Information2014, 5(4), 612-621; doi:10.3390/info5040612 - published 14 November 2014 Show/Hide Abstract
Abstract: In recent years, a large portion of smartphone applications (Apps) has targeted context-aware services. They aim to perceive users’ real-time context like his/her location, actions, or even emotion, and to provide various customized services based on the inferred context. However, context-awareness in mobile environments has some challenging issues due to limitations of devices themselves. Limited power is regarded as the most critical problem in context-awareness on smartphones. Many studies have tried to develop low-power methods, but most of them have focused on the power consumption of H/W modules of smartphones such as CPU and LCD. Only a few research papers have recently started to present some S/W-based approaches to improve the power consumption. That is, previous works did not consider energy consumed by context-awareness of Apps. Therefore, in this paper, we focus on the power consumption of context-aware Apps. We analyze the characteristics of context-aware Apps in a perspective of the power consumption, and then define two main factors which significantly influence the power consumption: a sort of context that context-aware Apps require for their services and a type of ways that a user uses them. The experimental result shows the reasonability and the possibility to develop low-power methods based on our analysis. That is, our analysis presented in this paper will be a foundation for energy-efficient context-aware services in mobile environments.
Information2014, 5(4), 602-611; doi:10.3390/info5040602 - published 12 November 2014 Show/Hide Abstract
Abstract: This paper describes the design of a high-efficiency vehicular roof-mounted antenna for wireless access for vehicular environment (WAVE) communication systems used for ubiquitous intelligent systems. The main objective of the ubiquitous intelligent system’s automotive IT technology is to enhance the connectivity among vehicles to ensure seamless communication and to reduce the initial access time using high-performance antenna systems. The efficiency of WAVE communication systems used for ubiquitous intelligent systems depends on the antenna efficiency. The proposed vehicular antenna for WAVE communication systems shows an improvement of approximately 4.77 dB in the return loss, as compared with a conventional antenna system.
Information2014, 5(4), 587-601; doi:10.3390/info5040587 - published 11 November 2014 Show/Hide Abstract
Abstract: In this paper, firstly, a new intuitionistic fuzzy (IF) entropy has been put forward, which considered both the uncertainty and the hesitancy degree of IF sets. Through comparing with other entropy measures, the advantage of the new entropy measure is obvious. Secondly, based on the new entropy measure, a new decision making method of a multi-attribute decision making problem was subsequently put forward, in which attribute values are expressed with IF values. In the cases of attribute weights, completely unknown and attribute weights are partially known. Two methods were constructed to determine them. One method is an extension of the ordinary entropy weight method, and the other method is a construction the optimal model according to the minimum entropy principle. Finally, two practical examples are given to illustrate the effectiveness and practicability of the proposed method.
Information2014, 5(4), 570-586; doi:10.3390/info5040570 - published 5 November 2014 Show/Hide Abstract
Abstract: The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam’s Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS) algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN) for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO) and back-propagation (BP), indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance criteria RMSE, MAPE, and R obtained by the CS-FNN in this scenario were calculated as 48.7161, 0.067268 and 0.8965, respectively. These results were highly correlated to actual values. It is expected that this work may be useful for hydrographic forecasting.
Information2014, 5(4), 558-569; doi:10.3390/info5040558 - published 28 October 2014 Show/Hide Abstract
Abstract: In this paper, the authors demonstrate the suitability of IT-supported knowledge repositories for knowledge retention. Successful knowledge retention is dependent on what is stored in a repository and, hence, possible to share. Accordingly, the ability to capture the right (relevant) knowledge is a key aspect. Therefore, to increase the quality in an IT-supported knowledge repository, the identification activity, which starts the capture process, must be successfully performed. While critical success factors (CSFs) for knowledge retention and knowledge management are frequently discussed in the literature, there is a knowledge gap concerning CSFs for this specific knowledge capture activity. From a knowledge retention perspective, this paper proposes a model that characterizes CSFs for the identification activity and highlights the CSFs’ contribution to knowledge retention.
Information2014, 5(4), 548-557; doi:10.3390/info5040548 - published 27 October 2014 Show/Hide Abstract
Abstract: Internet of Things (IoT) for Smart Environments (SE) is a new scenario that collects useful information and provides convenient services to humans via sensing and wireless communications. Infra-Red (IR) motion sensors have recently been widely used for indoor lighting because they allow the system to detect whether a human is inside or outside the sensors’ range. In this paper, the performance of a position estimation scheme based on IR motion sensor is evaluated in an indoor SE. The experimental results show that we can track the dynamic position of a pedestrian in straight moving model as well as two dimensional models. Experimental results also show that higher performance in accuracy and dynamic tracking in real indoor environment can be achieved without other devices.