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Fine-grained Emotion Role Detection Based on Retweet Information

User behaviors in online social networks convey not only literal information but also one’s emotional attitudes towards the information. To... (more)

Grouping Peers Based on Complementary Degree and Social Relationship using Genetic Algorithm

The aim of this article is to propose a new innovative grouping approach using the genetic algorithm (GA) to enhance the interaction and collaboration... (more)

Making Constrained Things Reachable: A Secure IP-Agnostic NAT Traversal Approach for IoT

The widespread adoption of the Internet of Things (IoT) has created a demand for ubiquitous connectivity of IoT devices into the Internet. While end-to-end connectivity for IoT requires in practice IPv6, a vast majority of nodes in Internet are only IPv4-capable. To address this issue, the use of Network Address Translation (NAT) at the IoT network... (more)

A Hybrid Approach for Improving the Design Quality of Web Service Interfaces

A key success of a Web service is to appropriately design its interface to make it easy to consume and understand. In the context of service-oriented... (more)

Context-Driven and Real-Time Provisioning of Data-Centric IoT Services in the Cloud

The convergence of Internet of Things (IoT) and the Cloud has significantly facilitated the provision and management of services in large-scale... (more)

An Autonomic Cognitive Pattern for Smart IoT-Based System Manageability: Application to Comorbidity Management

The adoption of the Internet of Things (IoT) drastically witnesses an increase in different domains and contributes to the fast digitalization of the... (more)

Latency-Aware Application Module Management for Fog Computing Environments

The fog computing paradigm has drawn significant research interest as it focuses on bringing cloud-based services closer to Internet of Things (IoT)... (more)

IoT Data Prefetching in Indoor Navigation SOAs

Internet-based Indoor Navigation Service-Oriented Architectures (IIN-SOA) organize signals collected by IoT-based devices to enable a wide range of novel applications indoors, where people spend 80--90% of their time. In this article, we study the problem of prefetching (or hoarding) the most... (more)

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About TOIT

The 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
Adaptive Resource Allocation for Computation Offloading: A Control-theoretic Approach

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.

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

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.

A Dynamic Data-Throttling Approach to Minimize Workflow Imbalance

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.

On the Profitability of Bundling Sale Strategy for Online Service Markets with Network Effects

In this paper, we aim to understand the dynamics of bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effect. We provide mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and formulate an optimization framework to determine the optimal sale strategy for the service provider. We analyze the impact of the key factors such as network effects, operating costs, as well as the variance and correlation of customers' valuations towards these services.

ContextAiDe: End to End Architecture for Mobile Crowd Sensing Applications

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.

Fog-based secure communications for low power IoT devices

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.

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.

Fog Computing for the Internet of Things: a Survey

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.

Cloud, Fog or Mist in IoT, that is the question

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.

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.

Guest Editors' Introduction to the Special Issue on Knowledge-Driven Business Process Management

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