Multilingual sentiment analysis remains challenging due to limited labeled data and strong cross-lingual variation. More specifically, the same expression may signal praise in one language but criticism in another, making sentiment detection highly context-dependent. At the same time, large language models (LLMs) often struggle to maintain stable classifications in low-resource and zero-shot settings. Therefore, we introduce MultiSent-RAG, a training-free retrieval-augmented framework that integrates structured sentiment corpora with unstructured multilingual evidence, and MultiSent-RAG-Cache, a semantic memory module that reuses prior label inferences based on embedding similarity. Our study uses 80,000 labeled instances across 12 languages, including low-resource and zero-shot settings. Retrieval augmentation leads to substantial improvements, with F1 gains of up to +0.65 points, and relative gains exceeding 70%–110% over strong LLM baselines. The semantic cache further reveals when memory reinforces consistent classifications and when it risks propagating errors. Beyond improving accuracy, our analysis reveals how retrieval strengthens contextual grounding across languages, while the semantic cache exposes a trade-off between classification stability and error propagation. These findings provide practical insights for designing robust and interpretable multilingual information processing systems.

Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Urban traffic congestion remains a critical challenge to mobility efficiency and environmental sustainability. This paper presents a Digital Twin framework for real-time urban traffic management in Issy-les-Moulineaux, France. The framework integrates heterogeneous traffic data sources, including historical, real-time, and predictive data, into a unified architecture for scenario-based analytics and decision support. For traffic forecasting, deep learning models, including Long Short-Term Memory (LSTM) and Transformer-based approaches, are evaluated alongside baseline models such as SARIMA and Neural Prophet. The LSTM model is selected based on its stable and consistent performance across heterogeneous traffic conditions, achieving mean absolute error (MAE) values ranging from approximately 2 to 33 vehicles/hour across different road segments. The system also supports dynamic event handling, including full and time-based road closures, with capacity-constrained and equal-allocation diversion strategies for first-order impact estimation. Quantitative evaluations demonstrate that the framework enables proactive congestion mitigation, comparative emission impact assessment, and improved interoperability with city platforms through NGSI-LD standardization. By combining predictive modeling, real-time monitoring, and scenario-driven analytics, the proposed Digital Twin delivers actionable insights for urban planners, reducing short-term disruptions and supporting long-term sustainable mobility management.

The widespread application of medical information systems has promoted the growth of personal electronic medical records (EMRs), which are typically produced in different medical institutions and stored in data centers. Consequently, data owners no longer retain control over their medical data, nor can they establish access control rules for their EMRs. Therefore, this study designs a patient-centered EMR access control system that integrates decentralized smart contracts and role-based access control (RBAC) to provide fine-grained data access control. In this system, we integrate a role-based access control model to achieve user-permission definition and adopt a personalized data access policy definition mechanism to achieve patient-centered data access control. The proposed system allows data owners to define a series of data access policies through smart contracts, achieving decentralized management of data access control permissions. In addition, we analyze the security features of this scheme and design a series of comparative experiments to evaluate the performance. The experimental results show that this system can efficiently achieve access control of personal electronic medical records and has higher reliability compared to traditional cloud-based EMR sharing systems.

International audience

We present a novel class of jailbreak adversarial attacks on LLMs, termed Task-in-Prompt (TIP) attacks. Our approach embeds sequence-to-sequence tasks (e.g., cipher decoding, riddles, code execution) into the model’s prompt to indirectly generate prohibited inputs. To systematically assess the effectiveness of these attacks, we introduce the PHRYGE benchmark. We demonstrate that our techniques successfully circumvent safeguards in six state-of-the-art language models, including GPT-4o and LLaMA 3.2. Our findings highlight critical weaknesses in current LLM safety alignment and underscore the urgent need for more sophisticated defence strategies.

Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from models such as Wav2Vec and Whisper, we explore cross-lingual abuse detection using the ADIMA dataset with FSL. Our approach integrates these representations within the Model-Agnostic Meta-Learning (MAML) framework to classify abusive language in 10 languages. We experiment with various shot sizes (50-200) evaluating the impact of limited data on performance. Additionally, a feature visualization study was conducted to better understand model behaviour. This study highlights the generalization ability of pre-trained models in low-resource scenarios and offers valuable insights into detecting abusive language in multilingual contexts.

Federated learning allows multiple users to collaboratively train models while protecting data privacy. However, for some users, the non-independent identically distributed nature of user data often reduces the accuracy of the global model. Existing personalized federated learning methods usually focus on individual users, which leads to problems such as bias and overfitting. This paper proposes a new distributed personalized federated learning framework based on Siamese neural networks (DPFL-SNN). First, a novel similarity calculation method is designed using the dual-branch structure of the Siamese neural network to effectively identify local users with similar data. Second, by combining this similarity calculation with blockchain technology, a new consensus algorithm is developed to achieve decentralization and reduce security risks. Simulations conducted on publicly available datasets demonstrate that the DPFL-SNN achieves higher accuracy compared to state-of-the-art personalized federated learning methods, thanks to enhanced collaboration among users with similar data.

Guanlin Li, Yuki Arase, Noel Crespi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The increasing demand for smart cities calls for advanced solutions to enhance urban sustainability. Digital twin technology offers transformative potential by synchronizing virtual and physical environments in real time. However, existing approaches struggle with scalability due to the reliance on numerous specialized prediction models for individual urban components, the lack of a unified framework for different use cases limits generalization, and high latencies in real-time synchronization. To address these, this paper presents a comprehensive software architecture for smart city DT and integrates correlation-aware model reduction and dynamic adaptive forecasting to support diverse urban applications to improve generalizability and scalability. This is done while adapting a smart distribution of DT software components between edge and core servers to ensure a low-latency performance. Validated using real-world traffic and air quality data, the system demonstrates significant improvements in traffic flow, emissions reduction, and public transportation efficiency, and enhances air quality monitoring, forecasting, and pollutant management. Key contributions include a scalable and generalizable DT architecture, AI-driven adaptability, edge-core deployment, and extensive validation through predictive analytics. This work establishes a replicable blueprint for metropolitan-scale DTs, balancing computational efficiency with responsive urban analytics.

The growing need for intelligent tools to support urban planning and resource management has positioned Digital Twin (DT) technology as a cornerstone of smart city development. DTs, as dynamic virtual replicas of physical systems, offer capabilities that extend beyond mere representation, enabling monitoring, diagnostics, forecasting, and optimization. In the context of urban traffic management, DTs provide a robust solution for real-time traffic monitoring and predictive analytics. However, existing approaches often lack a systematic design methodology, leading to challenges in scalability and adaptability, particularly in heterogeneous environments. This paper presents a novel methodology for developing scalable and adaptive smart city DT architectures, with a focus on real-time traffic management. A modular and unified software framework is proposed, leveraging AI-driven approaches to address the complexity of managing diverse traffic data sources. A sequential learning model is integrated into the architecture to enhance the DT's adaptability to evolving traffic conditions and congestion patterns. The proposed framework is validated using real-world traffic data from an IoT network deployed in Madrid, demonstrating its scalability and low-latency performance. Experimental results highlight the effectiveness of the framework in handling heterogeneous traffic scenarios and its ability to deliver accurate predictions while minimizing resource overhead.

Network Digital Twin (NDT) collects data from physical, virtual, and software components and supports real-time network performance analysis, emulation, and intelligent physical network control. This paper surveys the current state of NDT specifications and explores NDT benefits for Network Operators (NOs) and its possible roles in future network management. It discusses the NDT key components, architecture, and integration of Machine Learning and Artificial Intelligence models in the NDT. Further, it covers virtualization technology management, suitability of Software-Defined Networking capabilities, and simulation tools to empower NDT. Two perspectives make the position of this survey different from existing studies; first, it highlights NDT limitations regarding Edge–Cloud Continuum (ECC) contextualization. ECC is a purposeful trending integration of Edge and Cloud Computing , involving multiple stakeholders like Service Providers, Customers, and Platform or Infrastructure Providers. However, current NDT specifications have not mentioned the ways to benefit stakeholders other than NOs. We also discuss notable computing and communication technologies transformations necessary to consider during NDT modeling, the existing data models, and reusable vocabularies that can be extended to achieve a detailed ECC representation for all stakeholders, essentially for Service Providers and Customers. Secondly, a data model is proposed that covers descriptive and prescriptive features and aims to provide a granular representation of ECC components to meet stakeholders’ requirements and render particular user information views. Different explored NDT perspectives, and proposed data model reduces the impact of existing NDT limitations in ECC representation.

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.

This paper presents a modular Digital Twin software architecture designed for smart cities, leveraging Edge-Cloud Continuum to enable the development of flexible and scalable DT-based solutions. Digital Twin technology provides a powerful framework for simulating, analyzing, and optimizing urban environments by integrating real-time and historical data from various city sensors through IoT, AI, and cloud computing. The proposed architecture addresses the limitations of existing DT frameworks by focusing on smart city-specific requirements such as dynamic resource management, real-time data processing, and autonomous decision-making. The viability of the proposed framework is demonstrated through a case study on autonomous traffic management in the city of Issy-les-Moulineaux. It shows how the proposed framework predicts traffic patterns and manages network resource allocation by adjusting the data sampling frequency to balance prediction accuracy and communication costs. The architecture’s modular design supports seamless integration and adaptability, making it suitable for various smart city applications, thereby advancing the development of more efficient, sustainable, and resilient urban environments.

Specifications for Network Digital Twin (NDT) from Standardization Development Organizations (SDOs), such as the Internet Engineering Task Force (IETF), and academic contributions focus primarily on benefiting network operators. However, they often overlook the needs of stakeholders in the Edge-Cloud Continuum (ECC), such as Service Providers, customers, and Platform or Infrastructure Providers. In ECC, resource heterogeneity, agile software component integration, and quality of service requirements are challenges. To address these challenges, continuous and granular monitoring of software and physical resources is required. In this paper, we present the design and ongoing implementation of a data model. It captures and characterizes the physical and software properties, i.e., Key Performance Indicators (KPIs), of Kubernetes-managed components in the ECC. Collected data is structured in NGSI-LD-compliant format and managed through interoperable context brokers for authenticating the requests of various stakeholder applications. We also demonstrate sample data curation from a lab-configured platform, its integration into the context broker, and how it responds to the queries concerning a particular component information, thereby representing a partial implementation of proposed data model to render the views for particular stakeholders.

Recent studies improve the cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. However, the alignment-based methods exhibit intrinsic limitations such as non-transferable linguistic elements, while most of the self-training based methods ignore the useful information hidden in the low-confidence samples. To address this issue, we propose CoNLST (Contrastive Negative Learning and Self-Training) to leverage the information of low-confidence samples. Specifically, we extend the negative learning to the metric space by selecting negative pairs based on the complementary labels and then employ self-training to iteratively train the model to converge on the obtained clean pseudo-labels. We evaluate our approach on the widely-adopted cross-lingual benchmark XNLI. The experiment results show that our method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods.

Redundancy reduction plays a critical role in optimizing sensor network performance. This research proposes a deep-learning approach to identify and eliminate redundant sensors in a traffic network. This strategy aims to create a more cost-effective, efficient and reliable traffic monitoring system, ultimately leading to improvements in the transportation infrastructure. Leveraging traffic data from the Madrid Open Data Portal (focusing on ’District 19’), we employed sensor correlation (cosine) and similarity analysis (VGG16-based model) to identify significant correlations among sensors. This allows for accurate prediction (using Long Short-Term Memory(LSTM)-based models) of values from highly correlated sensors, leading to a potential reduction in District 19’s sensor nodes by 43% (from 32 to 18) and connectivity edges by 82% (from 106 to 19). Notably, the predictive accuracy for ’highly similar’ sensors achieved an average R-squared score of 0.82, validating the reliability of LSTM model predictions. These initial results encourage a larger analysis of the methodology to better prove the potential of our deep learning approach in optimizing and streamlining smart city infrastructure. This promising approach can be extended to analyze districts with higher sensor density and be adapted for application in other cities. We aim to utilize deep learning algorithms to optimize future sensor deployment planning.

Bitcoin’s success as a global cryptocurrency has paved the way for the emergence of blockchain, a revolutionary category of distributed systems. However, the growing popularity of blockchain has led to a significant divergence from its core principles in many systems labeled as “blockchain.” This divergence has introduced complexity into the blockchain ecosystem, exacerbated by a lack of comprehensive reviews on blockchain and its variants. Consequently, gaining a clear and updated understanding of the diverse spectrum of current blockchain and blockchain-like systems has become challenging. This situation underscores the necessity for an extensive literature review and the development of thematic taxonomies. This survey seeks to offer a comprehensive and current assessment of existing blockchains and their variations while delineating the boundaries between blockchain and blockchain-like systems. To achieve this objective, we propose a holistic reference model for conceptualizing and analyzing these systems. Our layer-wise framework envisions all distributed ledger technologies (DLTs) as composed of four principal layers: data, consensus, execution, and application (DCEA). In addition, we introduce a new taxonomy that enhances the classification of blockchain and blockchain-like systems, offering a more useful perspective than existing works. Furthermore, we conduct a state-of-the-art review from a layered perspective, employing 23 evaluative criteria predefined by our framework. We perform a qualitative and quantitative comparative analysis of 44 DLT solutions and 26 consensus mechanisms while discussing differences and boundaries between blockchain and blockchain-like systems. We emphasize the significant challenges and tradeoffs encountered by distributed ledger designers, decision-makers, and project managers during the design or adoption of a DLT solution. Finally, we outline crucial research challenges and directions in the field of DLTs.

As Mobile Network Operators (MNOs) prepare to interconnect diverse technologies to existing cellular networks, it is critical to assess the capabilities of the network architecture to handle such changes. One key area to consider is the subscription management process, which governs the user's profile management, authentication, and access control. This process operates centrally, which affects users' security, accessibility, and privacy. Additionally, it increases the system's complexity when handling the large volume of messages sent over networks like IoT. In this work, we propose a Blockchain-based subscription management approach for next generation cellular networks to address the challenges in user profile management and the Authentication and Key Agreement (AKA) process. The method uses a hybrid cryptosystem technique to protect the user's privacy. Based on the evaluation, the system can handle the AKA process with fewer messages passing while improving system availability by utilizing distributed network functions and storage. Finally, we highlight some key points to consider when implementing our proposed approach.

The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusive content. Therefore, automatically detecting this content is essential for banning inappropriate information, and reducing toxicity and violence on social media platforms. The existing works on hate speech and offensive language detection produce promising results based on pre-trained transformer models, however, they considered only the analysis of abusive content features generated through annotated datasets. This paper addresses a multi-task joint learning approach which combines external emotional features extracted from another corpora in dealing with the imbalanced and scarcity of labeled datasets. Our analysis are using two well-known Transformer-based models, BERT and mBERT, where the later is used to address abusive content detection in multi-lingual scenarios. Our model jointly learns abusive content detection with emotional features by sharing representations through transformers' shared encoder. This approach increases data efficiency, reduce overfitting via shared representations, and ensure fast learning by leveraging auxiliary information. Our findings demonstrate that emotional knowledge helps to more reliably identify hate speech and offensive language across datasets. Our hate speech detection Multi-task model exhibited 3% performance improvement over baseline models, but the performance of multi-task models were not significant for offensive language detection task. More interestingly, in both tasks, multi-task models exhibits less false positive errors compared to single task scenario.

The telecommunications sector is devoting an initial interest in the representation of complex networks as Digital Twins. The concept of a Digital Twin Network (DTN) is a research topic, but it promises to be an important step for harmonizing different models of the Edge-Cloud Continuum. The DTN software framework aims at helping network operations by providing updated and complete views on the network or parts of it, and it also introduces the possibility to simulate the network behavior or to learn from network events history (Machine Learning) without jeopardizing the actual operations of resources. In addition, thanks to the representation capabilities of the DT, its usage in the network promises to support different stakeholders’ views on their virtualized and physical infrastructure. This work tries to consolidate a DTN data model representing the elements of the Edge-Cloud Continuum by providing a layered (horizontal) and segmented (vertical) view of the infrastructure to all the involved stakeholders. The DTN model is an ontology where the linked classes represent properties and relations of networked components. This work aims to design a flexible and extensible ontology that describes the Edge-Cloud continuum usable in the telecommunications as well in the Cloud (IT and web) industries creating a bridge between the two.

Cities have undergone significant changes due to the rapid increase in urban population, heightened demand for resources, and growing concerns over climate change. To address these challenges, digital transformation has become a necessity. Recent advancements in Artificial Intelligence (AI) and sensing techniques, such as synthetic sensing, can elevate Digital Twins (DTs) from digital copies of physical objects to effective and efficient platforms for data collection and in-situ processing. In such a scenario, this paper presents a compre-hensive approach for developing a Traffic Monitoring System (TMS) based on Edge Intelligence (EI), specifically designed for smart cities. Our approach prioritizes the placement of intelligence as close as possible to data sources, and leverages an “opportunistic” interpretation of DT (ODT), resulting in a novel and interdisciplinary strategy to re-engineering large-scale distributed smart systems. The preliminary results of the proposed system have shown that moving computation to the edge of the network provides several benefits, including (i) enhanced inference performance, (ii) reduced bandwidth and power consumption, (iii) and decreased latencies with respect to the classic cloud -centric approach.

Trust is an important factor in the success of P2P networks. It is needed to ensure that nodes in the network can be trusted to behave honestly and to deliver on their promises (e.g sharing resources). While traditional reputation trust management systems (RTMS) such as BTrust or EigenTrust have proven effective, there is room for further enhancement by integrating advanced graph neural network (GNN) models. This paper proposes a novel approach to enhance Trust Management Systems (TMS) by incorporating an Edge-Feature Attention Mechanism into the Edge Graph Neural Network (EGNN) model, which takes into account the direction of edges. The proposed GBTrust model is specifically designed for trust management in P2P networks, leveraging the interactions and relationships among peers. By incorporating the Edge-Feature Attention Mechanism, the model dynamically assigns importance to different edge features based on their relevance, thereby improving the discrimination of the importance of various neighbors and edge features in the network graph. The GBtrust model aims to provide more accurate and adaptive detection of malicious peers, thereby enhancing the overall security and reliability of P2P networks.

The Internet of Things (IoT) consists of a myriad of smart devices and offers tremendous innovation opportunities in industry, homes, and businesses to enhance the productivity and the quality of life. However, ecosystem of infrastructures and the services associated with IoT devices have introduced a new set of vulnerabilities and threats, resulting in abnormal values of information collected by sensors, jeopardizing system security. To secure sensor networks, it must be possible to detect such anomalies or sequences of patterns in IoT devices that significantly deviate from normal behavior. To perform this task, this paper proposes a real-time streaming anomaly detection method based on a Bloom filter combined with hashing. This method expands the data dimensions through a hashing algorithm, and then adopts competitive learning (Winner-Take-All) to build a multi-layer Bloom Filter anomaly detection model. The feasibility of the proposed algorithm is verified theoretically using two datasets, KDD (to detect anomalies at the TCP/IP network level) and Credit (to detect anomalies during credit card transactions). The simulation results show that the proposed in this paper can effectively identify anomalies in the simulation data streams, with almost 95% accuracy for both datasets.

Video anomaly detection has always been a hot topic and attracting an increasing amount of attention. Much of the existing methods on video anomaly detection depend on processing the entire video rather than considering only the significant context. This paper proposes a novel video anomaly detection method named COVAD, which mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an auto-encoded convolutional neural network and coordinated attention mechanism, which can effectively capture meaningful objects in the video and dependencies between different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can more effectively predict the future motion and appearance of objects in the video. Our proposed algorithm obtained better experimental results on multiple data sets and outperformed the baseline models considered in our analysis. At the same time we improve a visual test that can provide pixel-level anomaly explanations.

We present the deployment procedure of a private 4G-LTE network with standard User Equipment in two different scenarios using OpenAirInterface and Magma core networks. Our lessons learned from deploying the segregated end-to-end cellular network testbed, comparison of connection performance in two scenarios, challenges of connecting smartphones to the network, and comparison among the possible use-cases with each scenario are the highlighted subjects provided in this paper.

With the freedom of communication provided in online social media, hate speech has increasingly generated. This leads to cyber conflicts affecting social life at the individual and national levels. As a result, hateful content classification is becoming increasingly demanded for filtering hate content before being sent to the social networks. This paper focuses on classifying hate speech in social media using multiple deep models that are implemented by integrating recent transformer-based language models such as BERT, and neural networks. To improve the classification performances, we evaluated with several ensemble techniques, including soft voting, maximum value, hard voting and stacking. We used three publicly available Twitter datasets (Davidson, HatEval2019, OLID) that are generated to identify offensive languages. We fused all these datasets to generate a single dataset (DHO dataset), which is more balanced across different labels, to perform multi-label classification. Our experiments have been held on Davidson dataset and the DHO corpora. The later gave the best overall results, especially F1 macro score, even it required more resources (time execution and memory). The experiments have shown good results especially the ensemble models, where stacking gave F1 score of 97% on Davidson dataset and aggregating ensembles 77% on the DHO dataset.

In recent years, Blockchain arose as a key technology in building autonomous decentralised financial systems. Its ability to digitize trust enables building trustless systems such as cryptocurrencies where users do not need to rely on any third party to exchange value. The success of cryptocurrencies to operate without any intermediaries draw the interest of business operators who seek to bypass intermediation and thus to reduce cost and gain competitive advantages. As a result, Blockchain was used outside the crypto-sphere to build decentralized systems. However, this portage led to the inception of new types of Blockchains adapted to different specifications and with different designs. Consequently, the technology has diverged from its baseline (Bitcoin) to the point where some systems marketed as “blockchain” share only a few design concepts with the original Blockchain design proposed by Satoshi Nakamoto. This conceptual divergence alongside the lack of comprehensive models and standards made it difficult for both system designers and decision-makers to clearly understand what is a blockchain or to choose a suitable solution.This survey has a double goal; on the one hand, it attempts to contribute to the discussion on the ontological status of DLTs by providing a taxonomy oriented-framework (DCEA) for conceptualizing and examining DLT. On the other hand, it also attempts to present an up-to-date review and evaluation of current blockchains and their variants as constructed of four layers: the data, consensus, execution and application layers.

In centralized infrastructures, users are not capable of authenticating themselves, in identity management systems, beyond their application’s domain. Users are forced to trust their service providers for identification and data management. Such solutions have experienced large-scale data breaches and are cumbersome for users to remember the credentials for multiple sites. Furthermore, users have very little control over their data. The concept of decentralized identity has raised the possibility of better managing these concerns. It allows users to share only the relevant part of their personal information with a service provider to verify their digital identity. We propose OrgID, a decentralized identity and user-centric data management platform including identity registration and authorization procedures. Our approach supports self-sovereign identity architecture leveraged by blockchain. This method consists of a one-time proof-verification mechanism that facilitates the secure access and credibility of digital information. Moreover, users can maintain their identities associated with specific attributes and rely on a proof mechanism using smart contracts. It deploys a platform where user registration and authorization no longer involve a central service provider. We implemented the proposed solution using smart contracts on a private Ethereum blockchain. We further analyzed the performance of these processes regarding the system’s scalability to evaluate how they manage latency and the number of users. The results state that the system is highly scalable to manage a large number of users and the system’s latency is adjustable based on the application needs.

This paper tackles online scheduling of electric vehicles (EVs) in an adaptive charging network (ACN) with local and global peak constraints. Given the aggregate charging demand of the EVs and the peak constraints of the ACN, it might be infeasible to fully charge all the EVs according to their charging demand. Two alternatives in such resource-limited scenarios are to maximize the social welfare by partially charging the EVs (fractional model) or selecting a subset of EVs and fully charge them (integral model). The technical challenge is the need for online solution design since in practical scenarios the scheduler has no or limited information of future arrivals in a time-coupled underlying problem. For the fractional model, we devise both offline and online algorithms. We prove that the offline algorithm is optimal. Using competitive ratio as the performance measure, we prove the online algorithm achieves a competitive ratio of 2. The integral model, however, is more challenging since the underlying problem is strongly NP-hard due to 0/1 selection criteria of EVs. Hence, efficient solution design is challenging even in offline setting. For offline setting, we devise a low-complexity primal-dual scheduling algorithm that achieves a bounded approximation ratio. Built upon the offline approximate algorithm, we propose an online algorithm and analyze its competitive ratio in special cases. Extensive trace-driven experimental results show that the performance of the proposed online algorithms is close to the offline optimum, and outperform the existing solutions.

Impersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake accounts that intend to disguise a legitimate account by making similar profiles and then striking social media by fake content, which makes it considerably harder to understand which posts are genuinely produced. In this study, we focus on three important communities with legitimate verified accounts. Among them, we identify a collection of 2.2K impersonator profiles with nearly 10k generated posts, 68K comments, and 90K likes. Then, based on profile characteristics and user behaviours, we cluster them into two collections of `bot' and `fan'. In order to separate the impersonator-generated post from genuine content, we propose a Deep Neural Network architecture that measures `profiles' and `posts' features to predict the content type: `bot-generated', `fan-generated', or `genuine' content. Our study shed light into this interesting phenomena and provides interesting observation on bot-generated content that can help us to understand the role of impersonators in the production of fake content on Instagram.

Recent years have seen a new form of advertisement campaigns emerge: those involving so-called social media influencers. These influencers accept money in return for promoting products via their social media feeds. We gather a large-scale Instagram dataset covering thousands of accounts advertising products, and create a categorisation based on the number of users they reach. We then provide a detailed analysis of the types of products being advertised by these accounts, their potential reach, and the engagement they receive from their followers. Based on our findings, we train machine learning models to distinguish sponsored content from non-sponsored, and identify cases where people are generating sponsored posts without labelling them.

Impersonators on Online Social Networks such as Instagram are playing an important role in the propagation of the content. These entities are the type of nefarious fake accounts that intend to disguise a legitimate account by making similar profiles. In addition to having impersonated profiles, we observed a considerable engagement from these entities to the published posts of verified accounts. Toward that end, we concentrate on the engagement of impersonators in terms of active and passive engagements which is studied in three major communities including “Politician”, “News agency and “Sports star” on Instagram. Inside each community, four verified accounts have been selected. Based on implemented approach in our previous studies [1], we have collected 4. 8K comments, and 2. 6K likes across 566 posts created from 3. 8K impersonators during 7 months. Our study shed light into this interesting phenomena and provides a surprising observation that can help us to understand better how impersonators engaging themselves inside Instagram in terms of writing Comments and leaving Likes.

Digital twin (DT) is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object (PO) into a software counterpart. The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely augmented and virtual reality (e.g., avatars), multiagent systems, and virtualization. This survey thereby allows for the identification of an extensive set of DT features that point to the “softwarization” of POs. To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT. Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the DT concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT. This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.

Slices were introduced in 5G to enable the co-existence of applications with different requirements on a single infrastructure. Slices may be delay-constrained for mission-critical applications such as Tactile Internet applications. When delay-constrained slices are implemented as collections of virtual network function (VNF) chains, a key challenge is to place the VNFs and route the traffic through the chains to meet a strict delay constraint. Parallel VNF processing has been proposed as a promising approach. However, this approach increases the number of physical nodes in the chains, and thus decreases the reliability, which is also critical for Tactile Internet applications. Furthermore, the cost depends upon the specific VNF placement and traffic routing, as nodes and links are heterogeneous. This article tackles the issues of reliability and cost when embedding delay-constrained slices. We model the problem as an optimization problem that minimizes reliability degradation and cost while ensuring the strict delay constraint when a parallel VNF processing approach is used. Due to the complexity of the formulated problem, we also propose a Tabu search-based algorithm to find sub-optimal solutions. The results indicate that our proposed algorithm can significantly improve cost and reliability while meeting a strict delay constraint.

Social networking sites (SNSs) facilitate the sharing of ideas and information through different types of feedback including publishing posts, leaving comments and other type of reactions. However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user. The phenomenon known as flaming goes hand-in-hand with this type of posting that can trigger almost instantly on SNSs. Most popular users such as celebrities, politicians and news media are the major victims of the flaming behaviors and so detecting these types of events will be useful and appreciated. Flaming event can be monitored and identified by analyzing negative comments received on a post. Thus, our main objective of this study is to identify a way to detect flaming events in SNS using a sentiment prediction method. We use a deep Neural Network (NN) model that can identity sentiments of variable length sentences and classifies the sentiment of SNSs content (both comments and posts) to discover flaming events. Our deep NN model uses Word 2Vec and FastText word embedding methods as its training to explore which method is the most appropriate. The labeled dataset for training the deep NN is generated using an enhanced lexicon based approach. Our deep NN model classifies the sentiment of a sentence into five classes: Very Positive, Positive, Neutral, Negative and Very Negative. To detect flaming incidents, we focus only on the comments classified into the Negative and Very Negative classes. As a use-case, we try to explore the flaming phenomena in the news media domain and therefore we focused on news items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews) to train and test the model. The experimental results show that flaming events can be detected with our proposed approach, and we explored main characteristics that trigger a flaming event and topics discussed in the flaming posts.

Fake accounts and Impersonators on Online Social Networks such as Instagram are turning difficulties for society. This has attended to an increasing interest in detecting fake profiles and investigating their behaviours. Questions like who are impersonators? what are their characteristics? and are they bots? will arise. To answer, we begin this research by collecting data from three important communities on Instagram including “Politician”, “News agency”, and “Sports star”. Inside each community, four verified top accounts are picked. Based on the users who reacted to their published posts, we detect 4K impersonators [1]. Then we employed well-known clustering methods to distribute impersonators into separated clusters to observe obscure behaviours and unusual profile characteristics. We also studied the cross-group analysis of clusters inside each community to explore engagements. Finally, we conclude the study by providing a complete investigation of the bot-like cluster.

There is an ever-growing number of users who duplicate the social media accounts of celebrities or generally impersonate their presence on online social media as well as Instagram. Of course, this has led to an increasing interest in detecting fake profiles and investigating their behaviour. We begin this research by targeting a few famous politicians, including Donald J. Trump, Barack Obama, and Emmanuel Macron and collecting their activity for the period of 3 months using a specifically-designed crawler across Instagram. We then experimented with several profile characteristics such as username, display name, biography, and profile picture to identify impersonator among 1,5M unique users. Using publicly crawled data, our model was able to distinguish crowds of impersonators and political bots. We continued by providing an analysis of the characteristics and behaviour of these impersonators. Finally, we conclude the analysis by classifying impersonators into four different categories.

The emerging Blockchain (BC) and Distributed Ledger technologies have come to impact a variety of domains, from capital market sectors to digital asset management in the Internet of Things (IoT). As a result, more and more BC-based decentralized applications for numerous cross-domain services have been developed. These applications implement specialized decentralized computer programs called Smart Contracts (SCs) which are deployed into BC frameworks. Although these SCs are open ato public, it is challenging to discover and utilize such SCs for a wide range of usages from both systems and end-users because such SCs are already compiled in form of byte-codes without any associated meta-data. This motivates us to propose a solution called Semantic SC (SSC) which integrates RESTful semantic web technologies in SCs, deployed on the Ethereum Blockchain platform, for indexing, browsing and annotating such SCs. The solution also exposes the relevant distributed ledgers as Linked Data for enhancing the discovery capability. To achieve this goal, the OWL-S service ontology is extended by incorporating some domain specific terminologies, which are used in the development of the proposed SSCs. As a result, SSC can be utilized to enrich queries for a domain-specific terms across multiple distributed ledgers, which greatly increases the discovery capability of decentralized IoT applications and services. Contribution in standardization is also discussed. We believe that our research work takes the first steps towards connecting BC-based decentralized services with semantic web services in order to provide better IoT ecosystems.

The fifth generation of mobile cellular networks (5G) is designed to support a set of new requirements and use cases, including connectivity for the IoT (Internet of Things). Due to the industry and the user's expectation of having connectivity embedded into IoT devices, the “wholesale wireless connectivity” concept is gaining more and more attention. According to this concept, connectivity providers sell connectivity to 3rdparties, which in turn provide it to their own devices. However, this concept brings also new architecture and security requirements that are not fully addressed by the state of the art. Taking advantage of the flexibility provided by virtualization technologies (including network slicing), we propose in this paper a new 5G-SSAAC (5G Slice Specific Authentication and Access Control) mechanism that delegates authentication and access control of the devices to the 3rdparties providing these devices, thereby decreasing the load of the connectivity provider's CN (core network), while increasing flexibility and modularity of the whole 5G network.

The rapid proliferation of electric vehicles has resulted in a drastic increase in the total energy demand of EVs. Given the limited charging rate capacity of charging stations and uncertainty of EV arrivals, the aggregate demand might go beyond the charging station capacity, even with proper scheduling. This paper formulates a social welfare maximization problem for EV charging scheduling with charging capacity constraint. Even though the underlying problem is linear, it is difficult to tackle since the input to the problem, i.e., the charging profile of EVs, reveals in online fashion. We devise charging scheduling algorithms that not only work in the online scenario, but also provide the following two key features: 1) on-arrival commitment; respecting the capacity constraint may hinder fulfilling charging requirement of the deadline-constrained EVs entirely. Therefore, committing a guaranteed charging amount upon arrival of each EV is highly essential; 2) (group)-strategy-proofness as a salient feature to promote EVs to reveal their true type and do not collude with other EVs. Extensive simulations using real traces demonstrate the effectiveness of our online scheduling algorithms as compared to the optimal non-committed offline solution.

The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions. To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc. This paper provides some insight into the social media presence of worldwide popular news media outlets. Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item producers, providers and consumers in the news media community in social media. To better understand these interactions, this work aims to analyze news items in two large social media, Twitter and Facebook. Towards that end, we collected all published posts on Twitter and Facebook from 48 news media to perform descriptive and predictive analyses using the dataset of 152K tweets and 80K Facebook posts. We explored a set of news media that originate content by themselves in social media, those who distribute their news items to other news media and those who consume news content from other news media and/or share replicas. We propose a predictive model to increase news media popularity among readers based on the number of posts, number of followers and number of interactions performed within the news media community. The results manifested that, news media should disperse their own content and they should publish first in social media in order to become a popular news media and receive more attractions to their news items from news readers.

Over the last decade, the evolution of the Internet of Things (IoT) has resulted in a drastic increase in the development of smart cities, including smart parking and intelligent transportation systems (ITS). Smart cities combine a variety of sensors (such as traffic, parking and weather sensors) deployed within these cities. These sensors are used for various applications, such as transportation, parking and weather forecasting. We propose an approach for the mapping of traffic sensors with route coordinates in order to analyze traffic conditions (e.g., level of congestion) on the roadways. We present an algorithm and provide two illustrative examples that cover all of the possible mapping scenarios. We also evaluate the performance of our proposed approach in terms of sensors' correct detection, missed detection and false detection on the routes. Our work can be used for the development of various smart city applications, such as traffic management and smart parking.

By the massive available people data in social media, many digital service providers exploit widely this information to improve their services by predicting future requirements of their customers. This prediction mainly needs to study users' previous behavior and interactions and identify their preferences to provide rigorous recommendations that fulfill their requirements more favorably. Meanwhile, experiments show the prediction methods which exploit representation learning instead of traditional hand-crafted features accomplish better results and more precise predictions. In this study, we take advantage of representation learning method to predict user's future interactions by extracting users embeddings from their reactions history and exploit them in predicting future reactions. In this approach, users embeddings are used in a neural network designed with one-hidden layer and a softmax function in the end layer in order to predict users reactions. The proposed method is evaluated when user embeddings come from two different sources; users reactions history and random walks on the user network. The performance of the method has been evaluated by using a large Flickr dataset including more than 2M users and 11M users reactions sequences. The results show outperforming of the prediction method when it uses the history of user reactions to derive user embeddings.

Achieving global interoperability among IoT systems has become a very real possibility due to the heterogeneity at all levels of IoT. Besides achieving interoperability, it will become very important to establish social relationships and communications among IoT devices (or things), humans and applications. Social relationships in IoT have been realized through the Social IoT (SIoT) paradigm which is one of the trending feature in the IoT. The SIoT is currently consisted of two types of communications: things-to-things and things-to-human communications; in addition, we propose social cross-domain IoT (SCDIoT), a third type of SIoT communication at a global level which enables application-to-application communication in the IoT. Although interoperability allows the exchange and reuse of data among various applications, it does not focus on the social relationships among IoT applications through which those applications can closely collaborate with each other. SCDIoT fills this gap by operating one level above interoperability. It allows collaboration among IoT applications by enabling them to talk to each other, building social relations and benefitting from each other via various useful services, truly exploiting the advantages of interoperability. We present the concept of SCDIoT, its logical framework and some potential use case scenarios, together with the challenges and possible future research directions.

The current research in opinion propagation is largely based on content analysis of social interactions of users on a network. However, the social power of users in propagating the opinion is not considered. In this paper, we study the impact of influential users on propagating the opinion on singed networks and propose an opinion propagation model considering influential users (OPIU). In particular, we identified two major factors involved in a user opinion propagation: (i) the influential users' effect, induced by the presence of a highly confident individual in the network, and (ii) the neighbors' effect, caused by the presence of the users who have a link with the current user. To assess the performance of the proposed method, we applied it to two large datasets of Epinions signed and Etsy unsigned networks. The results are compared with similar opinion propagation algorithm which indicates the influential users have a significant impact on propagation and considering them can effectively improve the propagation extend.

This paper studies the classical problem of online scheduling of deadline-sensitive jobs with partial values and investigates its extension to Electric Vehicle (EV) charging scheduling by taking into account the processing rate limit of jobs and charging station capacity constraint. The problem lies in the category of time-coupled online scheduling problems without availability of future information. This paper proposes two online algorithms, both of which are shown to be (2-[1/U])-competitive, where U is the maximum scarcity level, a parameter that indicates demand-to-supply ratio. The first proposed algorithm is deterministic, whereas the second is randomized and enjoys a lower computational complexity. When U grows large, the performance of both algorithms approaches that of the state-of-the-art for the case where there is processing rate limits on the jobs. Nonetheless in realistic cases, where U is typically small, the proposed algorithms enjoy a much lower competitive ratio. To carry out the competitive analysis of our algorithms, we present a proof technique, which is novel to the best of our knowledge. This technique could also be used to simplify the competitive analysis of some existing algorithms, and thus could be of independent interest.

Value-added services (e.g., overlaid video advertisements) have become an integral part of today's content delivery networks (CDNs). To offer cost-efficient, scalable, and more agile provisioning of new value-added services in CDNs, network functions virtualization paradigm may be leveraged to allow implementation of fine-grained services as a chain of virtual network functions (VNFs) to be placed in CDN. The manner in which these chains are placed is critical as it both affects the quality of service (QoS) and provider cost. The problem is however, very challenging due to the specifics of the chains (e.g., one of their end-points is not known prior to the placement). We formulate it as an integer linear program and propose a cost efficient proactive VNF placement and chaining algorithm. The objective is to find the optimal number of VNFs along with their locations in such a manner that the cost is minimized while QoS is met. Apart from cost minimization, the support for large-scale CDNs with a large number of servers and end-users is an important feature of the proposed algorithm. Through simulations, the algorithm's behavior for small-scale to large-scale CDN networks is analyzed.

Content originality detection is an interesting research topic in large-scale scenarios especially in social media where anyone has the ability to produce and disseminate content in different forms through their profiles and activities. What is missing in these communication sites is to be able to identify original content producers as some users spread information copied from other users without indicating its original producer, or where they found it. This paper provides a conceptualized approach for content originality detection and illustrates the efficiency of the model when applying it to a Twitter dataset. This approach amalgamates user's linguistic features and their online circadian behaviors to identify accurately the content originator for a given text. The proposed approach is evaluated using an F1-measure and the results indicate an accuracy of 95% or higher for all test scenarios. While achieving high accuracy in the test results, our approach, as a usecase, was applied in the context of news agencies popular worldwide to identify news producers and consumers by analyzing their Tweets. We investigated intra and inter news flows among several major news agencies considered in our dataset. Our results show that this proposed approach can distinguish News Story Tellers from News Propagators in the news agencies community as well as provide information that helps to understand the flow patterns between different news groups.

International audience

Network services have been significantly increased in today's enterprise networks. The time and cost of deploying these services are recently considered as critical challenges for enterprise networks. Network Functions Virtualization (NFV) is a promising solution to offer cost-efficient, scalable and more rapid deployment of such services. It allows the implementation of fine-grained services as a chain of Virtual Network Functions (VNFs). These chains need to be placed in the network. The chain placement is critical since it effects on both quality of service (QoS) and the provider cost. This paper formulates the problem of VNF placement and chaining as an Integer Linear Program (ILP) and proposes a Cost-efficient Centrality-based VNF Placement and chaining algorithm (CCVP). The objective is to find the optimal number of VNFs along with their locations in such a manner that the provider cost is minimized. Apart from cost minimization, the support for large-scale environments with a large number of servers and end-users is an important feature of the proposed algorithm. Finaly, the algorithm behavior is analyzed through simulations.

Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, namedNetSpam, which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features helps us to obtain better results in terms of different metrics experimented on real-world review data sets from Yelp and Amazon Web sites. The results show thatNetSpamoutperforms the existing methods and among four categories of features, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the first type of features performs better than the other categories.

This study proposes an eco-friendly navigation algorithm, eRouting, to save energy and reduce CO2emission. The important research issue of traffic information industry, eco-friendly navigation, has been widely studied. As an improvement, in this paper, combining real-time traffic information and a representative factor-based energy/emission model, a calculated route is dynamically adjusted during the travel of a vehicle. eRouting is a centralized algorithm. It profits from the following aspects to achieve improved performance: a representative factor-based energy/emission model, a real-time traffic information-based dynamic adjustment, and an objective function to control the optimization direction to optimize the final energy consumption of vehicle's travel. As a peculiarity of this paper, the design of the representative factor-based model mines the impact of road-level parameters on energy consumption and CO2emission. Such mining is helpful to improve the pertinence of a model by formulating the key influence factors into the model. Experimental results are presented to prove the validity of eRouting. In addition, by contrast experiments, eRouting shows improved performance compared with an eco-friendly navigation algorithm and three traditional navigation algorithms.

Sharing a deployed Wireless Sensor Network Infrastructure (WSNI), (using virtualization), among multiple, concurrent applications can help realize the true potential of Internet-of-Things (IoT). Virtualized WSNs can be used by multiple applications and services concurrently including semantic applications to help end-users to understand the context of the events and make informed decisions. This paper proposes a heuristic-based genetic algorithm to select capable nodes to perform efficient in-network sensor data annotation in virtualized WSNs. We also present early simulations results.

The data generated by the devices and existing infrastructure in the Internet of Things (IoT) should be shared among applications. However, data sharing in the IoT can only reach its full potential when multiple participants contribute their data, for example when people are able to use their smartphone sensors for this purpose. We believe that each step, from sensing the data to the actionable knowledge, requires trust-enabled mechanisms to facilitate data exchange, such as data perception trust, trustworthy data mining, and reasoning with trust related policies. The absence of trust could affect the acceptance of sharing data in smart cities. In this study, we focus on data usage transparency and accountability and propose a trust model for data sharing in smart cities, including system architecture for trust-based data sharing, data semantic and abstraction models, and a mechanism to enhance transparency and accountability for data usage. We apply semantic technology and defeasible reasoning with trust data usage policies. We built a prototype based on an air pollution monitoring use case and utilized it to evaluate the performance of our solution.

Understanding the propagation dynamics of information/an epidemic on complex networks is very important for discovering and controlling a terrorist attack, and even for predicting a disease outbreak. As an effective method, with analyzing the structure of a propagation network, a large number of previous studies have analyzed the propagation dynamics. Most of these studies are based on a special network structure to make such analysis. However, a propagation network has dynamically changed structure during the propagation. How to track, recognize and model such dynamic change is a big challenge. Along with the popularity of smart devices and the rapid development of the Internet of Things (IoT), massive mobile data is automatically collected. In this article, as a typical use case, we investigate the impact of network structure on epidemic propagation dynamics by analyzing the massive mobile data collected from smart devices carried by the volunteers of Ebola outbreak areas. From this investigation, we obtain two observations. Based on these observations and the analytical ability of Apache Spark on streaming data and graphs, we propose a simple model to track and recognize the dynamic structure of a network. Moreover, we introduce and discuss open issues and future work for developing this proposed recognition model.

The papers in this special section focus on the topic of industrial sensing intelligence. Examines the sensor and communications technologies that support these services and reports on industrial applications for their use.

IPv6-enabled low-power wireless personal area networks of smart objects (6LoWPANs) play an important part in the Internet of Things (IoT), especially on account of the Internet integration (IPv6), energy consumption (low-power), and ubiquitous availability (wireless). This paper presents our experience of designing and implementing 6LoWPANs for developing IoT applications, particularly for home and building automation. The performance evaluation provides a comprehensive analysis on several communication aspects between 6LoWPANs and regular IPv6 networks such as energy consumption, network performance, and service communication.

Online Social Networks (OSNs) are being intensively used by professional users (e.g., companies, politician, athletes, celebrities, etc) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation, etc). Hence, due to the large catalog of existing OSNs, professional users usually count with OSN accounts in different systems. In this context an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. In this paper we aim at characterizing the cross-posting activity of professional OSN users across three major OSNs, Facebook, Twitter and Google+. To achieve this goal we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs.

With the recent advances in sensor hardware and software, architectures for virtualized Wireless Sensor Networks (vWSNs) are now emerging. Through node- and network-level virtualization, vWSNs can be offered as Infrastructure-as-a-Service (IaaS) which can aid in realizing the true potential of Internet-of-Things (IoT). Cloud computing offers elastic provisioning of large-scale infrastructures to multiple concurrent users where Platform-as-a-Service (PaaS) interacts with IaaS in order to efficiently host and execute applications over these infrastructures. Amalgamating IoT with cloud computing potentially allows rapid application and service provisioning in an efficient, scalable and robust manner. However, interactions between vWSNs and PaaS are largely an unexplored area. Indeed, existing vWSN IaaS are not yet ready for PaaS. This paper proposes a vWSN IaaS architecture which is ready for interactions with PaaS. The proposed architecture is based on our previous works and is rooted in the fundamental differences between traditional IaaS and vWSN IaaS. We built a prototype using Java Sunspot as the WSN tool kit and made early performance measurements.

Data in smart cities is commonly generated by a large variety of participants including institutional actors, equipment manufacturers, network operators, infrastructure providers, service providers, and end users. This data potentially undergoes several transformations such as aggregation and/or composition before finally being consumed. In this context of sharing data between diverse consumers, it is essential to provide the data producers the means by which they can exercise control over how and by whom the data is used. To date, usage control has received attention in the domains of the web and social networks, in terms of confidentiality, privacy and access control aspects. However, it has not yet been fully applied in a rigorous manner in the context of smart cites. In this paper we study usage control with the goal to address the problem of providing stakeholders more control over their data and enforcing accountable management of such data. We first propose a new data usage policy, called DUPO, which captures the diversity of obligations and constraints resulting from the usage control requirements for smart cities. Next, we apply a defeasible logic based approach on DUPO to formally define rule language, solve rule conflicts, and elaborate reasoning. We then introduce the data handling mechanism, which provides useful functionality to process consumer's request, ensuring the accountability of the policy enforcement, and traceability of the data usage. To this end we benefit from SPINdle reasoner to implement the proposed usage control module covered main functionalities of the mechanism.

Link prediction for new users who have not created any link is a fundamental problem in Online Social Networks (OSNs). It can be used to recommend friends for new users to start building their social networks. The existing studies use cross-platform approaches to predict a new user's links on a certain OSN by porting his existing links from other OSNs. However, it cannot work when OSNs are not willing to share their data or users do not want to connect different OSN accounts. In this paper, we use a single-platform approach to carry out the link prediction. We explore the users' profile attributes (e.g., workplace, high school and hometown) which can be easily obtained during the new users' sign up procedure. Based on the limited available information from the new user, along with the attributes and links from existing users, we extract three types of social features: basic feature, derived feature and latent relation feature. We propose a link prediction model using these social features based on Support Vector Machines. Eventually, we rely on a large Facebook data set consisting of 479,000 users to evaluate our proposed model. The result reveals that our model outperforms the baselines by achieving the AUC value of 0.83; it also demonstrates that each of the proposed social features contribute significantly to the prediction model.

Wireless Sensor Networks (WSNs) have become very popular and are being used in many application domains (e.g. smart cities, security, gaming and agriculture). Virtualized WSNs allow the same WSN to be shared by multiple applications. Semantic applications are situation-aware and can potentially play a critical role in virtualized WSNs. However, provisioning them in such settings remains a challenge. The key reason is that semantic applications' provisioning mandates data annotation. Unfortunately it is no easy task to annotate data collected in virtualized WSNs. This paper proposes a data annotation architecture for semantic applications in virtualized heterogeneous WSNs. The architecture uses overlays as the cornerstone, and we have built a prototype in the cloud environment using Google App Engine. The early performance measurements are also presented.

In the distributed cloud paradigm, data centers are geographically dispersed and interconnected over a wide-area network. Due to the geographical distribution of data centers, communication networks play an important role in distributed clouds in terms of communication cost and QoS. Large-scale, processing-intensive tasks require the cooperation of many VMs, which may be distributed in more than one data center and should communicate with each other. In this setting, the number of data enters serving the given task and the network distance among those data centers have critical impact on the communication cost, traffic and even completion time of the task. In this paper, we present the NACER algorithm, a Network-Aware Cost-Efficient Resource allocation method for optimizing the placement of largemulti-VM tasks in distributed clouds. NACER builds on ideas of the A* search algorithm from Artificial Intelligence research in order to obtain better results than typical greedy heuristics. We present extensive simulation results to compare the performance of NACER with competing heuristics and show its effectiveness.

Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-time and the building of the diagnosis model of any SDN infrastructure using our templates.

In this paper we present the concept of context- aware Emergency Notification and Rescue Service (ENRS) utilizing user information from the 4G EPC network. The 4G EPC is an all IP next generation architecture that makes it easy for various 3GPP and non-3GPP standards to co-exist and provide network access to the end user. The proposed architecture consists of two parts; first part is emergency notification and information gathering to use for the event notification. The second part consists of an intelligent context-aware decision engine that processes the collected information in real time and generates notification messages for the end user. Our proposed service is based on the RESTful concepts and can be used to effectively notify and help users during the emergency. Currently a prototype of the service is being developed to validate the ENRS.

The devices profile for Web services (DPWS) standard enables the use of Web services for resource-constrained devices, main components of the Internet of Things (IoT). DPWS can power the next generation of IoT applications by connecting millions of networked devices and services on the Web. This paper presents a simulator, called DPWSim, to support the use of this technology. DPWSim featuring secure messaging, dynamic discovery, service description, service invocation, and publish-subscribe eventing can be used to prototype, develop, and test products in terms of DPWS communication protocols. It can also support the collaboration among manufacturers, developers, and designers during the new product development process.

This paper presents a comprehensive empirical study on the correlations between users' interest similarity and various social features across three interest domains (i.e., movie, music and TV). This study relies on a large dataset, containing 479, 048 users and 5, 263, 351 user-generated interests, captured from Facebook. We identify the social features from three types of the users' information - demographic information (e.g., age, gender, location), social relations (i.e., friendship), and users' interests. The results reveal that the interest similarity follows the homophily principle. Particularly, the results show that two users are more likely to be alike in their interests 1) if they exhibit more similarity in their demographic characteristics (e.g., similar age, same gender, or close to each other geographically), or 2) if they are more intimate in their friendship, or 3) if they present a higher average interest individuality (i.e., a measurement for estimating the personalized characteristics of a user's interests). The empirical observations could be exploited to infer how two users are alike in their interests according to the social features, which could be further harnessed by various practical applications and services, such as recommendation system and advertisement service.

Content discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384,494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features–Knowledge and Similarity–and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node’s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods.

The cluster between Internet of Things (IoT) and social networks (SNs) enables the connection of people to the ubiquitous computing universe. In this framework, the information coming from the environment is provided by the IoT, and the SN brings the glue to allow human-to-device interactions. This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT). Although there have been early-stage studies in social-driven IoT, they merely use one or some properties of SIoT to improve a number of specific performance variables. Therefore, this paper first addresses a complete view on SIoT and key perspectives to envision the real ubiquitous computing. Thereafter, a literature review is presented along with the evolutionary history of IoT research from Intranet of Things to SIoT. Finally, this paper proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.

Service-oriented architecture (SOA) is realized by independent, standardized, and self-describing units known as services. This architecture has been widely used and verified for automatic, dynamic, and self-configuring distributed systems such as in building automation. This paper presents a building automation system adopting SOA paradigm with devices implemented by device profile for web service (DPWS) in which context information is collected, processed, and sent to a composition engine to coordinate appropriate devices/services based on the context, composition plan, and predefined policy rules. A six-phased composition process is proposed to carry out the task. In addition, two other components are designed to support the composition process: building ontology as a schema for representing semantic data and composition plan description language to describe context-based composite services in form of composition plans. A prototype consisting of a DPWSim simulator and SamBAS is developed to illustrate and test the proposed idea. Comparison analysis and experimental results imply the feasibility and scalability of the system.

Facebook, the most popular Online social network is a virtual environment where users share information and are in contact with friends. Apart from many useful aspects, there is a large amount of personal and sensitive information publicly available that is accessible to external entities/users. In this paper we study the public exposure of Facebook profile attributes to understand what type of attributes are considered more sensitive by Facebook users in terms of privacy, and thus are rarely disclosed, and which attributes are available in most Facebook profiles. Furthermore, we also analyze the public exposure of Facebook users by accounting the number of attributes that users make publicly available on average. To complete our analysis we have crawled the profile information of 479K randomly selected Facebook users. Finally, in order to demonstrate the utility of the publicly available information in Facebook profiles we show in this paper three case studies. The first one carries out a gender-based analysis to understand whether men or women share more or less information. The second case study depicts the age distribution of Facebook users. The last case study uses data inferred from Facebook profiles to map the distribution of worldwide population across cities according to its size.

The concept of connectedness, as inspired by Social Networking Service, is a key factor which participates in changing the way people interact with each other over the Internet. On the other hand, connected world as envisioned by the Internet of Things aims to expand the idea of connectivity to include everything in the physical world to a big network called the Internet. In order to realize the integration between the world of connected people and the world of connected devices, intelligence including semantics and recommendation acts as a key factor to expand the basic communication functionalities to include search, discovery, mashup of new services and filtering. We propose a framework to facilitate the next generation of communication between people and devices, and a preliminary prototype including three modules DPWSim, ThingsGate, ThingsChat along with a use case discussion.

Location-Based services with social networks improve users' experience and enrich people's social live. However, location information is often inadequate due to privacy and security concerns. We seek to infer users' ‘Current City’ on Facebook for coarse location based applications. We first extract users' multiple explicit and implicit location attributes, and analyze correlations of these attributes from two perspective: user-centric and user-friends. We observe that both user-centric and user-friends location attributes tightly correlate to a user's Current City (e.g., 60% of users stay in their hometown, 60% of users live in the same city as 50% of their friends). Based on extensive analysis and observations on location attributes correlations, we have constructed a Current City Prediction model (CCP) using artificial neural network (ANN) learning frameworks. The experimental results indicate that we achieve accuracy levels of 84% for city-level prediction and 98% for country-level which are increases of 9% and 18%, respectively than what is possible with Tweecalization.

In legacy networks, overlay multicast protocols are stateless multicast relying on unicast with IP addresses regarding multicast routing states on routers, and thus they offer cost-effectiveness and robustness. Wireless sensor networks (WSNs) consist of a large number of randomly deployed sensors with unattended batteries and constrained device capabilities. For non-uniform and resource-sensitive WSNs multicast protocols also have taken an overlay approach. The protocols establish a cost-optimal structure, a Steiner tree, among destinations as an overlay structure and exploit geographic routing for unicasting. However, such concentration on the Steiner tree structure may paradoxically burden the irregular and resource-constrained WSNs with heavy loads for heuristic structure construction and fault-tolerant maintenance. In addition, it leads to frequent tree reconstruction, thus it could harm data multicast transmission seriously. Regarding these problems, we consider a proxy district per a destination for overlay tree formation to prevent structural dynamic alteration and support local state management. This district-based overlay multicast might be able to keep the stateless advantages of traditional overlay multicast. By overhead analysis and various computational simulations, we prove that our proposal provides robust and efficient multicast transmission.

During recent years, a few countries have put in place online antipiracy laws and there has been some major enforcement actions against violators. This raises the question that to what extent antipiracy actions have been effective in deterring online piracy? This is a challenging issue to explore because of the difficulty to capture user behavior, and to identify the subtle effect of various underlying (and potentially opposing) causes. In this paper, we tackle this question by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law, on publishers in the largest BitTorrent portal who are major providers of copyrighted content online. We capture snapshots of BitTorrent publishers at proper times relative to the targeted antipiracy event and use the trends in the number and the level of activity of these publishers to assess their reaction to these events. Our investigation illustrates the importance of examining the impact of antipiracy events on different groups of publishers and provides valuable insights on the effect of selected major antipiracy actions on publishers' behavior.

Most indoor localization algorithms are based on Received Signal Strength (RSS), in which RSS signatures of an interested area are annotated with their real recorded locations. However, according to our experiments, RSS signatures are not suitable as the unique annotations (like Fingerprints) of recorded locations. In this study, we investigate the characteristics of RSS (e.g., how the RSS values change as time goes on and between consecutive positions?). On this basis, we design LuPI (Locating using Prior Information) that exploits the characteristics of RSS: with user motion, LuPI uses novel sensors integrated in smartphones to construct the RSS variation space (like radio map) of a floor plan as prior information. The deployment of LuPI is easy and rapid since little human intervention is needed. In LuPI, the calibration of ``radio map'' is crowd-sourced, automatic and scheduled. Experimental results show that LuPI achieves comparable location accuracy to previous approaches, even without the statistical information of site survey.

Several frameworks have already been proposed to simplify the development of context-aware applications. These frameworks are focused on collecting and publishing contextual data, and on providing common semantics, definitions and representations of these data. This implies that applications share the same semantics, which limits the range of use cases where a framework can be used since that assumption induces a strong coupling between context management and application logic. This article proposes a framework that decouples context management from application business logic. The aim is to reduce the overhead for applications that run on resource-limited devices while still providing efficient mechanisms to support context-awareness and behavior adaptation. This framework implements an innovative approach that involves third parties in the process of context processing definition by structuring it in atomic functions, and describing it with an XML-based programming language. Its implementation and evaluation demonstrates the benefits, in terms of flexibility, of using trusted design patterns from software engineering for developing context-aware application.

Empowering the enterprise to control their own session policy for mobile broadband is not only necessary for consumerization, but is beneficial in controlling budgets and protecting corporate network resources. We propose a practical method of establishing dynamically enterprise-Business-Context (eBC) status to determine whether or not the enterprise should fund employees'' service requests and what QoS and funding levels should be assigned. To do that, the enterprise can use context information that is not available externally and apply corporate business objectives. This paper describes the eBC Function process, platform, logic, data sources and call flows, and details the computation method.

Content management appears as an essential requirement in order to deploy enhanced P2P networks. In P2P networks, the content that is requested by the query node could be located at different locations/nodes; therefore an efficient search mechanism is required. The proposed search algorithm in this paper, called socP2P, relies on peers' social relationships (friendships, shared interests, shared background and experiences) to improve the content discovery compared to similar solutions. With socP2P nodes can improve searches by using knowledge gained by `overheard information' during their stay in the network. In addition, our algorithm exploits peers' common interests, friendships, and capability of memorizing experiences (received and routed queries) by them. Simulation results show that socP2P is able to achieve a high success rate, low delay and low overhead. We have verified that our algorithm is not only useful in finding popular contents in the network but also good enough to locate rare files. The obtained results reflect that exploiting social information in P2P networks leads to a more efficient content search mechanism.

Quality of Experience (QoE) provides human centric assessment of multimedia quality. QoE of a multimedia service is affected by various application and network layer QoS parameters; content and business parameters. In this paper, we propose a new QoE framework for Multimedia services (named as QoM) for run time quality evaluation of video streaming services based on the influence of QoE factors, various network and application level QoS parameters. The newly proposed QoM framework monitors QoS and QoE data, evaluates it and moreover, in the event of decline in QoE, it can also send alert messages to the Administrator based on some policy rules. Our new QoM framework is being launched as an open-source QoE evaluation tool for the industry and research community. More realistic experimental work is also underway and we intend to conduct user tests to evaluate the performance of the proposed QoM framework in a context of real 4G WiMax wireless networks.

During off-peak hours of a day, when incoming and outgoing traffic arrival rate used to be low, a Mobile Terminal (MT) can stay in idle mode for long time. For tracking the idle MTs, a wireless access network invokes them to conduct location update whenever they enter into a new location. This allows the network to route a call successfully. To provide any IP packet based service, Mobile IP (MIP) is very important protocol undeniably. However, Proxy Mobile IP was developed by IETF without considering idle mode condition of MTs. Consequently, an idle MT needs to conduct MIP binding (Layer 3 location update) whenever it moves to a new area of a Mobile Access Gateway (MAG) although it does not have any incoming or outgoing packets during idle periods. This phenomenon unnecessarily increases location update signaling cost. In our previous work, we proposed a mechanism using which only Layer 2 location update is needed when an MT is in idle mode and the Layer 3 location update is conducted after a call arrives for an idle MT in Optical and Wireless Converged Access Networks (OWCAN). This allows saving energy in the mobility management nodes (e.g. MAG, Base Stations) significantly. Based on our previous work, in this paper we propose a novel algorithm for the Optical Network Units (ONUs) of an OWCAN. This algorithm decides when to interrupt modules of a sleeping ONU to wake up and which frames should be forwarded for uplink transmission. We have found that adopting this algorithm an ONU of an OWCAN can save energy significantly. Therefore, our solutions mainly contribute in two-fold to make a green OWCAN. First, it minimizes energy consumption in the mobility management nodes. Second, our solution also reduces energy consumption and improves bandwidth utilization in the optical backhaul.

This research proposes a new architecture for the inter-connectivity between UEs running on the LTE infrastructure participating in an Application Layer Multicast-based distributed conference. The main contribution is that the proposal replaces the standard centralized architecture of the IMS-based conference with a more robust solution utilizing intelligence and computational capacity of LTE's eNodeBs. The costly Media Resource Function Controller (MRFC) can be fully omitted from the IMS without effecting the conference. A prototype has been built to prove the feasibility of the proposed architecture and evaluate its performances.

In this paper, we describe our work in progress on Web services recommendation for services composition in a Mashup environment, by proposing a new approach to assist end-users based social interactions capture and analysis. This approach uses an implicit social graph inferred from the common composition interests of users. We describe the transformation of users-services interactions into a social graph and a possible means to leverage that graph to derive service recommendation. As this work is in progress, this proposal was implemented within a platform called SoCo where preliminary experiments show interesting results.

This paper presents an event based functionality integration framework to approach the issue of service personalization and service mashups. In contrast to existing data integration approaches, the proposed framework addresses the mashup issue from a new perspective by extracting and reasoning the context through user generated event, while recommending and aggregating the contextual services dynamically in response to the user's functional requirements. An event hierarchy is proposed to retrieve contextual information and analyze underlying functionalities. The three layer system framework, service recommendation logic, and the functionality integration are also presented.

Mashups have been gaining wide popularity over the past few years. Several tools and platforms exist to support user-created mashups, however working with them is still complex, and their inability to directly impact existing activities and daily lives of endusers provide little motivation for their adoption and sustained use. This paper aims to design and implement a user-centered mashup system which provides greater motivation for mashups usage, by relating every-day calendar events to useful gadgets. The system offers high level of abstraction to end users, which eliminates the need for programming and the burden of knowing about data flows from one service to the other. The platform exhibits context-orientation, personalization and socialization features which are believed to improve user experience in the system. Strong focus on functionality integration rather than data integration is believed to create greater usefulness and motivation in using the system. The system is evaluated by 131 end-users to test for usability. Also, the system is used as a representative example in proposing a user-acceptance model for consumer mashups.

In this paper we present the system design and rational for a novel activity-oriented social networking site called Dig-Event. This system extends our previous work on event based consumer mashups and is inspired by research on popular social network applications. Dig-Event provides an open, social space for users to share events and discover the activity of mutual interest among social contacts like schoolmates, families, friends and colleagues. It allows users to share their activities to the customized social circle, conduct events by selecting activity-based gadgets, and socialize around them. The features of event recommendation and integration with existing social networks further boost the event socialization experience.

Nowadays Passive Optical Network (PON) requires that Optical Network Units (ONUs) wake up periodically to check if the Optical Line Terminal (OLT) has any message directed to them. This implies that ONUs change from sleeping mode in which they just consume 1 W to active mode in which the consumption goes up to 10 W. In many cases, the OLT does not have any packets for the ONU and it goes to sleep again, what supposes a waste of energy. In this paper, we propose a novel Hybrid ONU that relies on a low-cost and low-energy technology, IEEE 802.15.4, to wake up those ONUs that are going to receive a packet. Our first estimations demonstrates that our solution would save around 25000$ per year and OLT.

As technology advances and becomes more pervasive, the DiY (Do-it-Yourself) paradigm that emerged on the furniture & home decoration market in the 70's is now experiencing a second birth in the digital realm. Continuing from the prosumer paradigm, where people are allowed not only to surf a network obtaining content and information, but also (co-)create such elements themselves, the user-centered participation is expected to further increase beyond the Web 2.0 as we know it.

To manage multicast group in wired or wireless domain, the hosts need to send internet group management protocol (IGMP) report message when they are queried by the multicast routers. In fact, the bandwidth of wireless domain and mobile nodes' battery power are scarce resources; therefore, it can be big burden for the receiver-driven layered video multicasting (RLVM) receiving mobile hosts to follow the conventional report sending phenomenon, where a host needs to send a report message for subscribing each of the video layers. In this paper, we propose a mechanism for reducing the number of IGMP report messages for RLVM receiving hosts. The protocol overhead and efficiency are evaluated, and compared with conventional IGMPs. Our results show that applying proposed idea in IGMP the number of report messages can be minimized significantly for RLVM services.

By utilizing various sorts of contextual data acquired from the users' devices, advance context-aware services and applications can be developed. But the current context management systems are based on server-client approach which hinders their widespread adoption in an ad-hoc network. In addition, performance issues introduced because of context dependency in a ubiquitous environment still need to be addressed. This paper presents the idea of profile context to address the problem of context dependency, and also proposes an open framework for context acquisition, management and distribution in a ubiquitous environment. Assembling together the context information and context updates from various sources, support for context-aware decisions can be implemented efficiently in a mobile environment by solving the problem of context dependency using profile context.

In pervasive environments, end-users have heterogeneous devices to access their different services. These services are usually distributed over different devices and each service should be able to run in the most appropriate device. However, current technologies do not address the integration between these services and as a consequence the end user does not have the possibility to access the services in an optimal way. In this paper, we define and implement new mechanisms that enable a seamless integration of a service in the end-user pervasive environment. First, these mechanisms enable the end-user to personalize his/her pervasive environment by running each functionality in the most suited (preferred) device, and then to make these services communicate with each other. The specificity of our approach is that we split each application into independent functionalities, and then, we define and implement on the end-user devices a distributed mechanism that detects automatically semantic compatibilities between these functionalities.

With the emergence of Web 2.0 and the related technologies, composing services has left the traditional frontiers of enterprises. In fact, end-users need to use a certain kind of composition in different situations since Web 2.0 has brought a set of technologies making it easy to create or collaborate on new services or use others' services, e.g., mashups. On the other hand, users participate in different communities and social networks to share common interests and find expertise offered by others. Even if Web services composition tools like mashups include a community dimension helping the service creation process (tagging, rating, ...), they completely ignore this social dimension at the composition level. Our approach is to bring the generated knowledge from interactions between users and services (and by extension from social environments) to enhance services composition. This paper reviews some related concepts and work and then introduces a first view of our framework, named Social Composer (SoCo), aiming at handling this issue from a social networking perspective. SoCo provides dynamic recommendations for services discovery and selection based on the users' interactions and a social network implicitly built from the interactions between users and services, and the different services compositions operated in the user's social network as well as the global social network.

The great success of social technologies such as media sharing, blogs and wikis, is transforming the Internet into a collaborative community. This paper presents our research towards the exploitation of P2P networks, semantic metadata and social tagging for home media sharing, with a vision of P2P-based Collaborative Home Media Community (CHMC). The goal of the proposed CHMC is to enable sharing, searching and tagging of multimedia contents based on semantic metadata within home networks as well as between homes connected by broadband networks. We will firstly present a hierarchical P2P network architecture where the super-peer model is applied. The super peers act as a gateway bridging the inner home network with the external Internet. We further propose the Resource Description Framework (RDF) triple-based metadata indexing, retrieving and tagging algorithms on structured P2P networks. Finally, a prototype is developed for the purposes of system validation and performance evaluation.

The main aim of Enterprise Architecture (EA) is to master the development and the evolutions of Information Systems (IS). The EA process consists in designing on several views the IS target architecture, according to the company strategy. The business view represents the target organization of the considered company. The functional view focuses on the target functional architecture of the considered IS. In this paper, we propose a new formal solution to analyze the consistency between the target functional view and the target business view of telecom services. This solution is based on the definition of a strategic alignment of the target functional view with the target business view. Alignment is illustrated with a real case study achieved with Orange - France Telecom on their messaging service. An alignment measure completing this analysis provides an estimation of the gap between a target functional view and a target business view.

Widget aggregators such as iGoogle and Netvibes are broadly adopted by the mass market. They enable end-users to personalize their environment with their preferred services (Widgets). However, the usage in an enterprise context is not yet investigated. In this paper, we firstly show that in addition to personalization capability, the integration of business processes should be considered. Secondly, we propose a new Widget aggregator that enables the end-user to personalize a business process by chaining Widgets according to his/her needs and habits. Thirdly, we introduce a new approach for specifying an end-user process; an approach which enables even ordinary end-users, without computing skills, to define their processes. Finally, we validate these concepts by implementing and testing a prototype. As a consequence, this work does not only impact Widget aggregators, but it also innovates in end-user service creation research by proposing an intuitive tool, understandable even by ordinary end-users, for specifying their processes (composite services).

The emergence of web mashups has opened interesting possibilities for searching, combining, and reusing information in a simple, lightweight manner. However, actual research on user perceptions and consumer acceptance of mashup technology has received little attention. This paper presents a user acceptance model for consumer mashups, to precisely identify what factors lead to their adoption and to what extent. Our empirical results show that 'performance expectancy' of a mashup platform is the most important attribute in predicting and explaining user's intention to use a consumer mashup platform, this attribute being further explained by the factor 'organization of existing day-today activities'. The presented model can serve as a reference for mashup designers to design platforms having better user acceptance.

SCTP faces three performance problems in hard handover scenarios: high network handover delay, high transport handover delay and throughput under-utilization. Existing solutions assume the mobile extension of SCTP (m-SCTP) as a unique way to handle handovers and rely on terminal mechanisms. Nevertheless, they are not efficient as they lack the necessary information to perform on-time and fine SCTP configuration tuning during handovers. We propose in this paper an overall mobility management architecture (UFA) replacing m-SCTP. UFA uses network-controlled mechanisms and SIP protocol to reduce the network handover delay and drive the optimal SCTP configuration on both SCTP endpoints. We simulate and compare UFA and m-SCTP. Results are promising and show better performances for UFA, even when compared with enhanced m-SCTP solutions.

Service creation by composing existing services and/or network resources is considered by Telecom and Internet industries as the trend for service provisioning in next generation networks. Related research work is being carried out for years, and various service composition approaches have been proposed by different standardization organizations and companies catering to different network requirements. However, the requirements of automaticity, flexibility and runtime adaptation are still open issues for operators and service providers in the actual service environment. This paper, relying on a SCIM (service capability interaction manager) based service composition model, attempts to extend the policy concept and introduces two additional functional modules, capability policies repository and policies comparator, for addressing some of the automatic service composition issues in service provisioning process. With these extended policy and functional modules, the SCIM based model is enhanced with attributes of intelligence, flexibility and runtime adaptability: it enables delivering competitive and revenue generating service at a more rapid pace; reducing service creation and maintenance costs; and optimizing service lifecycle.

This demo presents some features of our platform What's up for P2P social networking. The main idea of What's Up is to provide spontaneous social networks in the events such as conferences and expositions. With no infrastructure requirement, What's up enables fast setup and deployment of a distributed social network that provides VoIP, instant messaging, community creation/management, V-card sharing, search and event alert. What's up is designed to be also deployable on ad hoc networks. Moreover it works on different operating systems including windows, Linux, Windows mobile and Symbian.

Due to their centralised design, current mobile network architectures as well as IMS layer will not be able to handle the increasing number of mobile users consuming high bitrate services. An Ultra Flat Architecture (UFA), based on SIP and designed with the primary goal of being scalable, has been introduced in a previous paper. In this paper, the limitations of current 3GPP architectures are discussed and detailed explanations of UFA interest and procedures are provided. UFA is entirely controlled by the operator and integrates QoS in its establishment and mobility procedures. To prove its concept and assess the performance of its mobility procedure, UFA is implemented on a testbed. UFA HO delay measured at the application level is 100ms in average; it is 12 times smaller than in a classic SIP-based mobility scheme.

In this demo, we exhibit some features of our platform called SCOPE. SCOPE provides an open DHT with unified application interface (API) for mobile users and allows them to provide and benefit from communication services such as instant messaging, file sharing and voice chat. The main goal in this demo is to demonstrate and evaluate this server-less P2P communication services on ad-hoc mode in a real scenario and get the feedback of the users about the performance.

This paper presents a policy-based framework to approach the issue of autonomous reconfiguration management in heterogeneous networks. In contrast to existing policy-based approaches, the proposed framework addresses the management issue from a new perspective through posing it as a problem of learning from current network behavior, while creating and updating policies dynamically in response to changing reconfiguration requirements, and this task is implemented by Reinforcement Learning methodology. A two-layer policy model is used to mapping users and operators' higher level goals into network level objectives. The autonomic reconfiguration procedures for policy creation, storage, evaluation are also presented in detail. Illustrative examples analysis and simulation results demonstrate the performance of the proposed work.

The challenge in the coming years for mobile networks will be to offer high bit rate data services to customers in mobility. Future mobile architectures are being standardized to offer mobility between heterogeneous access technologies. The design of these architectures does not take into account scalability requirement since they are centralized with many network levels and dependency. This paper proposes a new architecture, called ultra flat architecture (UFA), that integrates scalability requirement. The key idea of UFA is the reduction of the number of network nodes to one node which is the base station, by the distribution of traditional user and control plane functions in this node. We detail UFA architecture and show how it optimizes service establishment and mobility procedures. We perform a first performance evaluation of the solution performance and expose the first requirements that guarantee seamless handover for real-time services.

In this paper, we investigate the various service composition mechanisms and provide the impact of each of them on user-centric service development issues. We classify service composition mechanisms into three categories: automatic service composition, semi-automatic service composition, and static service composition. As services are today mainly driven by the user's needs, the following survey essentially focus on automatic service composition and semi-automatic service composition. This enables users to conceive their own personalized applications.

Personal communications are facing many challenges created by mobility and convergence in today's communication networks. People often find themselves interacting with their devices in attention-constrained environments and deal with a bewildering variety of communication services, devices and access technologies. Many solutions tried to resolve this issue by relying on context-awareness. However, they suffered from drawbacks that hinder their deployment in the consumer market. In this paper, we discuss the implementation of INCA (intelligent network-based communication assistant), a multi-layered agent for context-aware adaptation of personal communications. This agent relies on a generic layer model that is applied to each of its layers, therefore enabling an intuitive and easy to implement architecture.

Wireless ad-hoc networks is the key technology for community networks and rural regions that lack an established networking infrastructure. On the applications front, IP Telephony is also a killer application among the various with the sought after auto-configuration feature of ad-hoc multimedia services. It is therefore natural that wireless ad-hoc networks are expected to provide IP Telephony services. In this paper, we define strategies for constructing a P2P service control overlay to support IP Telephony services in wireless ad-hoc networks with no mobility, and validate the efficiency of our approach via simulations. This service control overlay provides a critical component to the distributed indexing to provide the required service to all deployment of various session-based multimedia applications in wireless ad-hoc networks.

IP Telephony is a potential killer application among the various multimedia applications and services. It is therefore natural to expect support for these services over new network architectures like wireless ad-hoc networks. Despite the proliferation of IP Telephony services in the Internet, the traditional client/server models that have been used are found to be highly inefficient for wireless ad-hoc networks as compared to peer-to-peer (P2P) models. On the other hand, P2P strategies require some tuning to work well in wireless ad-hoc networks. In this paper, we discuss some undesirable situation that may happen if P2P systems are deployed over wireless ad-hoc networks without adaptation. We then define our strategies for positioning Super Nodes in the physical network underlay as well as P2P ID space according to the constraints of these network technologies. We evaluate the efficiency of our approche in reducing the session establishment time and request failure rate as two important criteria for the performance of IP telephony systems

Personal communications are facing many challenges created by mobility and convergence in today's communication networks. People often find themselves interacting with their devices in attention-constrained environments and deal with a bewildering variety of communication services, devices and access technologies. Although context awareness seems to be the best answer to these challenges, most of the already developed context-aware solutions did not make their way to the consumer market because they focused on context provisioning (acquisition and modeling) and fell short from proposing concrete architectures for context-based adaptation of user communications. In this paper, we discuss the basic requirements for communication adaptation and we define the main characteristics of a communication session. Then, we propose INCA (intelligent network-based communication assistant) by describing its architecture and its behavior according to a previously defined reference scenario.

Design of handover mechanisms in converged wireless and wire-line networks where different access technologies overlap are challenging. As the first step, defining the handover criteria for selecting the best candidate among available accesses plays an important role for efficient use of network resources. Different parameters should be considered such as: QoS, load balancing factor and inter technology handover cost. For load balancing the goal is selecting an access technology for a client according to its active sessions in a manner that the set of overlapping accesses can admit the most number of clients. It does not always mean selecting the access with the most available resources. We define the handover criteria according to access characteristics, network load condition and end user preferences. We also consider distributing of different sessions belonging to one user over different access technologies as a new feature. We analyze the effect of our approach in efficient use of resources in different test scenarios.

The standardization of the IMS as a multi-access network implies that it can be accessed by different classes of terminals. Even at the heart of the same class of terminals (mobile phones for example), we are witnessing an increasingly divergent range of hardware and software capabilities (screen size, memory, OS). As a result of this heterogeneity, users may receive content that is not compatible with their device capabilities, and may even be unable to display this content. In order to ensure a better service and an optimal user experience, terminals should be able to communicate their capabilities to the network. The network could then use these capabilities to adapt its services and it may communicate them to other users as well. In this paper, we establish a state of the art concerning existing methods for indicating terminal capabilities in IMS. This state of the art will be used to design the architecture and the necessary mechanisms to implement this service in IMS, while taking into account the particularity of the IMS architecture and the possibility of integrating this service with presence.

Reusing service capabilities to implement different integrated services in Next Generation networks (NGN) provides flexible and open service architecture capable of creating innovative services that do not need to be standardized and are based on the standardized service capabilities. In this architecture, invoking each integrated service may provoke the invocation of multiple service capabilities. The service architecture must therefore provide a mechanism to manage the interactions and the incompatibilities that may occur between these service capability invocations. Due to the shortcomings in the NGN service architecture domain for defining such mechanisms, we define in this paper, new architectural concepts regarding the implementation of a service capability interaction manager as well as the functionalities that this new entity must provide to avoid conflicts between service capability interactions in order to implement integrated services based on the standardized service capabilities.

Creating a background to give access to the IP multimedia subsystem (IMS) of the UMTS domain over different kinds of access networks (like WLAN, PacketCable and DSL), which we name multi-access to IMS, can provide many benefits according to the capabilities of IMS in offering different types of IP-based multimedia services. As a part of multi-access idea, we have worked on the access to IMS via PacketCable. The PacketCable architecture seeks to enable a wide variety of IP-based multimedia services over two-way hybrid fiber-coax (HFC) cable access systems. We have proposed some solutions for the different phases of this interconnection between the PacketCable network and IMS of UMTS: from attaching to the UMTS core network (UMTS-CN) and making a secure connection, until accessing the services and session establishment. We suggest some modification and development in PacketCable entities to allow them to be capable of establishing such an inter-connection.

Creating end-to-end QoS in heterogeneous wireless-wired networks beyond 3G networks in which the access to the IP core network can be accomplished via different kinds of access networks with different technologies is essential for supporting real time application. In this paper we have defined new functionalities and interfaces in addition to some extension to the existing SIP signalling to resolve some of the existing problems existing in UMTS that don't let end-to-end QoS control between different technologies and domains.