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

Middleware platform for distributed applications incorporating robots, sensors and the cloud

Cyber-physical systems in the factory of the future will consist of cloud-hosted software governing an agile production process executed by autonomous mobile robots and controlled by analyzing the data from a vast number of sensors. CPSs thus operate on a distributed production floor infrastructure and the set-up continuously changes with each new manufacturing task. In this paper, we present our OSGibased middleware that abstracts the deployment of servicebased CPS software components on the underlying distributed platform comprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop components based on artificial neural networks, a technique that recently became very popular for sensor data analytics and robot actuation. We demonstrate a system where a robot takes actions based on the input from sensors in its vicinity

Mobile Cloud Computing: A Comparison Study of Cuckoo and AIOLOS Offloading Frameworks

Currently, smart mobile devices are used for more than just calling and texting. They can run complex applications such as GPS, antivirus, and photo editor applications. Smart devices today offer mobility, flexibility, and portability, but they have limited resources and a relatively weak battery. As companies began creating mobile resource intensive and power intensive applications, they have realized that cloud computing was one of the solutions that they could utilize to overcome smart device constraints. Cloud computing helps decrease memory usage and improve battery life. Mobile cloud computing is a current and expanding research area focusing on methods that allow smart mobile devices to take full advantage of cloud computing. Code offloading is one of the techniques employed in cloud computing with mobile devices. This research compares two dynamic offloading frameworks to determine which one is better in terms of execution time and battery life improvement

Leveraging cloudlets for immersive collaborative applications

To enable immersive applications on mobile devices, the authors propose a component-based cyber foraging framework that optimizes application-specific metrics by not only offloading but also configuring application components at runtime. It also enables collaborative scenarios by sharing components between multiple devices

A Dynamic Application Partitioning and Offloading Framework to Enhance the Capabilities of Transient Clouds Using Mobile Agents

Mobile cloud computing has emerged as a prominent area of research, a natural extension of cloud computing that proposes to offer solutions for enhancing the capabilities of smart mobile devices commonly plagued by resource constraints. As one of its promising models, transient clouds aim to address the internet connectivity shortfall inherent in most solutions through the formation of ad hoc networks by devices in close proximity, then the offloading some computations (Cyber Foraging) to the created cloud. However, transient clouds, at their current state, have several limitations, concerning their expansion on a local network having a large number of devices and the management of the instability of the network due to the constant mobility of the devices. Another issue is the fact code partitioning and offloading are not addressed to fit the need of such networks, thereby rendering the distributed computing mechanism barely efficient for the Transient Cloud. In this study, we propose a transient cloud-based framework that exploits the use of multi-agent systems, enabling a dynamic partitioning and offloading of code, and facilitating the movement and the execution of code partition packets in a multi-hop ad-hoc mesh network. When created and deployed, these intelligent mobile agents operate independently or collaboratively and adapt to the continual entry and exit of devices in the neighbourhood. The integration of these trending concepts in distributed computing within a framework offers a new architecture for resource-sharing among cooperating devices that addresses the varied issues that arise in dynamic environments

Integrating mobile and cloud resources management using the cloud personal assistant

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources