Mobile, collaborative augmented reality using cloudlets

The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment

Large-scale offloading in the Internet of Things

Large-scale deployments of IoT devices are subject to energy and performance issues. Fortunately, offloading is a promising technique to enhance those aspects. However, several problems still remain open regarding cloud deployment and provisioning. In this paper, we address the problem of provi- sioning offloading as a service in large-scale IoT deployments. We design and develop an AutoScaler, an essential component for our offloading architecture to handle offloading workload. In addition, we also develop an offloading simulator to generate dynamic offloading workload of multiple devices. With this toolkit, we study the effect of task acceleration in different cloud servers and analyze the capacity of several cloud servers to handle multiple concurrent requests. We conduct multiple experiments in a real testbed to evaluate the system and present our experiences and lessons learned. From the results, we find that the AutoScaler component introduces a very small overhead of ≈150 milliseconds in the total response time of a request, which is a fair price to pay to empower the offloading architectures with multi-tenancy ability and dynamic horizontal scaling for IoT scenarios

Universal Mobile Service Execution Framework for Device-To-Device Collaborations

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit these technologies from being broadly applied in the mobile industry. To address the above problems, we designed and developed UMSEF - an Universal Mobile Service Execution Framework, which is an innovative and radical approach for mobile computing in opportunistic networks. Our solution is built as a component-based mobile middleware architecture that is flexible and adaptive with multiple network topologies, tolerant for network errors and compatible for multiple platforms. We provided an effective algorithm to estimate the resource availability of a device for higher performance and energy consumption and a novel platform for mobile remote method invocation based on declarative annotations over multi-group device networks. The experiments in reality exposes our approach not only achieve the better performance and energy consumption, but can be extended to large-scaled ubiquitous or IoT systems

Novel Mobile Computation Offloading Framework for Android Devices

The thesis implements an offloading framework for GoogleTM AndroidTM based on mobile devices. Today, the full potential for smartphones may be constrained by certain technical limits such as battery endurance and computational performance. Modern mobile applications own more powerful functions but need larger computation and faster frame rate, which consume more battery energy. Using the proposed offloading framework, mobile devices can offload computational intensive workload to servers to save battery energy consumption and reduce the execution time. The framework can also enable software developers to easily build and deploy services on the servers to support mobile devices to run computationally intensive jobs. Compared with other offloading schemes for android cell phones, the scheme enables developers to choose which parts of the codes are potentially offloading. As developers fully understand the data flow models of the apps, they are considered most capable of making offloading decisions. Developers can minimize communication overhead brought by offloading by carefully partitioning source code by data dependency. Experiment results and data showed that the proposed offloading scheme could significantly reduce computational time and battery energy consumption

Towards Smart Cloud Gate Middleware : An approach based on Profiling Technique

International audienceMobile Cloud Computing (MCC) is a new paradigm whose mobile technology aims to exploit the advantages offered by the Cloud in order to provide ubiquitous PC-like functionality to mobile users. Cloud services provisioning is a continuously operating activity, unfortunately, services that has recently been deployed in the Cloud infrastructure remain unused or unknown by mobile devices. It is noteworthy that despite the benefits associated with the adoption of the Cloud by mobile technology the gate to the Cloud remains frozen. This means that mobile applications often use the same services without having an update of the novelty in Cloud. Thus, applications lack awareness of new services which are more advantageous in terms of features and qualities than the currently used ones. This is due to the fact that the interest of researchers in the field of MCC has been focused on how to enhance the performance of the computing counterpart of mobile technology. Actually, Cloud Computing is largely unexplored and considered only as a resource for provisioning on demand services. To enable mobile applications to exploit the Cloud intelligently, we propose Smart Mobile Cloud Architecture (SMCA). We consider this new architecture as referential allowing MCC users to have a full awareness of both contexts (Cloud and Mobile) at the same time. We introduce a new concept called Smart Cloud Gate (SCG), which aims to profile both mobile applications and the Cloud to extract knowledge that will be used as a criterion to select the appropriate services, which will be suggested to mobile applications and give each different application the appropriate view of the Cloud

Multisite adaptive computation offloading for mobile cloud applications

The sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints. The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others. To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-repo

Offloading cryptographic services to the SIM card in smartphones

Smartphones have achieved ubiquitous presence in people’s everyday life as communication, entertainment and work tools. Touch screens and a variety of sensors offer a rich experience and make applications increasingly diverse, complex and resource demanding. Despite their continuous evolution and enhancements, mobile devices are still limited in terms of battery life, processing power, storage capacity and network bandwidth. Computation offloading stands out among the efforts to extend device capabilities and face the growing gap between demand and availability of resources. As most popular technologies, mobile devices are attractive targets for malicious at- tackers. They usually store sensitive private data of their owners and are increasingly used for security sensitive activities such as online banking or mobile payments. While computation offloading introduces new challenges to the protection of those assets, it is very uncommon to take security and privacy into account as the main optimization objectives of this technique. Mobile OS security relies heavily on cryptography. Available hardware and software cryptographic providers are usually designed to resist software attacks. This kind of protection is not enough when physical control over the device is lost. Secure elements, on the other hand, include a set of protections that make them physically tamper-resistant devices. This work proposes a computation offloading technique that prioritizes enhancing security capabilities in mobile phones by offloading cryptographic operations to the SIM card, the only universally present secure element in those devices. Our contributions include an architecture for this technique, a proof-of-concept prototype developed under Android OS and the results of a performance evaluation that was conducted to study its execution times and battery consumption. Despite some limitations, our approach proves to be a valid alternative to enhance security on any smartphone.Los smartphones están omnipresentes en la vida cotidiana de las personas como herramientas de comunicación, entretenimiento y trabajo. Las pantallas táctiles y una variedad de sensores ofrecen una experiencia superior y hacen que las aplicaciones sean cada vez más diversas, complejas y demanden más recursos. A pesar de su continua evolución y mejoras, los dispositivos móviles aún están limitados en duración de batería, poder de procesamiento, capacidad de almacenamiento y ancho de banda de red. Computation offloading se destaca entre los esfuerzos para ampliar las capacidades del dispositivo y combatir la creciente brecha entre demanda y disponibilidad de recursos. Como toda tecnología popular, los smartphones son blancos atractivos para atacantes maliciosos. Generalmente almacenan datos privados y se utilizan cada vez más para actividades sensibles como banca en línea o pagos móviles. Si bien computation offloading presenta nuevos desafíos al proteger esos activos, es muy poco común tomar seguridad y privacidad como los principales objetivos de optimización de dicha técnica. La seguridad del SO móvil depende fuertemente de la criptografía. Los servicios criptográficos por hardware y software disponibles suelen estar diseñados para resistir ataques de software, protección insuficiente cuando se pierde el control físico sobre el dispositivo. Los elementos seguros, en cambio, incluyen un conjunto de protecciones que los hacen físicamente resistentes a la manipulación. Este trabajo propone una técnica de computation offloading que prioriza mejorar las capacidades de seguridad de los teléfonos móviles descargando operaciones criptográficas a la SIM, único elemento seguro universalmente presente en los mismos. Nuestras contribuciones incluyen una arquitectura para esta técnica, un prototipo de prueba de concepto desarrollado bajo Android y los resultados de una evaluación de desempeño que estudia tiempos de ejecución y consumo de batería. A pesar de algunas limitaciones, nuestro enfoque demuestra ser una alternativa válida para mejorar la seguridad en cualquier smartphone

Enhancing Mobile Capacity through Generic and Efficient Resource Sharing

Mobile computing devices are becoming indispensable in every aspect of human life, but diverse hardware limits make current mobile devices far from ideal for satisfying the performance requirements of modern mobile applications and being used anytime, anywhere. Mobile Cloud Computing (MCC) could be a viable solution to bypass these limits which enhances the mobile capacity through cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. Traditional schemes either restrict to share a specific type of hardware resource within individual applications, which requires tremendous reprogramming efforts; or disregard the runtime execution pattern and transmit too much unnecessary data, resulting in bandwidth and energy waste.To address the aforementioned challenges, we present three novel designs of resource sharing frameworks which utilize the various system resources from a remote or personal cloud to enhance the mobile capacity in a generic and efficient manner. First, we propose a novel method-level offloading methodology to run the mobile computational workload on the remote cloud CPU. Minimized data transmission is achieved during such offloading by identifying and selectively migrating the memory contexts which are necessary to the method execution. Second, we present a systematic framework to maximize the mobile performance of graphics rendering with the remote cloud GPU, during which the redundant pixels across consecutive frames are reused to reduce the transmitted frame data. Last, we propose to exploit the unified mobile OS services and generically interconnect heterogeneous mobile devices towards a personal mobile cloud, which complement and flexibly share mobile peripherals (e.g., sensors, camera) with each other