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.

Group awareness can affect student online learning while self-regulation also can substantially influence student online learning. Although some studies identify that these two variables may partially determine learning behavior, few empirical studies or thorough analyses elucidate the simultaneous impact of these two variables (group awareness and self-regulation) on online learning behavior. This paper compared one online collaboration environments with GA support with one without group awareness (NA) support and further investigated how these two variables, different system types (i.e., GA and NA) and different self-regulation levels (i.e., high and low), influence learning task (i.e., assessment) participation, and peer interaction (i.e., asking for help and willing to help) using two-way analysis of variance (ANOVA). Analytical results first showed that both variables have significant interaction on assessment participation and requesting rate. GA can particularly stimulate students with high-level self-regulation to engage more learning task (assessment) participation and ask for help more, compared with students with low-level self-regulation. Second, both variables have no significant interaction on willingness to help. The GA class can enhance a student's willingness to help regardless of his/her self-regulation level.

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SUMMARYSubscriber stations located in different places encounter various interferences in an IEEE 802.16 network, resulting in that their communication channels experience varying channel conditions. Thus, an excellent bandwidth allocation algorithm should not only satisfy various QoS required by heterogeneous traffic, but also consider the channel quality to maximize bandwidth utilization. In this paper, a bandwidth allocation algorithm with channel quality awareness and QoS guarantee, called CQQ, is proposed. CQQ not only satisfies each connection's QoS requirement, but also dynamically adjusts the downlink/uplink bandwidth to match current downlink/uplink traffic ratio. CQQ allocates more bandwidth to the connections having better channel quality by applying weighted fair queuing strategy to raise the bandwidth usage. CQQ provides lower delay violation ratio and higher goodput than the previous algorithms, as observed from the simulation results. Copyright © 2012 John Wiley & Sons, Ltd.