Unobtrusive Movement Interaction for Mobile Devices

Gesture control of mobile devices is an emerging user interaction modality. Large-scale deployment has been delayed by two main technical challenges: detecting gestures reliably and power consumption. There have also been user-experience- related challenges, such as indicating the start of a gesture, social acceptance, and feedback on the gesture detection status. This chapter evaluates a solution for the main challenges: an event-based movement interaction modality, tapping, that emphasizes minimal user effort in interacting with a mobile device. The technical feasibility of the interaction method is examined with a smartphone equipped with a sensor interaction cover, utilizing an enabling software framework. The reliability of detecting tapping is evaluated by analyzing a dataset collected with the smartphone prototype. Overall, the results suggest that detecting tapping is reliable enough for practical applications in mobile computing when the interaction is performed in a stationary situation.

The explosive growth in the number of mobile devices such as Internet-enabled cellular phones, wireless handheld devices, wireless laptops, and tablet PCs has driven the corresponding growth in applications for mobile computing. These applications usually belong to one of two classes: collaborative applications and individual application. While collaborative applications require several mobile devices to work together and include peer-to-peer computing and grid computing, individual applications are local to the mobile device and its user. In this paper, we present a framework that allows mobile devices to collaboratively work on a computationally-expensive problem. Such a problem is decomposed into smaller tasks and distributed across other mobile devices willing to share their computational power with others. This framework is based on the paradigm of grid computing applied to the domain of wireless mobile devices. This paper presents our current implementation of the framework architecture and simulation of its functionality. Also, we focus on the issues of mobility, QoS and network stability and their effect on the performance of our collaborative problem-solving framework.

BackgroundThe application of mobile computing and communication technology is rapidly expanding in the fields of health care and public health. This systematic review will summarise the evidence for the effectiveness of mobile technology interventions for improving health and health service outcomes (M-health) around the world.FindingsTo be included in the review interventions must aim to improve or promote health or health service use and quality, employing any mobile computing and communication technology. This includes: (1) interventions designed to improve diagnosis, investigation, treatment, monitoring and management of disease; (2) interventions to deliver treatment or disease management programmes to patients, health promotion interventions, and interventions designed to improve treatment compliance; and (3) interventions to improve health care processes e.g. appointment attendance, result notification, vaccination reminders.A comprehensive, electronic search strategy will be used to identify controlled studies, published since 1990, and indexed in MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, the Cochrane Library, or the UK NHS Health Technology Assessment database. The search strategy will include terms (and synonyms) for the following mobile electronic devices (MEDs) and a range of compatible media: mobile phone; personal digital assistant (PDA); handheld computer (e.g. tablet PC); PDA phone (e.g. BlackBerry, Palm Pilot); Smartphone; enterprise digital assistant; portable media player (i.e. MP3 or MP4 player); handheld video game console. No terms for health or health service outcomes will be included, to ensure that all applications of mobile technology in public health and health services are identified. Bibliographies of primary studies and review articles meeting the inclusion criteria will be searched manually to identify further eligible studies. Data on objective and self-reported outcomes and study quality will be independently extracted by two review authors. Where there are sufficient numbers of similar interventions, we will calculate and report pooled risk ratios or standardised mean differences using meta-analysis.DiscussionThis systematic review will provide recommendations on the use of mobile computing and communication technology in health care and public health and will guide future work on intervention development and primary research in this field.

A framework for using mobile computing for information management on construction sites

Mobile edge computing is becoming a novel ubiquitous computing platform to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can reduce energy consumption for the mobile devices, it may also lead to greater processing latency. Usually the mobile users have to pay for cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption and cost of using resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on Non-dominated Sorting Genetic Algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show the efficiency and effectiveness of our proposed method.

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

Summary form only given. Mobile wireless devices have become a necessity in everyday life, are redefining mobile possibilities for people everywhere, and are enabling significant societal changes. They have also become important product drivers for the semiconductor industry. The product market landscape has been driven by an insatiable demand for data speeds, device features, sleek appearances and increased battery life. By integrating the functionality of the CPU, the GPU, connectivity, multimedia and GPS with optimal performance and power consumption, Qualcomm's chipsets are finding applications in Smartphones as well as other Mobile Computing applications. This talk will outline the key technology elements required in the implementation of Qualcomm's chip sets. The development and cost-effective fabrication of ever more complex chip sets to meet the needs of future products requires increased innovation and integration in critical areas such as architecture, circuit design, process technology and packaging. In addition a continued focus on evolving the successful fabless model and managing cost will be discussed.

Mineral resources are indispensable in the development of human society and are the foundation of national economic development. As the prospecting target shifts from outcrop ore to concealed ore, from shallow to deep, the difficulty of prospecting becomes more and more difficult. Therefore, the prediction of mineralization prospects is of great significance. This paper is aimed at completing the prediction of mineralization prospects by constructing geological semantic models and using mobile computer learning to improve the accuracy of prediction of mineralization prospects and expanding the application of semantic mobile computing. We use five different semantic relations to build a semantic knowledge library, realize semantic retrieval, complete information extraction of geological text data, and study mineral profiles. Through the distributed database of mobile computing, the association rules and random forest algorithm are used to describe the characteristics of minerals and the ore‐controlling elements, find the association rules, and finally combine the geological and mineral data of the area and use the random forest algorithm to realize the prospect of mineralization district forecast. The geological semantic model constructed in the article uses the knowledge library for associative search to achieve an accuracy rate of 87.9% and a recall rate of 96.5%. The retrieval effect is much higher than that of traditional keyword retrieval methods. The maximum value of the posterior result of the mineralization prospect is 0.9027, the average value is 0.0421, and the standard deviation is 0.1069. The picture is brighter, and the probability of mineralization is higher.

In mobile edge computing(MEC), application partitioning can split the executions into local and edge server parts. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Edge Computing (MEC). In this paper, a new approach on task offloading scheduling for application of mobile edge computing is proposed. We divide the computing task into several subtasks. Then, we analyze the overhead of the system, and propose a fine-grained strategy for task scheduling and offloading in a multi-user MEC system. For reducing the energy consumption and delay, the computation offloading is considered as a constrained multi-objective optimization problem (CMOP), which can be solved by an improved NSGA-II algorithm. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

With the development of mobile edge computing and neural network Deep Learning (DL), more and more scholars are studying the combination of the two. This paper mainly studies the application of mobile edge computing and neural network DL in IoT computing collaboration and data-aware routing algorithms. Therefore, this paper proposes the deployment options of MEC technology and ETSIMEC in mobile edge computing, combining mobile edge computing with DL, designs an optimization algorithm based on Markov decision process and feature expression learning, and then analyzes and optimizes IoT computing and VANET routing algorithm. In order to have a clearer direction for the optimization algorithm, this paper also designs the edge computing model training and experiment comparison, the DL algorithm comparison experiment, and the routing algorithm simulation experiment and performance analysis. Combined with the experimental results, it is optimized and compared with traditional IoT computing and routing algorithms. Finally, it is concluded that the computing efficiency of IoT computing based on edge computing and DL designed in this paper is 21.33% higher than that of traditional IoT computing. The efficiency of the routing algorithm based on edge computing and DL designed in this paper is 9.29% higher than that of the traditional routing algorithm.

Traditional students are starting to disappear as more and more computer technology is introduced to the world of Academia. Many adult learners who are interested in pursuing additional education and potential degrees need to be practical in their strategic decision-making processes, as well as creative in their academic pursuits. With the onset of online learning, especially mobile computing, many adult learners are embracing this new form of non-traditional learning in order to achieve their academic dreams for a better future and the attainment of future educational degrees. In particular, more and more instructors are either moving towards this newer form of non-traditional teaching or being asked by their superiors to pursue a broader interest in current and future technological changes and developments. As a result, this paper will address these changing student demographics, as well as computer technology being used to help students move from learning in a linear fashion to a more cyclical modality in order to catch up with other nations and compete on a stronger foothold and help participate with further changes in the workplace, as well as the marketplace. This paper will explore how mobile computing is being used to address the needs of the "underserved" in some instances in order to broadened one's academic offerings and reach out for a broader and more culturally diverse student population.

In recent years, researches have been done many works in the area of application of mobile computing technologies in construction to improve the low productivity of construction. This paper gives a brief review of the implanting of mobile computing in construction. It starts with the necessity of mobile computing in construction. It then reviews the researches on how people use mobile computing to solve communication problems and to improve the productivity in construction processes, the implementation of mobile computing technology in construction. Finally, it lists challenges for the future of research in mobile computing in construction

Due to the enormous data sizes involved in mobile computing and multimedia data transfer, it is possible that more data traffic may be generated, necessitating the use of data compression. As a result, this paper investigates how mobile computing data are compressed under all transmission scenarios. The suggested approach integrates deep neural networks (DNN) at high weighting functionalities for compression modes. The proposed method employs appropriate data loading and precise compression ratios for successful data compression. The accuracy of multimedia data that must be conveyed to various users is higher even though compression ratios are higher. The same data are transferred at significantly higher compression ratios, which save time while also minimizing data mistakes that may occur at the receiver. The DNN process also includes a visible parameter for handling high data-weight situations. The visible parameter optimizes the data results, allowing simulation tools to readily observe the compressed data. A comparison case study was created for five different scenarios in order to confirm the results, and it shows that the suggested strategy is significantly more effective than existing methods in roughly 63 percent of the cases.

The value of a mobile communication device is its ability to organize and access important information quickly. Mobile devices are often associated with the cutting-edge technology of the last 4 years. Although having gained an increasing popularity over recent years, mobile devices have a history of 15 years. The first generation of mobile devices was the analog mobile phone. They were large and cumbersome, resembling a small briefcase. With the launch of the second generation, mobile phone units became smaller and easier to handle. Currently, third-generation phones are starting to be released, advancing in design and dynamic capabilities. By the year 2005, 76% of consumers will own an Internet compatible device, 66% will own a cell phone, 45% will own an Internet cell phone, and 10% will own a personal digitial assistant (Forrester, 2001). In today's information surge, business never stops. IDC Research has forecasted that an average of more than 36 billion person-to-person e-mails will be sent daily by 2005 (Juptner, 2001). Hence, mobile communication plays a major role in the success of most of today's business operations (Jones, 2001), and all businesses are continually faced with the challenge of engaging with the rapidly changing information technology age. One area of mobile communications that has been growing exponentially is mobile computing. Keywords: code division multiple access (CDMA); general packet radio service (GPRS); global positioning system (GPS); Internet cell phone; mobile communication device; personal digital assistant (PDA)

Mobile edge computing enables the execution of compute-intensive applications, e.g. deep learning applications, on the end devices with limited computation resources. However, the deep learning applications bring the performance bottleneck in mobile edge computing, due to the movements of a large amount of data incurred by the large number of layers and millions of weights. In this paper, the computing model for parallel deep learning applications in mobile edge computing is proposed, by considering the occupancy allocation of processors, cost of context switch, and multi-processors in edge server and remote cloud. The problem of minimizing the completion time for deep learning applications is formulated, and the NP-hardness of the problem is proved. To solve the problem, an integrated algorithm by merging and scheduling is proposed. Moreover, a real-world distributed platform is developed for evaluating the proposed algorithm. Experimental results show that, the completion time of deep learning application for the proposed algorithm is decreased by 63% and 75%, respectively, without extra control costs, compared with the existing algorithms.