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Fog Computing for the Internet of Things: A Survey

Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host... (more)

ContextAiDe: End-to-End Architecture for Mobile Crowd-sensing Applications

Mobile crowd-sensing (MCS) enables development of context-aware applications by mining relevant information from a large set of devices selected in an ad hoc manner. For example, MCS has been used for real-time monitoring such as Vehicle ad hoc Networks-based traffic updates as well as offline data mining and tagging for future use in applications... (more)

A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices

The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of... (more)

Oops: Optimizing Operation-mode Selection for IoT Edge Devices

The massive increase of IoT devices and their collected data raises the question of how to analyze all that data. Edge computing provides a suitable compromise, but the question remains: How much processing should be done locally vs. offloaded to other devices? The diverse application requirements and limited resources at the edge extend the... (more)

Adaptive Resource Allocation for Computation Offloading: A Control-Theoretic Approach

Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive... (more)

DM2-ECOP: An Efficient Computation Offloading Policy for Multi-user Multi-cloudlet Mobile Edge Computing Environment

Mobile Edge Computing is a promising paradigm that can provide cloud computing capabilities at the edge of the network to support low latency mobile services. The fundamental concept relies on bringing cloud computation closer to users by deploying cloudlets or edge servers, which are small clusters of servers that are mainly located on existing... (more)

Cloud, Fog, or Mist in IoT? That Is the Question

Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be... (more)

Practical Privacy-preserving High-order Bi-Lanczos in Integrated Edge-Fog-Cloud Architecture for Cyber-Physical-Social Systems

Smart environments, also referred to as cyber-physical-social systems (CPSSs), are expected to... (more)

Fog-based Secure Communications for Low-power IoT Devices

Designing secure, scalable, and resilient IoT networks is a challenging task because of resource-constrained devices and no guarantees of reliable... (more)

Enabling Workload Engineering in Edge, Fog, and Cloud Computing through OpenStack-based Middleware

To enable and support smart environments, a recent ICT trend promotes pushing computation from the... (more)

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About TOIT

ACM Transactions on Internet Technology (TOIT) brings together many computing disciplines including computer software engineering, computer programming languages, middleware, database management, security, knowledge discovery and data mining, networking and distributed systems, communications, performance and scalability etc. TOIT will cover the results and roles of the individual disciplines and the relationships among them.

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Forthcoming Articles
Constructing Novel Block Layouts for Webpage Analysis

Webpage segmentation is the basic building block for a wide range of webpage analysis methods. The rapid development of Web technologies results in more dynamic and complex webpages, which brings new challenges to this area. To improve the performance of webpage segmentation, we propose a two-stage segmentation method that can combine visual, logic and semantic features of the contents on a webpage. This two-stage method can effectively conduct webpage segmentation on complicated and dynamic webpages. The experiment results show that the proposed method significantly outperforms the existing state-of-the-art in terms of higher precision, recall and accuracy.

Universal Social Network Bus: Towards the Federation of Heterogeneous Online Social Network Services

Online social network services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the last decades, multiple competing OSNSs have emerged. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about the people they can interact with. Our work aims at enabling users to meet and interact beyond the boundary of their OSNSs. We introduce USNB -Universal Social Network Bus which revisits the "service bus" paradigm to address the requirements of social interoperability.

Trust Prediction via Matrix Factorization

We propose PTP-MF (Pairwise Trust Prediction through Matrix Factorization), an algorithm to predict the intensity of trust/distrust relations in Online Social Networks. The PTP-MF algorithm maps each user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and computes the trust j places in j as the dot product of trustor profile of i and the trustee profile of j. Experiments indicate that the PTP-MF algorithm is more accurate than the state-of-the-art approaches (up to 9.65%) and scales well on real life graphs

Threat Management in Data-Centric IoT-Based Collaborative Systems

In this paper, we propose a Threat Management System (TMS) for Data-driven Internet-of-Things-based Collaborative Systems (DIoTCS). The novelty of the system is that it con nes the damage into partitions generated for the global dataset shared by the DIoTCS applications. We formulate the partitioning problem as a cost-driven optimization problem, which is proven to be NP-hard. Accordingly, two heuristics are proposed to solve this problem. For TMS, we propose response and recovery subsystems. We evaluate TMS experimentally and demonstrate that intelligent partitioning of global dataset improves the overall availability of the DIoTCS.

Source-aware Crisis-relevant Tweet Identification and Key Information Summarization

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.

Social Network De-Anonymization: More Adversarial Knowledge, More Users Re-Identified?

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.

Policy Adaptation in Hierarchical Attribute-Based Access Control Systems

In Attribute-Based Access Control (ABAC), access to resources is given based on attributes of subjects, objects, and environment. There is an imminent need for the development of efficient algorithms that enable migration to ABAC. However, existing policy mining approaches do not consider possible adaptation to the policy of a similar organization. In this article, we address the problem of automatically determining an optimal assignment of attribute values to subjects for enabling the desired accesses to be granted while minimizing the number of ABAC rules used by each subject. We show the problem to be NP-Complete and propose a heuristic solution.

Understanding The Influences of Past Experience on Trust in Human-Agent Teamwork

This study empirically investigates the impact of past experience on human trust in and reliance on agent teammates. We developed a repeated team coordination game, in which two players repeatedly cooperate to complete team tasks. The results show that positive (negative) past experience increases (decreases) human trust in and reliance agent teammates; lack of past experience lead to higher trust levels compared to positive past experience. These findings provide clear and significant evidence and enhance our understanding of the changes in human trust in peer-level agent teammates with respect to past experience

Multi-Level Tag Recommendation for Question Answering

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.

CloseUp: Community-Driven Live Online Search

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.

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