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This paper aims to balance the total energy consumption and the transmission delay for data gathering application in wireless sensor networks.

This paper adopts a hierarchical grid structure to reduce the total energy consumption, and utilizes a tree architecture to decrease the transmission delay.

In the results, the proposed method performs better, in terms of the number of rounds and the energy × delay cost, than other data gathering protocols with different network sizes and node densities. Moreover, the proposed method also provides good coverage preservation in different environments.

In this paper, sensor nodes are assumed to be uniformly distributed, homogenous, energy‐constrained. Each sensor node also has ability to adjust its transmission power. For practice, the proposed method needs location information of sensor nodes and the radio interference between sensor nodes during data transmissions should be considered.

The proposed method can significantly reduce the delay time and may be suitable for real‐time data gathering applications.

This paper combines hierarchical grid structure with tree architecture to minimize the energy × delay cost for data gathering application.

The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.

The original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.

The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.

There is a need to have accurate location information of reference nodes.

This is an effective low‐cost solution for the localization of sensor nodes.

An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.

This paper aims to propose an open service access (OSA) service capability server (SCS) architecture that supports the network capabilities to the Application Server (AS).

Based on this architecture, the service capability feature (SCF) provides the OSA Application programming interface functions by implementing the SCF service logic module and callback module. The SCF uses the XML communication module to interact with service capability (SC), which is the bearer to realize services by implementing the SC service logic module. The SC service logic interacts with mobile core network through the session initiation protocol (SIP)‐based session control module and the SIP Callback module.

The push to talk over cellular service is used to illustrate how the proposed OSA SCS interacts with the AS and the mobile core network elements.

In the design, when implementing a new service (i.e. to create a new SCS), one only needs to create the Callback module and the Service Logic modules, and other SC/SCF modules can be reused.

Through this modulized SCS design, the OSA service deployment can be speeded up.

In this paper, the design of multiple channels to achieve the goal of a high‐performance medium access control (MAC) protocol is to be proposed to solve the problem of wasting bandwidth resources due to waiting for the backoff time.

In the MAC design of this paper, a control channel and a data channel are used to improve bandwidth utilization. When the control channel waits for the backoff time, the data channel may transfer data. As a result, bandwidth utilization can be improved. In order to have better bandwidth utilization in multiple channels, the authors also propose a bandwidth allocation strategy for control channels and data channels. According to the strategy, the control and data signals can be smoothly transmitted without blocking or waiting, thereby not wasting bandwidth resources. Finally, the authors propose multiple control sub‐channels and data sub‐channels to further reduce the backoff time penalty and make more communication pairs work in a transmission range to increase the throughput.

The paper solves the following problems bandwidth waste that results from waiting for the backoff time in the single channel model and bandwidth allocation strategy for the control and data sub‐channels in the multiple channel model to achieve throughput enhancement in mobile ad‐hoc networks.

The proposed method needs the support of multiple channels.

From the result, the bandwidth allocation ratio of the proposed method performs better than other various allocation ratios. In addition, the proposed method with the bandwidth allocation strategy and multiple data and control sub‐channels results in a better throughput than IEEE 802.11 DCF by 22.3 per cent.

The proposed method using multiple control and data sub‐channels can improve the throughput and reduce bandwidth waste over IEEE 802.11 DCF.

The purpose of this paper is to examine and predict users' information‐seeking intention regarding academic digital library services, using the theory of reasoned action (TRA) and the theory of planned behavior (TPB).

Data are collected from 224 Taiwanese undergraduate and graduate students to assess the influence of attitude, subjective norm, and perceived behavioral control on the intention to seek information in an academic digital library. The results of structural equation model‐fitting analyses show that the TPB is better than the TRA in predicting the information‐seeking intention in an academic digital library.

Specially, the empirical results indicated that perceived behavioral control is a better predictor of behavioral intention than is attitude or subjective norm.

This paper assessed self‐reported information‐seeking intention as part of the survey and, as a result, could have introduced inaccuracies.

The findings of the paper will help academic digital libraries to address the key factor which influences users' intention to seek information and to intensify their performance to meet user needs.

Although the nature of the user experience in the digital environment appears to be quite different from the experience of looking through archival boxes or folders, research on the use of academic digital library services is scarce – particularly regarding user intention in the process of seeking information. The TRA and TPB are novel and usable in explaining the intention of online users to seek information, and these findings may be generally applicable to academic digital libraries and users.

This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often encounter some teaching problems. These are frequently related to the fact that the teacher cannot clearly know the learning status of students, such as their degree of learning concentration and capacity to absorb knowledge. In order to deal with this situation, this study uses a learning concentration detection system (LCDS), combining sensor technology and an artificial intelligence method, to better understand the learning concentration of students in a learning environment.

The proposed system uses sensing technology to collect information about the learning behavior of the students, analyzes their concentration levels, and applies an artificial intelligence method to combine this information for use by the teacher. This system includes a pressure detection sensor and facial detection sensor to detect facial expressions, eye activities and body movements. The system utilizes an artificial bee colony (ABC) algorithm to optimize the system performance to help teachers immediately understand the degree of concentration and learning status of their students. Based on this, instructors can give appropriate guidance to several unfocused students at the same time.

The fitness value and computation time were used to evaluate the LCDS. Comparing the results of the proposed ABC algorithm with those from the random search method, the algorithm was found to obtain better solutions. The experimental results demonstrate that the ABC algorithm can quickly obtain near optimal solutions within a reasonable time.

A learning concentration detection method of integrating context-aware technologies and an ABC algorithm is presented in this paper. Using this learning concentration detection method, teachers can keep abreast of their students' learning status in a teaching environment and thus provide more appropriate instruction.

A multimedia computer system is one that can create, import, integrate, store, retrieve, edit, and delete two or more types of media materials in digital form, such as audio, image, full‐motion video, and text information. This paper surveys four possible types of multimedia computer systems: hypermedia, multimedia database, multimedia message, and virtual reality systems. The primary focus is on advanced multimedia systems development projects and theoretical efforts that suggest long‐term trends in this increasingly important area.

As the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become challenging tasks for consumers. Hence, knowing the closing price of a given auction would be an advantage, since this information will ensure a win in a given auction. However, predicting a closing price for an auction is not easy, since it is dependent on many factors. The purpose of this paper is to report on a predictor agent that utilises grey system theory to predict the closing price for a given auction.

The focus of the research is on grey system agent. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price in order to maximise the bidder's profit. The performance of this predictor agent is compared with two well‐known techniques, the Simple Exponential Function and the Time Series, in a simulated auction environment and in the eBay auction.

The grey theory agent gives a better result when less input data are made, while the Time Series Agent can be used with the availability of a lot of information. Although the Simple Exponential Function Agent is able to predict well with less input data, it is not an appropriate method to be applied in the prediction model since its formula is not realistic and applicable in predicting the online auction closing price. The experimental results also showed that using moving historical data produces a higher accuracy rate than using fixed historical data for all three agents.

Grey system theory prediction model, GM(1, 1) has not been applied in online auction prediction. In this paper the authors have applied grey theory into an agent to predict the closing price of an online auction, in order to increase the profit of bidders in the bidding stage. The experimental results show that the accuracy of the grey prediction model is more then 90 per cent, with less then eight historical data inputs.