传感系统提供了令人满意的区域定位预测精度，而与各个传感器所发生的数据丢失问题无关。提议的方案的有效性通过部署在办公大楼内的真实WSN测试平台进行了验证。结果表明，所提出的方案为普适传感系统提供了令人满意的区域定位预测精度，而与各个传感器所发生的数据丢失问题无关。