The increasing complexity of smartphone applications and services necessitate high battery consumption but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper we advance on previous ideas, by proposing and implementing the first known Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, with its better security, and its low battery consumption. We design a new NFC communication protocol that overcomes the limitations of the default protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. We present experimental results of the energy consumption and the time duration of two computationally intensive representative applications: (i) RSA key generation and encryption, and (ii) gaming puzzles. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.
More recently, Serendipity @cite_2 introduces the concept of mobile--to--mobile offloading in an environment with intermittent connectivity. This system is capable of conserving energy and increasing computation speed of low--power devices when these offload heavy computations to more powerful ones. Nevertheless, similar to other previous frameworks, Serendipity relies on Wi-Fi, and the paper does not specify how to search for and detect the available devices that are willing to help. OPENRP advances in this direction by collecting data from interactions between mobile users and building reputation scores per mobile user and application type @cite_7 . Honeybee @cite_6 is an offloading framework for mobile computing on Bluetooth channels. Without having to rely on Wi-Fi, it guarantees connectivity as long as other mobile devices equipped with Bluetooth are available as well.
@cite_6 @cite_7 @cite_2
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Although smartphones are increasingly becoming more and more powerful, enabling pervasiveness is severely hindered by the resource limitations of mobile devices. The combination of social interactions and mobile devices in the form of ‘crowd computing’ has the potential to surpass these limitations. In this paper, we introduce Honeybee; a crowd computing framework for mobile devices. Honeybee enables mobile devices to share work, utilize local resources and human collaboration in the mobile context. It employs ‘work stealing’ to effectively load balance tasks across nodes that are a priori unknown. We describe the design of Honeybee, and report initial experimental data from applications implemented using Honeybee. The concepts of wisdom of crowd and collective intelligence have been utilized by mobile application developers to achieve large-scale distributed computation, known as crowd computing. The profitability of this method heavily depends on users' social interactions and their willingness to share resources. Thus, different crowd computing applications need to adopt mechanisms that motivate peers to collaborate and defray the costs of participating ones who share their resources. In this article, we propose OPENRP, a novel, lightweight, and scalable system middleware that provides a unified interface to crowd computing and opportunistic networking applications. When an application wants to perform a device-to-device task, it delegates the task to the middleware, which takes care of choosing the best peers with whom to collaborate and sending the task to these peers. OPENRP evaluates and updates the reputation of participating peers based on their mutual opportunistic interactions. To show the benefits of the middleware, we simulated the behavior of two representative crowdsourcing applications: message forwarding and task offloading. Through extensive simulations on real human mobility traces, we show that the traffic generated by the applications is lower compared to two benchmark strategies. As a consequence, we show that when using our middleware, the energy consumed by the nodes is reduced. Finally, we show that when dividing the nodes into selfish and altruistic, the reputation scores of the altruistic peers increase with time, while those of the selfish ones decrease. Mobile devices are increasingly being relied on for services that go beyond simple connectivity and require more complex processing. Fortunately, a mobile device encounters, possibly intermittently, many entities capable of lending it computational resources. At one extreme is the traditional cloud-computing context where a mobile device is connected to remote cloud resources maintained by a service provider with which it has an established relationship. In this paper we consider the other extreme, where a mobile device's contacts are only with other mobile devices, where both the computation initiator and the remote computational resources are mobile, and where intermittent connectivity among these entities is the norm. We present the design and implementation of a system, Serendipity, that enables a mobile computation initiator to use remote computational resources available in other mobile systems in its environment to speedup computing and conserve energy. We propose a simple but powerful job structure that is suitable for such a system. Serendipity relies on the collaboration among mobile devices for task allocation and task progress monitoring functions. We develop algorithms that are designed to disseminate tasks among mobile devices by accounting for the specific properties of the available connectivity. We also undertake an extensive evaluation of our system, including experience with a prototype, that demonstrates Serendipity's performance.