The Internet of Things (IoT) networks have become the infrastructure to enable the detection and reaction of anomalies in various domains, where an efficient sensory data gathering mechanism is fundamental since IoT nodes are typically constrained in their energy and computational capacities. Besides, anomalies may occur occasionally in most applications, while the majority of time durations may reflect a healthy situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented in terms of the category of sensory data. To decrease the energy consumption of IoT networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an IoT node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this IoT node is different from the category of the forecasted one in the cloud. Experiments are conducted and evaluation results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption.

Accurate air quality prediction is of great significance for pollution prevention and disaster prevention. Effective and reliable prediction models are needed not only for short time prediction, but are even more important for long time-series future predictions. In the long time series, most of the current models might not function as accurately as in the short period and thus a new model is required. In this paper, the new PM_2.5 predictor is proposed to achieve accurate long time series PM_2.5 prediction in Beijing. The predictor simplifies the input parameters through Spearman correlation analysis and implements the long time series prediction through Informer. The results show that AQI, CO, NO₂, and PM₁₀ concentrations are selected from the air quality data, and Dew Point Temperature (DEWP) and wind speed are incorporated from two meteorological data to better improve the prediction efficiency by almost 27%. By comparing with LSTM and attention-LSTM models, the model constructed in this paper performs well in different prediction time periods, with at least 21%, 19%, 28%, and 35% improvement in accuracy in four prediction time series: 48 h, 7 days, 14 days, and 30 days. In conclusion, the proposed model is proved to solve the problem of predicting long time series PM_2.5 concentrations in the future, which can make up for the shortcomings of the currently existing models and have good application value.

In microservice systems, availability can be ensured through a variety of measures, such as fault tolerance and flow limiting, which are collectively called the flow control. In the current mainstream system design, the flow control rules are usually fixed and set manually, which cannot be dynamically adjusted according to the flow shape. The performance of the system is thus not fully explored. To mitigate this problem, an adaptive dynamic flow control algorithm is proposed. Based on the system's monitoring data and current flow, the algorithm calculates the flow‐limiting threshold in real time, and then it implements fine‐grained service adaptive flow control to improve the resource utilization. Experimental results show that the performance of the adaptive automatic flow control is better than that of the traditional static method on resource utilization.