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.