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Modern mobile devices suffer from severe power and thermal issues due to the adoption of high-frequency multi-core processors. However, methods of resolving the thermal problems in mobile environments are restricted due to their size, semiconductor process technology, and battery limitations. Mobile devices generally rely on a software framework called the Thermal Management Model (TMM). However, conventional TMMs suffer from inevitable performance degradation due to the thermal response. We propose a new Thermal Mitigation Algorithm (TMA) to optimize performance and thermal mitigation by considering the multi-core power curve of a mobile CPU which is experimentally derived using a typical smartphone and thus resolve this issue. Our proposed scheme aims to reduce thermal problems and enhance system performance by limiting the usage of power-inefficient frequencies when operating on a multi-core. We evaluate the proposed algorithm on a real system using a typical smartphone and conduct the performance measurement using the AnTuTu benchmark v4.5.1. In addition, we use the on-chip temperature sensors of CPU cores and a crystal oscillator embedded in the Application Processor (AP) to monitor the temperature of the mobile device. The result shows that our scheme, respectively reduce the average temperatures of CPU cores and a crystal oscillator by 6.61% and 5.02% while improving system performance by 3.24%. In conclusion, we reduce the thermal problems and enhance performance simultaneously using the proposed TMA.

Cloud computing has invaded our lives so beautifully, that it is time to make cloud more productive. What makes Cloud computing novel is the possibility of an almost immediate resizing of resources on a pay-as-you-go usage model. With companies moving forward with various cloud initiatives, cloud consumers are discouraged from relinquishing their control of the infrastructure to the cloud providers. The rivalry among cloud providers to stay competitive in the market makes it necessary to lock-in its customers, and therefore customers cannot migrate easily to another due to non-interoperable APIs (Application Programming Interface), portability and migration issues. In addition, today’s cloud users are mobile devices and consuming a cloud service onto mobile device poses another set of risks. One way to handle this complexity is to devise an intermediary which can take care of the heterogeneity at the cloud and mobile level, and ensure a multi-cloud deployment of application by taking advantage of the best features from different vendors simultaneously. Thus, this paper is a sincere initiative to understand the problem beneath multi-cloud solutions and their embrace for the mobile world. Hence, the following paper begins with a broad coverage of existing work, gives an outline of a multi-cloud middleware, and discusses existing issues with API heterogeneity which is the prime point of concern in the vendor lock-in issue.

Falls are a major health concern leading cause of fatal and non-fatal injuries for neurological disorders. Balance dysfunction is one of the common factors to determine fall risk in neurological patients. Preventative measures may help to reduce the incidence and severity of falls for detecting balance function and fall risk factors. However, the objective measures for balance require expensive equipment with the assessment of clinical expertise. A main gap remains in the evaluation method to objectively characterize the balance functions in individuals with high risk of falling. With the development of wearable and mobile devices, recent advances in smart mobile devices may provide a potential opportunity to manage the gap in the detailed quantification of balance impairments. The purpose of this study is to identify whether the biomechanical data measured by the mobile device is reliable to characterize the posture stability in various balance test conditions. A total of 39 children with Down syndrome completed four balance-testing tasks under altered base of support and vision. Simultaneous biomechanical measurements were gathered from the iPod and force plate analysis system during functional balance testing. The force plate and mobile system provided similar patterns of stability across groups. Correlation ($r^2)$ between two systems for path length, 95% ellipse area, peak-to-peak, standard deviation and mean ranged from 0.60 to 0.99. We expect that the smart mobile device can provide reliable and accurate information to quantify the postural stability in individuals with elderly people or neurological disorders. The objectivity, portability and easy use of such mobile device make it ideal to apply in clinical environments for detecting balance functions and reducing the risk of falls in Down syndrome or other neurological patients.

Mind wandering is a state transition from task-related thinking to task-unrelated thinking and it has possible negative effects on learners’ performance in online education. The detection of mind wandering can improve learners’ performance by signaling, intervening and restoring attention to the process of learning. Traditional methods of detection of mind wandering involve specific devices such as eye gaze or physiologic sensors, tracking movements of eye ball or detecting electro-physiological activities of neurons or skin. This paper presents a new detection method using the camera of a mobile device to detect mind wandering when learners are tasked to watch a teaching video. Through the constant image capturing of the learner’s face, his or her eye features can be captured and used to predict the learner's state of mind wandering if any, using methods of computer imagery and supervised machine learning. Through our experiment, we have attained an accuracy of 53%, which exceeded expectations, considering the hardware platform. The performance of the detection system based on the MCPP mechanism outperforms the detection system based on the traditional mechanism.

Authentication in multi-server environment plays an important role in distributed systems. Multi-server authenticated key agreement scheme allows users to register only once, and then access multiple services with session key establishment. However, security and privacy are crucial issues to guarantee identity authentication, data confidentiality and participant privacy. In this paper, we cryptanalyze Mishra et al.'s ‘A secure user anonymity-preserving biometric-based multi-server authenticated key agreement scheme using smart cards’, and point out that their scheme is vulnerable to session key compromised attack by insider curious service servers, and user behavior traced attack by common malicious attackers. In order to remedy the above weaknesses, we propose an improved scheme to enhance the security guarantee and privacy protection. Our improvement introduces public key for service servers to safeguard user identity and session key freshness. It not only remains the merits of the original scheme, but also achieves more security and privacy requirements. Finally the analysis shows that our improved scheme is more practical, but there is a tradeoff between security/privacy and performance.