Tag anticollision has long been an important issue in RFID systems. To accelerate tag identification, some researchers have recently adopted bit tracking technology that allows the reader to detect the locations of collided bits in a collision slot. However, these methods still encounter the problem of too many collisions occurring at the beginning of identification. This paper proposes an optimal query tracking tree protocol (OQTT) that tries to separate all of the tags into smaller sets to reduce collisions at the beginning of identification. Using bit tracking technology, OQTT mainly adopts three proposed approaches, bit estimation, optimal partition, and query tracking tree. Bit estimation first estimates the number of tags based on the locations of collided bits. Optimal partition then determines the optimal number of the initial sets based on this estimation. Query tracking tree splits a set of collided tags into two subsets using the first collided bit in the tag IDs. This paper analyzes the efficiency of OQTT, which represents how many tags can be identified in a slot. Results show that its efficiency is close to 0.614, the highest efficiency published to date. The simulation results further show that OQTT outperforms other existing algorithms.

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally, self-regulation behavior significantly influences online learning of students. However, exactly how network centrality and self-regulation influence learning behavior and effectiveness in an e-learning environment remains unclear. Therefore, this study investigates how both variables (ie, network centrality and self-regulation) impact student learning in an SNA-related e-learning environment. Analytical results indicate that the student group with high-level centrality and low-level self-regulation more significantly progresses in learning achievement than the other groups. The second finding shows the group also has the highest number of students asking for help, revealing they have the highest system utilization rate.

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