Abstract IEEE 802.11ah is a new protocol designed for Internet of Things (IoT). IEEE 802.11ah uses a hierarchical ID assignment schema and designs the corresponding channel access method such that stations can access channel alternatively through the four-level’s hierarchical channel access structure. Nevertheless, collisions still cannot be effectively avoided in a dense IOT networking environment using the legacy IEEE 802.11ah. The work proposed the registration-based situation-aware access extension (RSAE) control scheme to avoid collisions, decrease backoff’s waiting time and improve slot utilization through the following two ways: (i) in contrast to the traditional CSMA/CA protocol that generates the backoff time for the next channel access after the collision happened, registering the backoff time to AP for scheduling the next channel access before the collision happens; (ii) extending the channel accessing’s privilege of those stations that did not complete the data transmission/receiving in the current slot to the available time of the next slot. Comparing with the legacy IEEE 802.11ah, the performance evaluation of the proposed RSAE scheme shown that it still can have a better throughput and a lower collision rate when there is a bigger number of stations in a slot.

View-based 3D model retrieval has become an important task in both computer vision and machine learning domains. Although deep learning methods have achieved excellent performances on view-based 3D model retrieval, the intrinsic correlation and the degree of view discrimination among multiple views in a 3D model have not been effectively exploited. To obtain a more efficient feature descriptor for 3D model retrieval, in this work, we propose the pairwise view weighted graph network (abbreviated PVWGN) for view-based 3D model retrieval where non-local graph layers are embedded into the network architecture to automatically mine the intrinsic relationship among multiple views of a 3D model. Furthermore, the view weighted layer is employed in the PVWGN to adaptively assign the weight to each view according to its aggregation information. In addition, the pairwise discrimination loss function is designed to improve the feature discrimination of the 3D model. Most importantly, these three issues are integrated into a unified framework. Extensive experimental results on the ModelNet40 and ModelNet10 3D model retrieval datasets show that PVWGN can outperform all state-of-the-art methods on the 3D model retrieval task with mAPs of 93.2% and 96.2%, respectively.