A Low-Energy Fast Cyber Foraging Mechanism for Mobile Devices

The ever increasing demands for using resource-constrained mobile devices for running more resource intensive applications nowadays has initiated the development of cyber foraging solutions that offload parts or whole computational intensive tasks to more powerful surrogate stationary computers and run them on behalf of mobile devices as required. The choice of proper mix of mobile devices and surrogates has remained an unresolved challenge though. In this paper, we propose a new decision-making mechanism for cyber foraging systems to select the best locations to run an application, based on context metrics such as the specifications of surrogates, the specifications of mobile devices, application specification, and communication network specification. Experimental results show faster response time and lower energy consumption of benched applications compared to when applications run wholly on mobile devices and when applications are offloaded to surrogates blindly for execution.Comment: 12 pages, 7 figures, International Journal of Wireless & Mobile Networks (IJWMN

Contrapositive local class inference prediction

Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Making more localized prediction (e.g., words or phrases), however, is relatively harder because the role of smaller text units depends on the context in which they are used (e.g., sentences or paragraphs), and training a supervised model to take the context into account, requires labeled training corpora, which is not available in many problem domains. Often, the global prediction at a greater context may be informative for a more localized prediction at a smaller semantic unit. However, directly inferring the most salient local features from the corresponding, easier to learn, global prediction may overlook the semantics of this relationship. This thesis argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes a more robust and accurate inference scheme, and shows how it can be implemented as a transfer function that rewrites a greater context from one class to another. We study the generalizability of the proposed framework to problem domains with varying data availability profiles and different levels of inference granularity. We demonstrate the robustness of the contrapositive inference to the noisy data and how data augmentation can facilitate the generation of weakly-labeled training data for resource-constrained problem domains. We discuss the transferability and adaptability of the contrapositive relationship to the problem domains with limited amount of training data. In addition, we show the robustness of the contrapositive inference scheme to variability in the size of the local and global contexts: from paragraphs to sentences, and from sentences to words and phrases