Computation Offloading contributes towards moving to a Mobile Cloud Computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers, offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems and linera controllers are designed to meet the system's constraints and QoS metrics, while at the upper level, an optimizer tackles the problems of load balancing and application placement towards maximizing the number the offloaded requests.
This paper proposes OpenStack-based middleware through which resource containers at the Edge, Fog, and Cloud levels can be discovered, combined, and provisioned to users/applications, thereby facilitating and orchestrating vertical, horizontal, and hybrid offloading processes. As demonstrated by a proof of concept in a smart environment scenario, by converging the Edge, Fog, and Cloud levels, the proposed architecture has the potential to enable faster data processing, as compared to processing either at the Edge, Fog, or Cloud levels separately, allowing architects to combine offloading patterns in a flexible and fine-grained manner a significant step towards a workload engineering discipline.
Scientific workflows enable to conduct analysis on large datasets and perform complex scientific simulations. These workflows are often mapped onto distributed computational infrastructures to speed up their execution. Prior execution, a workflow structure may suffer transformations to accommodate the computing infrastructures. However, these transformations may cause workflow imbalance because of runtime or data imbalance. To mitigate these imbalances, in this paper we propose an autonomic data-throttling approach to compute how data transmission must be throttled throughout workflow jobs. Our approach relies on structural analysis of Petri nets, obtained by model transformation of data-intensive workflows, and Linear Programming techniques.
Mobile crowd sensing (MCS) has been utilized to develop several context aware applications that obtain knowledge about the environment from large set of devices. We present case of MCS to be potentially used for much more demanding applications such as real time perpetrator tracking by online mining of images taken from nearby mobile surrogates. ContextAiDe architecture, a combination of API, middleware, and optimization engine, that enables optimization of MCS operational overheads in addition to computation and communication requirements of the MCS application to enable real time operation under demanding workloads. Results of sample run of perpetrator tracking app are presented.
Standard security protocols are characterized by high computational complexity that is unsuitable to networks of low-power devices. The typical solution based on cloud services that facilitate deployment, intermediate all messages among things and enable secure communications has the disadvantage of requiring permanent Internet connectivity even for things connected over a local network. This paradigm is inappropriate in several scenarios, hence we propose an efficient fog-based system that enables secure communications and preserves easy management of cloud-assisted IoT. The proposal is based on an original lightweight proxy re-encryption scheme that can be executed even by large networks of low-power devices.
In this paper, we propose an automatic labeling approach to distinguish crisis-relevant tweets while differentiating source types (e.g., government or personal accounts) simultaneously. We first analyze and identify tweet-specific linguistic, sentimental and emotional features based on statistical topic modeling. Then,we design a novel correlative convolutional neural network which uses a shared hidden layer to learn effective representations of the multi-faceted features. The model can discover salient information robust to the variations and noises in tweets and sources. To obtain a bird-view of crisis event, we further develop an approach to automatically summarize key information of identified tweets.
Previous work in social network de-anonymization mainly focuses on designing accurate and efficient de-anonymization methods. We attempt to investigate the intrinsic relation between the attacker's knowledge and the expected de-anonymization gain. A common intuition is that more knowledge results in more successful de-anonymization. However, our analysis shows their relation is much more sophisticated than that. Though based on a few assumptions, our findings leave intriguing implications for the attacker to make better use of the background knowledge when performing de-anonymization, and for the data owners to better measure the privacy risk when releasing their data to third parties.
This article presents a comprehensive survey on Fog Computing for the IoT. Hereinafter, we detail the principles characterizing Fog Computing and provide the historical background of this paradigm. We also highlight the IoT application domains that may benefit from it and review the existing literature for each of them. Furthermore, we analyze the challenges afflicting the Fog, report how the research community is facing them, and point out the open issues and future research directions. Last but not least, we provide an overview of the main Fog Computing platforms for the Internet of Things.
Guest Editors' Introduction to the Special Issue on Fog, Edge, and Cloud Integration for Smart Environments
We focus on using unstructured textual Knowledge Bases (KBs) to answer questions from community based Question-and-Answer (Q&A) websites. We propose a novel framework that integrates multi-level tag recommendation with external KBs to retrieve the most relevant KB articles to answer user posted questions. A post-tag co-clustering model, augmented by a two-step tag recommender, is used to predict tags at different levels for a given user posted question. A KB article retrieval component leverages the recommended multi-level tags to select the appropriate KBs and search/rank the matching articles thereof. We conduct extensive experiments to demonstrate the effectiveness of the proposed approach.
Cloud of Things (CoT) is a Cloud Computing platform dedicated to Internet of Things (IoT), often used commercially to explore IoT. The use of CoTs in latency-sensitive applications has shown to be unfeasible due to the inherent latency of Cloud computing services. The Fog and Mist computing are solutions to this problem. However, choosing the best platform to provide the resources is not always a simple task. Then, this work proposes a protocol and an algorithm to select the best computational infrastructure to provide resources/services considered as the constraints imposed by the client device. The simulation results are promising.
Search engines cannot answer time and location-specific queries; this often requires humans on-site. While community question answering (CQA) platforms are popular, few exceptions consider users physical locations. Here, we present CloseUp, our prototype for the integration of community-driven live search into a Google-like search experience. We bridge the gap between Web search and CQA, namely the formulation of search requests and the expected response times. CloseUp features a deep learning pipeline to translate relevant queries into questions. CloseUp provides a mobile application for submitting and replying to questions. Using a field study, we evaluated the feasibility of our approach.