From smartphones to iPad: Power users’ disposition toward mobile media devices

The spread of mobile media devices (MMDs), such as smartphones and tablet computers, has been markedly rapid around the world in recent years. The diffusion of mobile media technology has turned some early adopters into “power users,” using MMD more innovatively, efficiently and thoroughly than others. Going beyond the research of innovative users and heavy users, this study proposes the concept of power users by investigating the interactions between the power use of MMD and personality attributes, online activities as well as traditional media use. The findings showed that personality attributes were strong predictors for power use, which was also partially predicated by media multitasking and traditional media use. Research in this stream should assist in understanding the diffusion of mobile media technology and its impact on human interaction.     

An energy-efficient task scheduling for mobile devices based on cloud assistant

Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.     

An economics-based negotiation scheme among mobile devices in mobile grid

In this paper, we propose an economics-based distributed negotiation scheme among mobile devices in mobile grid. In our model, there are energy negotiation and transactions between buyer devices and seller devices. Dynamic allocation of energy resources in mobile grid is performed through online transactions within markets. Mobile devices can be sellers and buyers that use optimization algorithms to maximize predefined utility functions during their transactions. Seller device agents sell the underlying energy resources of the mobile device. Buyer device agent makes buying decisions within the budget constraints to acquire energy resources. An economics-based negotiation algorithm among mobile devices is proposed. The proposed algorithm decomposes mobile grid system optimization problem into a sequence of two sub-problems. In the simulation, the performance evaluation of economics-based negotiation algorithm is evaluated.     

A survey on mobile affective computing

The spontaneous recognition of emotional states and personality traits of individuals has been puzzling researchers for years whereas pertinent studies demonstrating the progress in the field, despite their diversity, are still encouraging. This work surveys the most well-known research studies and the state-of-the-art on affect recognition domain based on smartphone acquired data, namely smartphone embedded sensors and smartphone usage. Inevitably, supplementary modalities employed in many eminent studies are also reported here for the sake of completeness. Nevertheless, the intention of the survey is threefold; firstly to document all the to-date relevant literature on affect recognition through smartphone modalities, secondly to argue for the full potential of smartphone use in the inference of affect, and thirdly to demonstrate the current research trends towards mobile affective computing.     

Provisioning of energy consumption information for mobile ads

Advertisement-embedded mobile applications have been reported to consume a non-trivial amount of energy. Although a few studies have focused on the energy consumption of mobile advertisements (ads), no previous work has addressed the mobile ad ecosystem, which consists of users, application developers, and ad providers. In this paper, we define the advertisement energy information (AEI) required for the mobile ad ecosystem, and we propose a set of application programming interfaces (APIs) to provide AEI that considers various requirements of the underlying ecosystem. To realize the APIs, we developed a system service in Android to collect the AEI accurately and with low overhead. The experiment results show the validity of the proposed scheme, and the case studies demonstrate the usefulness of the proposed APIs.     

Wukong: A cloud-oriented file service for mobile Internet devices

Along with the rapid growth of heterogeneous cloud services and network technologies, an increasing number of mobile devices use cloud storage services to enlarge their capacity and share data in our daily lives. We commonly use cloud service client-side software in a straightforward fashion. However, when more devices and users participate in heterogeneous services, the difficulty of managing these services efficiently and conveniently increases. In this paper, we report a novel cloud-oriented file service, Wukong, which provides a user-friendly and highly available facilitative data access method for mobile devices in cloud settings. Wukong supports mobile applications, which may access local files only, transparently accessing cloud services with a relatively high performance. To the best of our knowledge, Wukong is the first file service that supports heterogeneous cloud services for mobile devices by using the innovative storage abstraction layer. We have implemented a prototype with several plugins and evaluated it in a systematic way. We find that this easily operable file service has a high usability and extensibility. It costs about 50 to 150 lines of code to implement a new backend service support plugin. Wukong achieves an acceptable throughput of 179.11 kB/s in an ADSL environment and 80.68 kB/s under a countryside EVDO 3G network with negligible overhead.     

Seed-based authentication for mobile clients across multiple devices

Authenticating users for mobile cloud apps has been a major security issue in recent years. Traditional passwords ensure the security of mobile applications, but it also requires extra effort from users to memorize complex passwords. Seed-based authentication can simplify the process of authentication for mobile users. In the seed-based authentication, images can be used as credentials for a mobile app. A seed is extracted from an image and used to generate one-time tokens for login. Compared to complex passwords, images are more friendly to mobile users. Previous work had been done in seed-based authentication which focused on providing authentication from a single device. It is common that a mobile user may have two or more mobile devices. Authenticating the same user on different devices is challenging due to several aspects, such as maintaining the same credential for multiple devices and distinguishing different users. In this article, we aimed at developing a solution to address these issues. We proposed multiple-device authentication algorithms to identify users. We adopted a one-time token paradigm to ensure the security of mobile applications. In addition, we tried to minimize the authentication latency for better performance. Our simulation showed that the proposed algorithms can improve the average latency of authentication for 40% at most, compared to single-device solutions.     

XMobile: A MB-UID environment for semi-automatic generation of adaptive applications for mobile devices

Ubiquitous Computing promises seamless access to information anytime, anywhere with different and heterogeneous devices. This kind of environment imposes new challenges to software development. For example, information and user interface should be adapted according to contextual characteristics such as user, environment, and access device. In case of device adaptation, the development challenge is related to the heterogeneity of the devices, which requires software engineers to create different versions for each type of device and every platform. This paper proposes a MB-UID (model-based user interface development) approach for semi-automatic generation of adaptive applications for mobile devices. An environment, called XMobile, offers a device-independent user interface framework and a code generation tool for providing fast development of multi-platform and adaptive applications according to device and platform features. A case study is also presented to illustrate how the environment can be used for constructing an application for heterogeneous devices with different network connectivity modes.     

Type,Talk, or Swype: Characterizing and comparing energy consumption of mobile input modalities

It is reported that mobile users spend most of their time on texting SMS, Social Networking, Emailing, or sending instant messaging (IM), all of which involve text input. There are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each one of them engages a different set of hardware and consequently consumes different amounts of battery energy. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three input modalities. We find that the length of interaction, or the message length, determines the most energy efficient modality. For short interactions, less than 14–30 characters, SK is the most energy efficient. For longer interactions, however, STT significantly outperforms both SK and Swype. When message length distributions of popular text activities are considered, STT provides near optimal energy consumption without requiring the user to predict the message length and decide between SK and STT. In terms of battery life, the choice of input modality makes significant differences. If users always choose SK for all their text activities, they will consume nearly 50% of the phone battery each day. Choosing STT over SK can save 30%–40% of the battery depending on the choice of STT software.     

Navigating large terrains using commodity mobile devices

We describe a hybrid client-server technique for remote adaptive streaming and rendering of large terrains in resource-limited mobile devices. The technique has been designed to achieve an interactive rendering performance on a mobile device connected to a low-bandwidth wireless network. The rendering workload is split between the client and the server. The terrain area close to the viewer is rendered in real-time by the client. The terrain located far from the viewer is portrayed as view-dependent impostors, rendered by the server on demand. A prototype has been built and an exhaustive set of experiments covering several platforms, wireless networks and a wide range of viewer velocities has been conducted. Results show that the approach is feasible, effective and robust.     

Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.     

Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior

The use of mobile applications continues to experience exponential growth. Using mobile apps typically requires the disclosure of location data, which often accompanies requests for various other forms of private information. Existing research on information privacy has implied that consumers are willing to accept privacy risks for relatively negligible benefits, and the offerings of mobile apps based on location-based services (LBS) appear to be no different. However, until now, researchers have struggled to replicate realistic privacy risks within experimental methodologies designed to manipulate independent variables. Moreover, minimal research has successfully captured actual information disclosure over mobile devices based on realistic risk perceptions. The purpose of this study is to propose and test a more realistic experimental methodology designed to replicate real perceptions of privacy risk and capture the effects of actual information disclosure decisions. As with prior research, this study employs a theoretical lens based on privacy calculus. However, we draw more detailed and valid conclusions due to our use of improved methodological rigor. We report the results of a controlled experiment involving consumers (n=1025) in a range of ages, levels of education, and employment experience. Based on our methodology, we find that only a weak, albeit significant, relationship exists between information disclosure intentions and actual disclosure. In addition, this relationship is heavily moderated by the consumer practice of disclosing false data. We conclude by discussing the contributions of our methodology and the possibilities for extending it for additional mobile privacy research.     

iFrame: Dynamic indoor map construction through automatic mobile sensing

Many pervasive computing applications depend upon maps for support of location based services. Individuals can determine their location outdoors on these maps via GPS. Indoor pervasive applications may also need to know the layout of buildings, however indoor maps are less prevalent. This paper presents iFrame, a dynamic approach that leverages existing mobile sensing capabilities for constructing indoor floor plans. We explore how iFrame users may collaborate and contribute to constructing 2-dimensional indoor maps by merely carrying smartphones. A deployment study shows iFrame is an unattended approach that provides a skeleton map of a real building effectively and automatically.     

Speech recognition for mobile devices

This article presents an overview of different approaches for providing automatic speech recognition (ASR) technology to mobile users. Three principal system architectures with respect to the employment of a wireless communication link are analyzed: Embedded Speech Recognition Systems, Network Speech Recognition (NSR) and Distributed Speech Recognition (DSR). An overview of the solutions having been standardized so far as well as a critical analysis of the latest developments in the field of speech recognition in mobile environments is given. Open issues, pros and cons of the different methodologies and techniques are highlighted. Special emphasis is placed on the constraints and limitations ASR applications are confronted with under different architectures.     

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.     

An empirical analysis of energy consumption of cross-platform frameworks for mobile development

The increasing fragmentation of mobile devices market has created the problem of supporting all the possible mobile platforms to reach the highest number of potential users. One possible solution is to use cross-platform frameworks, that let develop only one application that is then deployed to all the supported target platforms. Currently available cross-platform frameworks follow different approaches to deploy the final application, and all of them has pros and cons. In this paper, we evaluate and compare together the current frameworks for cross-platform mobile development considering one of the most important aspect when dealing with mobile devices: energy consumption. Our analysis shows and measure how the adoption of cross-platform frameworks impacts energy consumption with respect to the native mobile development, identifies which are the most consuming tasks, and tries to define a final rank among all the different approaches. Moreover, we highlight future development necessary to improve performances of cross-platform frameworks to reach native development performances.     

Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing

Nowadays, in order to deal with the increasingly complex applications on mobile devices, mobile cloud offloading techniques have been studied extensively to meet the ever-increasing energy requirements. In this study, an offloading decision method is investigated to minimize the energy consumption of mobile device with an acceptable time delay and communication quality. In general, mobile devices can execute a sequence of tasks in parallel. In the proposed offloading decision method, only parts of the tasks are offloaded for task characteristics to save the energy of multi-devices. The issue of the offloading decision is formulated as an NP-hard 0–1 nonlinear integer programming problem with time deadline and transmission error rate constraints. Through decision-variable relaxation from the integer to the real domain, this problem can be transformed as a continuous convex optimization. Based on Lagrange duality and the Karush–Kuhn–Tucker condition, a solution with coupled terms is derived to determine the priority of tasks for offloading. Then, an iterative decoupling algorithm with high efficiency is proposed to obtain near-optimal offloading decisions for energy saving. Simulation results demonstrate that considerable energy can be saved via the proposed method in various mobile cloud scenarios.     

The action of looking at a mobile phone display as nonverbal behavior/communication: A theoretical perspective

This study investigates the now-common action of looking at a mobile phone display, thereby offering insight into the present communication situation in an era in which the use of high-performance mobile phones has become ubiquitous. In this study, the action of looking at a mobile phone display is considered nonverbal behavior/communication. This study applies a basic, general model to elucidate the present situation of face-to-face communication in light of the increasing prevalence of social interaction via mobile phone use. The results derived from the model include mobile phone users’ increasing social power and an accumulation of potential discontent in relation to different interpretations. This study concludes that in an era of high-performance mobile phones, the social context in face-to-face communication can be influenced by the act of looking at a mobile phone display.     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.