Remaining Useful Life (RUL) estimation plays a critical role in Prognostics and Health Management (PHM). Traditional machine health maintenance systems are often costly, requiring sufficient prior expertise, and are difficult to fit into highly complex and changing industrial scenarios. With the widespread deployment of sensors on industrial equipment, building the Industrial Internet of Things (IIoT) to interconnect these devices has become an inexorable trend in the development of the digital factory. Using the device's real-time operational data collected by IIoT to get the estimated RUL through the RUL prediction algorithm, the PHM system can develop proactive maintenance measures for the device, thus, reducing maintenance costs and decreasing failure times during operation. This paper carries out research into the remaining useful life prediction model for multi-sensor devices in the IIoT scenario. We investigated the mainstream RUL prediction models and summarized the basic steps of RUL prediction modeling in this scenario. On this basis, a data-driven approach for RUL estimation is proposed in this paper. It employs a Multi-Head Attention Mechanism to fuse the multi-dimensional time-series data output from multiple sensors, in which the attention on features is used to capture the interactions between features and attention on sequences is used to learn the weights of time steps. Then, the Long Short-Term Memory Network is applied to learn the features of time series. We evaluate the proposed model on two benchmark datasets (C-MAPSS and PHM08), and the results demonstrate that it outperforms the state-of-art models. Moreover, through the interpretability of the multi-head attention mechanism, the proposed model can provide a preliminary explanation of engine degradation. Therefore, this approach is promising for predictive maintenance in IIoT scenarios.

As one of the most striking advances in online advertising, real-time bidding (RTB) [3] has received increasing attention since it improves the efficiency and transparency of the ad ecosystem [4]. In RTB, the publishing media sells ad impressions through public auctions, and advertisers bid on their targeting ad impressions in real-time and pay for their winning impressions. Therefore, it requires the bidding agent to make accurate user feedback predictions for each ad impression and determine a reasonable bidding price to maximize the long-term revenue [5] of the ad campaign. Figure 1 illustrates the entire process of an advertiser participating in bidding for an ad impression. Initially, the advertiser registers an ad campaign on the Demand Side Platform (DSP) and specifies the campaign's budget as well as targeting rules for each ad delivery period (usually a day). Bidding agents running on DSP participate in RTB on behalf of advertisers.