Machine learning has significantly advanced Intrusion Detection Systems in cybersecurity. However, current ML-based IDS solutions often struggle to keep pace with evolving attack patterns and new types of threats, as most models require complete retraining. Additionally, training these models requires large datasets, which are difficult to obtain due to privacy concerns. Moreover, in real-world environments, attacks occur with varying frequencies across organizations, resulting in non-identically distributed (non-IID) data, diminishing detection effectiveness. To address these challenges, we propose a novel Federated Multi-Agent Reinforcement Learning architecture. This architecture consists of a two-level reinforcement learning framework composed of N independent RL agents at the first level, each trained in a federated manner using a class-level FedAvg aggregation scheme to detect a specific attack type, while the second level features a decision agent that aggregates their outputs for final classification. Each RL agent employs an enhanced Deep Q-Network (DQN) incorporating cost-sensitive learning and a weighted mean square loss function to handle class imbalance and adapt to heterogeneous non-IID data. Reinforcement learning enables adaptation to evolving attack patterns, while federated learning addresses data scarcity and privacy concerns. Additionally, the modular design reduces model bias and enables seamless updates in response to new attacks. Experimental results using the CIC-IDS-2017 dataset confirm FMARL’s robustness, adaptability, and efficiency, achieving 99% accuracy across most configurations, maintaining a minimum of 97% accuracy even in extreme non-IID scenarios, with a notably low false positive rate.

• Introduces a workflow-oriented taxonomy for backdoor attacks in FL. • A bi-dimensional taxonomy classifies defenses by mechanism and FL stage. • Empirically evaluates the effectiveness of SoTA attacks and defenses. • Identifies key limitations and outlines future research directions. Federated Learning (FL) is a collaborative paradigm that enables decentralized model training without centralizing data. However, FL remains highly vulnerable to backdoor attacks, where adversaries poison local updates to insert hidden malicious behaviors into the global model. Over the past few years, a rapidly growing body of work has proposed both attack strategies and defense mechanisms, yet the field remains chaotic, with inconsistent assumptions, evaluation practices, and a lack of clear understanding of the core trade-offs. In this paper, we present a comprehensive Systematization of Knowledge (SoK) of the field. We introduce novel, multi-dimensional taxonomies to deconstruct attacks and categorize defenses by their intervention point and underlying techniques. Ultimately, our analysis reveals a critical gap between research and practice, highlighting unaddressed challenges in scalability, data heterogeneity, and the conflict between privacy and robustness.

International audience

Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations—namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.

Effective incident response (IR) is critical for mitigating cyber threats, yet security teams are overwhelmed by alert fatigue, high false-positive rates, and the vast volume of unstructured Cyber Threat Intelligence (CTI) documents. While CTI holds immense potential for enriching security operations, its extensive and fragmented nature makes manual analysis time-consuming and resource-intensive. To bridge this gap, we introduce a novel Retrieval-Augmented Generation (RAG)-based framework that leverages Large Language Models (LLMs) to automate and enhance IR by integrating dynamically retrieved CTI. Our approach introduces a hybrid retrieval mechanism that combines NLP-based similarity searches within a CTI vector database with standardized queries to external CTI platforms, facilitating context-aware enrichment of security alerts. The augmented intelligence is then leveraged by an LLM-powered response generation module, which formulates precise, actionable, and contextually relevant incident mitigation strategies. We propose a dual evaluation paradigm, wherein automated assessment using an auxiliary LLM is systematically cross-validated by cybersecurity experts. Empirical validation on real-world and simulated alerts demonstrates that our approach enhances the accuracy, contextualization, and efficiency of IR, alleviating analyst workload and reducing response latency. This work underscores the potential of LLM-driven CTI fusion in advancing autonomous security operations and establishing a foundation for intelligent, adaptive cybersecurity frameworks.

The rapid growth of decentralized finance (DeFi) has led to the widespread use of automated agents, or bots, within blockchain ecosystems like Ethereum, Binance Smart Chain, and Solana. While these bots enhance market efficiency and liquidity, they also raise concerns due to exploitative behaviors that threaten network integrity and user trust. This paper presents a decentralized federated learning (DFL) approach for detecting financial bots within Ethereum Virtual Machine (EVM)-based blockchains. The proposed framework leverages federated learning, orchestrated through smart contracts, to detect malicious bot behavior while preserving data privacy and aligning with the decentralized nature of blockchain networks. Addressing the limitations of both centralized and rule-based approaches, our system enables each participating node to train local models on transaction history and smart contract interaction data, followed by on-chain aggregation of model updates through a permissioned consensus mechanism. This design allows the model to capture complex and evolving bot behaviors without requiring direct data sharing between nodes. Experimental results demonstrate that our DFL framework achieves high detection accuracy while maintaining scalability and robustness, providing an effective solution for bot detection across distributed blockchain networks.

The Internet of Vehicles (IoV) is transforming transportation by enhancing connectivity and enabling autonomous driving. However, this increased interconnectivity introduces new security vulnerabilities. Bot malware and cyberattacks pose significant risks to Connected and Autonomous Vehicles (CAVs), as demonstrated by real-world incidents involving remote vehicle system compromise. To address these challenges, we propose an edge-based Intrusion Detection System (IDS) that monitors network traffic to and from CAVs. Our detection model is based on a meta-ensemble classifier capable of recognizing known (N day) attacks and detecting previously unseen (zero-day) attacks. The approach involves training multiple Isolation Forest (IF) models on Multi-access Edge Computing (MEC) servers, with each IF specialized in identifying a specific type of botnet attack. These IFs, either trained locally or shared by other MEC nodes, are then aggregated using a Particle Swarm Optimization (PSO) based stacking strategy to construct a robust meta-classifier. The proposed IDS has been evaluated on a vehicular botnet dataset, achieving an average detection rate of $92.80 \%$ for N -day attacks and $77.32 \%$ for zero-day attacks. These results highlight the effectiveness of our solution in detecting both known and emerging threats, providing a scalable and adaptive defense mechanism for CAVs within the IoV ecosystem.

The rapid global adoption of electric vehicles (EVs) has established electric vehicle supply equipment (EVSE) as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, including network reconnaissance, backdoor intrusions, and distributed denial-of-service (DDoS) attacks. These emerging threats, driven by the interconnected and autonomous nature of EVSE, require innovative and adaptive security mechanisms that go beyond traditional intrusion detection systems (IDS). Existing approaches, whether network-based or host-based, often fail to detect sophisticated and targeted attacks specifically crafted to exploit new vulnerabilities in EVSE infrastructure. This paper proposes a novel intrusion detection framework that leverages multimodal data sources, including network traffic and kernel events, to identify complex attack patterns. The framework employs a distributed learning approach, enabling collaborative intelligence across EVSE stations while preserving data privacy through federated learning. Experimental results demonstrate that the proposed framework outperforms existing solutions, achieving a detection rate above $98 \%$ and a precision rate exceeding $97 \%$ in decentralized environments. This solution addresses the evolving challenges of EVSE security, offering a scalable and privacypreserving response to advanced cyber threats.

The Internet of Electric Vehicles (IoEV) envisions a tightly coupled ecosystem of electric vehicles (EVs), charging infrastructure, and grid services, yet remains vulnerable to cyberattacks, unreliable battery-state predictions, and opaque decision processes that erode trust and performance. To address these challenges, we introduce a novel Agentic Artificial Intelligence (AAI) framework tailored for IoEV, where specialized agents collaborate to deliver autonomous threat mitigation, robust analytics, and interpretable decision support. Specifically, we design an AAI architecture comprising dedicated agents for cyber-threat detection and response at charging stations, real-time State of Charge (SoC) estimation, and State of Health (SoH) anomaly detection, all coordinated through a shared, explainable reasoning layer; develop interpretable threat-mitigation mechanisms that proactively identify and neutralize attacks on both physical charging points and learning components; propose resilient SoC and SoH models that leverage continuous and adversarial-aware learning to produce accurate, uncertainty-aware forecasts with human-readable explanations; and implement a three-agent pipeline, where each agent uses LLM-driven reasoning and dynamic tool invocation to interpret intent, contextualize tasks, and execute formal optimizations for user-centric assistance. Finally, we validate our framework through comprehensive experiments across diverse IoEV scenarios, demonstrating significant improvements in security and prediction accuracy. All datasets, models, and code will be released publicly.

With the rapid growth of Electric Vehicle (EV) technology, EVs are destined to shape the future of transportation. The large number of EVs facilitates the development of the emerging vehicle-to-grid (V2G) technology, which realizes bidirectional energy exchanges between EVs and the power grid. This has led to the setting up of electricity markets that are usually confined to a small geographical location, often with a small number of participants. Usually, these markets are manipulated by intermediaries responsible for collecting bids from prosumers, determining the market-clearing price, incorporating grid constraints, and accounting for network losses. While centralized models can be highly efficient, they grant excessive power to the intermediary by allowing them to gain exclusive access to prosumers’ price preferences. This opens the door to potential market manipulation and raises significant privacy concerns for users, such as the location of energy providers. This lack of protection exposes users to potential risks, as untrustworthy servers and malicious adversaries can exploit this information to infer trading activities and real identities. This work proposes a secure, decentralized exchange market built on blockchain technology, utilizing a privacy-preserving Automated Market Maker (AMM) model to offer open and fair, and equal access to traders, and mitigates the most common trading-manipulation attacks. Additionally, it incorporates a scalable architecture based on geographical dynamic sharding, allowing for efficient resource allocation and improved performance as the market grows.

Blockchain-based Federated Learning (BFL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and in-crementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training accuracy and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.

Blockchained federated learning (BFL) combines the concepts of federated learning and blockchain technology to enhance privacy, security, and transparency in collaborative machine learning models. However, implementing BFL frameworks poses challenges in terms of scalability and cost-effectiveness. Reputation-aware BFL poses even more challenges, as blockchain validators are tasked with processing federated learning transactions along with the transactions that evaluate FL tasks and aggregate reputations. This leads to faster blockchain congestion and performance degradation. To improve BFL efficiency while increasing scalability and reducing on-chain reputation management costs, this paper proposes AutoDFL, a scalable and automated reputation-aware decentralized federated learning framework. AutoDFL leverages zk-Rollups as a Layer-2 scaling solution to boost the performance while maintaining the same level of security as the underlying Layer-1 blockchain. Moreover, AutoDFL introduces an automated and fair reputation model designed to incentivize federated learning actors. We develop a proof of concept for our framework for an accurate evaluation. Tested with various custom workloads, AutoDFL reaches an average throughput of over 3000 TPS with a gas reduction of up to 20X.

Artificial intelligence (AI) is increasingly used in the automotive industry for applications such as driving style classification, which aims to improve road safety, efficiency, and personalize user experiences. While deep learning (DL) models, such as Long Short-Term Memory (LSTM) networks, excel at this task, their "black-box" nature limits interpretability and trust. This paper proposes a machine learning (ML)-based method that balances high accuracy with interpretability. We introduce a high-quality dataset, "CARLA-Drive", and leverage ML techniques like Random Forest (RF), Gradient Boosting (XGBoost), and Support Vector Machine (SVM), which are efficient, lightweight, and interpretable. In addition, we apply the SHAP (Shapley Additive Explanations) explainability technique to provide personalized recommendations for safer driving. Achieving an accuracy of 0.92 on a three-class classification task with both RF and XGBoost classifiers, our approach matches DL models in performance while offering transparency and practicality for real-world deployment in intelligent transportation systems.

As the Internet of Things (IoT) continues to expand, botnet-driven threats pose a growing and severe risk to the security of IoT-enabled infrastructures. These threats exploit large numbers of compromised devices to establish covert control channels and, eventually, launch large-scale cyberattacks such as Distributed Denial of Service (DDoS), capable of severely disrupting critical services and causing substantial economic damage. This paper highlights the urgent need for detecting botnets at an early stage, particularly by identifying stealthy command and control (C&C) traffic that precedes the execution of such attacks. We propose an anomaly-based detection framework that combines semi-supervised learning with explainable Artificial Intelligence (XAI). Unlike most existing approaches, our method requires only benign traffic for training, thereby enabling the detection of previously unseen or evolving botnet threats without relying on labeled malicious data. The framework supports multiple traffic representations, including raw bytes, packet-level data, and unidirectional or bidirectional flows, enriched with diverse network features to enhance detection coverage and adaptability. Experimental evaluations using the IoT-23 dataset demonstrate a 99.51% detection rate and a 1.09% false positive rate for stealthy C&C communications, underscoring the method’s effectiveness and robustness. The integration of XAI enhances transparency and interpretability, enabling security professionals to better understand model decisions and refine detection strategies.

Edge Computing (EC) extends computing functionalities from remote cloud data centers to the proximity of end-user devices at the network edges, thereby reducing application response time . However, existing solutions face challenges in scalability, efficient resource management, and near real-time adaptation in dynamic environments. In this paper, we jointly investigate the application placement and load distribution challenges in an EC-enabled mobile network. We propose a hierarchical distributed Limited Look-Ahead Control (LLC) approach that mitigates centralized control bottlenecks by breaking down the overall decision problem into a series of local problems solved cooperatively through a two-tier architecture. The global controller processes system-wide information and sets local constraints, while local controllers make autonomous decisions coordinated through mutual information exchange. Utilizing LLC allows for anticipation and adaptation to load fluctuations. Even though the results indicate that the trade-off between system performance and scalable decisions depends on how the overall problem is decomposed, our distributed solution significantly reduces the controller’s execution time compared to a centralized approach.

Current blockchain-based reputation solutions for crowdsourcing fail to tackle the challenge of ensuring both efficiency and privacy without compromising the scalability of the block chain. Developing an effective, transparent, and privacy-preserving reputation model necessitates on-chain implementation using smart contracts. However, managing task evaluation and reputation updates alongside crowdsourcing transactions on-chain substantially strains system scalability and performance. This paper introduces RollupTheCrowd, a novel blockchain-powered crowdsourcing framework that leverages zkRollups to enhance system scalability while protecting user privacy. Our framework includes an effective and privacy-preserving reputation model that gauges workers' trustworthiness by assessing their crowdsourcing interactions. To alleviate the load on our blockchain, we employ an off-chain storage scheme, optimizing RollupTheCrowd's performance. Utilizing smart contracts and zero-knowledge proofs, our Rollup layer achieves a significant 20x reduction in gas consumption. To prove the feasibility of the proposed framework, we developed a proof-of-concept implementation using cutting-edge tools. The experimental results presented in this paper demonstrate the effectiveness and scalability of RollupTheCrowd, validating its potential for real-world application scenarios.

Large Language Models (LLMs) have witnessed a rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors under-mine trust in LLMs and pose significant hurdles to their adoption in real-world applications, such as legal assistance and medical diagnosis, where precision, reliability, and ethical considerations are paramount. These could also lead to user dissatisfaction, which is currently inadequately assessed and captured. Therefore, to effectively and transparently assess users' satisfaction and trust in their interactions with LLMs, we design and develop LLMChain, a decentralized blockchain-based reputation system that combines automatic evaluation with human feedback to assign contextual reputation scores that accurately reflect LLM's behavior. LLMChain helps users and entities identify the most trustworthy LLM for their specific needs and provides LLM developers with valuable information to refine and improve their models. To our knowledge, this is the first time that a blockchain-based distributed framework for sharing and evaluating LLMs has been introduced. Implemented using emerging tools, LLMChain is evaluated across two benchmark datasets, showcasing its effectiveness and scalability in assessing seven different LLMs.

The integration of Internet of Things (IoT) technology in various domains has led to operational advancements, but it has also introduced new vulnerabilities to cybersecurity threats, as evidenced by recent widespread cyberattacks on IoT devices. Intrusion detection systems are often reactive, triggered by specific patterns or anomalies observed within the network. To address this challenge, this work proposes a proactive approach to anticipate and preemptively mitigate malicious activities, aiming to prevent potential damage before it occurs. This paper proposes an innovative intrusion prediction framework empowered by Pre-trained Large Language Models (LLMs). The framework incorporates two LLMs: a fine-tuned Bidirectional and Auto-Regressive Transformers (BART) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model for evaluating the predicted traffic. By harnessing the bidirectional capabilities of BART the framework then identifies malicious packets among these predictions. Evaluated using the CICIoT2023 IoT attack dataset, our framework showcases a notable enhancement in predictive performance, attaining an impressive 98% overall accuracy, providing a powerful response to the cybersecurity challenges that confront IoT networks.

Amidst the emergence of immersive applications, such as, the metaverse, Virtual Reality (VR), Augmented Reality (AR), and Holography, it is clear that substantial enhancements to our existing internet infrastructure are imperative. Fulfilling the stringent Quality of Service (QoS) requirements—which include ultra-low latency, high bandwidth, and optimal frame refresh rates—hinges on the seamless integration of Communication, Caching, and Computing, collectively referred to as the "3C". These elements must be interwoven within the network fabric. Our study presents a novel approach to optimize these 3C resources within a network framework that incorporates Edge and Cloud computing, and In-Network Computing (INC). We propose a resource allocation solution tailored to networks enabled by INC. We aim to efficiently manage the distribution of Service Function Chains and the storage of relevant data for immersive applications. Given the inherent complexity of the tackled problem, we propose two solutions: a Particle Swarm Optimization (PSO)-based meta-heuristic and a simpler, yet effective, greedy heuristic. Our comprehensive simulations, grounded in realistic VR scenarios, validate the effectiveness of the proposed solution, which not only enhances resource efficiency and reduces operational costs but also guarantees high refresh rates and maintains a Motion-To-Photon latency under 22 ms.

In the rapidly evolving landscape of cyber threats targeting the Internet of Things (IoT) ecosystem, and in light of the surge in botnet-driven Distributed Denial of Service (DDoS) and brute force attacks, this study focuses on the early detection of IoT bots. It specifically addresses the detection of stealth bot communication that precedes and orchestrates attacks. This study proposes a comprehensive methodology for analyzing IoT network traffic, including considerations for both unidirectional and bidirectional flow, as well as packet formats. It explores a wide spectrum of network features critical for representing network traffic and characterizing benign IoT traffic patterns effectively. Moreover, it delves into the modeling of traffic using various semi-supervised learning techniques. Through extensive experimentation with the IoT-23 dataset-a comprehensive collection featuring diverse botnet types and traffic scenarios-we have demonstrated the feasibility of detecting botnet traffic corresponding to different operations and types of bots, specifically focusing on stealth command and control (C2) communications. The results obtained have demonstrated the feasibility of identifying C2 communication with a $100 \%$ success rate through packet-based methods and $94 \%$ via flow-based approaches, with a false positive rate of $1.53 \%$.

The introduction of 6G technology into the Internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings significant challenges, particularly for the dynamic and diverse IoV landscape, which must meet the rigorous reliability and security requirements of 6G networks. Furthermore, integrating 6G will likely increase the IoV’s susceptibility to a spectrum of emerging cyber threats. Therefore, it is crucial for security mechanisms to dynamically adapt and learn new attack patterns, keeping pace with the rapid evolution and diversification of these threats - a capability currently lacking in existing systems. This paper presents a novel intrusion detection system leveraging the paradigm of life-long (or continual) learning. Our methodology combines class-incremental learning with federated learning, an approach ideally suited to the distributed nature of the IoV. This strategy effectively harnesses the collective intelligence of Connected and Automated Vehicles (CAVs) and edge computing capabilities to train the detection system. To the best of our knowledge, this study is the first to synergize class-incremental learning with federated learning specifically for cyber attack detection. Through comprehensive experiments on a recent network traffic dataset, our system has exhibited a robust adaptability in learning new cyber attack patterns, while effectively retaining knowledge of previously encountered ones. Additionally, it has proven to maintain high accuracy and a low false positive rate.

Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large volumes of data. However, current ML-based IDS solutions often struggle to keep pace with the ever-changing nature of attack patterns and the emergence of new attack types. Additionally, these solutions face challenges related to class imbalance, where the number of instances belonging to different classes (normal and intrusions) is significantly imbalanced, which hinders their ability to effectively detect minor classes. In this paper, we propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. To enhance the capabilities of the proposed model, we have improved the DQN algorithm by implementing the weighted mean square loss function and employing cost-sensitive learning techniques. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns. Experimental results realized using CIC-IDS-2017 dataset, demonstrate that our approach can effectively handle the class imbalance problem and provide a fine-grained classification of attacks with a very low false positive rate. In comparison to the current state-of-the-art works, our solution demonstrates superiority in both detection rate and false positive rate.

The need for document exchange between people, companies and government increases every day. Consequently, safeguarding documents against potential attackers becomes increasingly crucial. Several attacks have been reported over the past years and the risk of document leak is more present nowadays. To prevent data violation, we need tools to determine the sensitivity degree of documents which allows us to guarantee that only authorized people have access to them and to adapt strategies to sensitivity levels. To achieve this, deep learning techniques have shown good performances in document classification and therefore in sensitivity identification. Such approaches require sufficiently large resources to learn robust models. However, due to the sensitive nature of documents, public datasets are missing to conduct research in this context. In this paper, we experiment with Large Language Models (LLM) to generate a multi-domain dataset of business documents in both english and french languages. Utilizing a two-step generation process, we employ several prompting strategies across six language models to create a first dataset of documents classified into 4 sensitivity classes: Public, Internal, Confidential and Restricted. We then relied on human experts to review validate the annotations generated in a sample of documents. The generated dataset has been tested over two robust baselines.

Multi-Access Edge Computing (MEC) is a popular and promising paradigm that allows service providers to serve their users from nearby servers. In order to fully leverage the advantages of MEC, the mapping between users and edge servers is of utmost importance for service providers. The Edge User Allocation (EUA) problem has been widely studied from the perspective of service providers with different objectives, e.g., maximizing the number of allocated users, respecting the latency threshold, minimizing overall system cost, etc. However, service providers tend to have dynamic priorities for different objectives over time. In certain situations, a service provider may opt to prioritize the minimization of their system cost at the expense of not meeting all their users expectations, or vice-versa, throughout a range of priority degrees. In this paper, we present a Deep Reinforcement Learning (DRL) approach for allocating users to edge servers according to dynamic priorities. We consider the online EUA problem where users arrive and depart dynamically, and propose a distributed solution that does not require full observation of all the servers to make allocation decisions. We offer a solution for both user-satisfaction and cost-effectiveness, while allowing the service provider to adjust their priority for each objective. A series of experiments have been conducted to evaluate the performance of our approach, under different priority degrees, against other baseline approaches. The results show the potential benefits of the proposed scheme in providing an adjustable multi-option solution for service providers.

Cloud and edge computing have proposed different functionalities to enable multiple applications requiring different communication, computing, and caching (3C) resources. The upcoming futuristic applications (e.g., metaverse, holographic, and haptic communication) impose further stringent requirements (e.g., ultra-low latency, ultra-high reliability) on the infrastructure. These requirements call for a paradigm shift in the infrastructure architecture where all resource components and owners collaborate from the cloud up to the edge, creating a cloud-to-edge continuum of integrated resources. Furthermore, we argue that artificial intelligence (AI) and collaborative-based decisions are promising techniques to efficiently manage the highly complex architecture that jointly leverages 3C in the continuum. This article presents a comprehensive survey of existing research, including AI and collaborative-based studies, targeting the effective and seamless provision of 3C resources and services in the cloud-to-edge continuum. Through an extensive analysis of driving use cases, the synergy between these three main services is scrutinized to highlight its crucial role in the next-generation network infrastructures (NGNI). Finally, a discussion on the opportunities and challenges brought by integrating 3C in NGNI from different perspectives, including architectural design as well as the regulatory and business aspects, are presented.

Current flow-based Network Intrusion Detection Systems (NIDSs) have the drawback of detecting attacks only once the flow has ended, resulting in potential delays in attack detection and increasing the risk of damage due to the infiltration of a greater number of malicious packets. Moreover, the delay provides attackers with an extended period of presence within the network, enabling them to execute subsequent attacks. To overcome this drawback, this work addresses the issue of early flow classification in NIDSs that incorporates a Deep Learning (DL) model. This model leverages Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), coupled with attention mechanisms. This strategic combination allows the system to harness the inherent sequential nature of packets within network flows, enhancing the efficiency of early flow classification. We conducted experiments on two up-to-date network intrusion datasets, namely CIC-IDS2017 and 5G-NIDD. Our findings demonstrate the effectiveness and accuracy of the proposed NIDS in classifying network flows. Additionally, our approach showcases its efficacy by promptly identifying and detecting attacks in their early stages without the need for flow termination. This results in a reduction in both the number of initial packets required for classification and the time needed for detection.

As part of the 5G, Connected and Automated Vehicles (CAVs) will benefit from Network Slicing (NS) in several tailored 5G- Vehicle-to-Everything (V2X) services running on the same physical infrastructure. However, the use of 5G- NS may also increase the risk of cyber-attacks that could compromise 5G-V2X network slices (5G-V2X-NSs) and cause significant harm to CAV's passengers. This risk is particularly high at cross-borders, where CAVs move from their Home Mobile Network Operator (H-MNO) to a Visited MNO (V-MNO), with similar 5G-V2X-NSs in place. Therefore, deploying security services to neutralize 5G- V2X NS threats in this scenario is mandatory. However, if H-MNO and V-MNO act independently, deploying these security services could be inefficient and may result in increased memory, processing, and network resource consumption. Thus, MNOs should collaborate to orchestrate their security services to neutralize 5G-V2X NS attacks and optimize their costs efficiently. In this context, this paper proposes a novel approach to enhance the security of 5G-V2X NS at cross-borders using Reinforcement Learning (RL) based security orchestration. Specifically, we trained and deployed an RL agent interacting with both H-MNO and V-MNO. The RL agent efficiently deploys security services to effectively remove threats, optimize resource utilization, and minimize the impact on 5G-V2X-NSs. The performance results show that the RL-based security orchestration neutralizes threats with an average success rate of almost 100%. Additionally, resource consumption is minimal at less than 8 %, and the acceptable impact on 5G- V2X - NSs is negligible, averaging less than 12 %.

The recent proliferation of computing paradigms, and among them the more prominent ones at the two endpoints of the network infrastructure, i.e. the edge and the cloud, paves the way for an edge-to-cloud continuum of resources and services that is able to meet the stringent Quality of Service (QoS) requirements of emerging applications. However, deploying modern microservice-based applications on such highly distributed and heterogeneous edge-to-cloud infrastructure is a complex challenge to be addressed. To overcome this challenge, we jointly investigate the placement and load distribution problems for applications composed of dependent microservices that can be deployed and scaled across the continuum. Furthermore, we propose a Deep Reinforcement Learning (DRL) based solution for solving this joint problem and we demonstrate through simulations that our proposal outperforms baseline methods in terms of QoS satisfaction and deployment cost.

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning-based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs' privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

Nowadays, services and applications are becoming more latency-sensitive and resource-hungry. Due to their high computational complexity, they can not always be processed locally in user equipment, and have to be offloaded to a distant powerful server. Instead of resorting to remote Cloud servers with high latency and traffic bottlenecks, service providers could map their users to Multi-Access Edge Computing (MEC) servers that can run computation-intensive tasks nearby. This mapping of users to MEC distributed servers is known as the Edge User Allocation (EUA) problem, and has been widely studied in the literature from the perspective of service providers. However, users in previous works can only be allocated to a server if they are in its coverage. In reality, it may be optimal to allocate a user to a distant server (e.g., two hops away from the user) if the latency threshold and system cost are both respected. This work presents the first attempt to tackle the multi-hop aware EUA problem. We consider the static EUA problem where users have a simultaneous-batch arrival pattern, and detail the added complexity compared to the original EUA setting. Afterwards, we propose a game theory-based distributed approach for allocating users to edge servers. We finally conduct a series of experiments to evaluate the performance of our approach against other baseline approaches. The results illustrate the potential benefits of allowing multi-hop allocations in providing better overall system cost to service providers.

Proactive content caching has been proposed as a promising solution to cope with the challenges caused by the rapid surge in content access using wireless and mobile devices and to prevent significant revenue loss for content providers. In this paper, we propose an end-to-end Deep Learning framework for proactive content caching that models the dynamic interaction between users and content items, particularly their features. The proposed model performs the caching task by building a probability distribution across different content items, per user, via a Deep Neural Network model and supports, both, centralized and distributed caching schemes. In addition, the paper addresses the key question: Do we need an explicit user-item pairs-based recommendation system in content caching? i.e., do we need to develop a recommendation system while tackling the content caching problem? To this end, an end-to-end Deep Learning framework is introduced. Finally, we validate our approach through extensive experiments on a real-world, public data set, coined MovieLens. Our experiments show consistent performance gains against its counterparts, where our proposed Deep Learning Caching module, dubbed as DLC, significantly outperforms state-of-the-art content caching schemes, serving as a baseline. Our code is available here: https://github.com/heshameraqi/Proactive-Content-Caching-with-Deep-Learning.

Unlike previous mobile networks, 5G and beyond (B5G) networks are expected to be the key enabler of various vertical industries such as eHealth, intelligent transportation, and Industrial IoT verticals. To support that, B5G networks enable to sharing of common physical resources (radio, computation, network) among different tenants, thanks to network slicing concept and network softwarization technologies, including Software Defined Networking (SDN) and Network Function Virtualization (NFV). Therefore, new research challenges related to B5G networks have emerged, such as resources management and orchestration, service chaining, security, and QoS management. However, there is a lack of a realistic platform enabling researchers to design and validate their solutions effectively, since B5G networks are still in their early stages. In this paper, we first discuss the different methods for deploying realistic B5G platforms for the V2X vertical, including the key B5G technologies. Then, we describe DRIVE-B5G, a novel platform that serves as an end-to-end test-bed to emulate a vehicular network environment, allowing researchers to provide proof of concept, validate, and evaluate their research approaches.

New Radio Vehicle-to-Everything (NR-V2X) distributed communication mode utilizes a semi-persistent scheduling (SPS) scheme, in which a reserved radio resource is used for a certain duration. However, attackers can exploit the predictability of SPS’s resource assignment to cause packet dropping, by selecting already reserved resources. In this paper, we first develop a feedback-based attack detection strategy then devise the optimal evasion policy based on a fuzzy inference system that dynamically adapts the resource reservation time. Simulation results show the effectiveness of our scheme in greatly reducing packet dropping-based attacks, and also in improving the packet reception ratio within the network.

The unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.

Connected and Automated Vehicles (CAVs) represent one of the main verticals of 5G to provide road safety, road traffic efficiency, and user convenience. As a key enabler of 5G, Network Slicing (NS) aims to create Vehicle-to-Everything (V2X) network slices with different network requirements on a shared and programmable physical infrastructure. However, NS has generated new network threats that might target CAVs leading to road hazards. More specifically, such attacks may target either the inner functioning of each V2X-NS (intra-slice) or break the NS isolation. In this paper, we aim to deal with the raised question of how to detect intra-slice V2X attacks. To do so, we leverage both Virtual Security as a Service (VSaS) concept and deep learning (DL) to deploy a set of DL-empowered security Virtual Network Functions (sVNFs) within V2X-NSs. These sVNFs are in charge of detecting such attacks, thanks to a DL model that we also build in this work. The proposed DL model is trained, validated, and tested using a publicly available dataset. The results show the efficiency and accuracy of our scheme to detect intra-slice V2X attacks.

In order to face the rise in data consumption and network congestion, caching structures like Content Delivery Networks (CDNs) are being more and more used and integrated into the network infrastructure. Knowing that the capacities of caching resources are most often limited due to their large operational cost, it has become very important that these entities are managed efficiently. Especially, at the caching operations level, the question that arises is what content should be cached or evicted from the cache when it becomes full. Having these in mind, we introduce a lightweight Artificial Intelligence-based caching scheme called Reversed OPT (RevOPT). In our proposal, we use a Long Short-Term Memory (LSTM) encoder-decoder model to learn future requests patterns from the past and exploit its outcome with a Counting Bloom Filter (CBF) structure to manage efficiently the caching decisions and to keep in the cache only contents expected to be reused in the near future. The conducted simulations show promising results of RevOPT in terms of the cache hit ratio compared to existing caching algorithms.

Network slicing is a key component of the envisioned 5G network. Slices are virtual networks purpose-built for tenants using a shared infrastructure. The slicing process is mathematically known as a virtual network embedding problem (VNE). Despite the plethora of VNE strategies in the literature, they do not take into account the fact that massive data transmission can be carried in a certain period. Embedding a large number of virtual networks to a real physical network over time is an NP-Hard problem, and it becomes more complex because of new considerations such as periodicity, amount of data and duration. Thus, we propose the NS4MIoT, a solution to allocate slices resources for each tenant, allowing it to be aware of each transmission’s periodicity, amount of data, and duration. NS4MIoT is an approach to increase the quantity of requisition mapped in an envisioned 5G network for IoT massive communication. To validate our solution, we incorporate it into two different embedding algorithms. Furthermore, we compare the same algorithms with and without the NS4MIoT approach, and the outcomes demonstrate an improvement in the mapping rate in all cases when it is incorporated.

Edge Computing (EC) is a promising concept to overcome some obstacles of traditional cloud data centers to support Internet of Things (IoT) applications, especially time-sensitive applications. However, EC faces some challenges, including the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. A relevant issue that needs to be addressed by a resource manager is the service placement problem, which is the decision-making process of determining where to place different services (or applications). A related issue of service placement is how to distribute workloads of an application placed on multiple locations. Hence, we jointly investigate the load distribution and placement of IoT applications to minimize Service Level Agreement (SLA) violations due to the limitations of EC resources and other conflicting objectives. In order to handle the computational complexity of the formulated problem, we propose a multi-objective genetic algorithm with the initial population based on random and heuristic solutions to obtain near-optimal solutions. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other conflicting objectives, such as operational cost and service availability.

Edge computing enables a wide variety of application services for the Internet of Things, including those with performance-critical requirements. To achieve this, it brings cloud computing capabilities to network edges. A key challenge therein is to decide where and when to place or migrate application services considering their load variation and seeking the optimization of multiple performance objectives. In this paper, we address this optimal service placement issue by further considering how to distribute the load of an application placed in different locations. By estimating the performance-cost trade-off of services migration, we propose a dynamic service placement and load distribution strategy that uses limited look-ahead prediction to handle load fluctuations. Evaluation analysis demonstrates that our proposal outperforms other benchmarks solutions in terms of multiple conflicting objectives.

Edge and Fog computing represent today a realistic alternative to traditional cloud data centers in order to support data-intensive and time-sensitive applications, such as those laying under the Internet of Things (IoT) umbrella. One of the main problems associated to this is the service placement problem, or how to efficiently manage the available computing and storage resources while deploying the plethora of application services, to be made available to clients, at the network edge/fog? Due to the similarities shared by this problem and the traditional data caching problem, multiple researches started looking at the adaptation of the Information-Centric Networking (ICN) paradigm to answer the aforementioned question, giving birth to what we depict as an ICN-Edge/Fog architecture. Leveraging such an architecture, we propose in this paper a novel service caching strategy, called 3Q, that is the first to argue on the caching of both, the service instances, consuming computing resources, as well as their associated source codes, consuming storage resources. Being characterized by its low-complexity and its low-overhead, 3Q proves to achieve near-optimal results in terms of cached services hits, latency and cloud usage.

Cloud-based interactive multimedia applications such as virtual games and video streaming are gaining high popularity. However, giving the high bandwidth consumption, the remote execution can negatively impact the quality of the multimedia traffic. In such a realm, data travel different communication networks from the cloud to the final users crossing the last meters the home's access point (AP). In such a scenario, the quality-of-service (QoS) support is a challenging task, particularly in the home network environment, with heterogeneous applications simultaneously running and consuming the available bandwidth. To address this issue, we propose ReiLeCS, a Reinforcement Learning-based Controller and Scheduler for interactive multimedia traffic in Home Area Networks (HAN). Through reinforcement learning and the maximization of a reward function, it enables the AP to schedule the arriving multimedia traffic from the cloud according to their required QoS. Simulation results using real multimedia traffic conditions demonstrate that ReiLeCS achieves better performances compared with existing packet scheduling policies.

Buildings are one of the biggest energy consumers and greenhouse gas producers. Technology could help reduce their environmental impact by deploying sensors to collect relevant data about the way energy is consumed, and with the aim to optimize it. For this sake, understanding building occupant's behavior and occupancy patterns can help minimize energy consumption while satisfying occupant's comfort. Indeed, occupants directly influence building appliances that consume energy, such as HVAC, ovens, hot water tanks, etc. In this paper, we aim at predicting occupants' movements among rooms and use the predicted movements to deduce room and space occupancy in the building. The latter is then used to preheat/pre-cool rooms. However, since prediction models are not always that accurate, it is possible to face situations where HVAC of some rooms are activated while these are empty or vice-versa, leading to either a waste of energy or a lack of occupant's comfort. To deal with this issue, we make use of sensors to detect real-time occupancy of building rooms and then correct the prediction when necessary. To achieve this, we developed a graph mining-based optimization approach that combines occupant behavior prediction and a real-time correction. We experimented our approach on simulated data and results showed that our model optimizes up to 39.09% of HVAC energy consumption, and provides up to 99.39% of occupants' comfort.

Using local caches is becoming a necessity to alleviate bandwidth pressure on cellular links, and a number of caching approaches advocate caching popular content at nodes with high centrality, which quantifies how well connected nodes are. These approaches have been shown to outperform caching policies unrelated to node connectivity. However, caching content at highly connected nodes places poorly connected nodes with low centrality at a disadvantage: in addition to their poor connectivity, popular content is placed far from them at the more central nodes. We propose reversing the way in which node connectivity is used for the placement of content in caching networks, and introduce a Low-Centrality High-Popularity (LoCHiP) caching algorithm that populates poorly connected nodes with popular content. We conduct a thorough evaluation of LoCHiP against other centrality-based caching policies and traditional caching methods using hit rate, and hop-count to content as performance metrics. The results show that LoCHiP outperforms significantly the other methods.

With the prevalence of smartphones, mobile crowd-sensing (MCS) becomes an appealing paradigm for sensing and collecting data. However, this rises new privacy concerns making participants reluctant to conduct sensing tasks. To cope with this problem, we develop a new privacy-preserving incentive framework for worker recruitment in MCS campaigns. This task is challenging as, on the one hand, the workers have heterogeneous requirements to protect their sensitive information, and on the other hand, the MCS platform looks for the participants who ensure its utility in terms of data quality. Therefore, our study concentrates specifically on the design of a privacy-preserving mapping mechanism to achieve an adequate privacy-utility trade-off. Particularly, we formulate an optimization problem that allows the platform to select among the participants those who maximize its utility after bidding and receiving their privacy needs. Once the workers are recruited to fulfill the sensing task, the platform provides them with incentives to cover their personal data exposure. Considering all these measures and the formulated optimization problem, we develop a meta-heuristic tabu-search based algorithm as the basis of our computationally efficient auction mechanism. The proposed meta-heuristic algorithm aims at revealing the false bids submitted by selfish workers who want to maximize their rewards while providing low data quality samples. As a result, we obtain a truthful and individually rational incentive process. The proposed solution is validated through extensive simulations using real-datasets. This demonstrates that our framework responds to the workers' heterogeneous privacy constrains while maximizing the platform utility.

Vehicular cloud (VC) is a promising environment, where intelligent transport applications can be developed relying on mobile vehicles, which can be both cloud users and cloud service providers. It enables vehicles that have sufficient resources to act as mobile cloud servers by offering a variety of services to users' vehicles. In this context, to consume a cloud service on the move, a user vehicle must first identify the most stable vehicles, relative to his/her motion, which are able to provide the service, and then select the most suitable service according to his/her preferences, while both provider vehicles and their services are described by attributes or quality constraints. Therefore, we introduce a generic relative motion model, as a generic link stability metric, upon which vehicles can form a stable cloud, and we address the VC service selection by using linguistic quantifiers and fuzzy quantified propositions, to define our flexible quantified service selection (FQSS) scheme, which aggregates efficiently both user preferences and service constraints and ranks service providers from the most to the least satisfactory. To break ties among the top-ranked service providers, we make use of our parameters for ranking refinement, called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp). The simulation results show that our link stability achieves generic motion, by modeling a wider range of vehicle motion types, and our FQSS scheme allows a good successful service consumption rate while reducing latency.

Vehicular Cloud (VC) has gained popularity today allowing mobile users to access a variety of on demand resources while on the move using low cost Vehicular Network. VC enables vehicles with sufficient resources to act as mobile cloud servers and provide their computing, communication and caching resources to nearby vehicles. However, due to high mobility and intermittent connectivity, it is challenging for mobile users to efficiently discover providers' services before request targeted services from them. Therefore, service advertising is of great interest with which offered services by Provider Vehicles (PVs) in the vehicular cloud can be fast propagated into the network. Given PVs' limited budget for renting advertiser vehicles, how to achieve the maximum service advertising coverage within a given period of time for a given budget requirements is NP-hard. This work aims to propose a new Centrality-based approach, PUBLISH, for PVs' services advertising in the vehicular cloud. We exploit the centrality score of both services and vehicles to find the best set of 'appropriate' vehicles as services advertisers. Results from scalable simulations show that PUBLISH efficiently identify the best services advertisers in comparison to other schemes in the literature.

Edge Computing emerges as a solution that overcomes some obstacles of traditional central data centers to support the performance-critical Internet of Things applications. However, a challenge therein is the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. In this paper, we investigate how to place replicas of applications, and distribute requests among these replicas to optimize multiple objectives. We propose a genetic algorithm based on Pareto fronts as a problem-solving meta-heuristic to prioritize latency-sensitive applications and optimize conflicted objectives. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other important and sometimes conflicting objectives, such as cost and availability.

Occupant behavior and space occupancy provide important information to controlling and optimizing energy use in buildings, especially when it comes to heating/cooling, where Heating, Ventilation, and Air Conditioning (HVAC) systems are in use. Besides, thermal comfort is mainly occupant behavior-dependent, based on his movements and space occupancy inside a building over the daytime. Traditional HVAC system functioning, based on turning OFF/ON of the system at the building level, without taking into account space occupancy, can lead to unnecessary heating/cooling of some rooms, which results in a waste of energy, or an under-heating/undercooling of the rooms leading to a lack of comfort. To optimize energy consumption and occupant comfort, we introduce in this paper a temporal graph-based approach for occupants' behavior modeling for energy consumption optimization at room level. Our approach combines a graph learning algorithm, a hierarchical clustering to identify frequent occupants movements within the optimal time interval decomposition of days, and a multi-objective problem resolution. We experimented our approach on a 4-week dataset of 4 occupants movements among office rooms. The first results showed that our model helps minimize energy consumption by up to 62.21% compared to conventional functioning of HVAC systems, and fulfills up to 94.02% of occupants' thermal comfort.

In cloud-based storage services, Ciphertext-Policy Attribute Based Encryption (CP-ABE) has been emerging as a promising outsourcing solution to provide data confidentiality protection and fine-grained data access management without relying on the Cloud. CP-ABE schemes enable data owner to encrypt data under a desired access structure before outsourcing it to the Cloud. Only those who own a certified attribute set, which matches with the access structure, can decrypt the encrypted data. Despite the advantages over conventional encryption algorithms, attribute and user revocations of CP-ABE schemes remain challenging. Firstly, existing revocation mechanisms cannot thoroughly solve the problem as they give rise to security issues and increases computational, communication overheads. Secondly, forward secrecy, in which revoked users are unable to decrypt data shared in the past, is not rigorously taken into account. In this paper, we investigate these problems and design two CP-ABE schemes that allow both efficient user and attribute revocations. The forward secrecy requirement of the proposed schemes is guaranteed by integrating re-encryption techniques. To demonstrate the feasibility of the proposals, we evaluate their performance and provide a security analysis.

As the adoption of digital technologies expands, it becomes vital to build trust and confidence in the integrity of such technology. The SPIRIT project investigates the proof of concept of employing novel secure and privacy-ensuring techniques in services set-up in the Internet of Things (IoT) environment, aiming to increase the trust of users in IoTbased systems. The proposed system integrates three highly novel technology concepts developed by the consortium partners. Specifically, a technology, ermed ICMetrics, for deriving encryption keys directly from the operating characteristics of digital devices; secondly, a technology based on a contentbased signature of user data in order to ensure the integrity of sentdata upon arrival; a third technology, termed semantic firewall, which is able to allow or deny the transmission of data derived from an IoT device according to the information contained within the data and the information gathered about the requester.

Building energy consumption depends on many factors, such as occupant behavior and occupancy. Many works study building occupancy modeling and its impact on energy consumption, based on sensors, such as CO2, humidity, presence, etc., which are deployed within buildings and their surrounding areas. These sensors collect different types of data at a high frequency, which can be used to build datasets used in building predictive models. In this context, existing datasets have been empirically built without considering the relevant sensor types and the frequency of data collection for building occupancy modeling. Therefore, in this paper, we introduce a method to select the data collection period and the relevant sensors for building occupancy prediction model with satisfying accuracy. Our approach uses feature selection and machine learning classifier algorithms, which are applied to different data collection periods, starting from 1 minute to 60 minutes. The experiments, carried out on a real dataset, with 5 different machine learning classifiers show that it is possible to build an occupancy predictive model with Random Forest having an accuracy of at least 90%, by using 8 sensors collecting data at a 20-min interval, or 5 sensors collecting data at a 15-min interval.

Participatory-sensing systems leverage mobile phones to offer unprecedented services that improve users' quality of life. However, the data collection process may compromise participants' privacy when reporting measurements tagged or correlated with their sensitive information. Therefore, existing privacy-preserving techniques introduce data perturbation, which ensures privacy guarantees, yet at the cost of a loss of data utility, a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring data quality for queriers and protecting participants' privacy. We propose a general privacy-preserving mechanism to capture the privacy inference threat encountered by a participant while considering utility requirements set by data queriers. We rely on a general probabilistic privacy mechanism, which is run on a trust-worthy entity to distort the collected data before its release. We consider two different adversary models and propose appropriate solutions for the both of them. Furthermore, we tackle the challenge of participatory collected data with large size alphabets by investigating quantization techniques. The proposed PRivacy-preserving Utility-aware Mechanism, PRUM, was evaluated on three different real datasets while varying the distribution of the collected data and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants' privacy while maintaining about 98 percent of the required data utility.

Vehicular Cloud Computing (VCC) exploits resources at vehicles, such as computing, storage and internet connectivity to provide services for applications supporting different ITS (Intelligent Transportation System) services. Current Vehicular Cloud (VC) systems allow Consumer Vehicles (CVs) to discover and consume offered services by nearby mobile cloud servers (vehicles). However, to consume the required services, the CVs must first select the most suitable service provider, given that each of providers is characterized by specific features, limitations and prices. To the best of our knowledge, no work to date addresses the critical question of how to select the best provider fitting the quality of services and costs requirements of the consumer vehicles. Similarly, Provider Vehicles (PVs) should adjust the provided services' features and prices under certain conditions such as the rate of consumers' requests which makes this issue even harder. In this paper, we propose GSS-VC as a new distributed game theory-based approach to manage the service provisioning in vehicular cloud. Our approach takes into account the benefit of each player and allows the CVs to find the most suitable PV based on the probability interaction between them. Simulation results are carried out using urban mobility model and illustrate the effectiveness of the proposed approach to answer the raised questions: what is the best condition under which the CVs may request the PVs for services? and how to select the best service with respect to the CV preferences? Results from extensive simulations on up to 1, 500 vehicles show that GSS-VC is a an efficient and reliable service selection scheme while achieving high QoS.

The realization of Smart Cities requires a huge number of connected devices, sensors and actuators as instances of Internet of Things (IoT) acting autonomously to collect data and provide different services to users. However, the environment in which these devices or sensors are deployed can largely impact the collected data and the provided services quality. We believe it is important that such devices are made conscious of the context of their surroundings, particularly readings from nearby sensors while collecting data. To do this, in this paper we propose the concept of “Context Awareness at the IoT Edge” by allowing an object to compute the spatio-temporal influence of the environment observed by nearby IoT devices, on its provided service. Specifically, we use Conditional Random Field (CRF) as a prediction model to analytically derive such an influence relation for a device. Our CRF-based model considers both the spatial and temporal impact of nearby sensors on a collected data by a specific device. We perform simulations and experimentation to evaluate the proposed CRF-based model. Results show that the proposed model not only estimates the context of a sensor with high accuracy (up to 98.5%) but also shows the effect of spatio-temporal variations in sensor readings.

Flexible querying exploits user preferences as pieces of information that help rank a set of alternatives from the most to the least satisfactory, based on the degrees of satisfaction of the criteria, describing the alternatives, to user preferences expressed by a user. Quantitative approaches for user preference evaluation and evaluation can perform such a ranking by defining total orders, based on a given aggregation function applied on satisfaction degrees to compute a global score. In this context, it becomes possible to end up with undistinguishable top-ranked alternatives, in the sense that they are equally scored. To break ties, we study in this paper a special type of user preferences involving linguistic quantifiers and fuzzy quantified propositions of the form "QX are A", where Q is a linguistic quantifier, X is a set of items and A is a fuzzy predicate. We define a new quantitative interpretation of the truth-value of fuzzy quantified propositions of the aforementioned form, in order to extract new useful information to be used in refining the ranking. To do so, two parameters, called least satisfactory proportion, denoted by lsp, and greatest satisfactory proportion, denoted by gsp, are defined as the lower bound and the upper bound, respectively, of the subset of the most satisfiable criteria, describing alternatives regarding a given set of user preferences. We show formally that lsp and gsp parameters can discriminate among tied top-ranked alternatives.

With the popularity of wireless devices, the possibility of implementing vehicular safety applications has been studied for years in the context of vehicular ad-hoc networks. Dedicated Short Range Communication (DSRC) is designed to serve the needs of vehicular safety applications. However, DSRC does not offer good enough coverage and range in highways for certain applications. Considering these drawbacks, LTE, an advanced cellular communication technology, is proposed as an alternative to DSRC. One problem is LTE capability to support regularly transmitted cooperative awareness messages due to high delay. Within this context, in this paper we propose a mechanism that uses a clustering approach in order to improve the wireless communications while using DSRC for intra-cluster communication and LTE for inter-cluster-communication. We show that our proposition improves the network performance while increasing the data packet delivery and decreasing the average delay.

LoRaWAN is the new transmission protocol for Low-Power Wide Areas (LPWA) networks. In addition to long range communications, one of the most solid arguments in favour of the LoRaWantechnology is its ability to operate at orthogonal spreading factors (SFs). This enables simultaneous transmissions at different data rates on the same frequency channel. But Recently, it has been shown that transmissions at different SFs are not perfectly orthogonal which generates cross-SF interferences (i.e. interferences between different spreading factors' transmissions). Therefore, we propose here an analytical model of a single cell LoRaWAN under unsaturated traffic, duty cycle and multi-channel deployment conditions. The model considers both perfect and imperfect SF orthogonality scenarios. The network performance are then derived in terms of achievable throughput at the gateway as a function of the offered load, the number of received packets per device and the percentage of successful transmissions. All the Results show that if spreading factors are assumed to be orthogonal, the capture effect improves the network performance in comparison with the theoretical pure Aloha access scheme. Whereas, the consideration of imperfect SF orthogonality revises the LoRaWAN performance downward.

Cluster-based cognitive radio sensor networks (CRSNs) are envisioned as a strong driver in smart grid (SG) network development. Added to the sensors' capability to exploit the temporally available licensed spectrum, a hierarchical CRSN allows a better network organization in such a harsh electrical environment. However, in CRSNs, one common control channel (CCC) is usually assumed to exist allowing sensors to exchange control messages before every data transmission. This assumption is not always possible given the licensed signal random activities. Furthermore, SG sensors have to be aware of their neighboring nodes' priorities. Indeed, sensors monitor different applications that have heterogeneous impacts on the SG. Accordingly, in this paper, we design a new solution that allows hierarchical data transmission in CRSNs without being dependent on a CCC and while considering the prioritization in the SG network. We first propose a new clustering algorithm. Then, channels are assigned to the sensors based on local estimates of other nodes' spectrum availability and priority. Given the opportunistic CRSN, we use a Partially Observable Markov Decision Process (POMDP) to allocate channels distributively. Performance evaluation reveals that the spectrum resources are fairly shared between sensors and that our solution outperforms existing work in terms of spectrum utilization.

Future mobile networks (5G) would be much more flexible, dynamic and faster adaptable to match the evolved demand. Network Function Virtualization (NFV) is investigated to take the advantage of information technology (IT) virtualization on telecom networks by separating network functions to be virtualized from underlying dedicated hardwares. Container-based micro-service is currently discussed as being lightweight virtualization approach enabling flexibility and scalability of future mobile networks. Virtualizing mobile network functions, the Serving Gateway (SGW) is analyzed as being the most sensible component, a bottleneck. The containerization seems to be the adequate approach to overcome this issue as it could enable rapid deployment by scaling SGW instances based on workload. In this paper, we discuss the cloudification of mobile network functions using containerization technology and 12 factors principles for enabling such cloudification. An implementation is made using Docker containerization while we employ Docker Swarm for cluster management and deployment. We also build an orchestration testbed for testing the scalability of SGW. This is realized by comparing the performances of two open-source orchestrators: Kubernetes and Mesos- Marathon. The proof of concepts shows clearly that a container-based approach is a viable option for achieving elasticity of future mobile networks.

Energy efficient neighbor discovery for multiple mobile devices in each others proximity is a challenge along duty cycling where low power devices are inactive for a large fraction of time. Existing schemes allow each device to employ a schedule to become active and send periodic messages or listen to neighboring devices to ensure a neighbor discovery in a bounded delay. However, collisions can occur due to simultaneous transmission of messages from multiple devices resulting in failure of neighbor discovery. We propose to reduce the number of message transmissions in a neighbor discovery process to avoid collisions and in result enhance the number of devices discovered.To do so, in this paper, we propose Welcome, a low latency and energy efficient neighbor discovery scheme. Instead of all nodes transmitting messages, only a single node can become a delegate to discover the nodes in vicinity and provide the neighborhood information to its neighbors. A node first finds its eligibility to become delegate based on its residual energy and association to the neighborhood. It then declares itself a delegate and listens to messages from its neighbors. Finally, it broadcasts the information regarding its neighbors to the devices in its communication range. Moreover, delegates can be rotated among neighbors where a node with high eligibility can content to become delegate. Welcome is compared with seven existing neighbor discovery schemes and it successfully discovers 100% of neighbors with low energy consumption and low latency for a neighborhood size of upto 100 nodes.

Vehicular Cloud (VC) is an emerging paradigm where vehicles having sufficient resources act as mobile cloud servers by offering a variety of services to user vehicles. To consume a cloud service on the move, a user vehicle must first identify the most stable vehicles, relatively to its motion, capable of providing the service, then select the most suitable service according to its preferences and service provider quality or constraints. In this paper, we introduce a link stability metric based on a generic relative motion model among vehicles to form a stable cloud and address vehicular cloud service selection by using linguistic quantifiers and fuzzy quantified propositions aggregating efficiently both user preferences and service constraints to rank service providers from the most to the least satisfactory. To break ties, we also define new parameters, called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp). Simulation results show that the link stability achieves generic motion and the selection approach allows a good successful service consumption rate while reducing latency.

In this demo, we present a home network traffic monitoring platform. Our solution is based on two main software components using standard IPFIX flow export architecture: a probe and a collector. The probe role is to capture flows in progress and to perform real time traffic classification. We implemented the probe on an actual home gateway prototype showing its feasibility on hardware constrained devices currently used by the consumers. Furthermore, we addressed hardware accelerators issue inherent to this kind of devices. The collector analyzes the records exported by the probe allowing to visualize various network monitoring data including flow rates, volumes and corresponding applications. Our collector is built upon an open source tool, namely nTopng.

Large scale networked systems, such as Identity Management (IdM) systems and software defined networks (SDN), have contributed to technological evolution. They simplify user and network device management. However, they strengthen Distributed Denial-of-Services (DDoS) attacks. These attacks are able to compromise system availability and harm legitimate users. The main approaches against DDoS attacks apply external resources (replication) or try to detect DDoS attacks. The first approach increases solution cost. The second is prone to high false positives. In other contexts, research into resilient approaches has increased for addressing emergent threats. In this work, we advocate that networked systems can self-manage to provide resilience. We propose a framework to guide the system design to follow the ideas defended in this thesis. The framework comprises the survival, collaboration, and analysis modules. Following the framework, networked systems can preserve their lifetime without external computer resources. The DDoS attack mitigation process starts when the system capacity overcomes a pre-established threshold. A protocol and a scheme showcase the framework over IdM systems and SDN. We conducted performance evaluations by experiments and simulations. Results show an increase in throughput and a decrease in latency of essential services when we use the proposed framework.

Wireless sensor networks (WSNs) are considered as a crucial technology that will definitely ensure a permanent growth to emergent applications as smart homes, smart grids, etc. In the particular home area network (HAN) context, sensor nodes will be in charge of the power monitoring between the smart home appliances. But, the communications in WSNs generally relay on the ISM bands, characterized by a narrow bandwidth and a high collision/interference ratio. Hence, the cognitive radio technology can be used to overcome the spectrum resource scarcity in WSNs. In cognitive radio networks, the sensor nodes will opportunistically access licensed channels if they are vacant of primary signals, thus ensuring a better exploitation of the available radio resources. Generally, a common control channel (CCC) is used to exchange control messages in the cognitive network. But, relying on a CCC is not always feasible especially if the number of contenders in the network is important or if the sensors do not share the same spectrum resources. Therefore, we propose in this paper a new framework for the by-domestic energy control in smart homes using cognitive radio sensor networks (CRSNs). Our proposed framework, called Cooperative Spectrum Resource Allocation (CSRA), aims to completely avoid the CCC and to achieve a distributed fair spectrum resource sharing among the sensors in smart homes. The fairness of CSRA is ensured through partially observable Markov decision process (POMDP) that performs the channel assignment, to each node, based on local spectrum utilization estimates. Performance evaluation, using the OMNeT++ simulator, reveals that the proposed CSRA approach achieves a fair spectrum allocation between sensor nodes in smart home systems.

A major advantage of wirelessly powered devices is the use of wire-free and sometimes battery-free nodes that can operate for extremely long times. However, to achieve infinite network lifetime a set of chargers is needed to periodically transmit energy to the nodes through the emission of RF signals. In this paper, we study the problem of finding the optimal number of chargers so that a group of nodes can operate without using power sources other than wireless charging. We show that this problem is equivalent to the set-cover problem which is NP-Complete. We propose an efficient heuristic that bypasses the high computational cost of finding the overlapping segments of the harvesting sensor disks and we compare to optimal and non-optimal set-cover solutions. The results show significant performance gains in terms of execution time while keeping the number of chargers close to the optimal.

In this paper, we propose a new distributed approach for the electrical substation monitoring in suburban environment using the cognitive radio (CR) technology. For instance, suburban areas are characterized by the scarcity of the free-accessed wireless bands due to their proximity to inhabitants' zones and the abundance of the usually underutilized spectrum resources provided by the operators. Thus, our focus is to fully benefit from the cognitive radio sensor network (CRSN) technology to provide efficient data aggregation and reporting processes in substations upon unexpected events' detection. Our approach, called Distributed Event-driven data Aggregation and constrained multipath Routing (DEAR), will completely emancipate from the common control channel (CCC) limitations, generally used for the channel assignment in cognitive networks. DEAR uses the graph coloring paradigm for the licensed spectrum allocation during the data aggregation and reporting phases and a constrained multipath ”Beam” routing to prevent packet loss and failures in the harsh substation environment. Performance evaluation reveals that DEAR ensures a rapid data transmission and an efficient channel assignment in CRSNs.

Cognitive radio sensor networks (CRSNs) have been widely considered to monitor the smart microgrid environment. In such an environment, different applications with various characteristics may coexist in the network. As a consequence, the sensor nodes deployed to collect the generated data need to access the available spectrum resources with different priorities. Thus, achieving a fair channel allocation in the microgrid driven CRSN is a very challenging task. In this paper, we propose a new Distributed Unselfish Spectrum Assignment approach (DUSA) for One-Hop smart microgrid communication network. The proposed approach exploits the promising cognitive radio technology and the heterogeneity of the traffic generated by sensor nodes to satisfy the requirements of the smart microgrid applications. It achieves channel allocation without relying on a predefined common control channel (CCC) to exchange control messages. As DUSA is a distributed approach, it aims to dynamically predict the primary channel availability. Thereafter, each sensor node estimates the buffer occupancies of its neighbors through discrete Markov chains. Unselfish distributed channel allocation is achieved based on a Partially Observable Markov Decision Process (POMDP) formulation. Performance evaluation using the OMNeT#x002B;#x002B; simulator, reveals that DUSA achieves a fair sharing of the spectrum resources and improves the spectrum utilization compared to the CCC-based resource allocation scheme.

Wireless sensor networks (WSNs) are widely used nowadays in various domains. In general, the applications in which the WSN is deployed need that this network presents a minimal degree of reliability, effectiveness and robustness. However, the specificity of the nodes deployed in this type of networks makes them prone to failures. In this paper, we discuss the recovery from simultaneous failures for a large scale WSN in a multichannel context. Indeed, we propose a deployment scenario which uses thousands of sensor nodes and relay nodes. Then, we propose Simultaneous Failure Recovery based on Relay Node Relocation (SFR-RNR) approach which aims to recover from simultaneous failures. These failures lead to the damage of a whole segment of the WSN, and hence, the loss of the monitored field coverage and/or connectivity. SFR-RNR rearranges a minimal set of relay nodes to restore, partially, the coverage and improve or restore the connectivity; then it reallocates the channels to minimize the interferences. The performance of the proposed approach is evaluated by simulation.

Nowadays, smart-devices come with a rich set of built-in sensors besides being full-fledged processing and communicating handsets. This empowers the crowd to collect and share sensed data about various city-related phenomena, a new paradigm denoted as Crowdsensing. In this context, we introduce Sensarena; an end-to-end general-use crowdsensing platform which consists of three main entities: two types of android-based mobile applications and a central server. The first application is destined to the participants to conduct sensing campaigns while the second is for requestors to submit their sensing requests. Besides, the server side is designed to host energy-aware sensing tasks assignment mechanisms and storage of different types of data. The developed platform has been exhaustively tested for different scenarios and proved a competitive performance while responding to both participants and requestors requirements.

With the emergence of IoT devices, such as smartphones, temperature and light devices, etc., the ways of creating IoT applications has changed. IoT applications are often created and managed by a set of central points (orchestration) for different users. However, users may desire to create and manage their own applications based on their own logic in a decentralized way (choreography). Hence, in this paper, we demonstrate BeC3, a tool for creating and deploying Crowd-based applications using the choreography method. BeC3 is based on D-LiTE, a lightweight RESTful virtual machine designed for IoT devices. The users could then compose the D-LiTe-enabled devices using the BeC3 tool. BeC3 provides a simple and intuitive way to compose interaction between IoT components.

Internet of Things (IoT)-based environments collect and generate huge amounts of data about users, their activities, and their surroundings, which can disclose some sensitive information and threat their privacy. Hence, user data collected and handled by IoT-based applications need to be exploited and secured in an appropriate way to protect personal data and user privacy. Therefore, we aim at designing a user-centric approach for user privacy protection based on two main blocks, namely (i) a habit-based approach for anomaly-based intrusion detection system, and (ii) semantic-based firewall for access control and communication security. We detail in this paper the design of the former block by introducing a generic algorithm for user habit learning as a pillar of our anomaly detection system, which is then instantiated by an intuitionistic fuzzy sets model to illustrate how it operates in a real world use-case.

Content distribution in vehicular networks is greatly impaired by high mobility and intermittent connectivity. Social-aware content distribution schemes based on typical centrality metrics address the challenge, however they suffer due to their network-centric nature instead of information centric. We suggest to exploit the recently proposed information-centric networking architecture which cater the issue by decoupling host-user and support in-network caching at intermediate nodes. In this paper, we propose a novel information-centric social-aware content distribution protocol, STRIVE confining the broadcast nature of interest/content by routing it selectively towards high centrality information facilitator vehicles. We use a three-tier forwarding strategy to discover potential local and global information facilitators in an urban environment for efficient content delivery. The performance evaluation implements a scalable simulation environment deploying up to 2986 vehicles using realistic vehicular mobility traces. Simulation results show that our proposed novel vehicle centrality based content distribution protocol, STRIVE outperforms existing social content distribution metrics used in the literature.

Software Defined Networks (SDN) provide a high simplification of the network management by decoupling the control plane from the data plane through the use of controllers. Distributed-Denial-of-Service (DDoS) attacks can make SDN controllers unavailable to process legitimate flow requests from switches. The main approaches to protect controllers against DDoS attacks are essentially based on the attack detection, that still yield high rates of false negatives and/or false positives, highlighting the importance of mitigating DDoS attacks. Existing mitigation techniques are fundamentally based on external and additional resources or on the network traffic analysis, increasing computational cost or being prone to high rates of false negatives and/or false positives. This work presents PATMOS, a novel Protocol for DDoS Attack miTigation in Multi-contrOller SDN networkS through controller's clustering. PATMOS procedures are organized in three phases. The first one exchanges control messages to identify overloaded controllers, eliminating the dependence on the network traffic analysis. The second phase elects the best performance level controller to coordinate the mitigation process. The third phase minimizes the DDoS attacks effects using operational controllers in the network, differently from the works that employ external resources. Simulations results show PATMOS reducing 52.39% of CPU usage rate, increasing 192.74 fold more the throughput and decreasing 2.5 fold less the latency of flow requests to a target controller.

In this paper, we propose a novel formulation for the efficient data gathering problem in Wireless Sensor Networks (WSNs) based on Matrix Completion technique. The objective here is to optimize the usage of WSN resources during the data gathering process by taking into account an a priori knowledge about the data to be gathered. More precisely, we model the prior knowledge about the target data via hard convex constraints where the involved regularization parameters are related to some physical properties of the data itself and are then easy to interpret. Hence, the problem of data gathering is reduced to a constrained minimization one. A new class of primal- dual algorithm is extended in order to solve the resulted optimization problem. Experiments carried out on two datasets show that the proposed algorithm outperforms state-of-the-art methods and achieves a good recovery accuracy even if the sampling rate is very low.

Wireless sensor networks (WSNs) consist of nodes with limited power resources. A potential method to prolong the lifespan of a node is the use of an antenna which can harvest energy from radio frequency (RF) signals. In this paper, we model a network consisting of nodes with energy harvesting capabilities and a number of dedicated energy transmitters (ETs) which send data to the nodes. We identify those parameters which affect the consumption of the nodes and we design a method to achieve multi-hop energy transfer between the nodes. However, the ultimate purpose of this paper is to examine whether the cost of the investment of using energy harvesting nodes can be covered by achieving a lower operation cost; that is longer operation times and, thus, less frequent maintenance. We consider three scenarios with different node densities and transmitter populations. Simulation results show that the use of RF-energy harvesting nodes can save a significant amount of energy, while the cost of the investment can be covered in less than 8 years for dense networks.

The introduction of machine-to-machine (M2M) communications to long term evolution and advanced (LTE-A) cellular networks can significantly degrade the performance of existing human-to-human (H2H) communications. In this paper, we consider a shared channel resource allocation in an H2H/M2M coexistence scenario. We first formulate the resource sharing problem between M2M and H2H communications as a bipartite graph (BG). In addition, we propose a power control scheme for the concurrently transmitting M2M nodes to mitigate the H2H performance degradation following a probability that is set based on a proportional integrative derivative (PID) controller reflecting the interference level. The impact of M2M radio resource allocation on the performance of conventional scheduling algorithms optimally designed for H2H communications in terms of data rate and fairness is evaluated. Simulation results are encouraging and our proposed scheme succeeds in reducing the impact of M2M communications on H2H services in terms of data rate and fairness.

In this paper, we proposed a novel matrix completion algorithm in Wireless Sensor Networks. Our contribution is twofold. First, we propose a general formulation of the problem of data gathering as a tractable convex optimization problem on the Hilbert space of data measurement matrices. The proposed criterion takes full advantage of both the global features, particularly low rank nature of data, and local features namely the sparsity and the spatio-temporal correlation in the sensors data to improve the recovery accuracy. Second, we design a new class of primal-dual optimization approach in order to optimize the resulting regularized criterion. Experiments carried out on two datasets show that the proposed algorithm outperforms state-of-the-art methods for low sampling rate and achieves a good recovery accuracy even if the sampling rate is very low.

Participatory Sensing is a new paradigm of mobile sensing where users are actively involved in leveraging the power of their smart devices to collect and share information. Motivated by its potential applications, we tackle in this paper the task assignment problem for a requester encountering a crowd of participants while considering their mobility model and sensing preferences. We aim to minimize the overall processing time of sensing tasks. Hence, we introduce first the Mobility-Aware Task Assignment scheme in both oFfline (MATAF) and oNline (MATAN) models where requesters investigate the participants' arrival model in different compounds of the sensing region. Further, we enhance such schemes by jointly taking into account participants' mobility and sensing preferences. We advocate then two other task assignment models, P-MATAF (offline) and P-MATAN (online). All proposed algorithms adopt a greedy-based selection strategy and address the minimization of the average makespan of all sensing tasks. We conduct extensive performance evaluation based on real traces while varying the number of tasks and associated workloads. Results proved that our proposed schemes have achieved lower average makespan and higher number of delegated tasks.

Wireless charging brings forward some new principles in designing energy efficient networks. A number of energy transmitters are placed in all over the network to recharge power constrained nodes. Since a few only nodes can remarkably benefit from the transmitter power emission, we organize the nodes in clusters and we propose an efficient localized algorithm as well as a centralized one to compute the charger position such that the cluster lifetime is maximized. Simulation results are presented to show the effectiveness of the approaches.

Device-to-device (D2D) communications, one of the major component of the evolving 5G networks, is showing promising advantages on supporting machine-to-machine (M2M) communications. In this paper, we consider the design of efficient transmission strategy selection algorithm for M2M communications underlaying cellular networks. First, a group of machine type-devices (MTDs) is matched with a particular user equipment (UE). MTDs belonging to the same group can access the same spectrum within its matched UE while the latter quality of service (QoS) is maintained. Next, we propose an efficient evolutionary game based transmission strategy selection algorithm for M2M communications using D2D mode. Specifically, MTDs switch opportunistically from a non-cooperative strategy to a cooperative strategy. Initially, we consider a non-cooperative scenario due to the selfish behavior of devices. In case the latter QoS is not satisfied, MTDs switch to a cooperative game. In a cooperative game, we propose two alternative power control schemes: a fixed mixed-strategy power control scheme where each MTD willing to play cooperatively selects the power strategy from a discrete level of powers and an adaptive mixed-strategy power control scheme. The latter technique enables to set efficiently the discrete power levels using a fuzzy logic and a proportional-integral-derivative (PID) controllers aiming to assure the desired QoS of UEs while maximizing the efficiency of M2M communications. Simulation results show that the evolutionary game based transmission strategy selection algorithm avoids significant degradation of traditional human-to-human (H2H) services in terms of throughput and fairness compared to a single non-cooperative game strategy. Besides, the adaptive mixed-strategy power control scheme outperforms the fixed mixed-strategy power control scheme by saving the battery life of MTDs while guaranteeing the latter QoS.

The ubiquity of sensors-equipped mobile devices has enabled citizens to contribute data via participatory sensing systems. This emergent paradigm comes with various applications to improve users' quality of life. However, the data collection process may compromise the participants' privacy when reporting data tagged or correlated with their sensitive information. Therefore, anonymization and location cloaking techniques have been designed to provide privacy protection, yet to some cost of data utility which is a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring the queriers' required data utility and protecting the participants' privacy. First, we introduce a trust worthy entity to the participatory sensing traditional system. Also, we propose a general privacy-preserving mechanism that runs on this entity to release a distorted version of the sensed data in order to minimize the information leakage with its associated private information. We demonstrate how to identify a near-optimal solution to the privacy-utility tradeoff by maximizing a privacy score while considering a utility metric set by data queriers (service providers). Furthermore, we tackle the challenge of data with large size alphabets by investigating quantization techniques. Finally, we evaluate the proposed model on three different real datasets while varying the prior knowledge and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants' privacy while maintaining about 98% of the required data utility.

Identity management (IdM) systems employ Identity Providers (IdPs), as guardians of users' critical information. However, Distributed Denial-of-Service (DDoS) attacks can make IdPs operations unavailable, compromising legitimate users. In the literature, the main countermeasures against DDoS attacks are based on either the application of external resources to extend the system lifetime (replication) or on the DDoS attacks detection. The first approach increases the solutions cost, and in general the second one is prone to high rates of false negatives and/or false positives. This work presents SAMOS, a first scheme to mitigate DDoS attacks in IdM systems through a novel approach: organizations of IdP clustering using optimization techniques. SAMOS is started based on the monitoring of processing and memory resources, differently from the solutions in the literature that are started based on the attack detection by the network traffic analysis. SAMOS minimizes the DDoS attacks effects using operational IdPs in the system, differently from the works that employ external computer resources. Results considering data from real IdM systems indicate the scheme viability.

Nowadays, smartphones and wearable devices are endowed with several sensors, which can harvest large quantities of data about urban areas (location information, pollution levels, etc.), going through a list of personal and surrounding contexts such as noise level, traffic awareness, to name a few. Exploiting this wealth of information provided by the crowd allows developers to design and build several applications over the so-called Mobile CrowdSensing, such as traffic regulation, environmental monitoring, tourism recommendation, etc. However, for this to be possible, many barriers still have to be overcome such as collecting, handling, structuring and representing the crowd data in a suitable way. In this paper, we propose a three-fold solution to the problem of data management in CrowdSensing systems. Firstly, we structure the collected data based on semantic ontologies. Secondly, we enrich the data based on a novel contextual awareness data interlinking. Finally, we refine recommendations with contexts, through taking into consideration meta-information interlinked to the main information of interest. We have implemented our model in a tourism recommendation application as a proof of concept. The experimental evaluation - which we carried out - has shown very promising results.

Participatory Sensing is an emerging paradigm that enables users carrying sensors-equipped smart devices to gather and share data about a particular phenomenon. However, involving individuals in sensing tasks raises new challenges such as the quality of collected data, denoted as Quality of Information (QoI), and the dedicated resources for its acquisition. Besides, a fair task assignment is highly recommended to ensure participants' commitment. This trade-off is challenging to achieve due to the two conflicting objectives of maximizing data quality and minimizing the sensing time among all participants. In this work, we design a Fair QoI and Energy-aware Mobile Sensing Scheme (F-QEMSS) to ensure both requestors' and participants' satisfaction. We formally model the corresponding multi-objective optimization problem and generate its weighted-sum objective function. Next, we employ our Tabu-Search based algorithm to solve it. Simulation results show the effectiveness of our solution. Particularly, our approach achieves up to 96% of fairness, measured by the Jain's index, while preserving the same data quality as non-fair algorithms.

This work proposes a novel Delay-aware Packet Prioritisation Mechanism (DPPM) to uniformly distribute the Quality of Service (QoS) level across all Voice Over IP (VoIP) calls in a Wireless Mesh Network (WMN). The method prioritises VoIP packets based on the amount of queueing delay that has been accumulated across multiple hops within the WMN. The accumulated queueing delay is piggybacked over every VoIP packet and is used at the enqueueing phase to place more delayed packets towards the head of the queue. This assures higher priority for more delayed VoIP packets over less delayed VoIP packets. The influence of the queueing delay on voice call quality is further reduced by utilising the proposed DPPM in conjunction with WiFi frame aggregation. This conjunction increases the network's VoIP call capacity, and this is validated through NS-3 simulations.

Vehicular Cloud (VC) is a new concept which enables vehicles to offer and rent out their advanced on-board resources to other vehicles. So, individual vehicles can be both service providers and cloud users. Vehicles users need to discover vehicles' services and request targeted services from them. To achieve this, a cloud directory must be used in which provider vehicles register their services and from which vehicle users discover offered services in order to consume them. In a previous work [1], we have designed a new protocol in VC, named Discovering and Consuming Cloud Services in Vehicular Cloud (DCCS-VC). Due to their predictability of time and space in urban scenarios, DCCS-VC was based on public buses as a cloud directory in order to form a dynamic index of provider vehicles. However, DCCS-VC provides a low efficiency of both registration and discovering operations, given the introduced high waiting time of vehicles to perform these operations. In this paper, we extend our previous protocol to minimize the provider and user vehicles' waiting time. To do so, we allow vehicles to exploit the providing real time bus information in order to discover existing public buses in the vicinity. In addition, we introduce an optimization technique which enables provider vehicles to select the most adequate public bus as a service registration node. We illustrate the superiority of this enhancement throughout the results obtained from simulation experiments, using an urban mobility model.

The efficient radio resource management and interference coordination schemes became an important requirement for the successful adoption of heterogeneous networks with small cells. In this approach, a large number of low-power devices is deployed to increase the spatial frequency reuse of the selected area. In this paper, we propose a distributed multi-agent strategy, where small cells locally control resource usage to maximize the overall system capacity. The main goal is to provide each cell with the ability to make its decision autonomously while taking into account the resource occupation of the surrounding cells. We study the coexistence with non-cooperative macroenvironment and propose a mechanism to increase the efficiency of learning with a smart safe-shift procedure. We illustrate the application of this distributed learning strategy for the subband allocation and propose several mechanisms to improve the convergence speed in the absence of communication. The performances of the proposed method are evaluated in the case of Long Term Evolution (LTE) setup and compared to a number of traditional resource allocation schemes. System level simulations show that it achieves a considerable improvement in system performance for heterogeneous deployment with non- cooperating agents, without compromising the efficiency of the system.

Wireless sensor networks (WSNs) are widely used nowadays and in a various domains. However, the specificity of the nodes deployed in this type of networks makes them prone to failures. To overcome this problem and guarantee the continuity of the network functioning even in the presence of node failure, fault tolerance mechanisms need to be designed and integrated to the operation of those networks. These fault tolerance mechanisms will more precisely deal with recovering from a failures and resuming the correct functioning of a WSN. With that aim, we propose in this paper a new centralized curative approach, called Rotating Nodes based Failure Recovery (RNFR), dedicated to restore connectivity in multi-channel WSNs. The proposed solution targets the failure of particular nodes designated as articulation nodes, which leads to the partitioning of the WSN into many segments isolated from each other and leading to connectivity loss. Therefore, the main tasks of RNFR are the restoration of the connectivity after an articulation node failure using reorganization and reallocation of channels. Moreover, the solution uses a node rotation technique to communicate the recovery information to all the disjoint parts of the network. The proposed approach proved to be interesting by giving motivating results while evaluated through simulation.

Traditional cellular networks have been considered the most promising candidates to support machine to machine (M2M) communication mainly due to their ubiquitous coverage. Optimally designed to support human to human (H2H) communication, an innovative access to radio resources is required to accommodate M2M unique features such as the massive number of machine type devices (MTDs) as well as the limited data transmission session. In this paper, we consider a simultaneous access to the spectrum in an M2M/H2H coexistence scenario. Taking the advantage of the new device to device (D2D) communication paradigm, enabled in long term evolution- advanced (LTE-A), we propose to combine M2M and D2D owing to the MTD low transmit power and thus enabling efficiently resource sharing. First, we formulate the resource sharing problem as a maximization of the sum-rate, problem for which the optimal solution has been proved to be non deterministic polynomial time hard (NP-Hard). We next model the problem as a novel interference-aware bipartite graph to overcome the computational complexity of the optimal solution. We propose two alternative algorithms, one centralized and one semi-distributed to perform the M2M resource allocation. The computational complexity of both introduced algorithms is of polynomial complexity. Simulation results show that the semi-distributed M2M resource allocation algorithm achieves quite good performance in terms of network aggregate sum-rate with markedly lower communication overhead compared to the centralized one.

Modern cars are transforming towards autonomous cars capable to make intelligent decisions to facilitate our travel comfort and safety. Such "Smart Vehicles" are equipped with various sensor platforms and cameras that are capable to constantly sense tremendous amount of heterogeneous data from urban streets. This paper aims to identify the appropriate vehicles, important to be selected as information hubs for the efficient collection, storage and distribution of such massive data. Therefore, we propose an Information-Centric algorithm, "GRank" for vehicles to autonomously find their importance based on their reachability for different location-aware information in a collaborative manner, without relying on any infrastructure network. GRank is the first step to identify socially important information hubs to be used in the network. Results from scalable simulations using realistic vehicular mobility traces show that GRank is an efficient ranking algorithm to find important vehicles in comparison to other ranking metrics in the literature.

One of the common challenges in Wireless Sensor Networks (WSNs) is the degradation of the performance due to several factors. In one hand, the interference between concurrent transmissions can affect the efficiency of the network and considerably degrades its performance. The exploitation of the multiple channels available in sensor technology and the development of protocols for WSNs can be a solution to mitigate this interference. In this case, the way the channels are assigned has a significant impact on the performance of multi-channel communication. In the other hand, the faults occurred in WSNs are another factor that degrades the WSN performance. In this paper, we propose a distributed energy-efficient solution for multi-channel allocation based on the routing. This solution implements a fault recovery mechanism to reconnect the network after an articulation node failure. A main task of the proposed approach is to minimize the number of interferences when allocating the limited number of available channels. As a second task, the approach minimizes the energy consumption by defining a sleeping/activity strategy. The fault recovery mechanism aims to restore the network connectivity and reallocate channels without affecting the whole WSN. The performance of the proposed solution is evaluated by simulation.

Vehicular Adhoc Networks (VANETs) have been attracted a lot of research recent years. Although VANETs are deployed in reality offering several services, the current architecture has been facing many difficulties in deployment and management because of poor connectivity, less scalability, less flexibility and less intelligence. We propose a new VANET architecture called FSDN which combines two emergent computing and network paradigm Software Defined Networking (SDN) and Fog Computing as a prospective solution. SDN-based architecture provides flexibility, scalability, programmability and global knowledge while Fog Computing offers delay-sensitive and location-awareness services which could be satisfy the demands of future VANETs scenarios. We figure out all the SDN-based VANET components as well as their functionality in the system. We also consider the system basic operations in which Fog Computing are leveraged to support surveillance services by taking into account resource manager and Fog orchestration models. The proposed architecture could resolve the main challenges in VANETs by augmenting Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Base Station communications and SDN centralized control while optimizing resources utility and reducing latency by integrating Fog Computing. Two use-cases for non-safety service (data streaming) and safety service (Lane-change assistance) are also presented to illustrate the benefits of our proposed architecture.

In the recent years, the research community proposed several protocols and applications for opportunistic networking. A common assumption is that all nodes have pro social behavior and are willing to cooperate with the network. However, in opportunistic networking applications, this assumption can lead to degradations in the network performance. People can be selfish and this behavior affects the operation of the network. In this work, we propose an incentive mechanism to improve routing, called MINEIRO, which aims to detect and avoid selfish nodes based on the source of the messages. We demonstrate under which constraints our algorithm leads to Bayesian equilibrium. Moreover, we show that without an incentive mechanism the network supports up to 60% of nodes with selfish behavior without performance degradation in a random mobility scenario. Meanwhile, in a scenario with social-based mobility, the performance decreases linearly for more than 20% of selfish nodes. Our proposal, on the other hand, improves the performance with any amount of selfish nodes by encouraging users to relay messages from third-parties.

The emerging Internet of Things (IoT) paradigm makes Wireless Sensor and Actuator Networks (WSANs) seem as a central element for data production and consumption. In this realm, where data are produced and consumed within the network, WSANs have as a challenge to perform in-network data storage considering their resource shortage. In this paper, we propose the Virtual Broking Coding (VBC) as a data storage scheme compliant with WSANs constraints. As such, VBC ensures a reliable data storage and an efficient mechanism for data retrievability. To evaluate our proposed solution, we present a theoretical analysis as well as a simulation study. Using both, we show that VBC reduces the cost incurred by the coding techniques; and increases the delivery ratio of the requested data. The results presented by VBC suggest this solution as a new direction on how to use network coding based schemes to address the WSAN in-network storage problem.

Home network complexity is dramatically growing in terms of topology (devices and connectivity technologies) and services leading to increasingly challenging management issues. In this context, enabling a better visibility of home network traffic usage and performance is a crucial step to provide efficient selfcare and customer care. In this paper, we study home network traffic monitoring architectural approaches. In particular, we study the feasibility of Home Gateway based flow monitoring approach, which will allow enhancing home network diagnostic and performance tracking. Our experimental evaluation aims to provide a better understanding of deployment possibilities and limits. The obtained results based on an open source tool are promising in terms of resource consumption (CPU and memory load, bandwidth utilization).

Future cars are becoming powerful sensor platforms capable to collect, store and share large amount of sensory data by constant monitoring of urban streets. It is quite challenging to upload such data from all vehicles to the infrastructure due to limited bandwidth resources and high upload cost. This invoke the need to identify the appropriate vehicles within the Vehicular Ad-hoc Network, that are important for different urban sensing tasks based on their natural mobility and availability. This paper address this problem leveraging the self-decision making ability of a "Smart Vehicle" regarding its importance in the network. To do so, we present Car Rank, an Information-Centric algorithm for a vehicle to first rank different location-aware information. It then uses the information importance, its spatio-temporal availability and neighborhood topology to analytically find its relative importance in the network. Car Rank is the first step towards identifying the best set of information hubs to be used in the network for the efficient collection, storage and distribution of urban sensory information. We evaluate Car Rank under a scalable simulation environment using realistic vehicular mobility traces. Results show that Car Rank is an efficient ranking algorithm to identify socially important vehicles in comparison to other ranking metrics used in the literature.

The evolution of the Internet of Things (IoT) allows the development of new services and applications but triggers as well a full set of new issues to be solved. Among them, there are the problems related to the integration of the WSNs in the IoT realm including those related to data access. In this paper, we address more precisely the in-network data storage and data retrieval performed in a WSN integrated in the IoT realm. In order to develop an adequate data storage scheme for this scenario, we first design a system that integrates the Virtual Broking Coding (VBC) data storage scheme in the IoT realm. Then, we propose an algorithm called Dynamic Adaptive Virtual Broking Coding (DA-VBC) that adapts dynamically the packet redundancy level adopted in VBC to the optimal redundancy level, regarding the actual condition of the network, in order to ensure a reliable data storage and data retrieval. To do so, we model the choice of the optimal redundancy level as a Markov Decision Process (MDP) problem. Using the optimal policy found by the MDP, DA-VBC always performs with the minimum cost-benefit for the network which means allowing more packet to be retrieved without overload the energy consumption. The simulation results confirm that the dynamic adaptation of the redundancy level improves the reliability of the data storage scheme while achieving an energy consumption comparable to the solution that does not use any redundancy. Besides, they show that the optimization of the cost-benefit metric is far more efficient than optimizing only one metric (for instance the cost or the packet delivery ratio), or using a fixed redundancy level.

The advanced on-board vehicles' resources have given birth to the Vehicular Cloud (VC) concept. The VC is an emerging paradigm where individual mobile vehicles can be both cloud users and service providers, it enables vehicles that have sufficient resources to act as mobile cloud servers and rent out them to other vehicles. However, and with the high mobility of vehicles, user vehicles need to discover vehicle providers, know their services, and request targeted services from them. In this paper, we propose a new protocol that enables user vehicles to discover and rent providers' services in VANet using public buses. Due to the predictability of time and space of these buses in urban scenarios, our protocol use them as cloud directories with which provider vehicles register and from which user vehicles discover all offered services. Hence, they hold a dynamic index of services' providers. We demonstrate the efficiency of our protocol, in terms of service discovery and consuming delays, by conducting an extensive set of simulation experiments using OMNet++ network simulator.

Modern cars are transforming towards autonomous cars capable to make intelligent decisions to facilitate our travel comfort and safety. Such "Smart Vehicles" are equipped with various sensor platforms and cameras to collect, store and share tremendous amount of heterogeneous data from urban streets. This paper addresses the efficient collection and distribution of such massive data by allowing a popular Smart Vehicle to autonomously decide its user relevant importance in the vehicular network without relying on the infrastructure network. Therefore, we propose an Information-Centric algorithm, "InfoRank" for a vehicle to rank different location- dependent information associated to it. It then uses the information importance to analytically find its influence in the network. InfoRank is the first step towards identifying the best information hubs to be used in the network for the efficient collection, storage and distribution of urban sensory information. Results from scalable simulations using realistic vehicular mobility traces show that InfoRank is an efficient ranking algorithm to find top information facilitator vehicles in comparison to other ranking metrics in the literature.

Mobile phones equipped with a rich set of embedded sensors enhance participatory sensing to collect data for different applications. However, many challenges arise when selecting participants to perform sensing tasks. Among these challenges, we can cite energy consumption, users' mobility impact and the quality of retrieved data, recently defined as Quality of Information (QoI). In this work, we study the QoI and Energy-aware Mobile Sensing (QEMSS) problem. Hence, for a given set of users, a sensing area and data quality requirements, the objective of QEMSS is to find the subset of users that maximizes QoI in terms of spatial and temporal metrics while minimizing the overall energy consumption and reducing the redundancy during the sensing process. We propose a meta-heuristic algorithm based on Tabu-Search to provide a sub-optimal solution. Simulation results, for both deterministic and unknown participants' trajectories, are compared to other state-of-the-art methods. This allows showing that our approach outperforms both the greedy- based and the random selection strategies. Particularly, the achieved data quality by our scheme is significantly higher in challenging scenarios such as low dense areas or scarce users' energy resources.

The articles in this special section were presented at the IEEE World Forum (WF) on IoT held in Seoul, Korea, from March 6–8, 2015.

This paper proposes a mechanism for uplink packet scheduler in LTE network in the context of Machine-to-Machine (M2M) communication. The proposed approach uses the current and past information of the system to satisfy the Quality of Service (QoS) requirements, to ensure fairness in resource allocation and to control the congestion caused by M2M devices. We carried out some network simulations by using a NS-3 simulator so as to show the effectiveness of the proposed approach. The results indicate that our solution can reduce the impact of M2M communication on Human-to-Human (H2H) communication and avoid the problem of starvation, when compared to related approaches.

With the increasing number of users subscribing to mobile Internet such as 3G and 4G networks, Wireless Internet Services providers (WISP) aim to provide a good service for customers while elevating the number of clients. Several proposals to offload the traffic of 3G networks were made in the last few years, including the use of femtocells, WiFi offloading and more recently mobile-to-mobile opportunistic offloading. In this paper, we propose a multi-criteria decision-making framework to manage the offload of data from 3G networks using mobile-to-mobile opportunistic communications. Primarily, we focus on building a decision framework that employs only user knowledge to select which users should handover from infrastructure to mobile-to-mobile network, avoiding changes in the infrastructure. Next, we evaluate our proposal and demonstrate its feasibility through trace-driven simulations, achieving 6% of data offload when there is no delay tolerance in the application and up to 36% when application can tolerate 20 minutes of delay.

Next-generation Long Term Evolution - Advanced (LTE-A) constitutes a promising solution to withstand the exponential growth of cellular connections related to user equipment devices (UEs) and their increasing demands of bandwidth. Relaying is a technique contemplated in this type of cellular networks where multi-hop communications can be established between nodes (UEs and relay nodes [either fixed or mobile]) to improve link quality and balance the network load between neighboring evolved Node Base stations (eNodeBs). Particularly, due to the dynamic behavior of Mobile Relay Nodes (MRNs), relaying becomes more challenging because there is a need to implement effective methods to choose multi-hop links that can guarantee acceptable levels of Quality of Service (QoS), while balancing the data traffic between different eNodeBs (for improving the robustness of the network), and with the handicap of mobility. This work presents a distributed algorithm in which MRNs act as intermediate relays between underloaded eNodeBs and UEs previously attached to other overloaded cell by dynamically assigning them a portion of the Resource Blocks initially scheduled for the corresponding intermediate MRN (by its serving eNodeB). Results show that multi-hop forwarding can be beneficial for the network performance enhancement in terms of per-user throughput and network load balancing.

One of the most common needs in wireless sensor networks (WSNs) is the continuity of network function even in the presence of some node failures. This is called fault tolerance. In general, fault tolerant solutions can be preventive or reactive: the preventive techniques aim to prevent the failure by minimizing and balancing the energy consumption in each node, while the reactive techniques intervene after a failure was detected. In this paper, we propose a new preventive/reactive fault tolerant scheme dedicated to manage the energy consumption and reconnect the WSN in case of articulation node failure. The specificity of this scheme is the use of multichannel communications, allowing simultaneous data transmission, which decreases the interferences between nodes and then decreases data retransmissions. The second task of this scheme targets the reorganization of the network after a network portioning episode. Such episode means that the WSN is partitioned in many segments after an articulation node failure. More precisely, we present here two heuristics for channel allocation/reallocation and WSN reorganization after a network failure. The performances of the proposed scheme is evaluated and proved through simulation.

Wireless Mesh Networks (WMNs) are becoming increasingly popular and user demand for high-quality rich media services is continuously growing. Despite the fact that WMNs offer significant flexibility, they suffer in respect to Quality of Service (QoS) provisioning. This paper proposes a novel mechanism for providing enhanced QoS support to video services in multi-hop WMNs. The mechanism makes use of an innovative hybrid hierarchical architecture which combines centralized and distributed approaches. The proposed solution relies on performance monitoring at WMN nodes and performs load balancing by off-loading traffic from the highest loaded nodes to less loaded neighbours. Simulation-based results presented outline the performance of our proposed mechanism in terms of QoS metrics (delay, throughput, packet losses and PSNR) in different network load scenarios. The results clearly demonstrate how our proposed mechanism outperforms the traditional OLSR protocol in terms of QoS performance.

Location-based services provide (and maintain) location information used by geographic routing protocols. Routing and location service are widely related, but handled separately in usual studies about Vehicular Ad hoc Network (VANET). In this paper, we propose a hybrid approach, denoted mobility-Prediction-based Hybrid Routing and Hierarchical Location Service (PHRHLS), coupling a VANET routing protocol, the Greedy Perimeter Stateless Routing (GPSR), and the Hierarchical Location Service (HLS) extended with a mobility prediction algorithm. We show that our approach, PHRHLS, reduces the localization overhead and enhances the routing performances. Indeed, our extensive simulations show promising results in terms of end-to-end latency, packet delivery ratio and control message overhead.

As the Internet of Things (IoT) grows in interest from both research and industrial parts, the lack of standard solutions to quickly and easily build and install IoT applications becomes a topic of high interest. In this paper, we introduce a language called SALT (Simple Application Logic description using Transducers) that allows describing and deploying the distributed logic needed in order to fulfil a complete desired application. This language aims at giving extended functionalities by filling the gap between the logical capabilities offered by services orchestration and the closeness efficiency of services choreography. SALT is interpreted by a virtual machine running on devices that introduces an abstraction layer in order to simplify access to hardware capabilities. SALT implements several mechanisms to comply with organisation issues presented in the Services Oriented Computing realm dealing with Services interactions, adapted to the specific constraints of the IoT. This work details SALT's concepts, formalism and implementation.

In order to check requirement specifications written in natural language, we have chosen to model domain knowledge through an ontology and to formally represent user requirements by its population. Our approach of ontology population focuses on instance property identification from texts. We do so using extraction rules automatically acquired from a training corpus and a bootstrapping terminology. These rules aim at identifying instance property mentions represented by triples of terms, using lexical, syntactic and semantic levels of analysis. They are generated from recurrent syntactic paths between terms denoting instances of concepts and properties. We show how focusing on instance property identification allows us to precisely identify concept instances explicitly or implicitly mentioned in texts.

In this paper, we propose a new approach to ensure fault tolerance in wireless sensor networks (WSNs) while guaranteeing both coverage and connectivity in the network. Hence after reviewing the fault tolerance related works in wireless sensor networks and enumerating the requirements that must be satisfied by our fault tolerance solution, we present the different mechanisms we propose to maintain both coverage and connectivity in the network in the case of node failure. Our approach is a proactive one in the sense that it aims to replace the “up to fail” node before its defection. In the case of impossible replacement of the “up to fail” node, a fast rerouting mechanism is proposed to forward the traffic initially routed via the “up to fail” node. Performance evaluation of our fault tolerance approach shows that the number of nodes potentially eligible for the “up to fail” node replacement depends on a threshold about the node redundancy as well as the network density metric. Moreover, we show that, compared to a classical routing algorithm, our fast rerouting mechanism reduces the packet loss rate in the network.

The primary contribution of this paper is the design of a novel architecture and mechanisms to enable voice services to be deployed over femtocells backhauled using a wireless mesh network. The architecture combines three mechanisms designed to improve Voice Over IP (VoIP) call quality and capacity in a deployment comprised of meshed femtocells backhauled over a WiFi-based Wireless Mesh Network (WMN), or femto-over-mesh. The three mechanisms are: (i) a Call Admission Control (CAC) mechanism employed to protect the network against congestion; (ii) the frame aggregation feature of the 802.11e protocol which allows multiple smaller frames to be aggregated into a single larger frame; and (iii) a novel delay-piggy-backing mechanism with two key benefits: prioritizing delayed packets over less delayed packets, and enabling the measurement of voice call quality at intermediate network nodes rather than just at the path end-points. The results show that the combination of the three mechanisms improves the system capacity for high quality voice calls while preventing the network from accepting calls which would result in call quality degradation across all calls, and while maximizing the call capacity available with a given set of network resources.

Fast transmission of event-driven warning messages and energy conservation are primary concerns to design robust Hybrid Sensors and Vehicular Networks (HSVNs). In last few years, several protocols have been proposed to address these issues. However, the tradeoff between energy consumption and latency has not been carefully studied and sometimes it is given higher priority than event detection efficiency which remains the first objective of HSVNs. Unlike the existing works, we propose a framework that provides equilibrium between the following three metrics of HSVNs; dangerous events detection, energy consumption and transmission delay. The main advantage of our framework is its ability to ensure an effective detection of dangers on the road and timely transmission of the corresponding warning messages towards the passing by vehicles. This is achieved through the proposed mechanism to switch the sensors' status between sleep and active modes as well as the devised communication scheme between WSN-Gateway and the vehicles cluster head. The preliminary simulation results confirm the effectiveness of our framework and encourage us to pursue further investigation to extend it.

Call Admission Control is a key function that guarantees the Quality of Service (QoS) for users. In radio networks, this function is usually based on traffic models and ensures that sessions are admitted only if the estimated available bandwidth is enough for the entire call duration. For video on IEEE 802.16, the CAC function must ensure that the bandwidth to be reserved is compatible with the resource availability. For the enhanced SVC (Scalable Video Coding) systems, the CAC function must take into account all the layers and their characteristics. In this paper, we propose an enhanced CAC function for SVC that adapts the admission according to the statistical behaviour of the video sessions. The main goal is to use measurements in the 802.16 base station (BS) to update the traffic model of SVC video flows, this for the different layers of SVC flows. We then use the variability of the traffics generated to adapt the CAC according to the characteristics of incoming flows. To perform that, we use a Markovian model that adapts for each flow instead of using a generic static one as used in most of the papers. Performance evaluation is given to illustrate the interest of our proposal.

In this paper, we propose a combination between a routing protocol Greedy Perimeter Stateless Routing (GPSR) and Hierarchical Location Service (HLS) that we denote Hybrid Hierarchical Location Service (HHLS). HLS and GPSR used to be combined in the original work with a direct method, i.e. GPRS takes care of routing packets and HLS is called to get the destination position when the target node position is not known or is not fresh enough. When a destination is quite far away from the sender, the exact position of the target is calculated, and an extra overhead is generated from sender to receiver. Our main purpose is to reduce this overhead in HHLS. We suggest to proceed as follows: when a packet has to be sent to the destination, it will be sent directly to the former position of the target instead of requesting for the exact position. When the packet is approaching the former position, the exact position request is then sent. We have proposed a patch over the NS-2 simulator for HHLS according to our proposal. We have conducted experimentations which show promising results in terms of latency, packet delivery rate and overhead.

Nowadays, Vehicle to Vehicle (V2V) communication is attracting an increasing attention from car manufacturers due to its expected impact in improving driving safety and comfort. IEEE 802.11P is the primary channel access scheme used by vehicles; however it does not provide sufficient spectrum to ensure reliable exchange of safety information. To overcome this issue, many efforts have been devoted to enhance the frequency spectrum utilization efficiency. To this end, the Cognitive Radio (CR) principle has been applied to assist the vehicles to gain extra bandwidth through an opportunistic use of the unused spectrums in their surrounding. In this paper, we focus on safety messages for which we propose an original scheme that makes their exchange among the nearby vehicles more reliable with a significant reduce in their dissemination delay. This improvement is due to the use of a Hidden Markov Model that enables the prediction of the available channels for the subsequent time slots, leading to faster channel allocation for the vehicles. The obtained simulation results confirm the efficiency of our scheme.

The main goal in wireless sensor networking remains the reduction of the network lifecycle and the enhancement of its reliability, keeping decent performances in terms of throughput and latency. Given the increasing interest of the research community on wireless network coding (NC), we think such challenges can be tackled using its innovative concepts, especially in the case of many-to-one communications where network coding has shown promising theoretical results. Yet, without a thoughtful adaptation to WSNs, the benefits of NC for sensor networking prove to be too “greedy” and impractical. In this paper, we propose index-coding, a simple and effective packet coding scheme that enhances significantly many-to-one communications in ZigBee sensor networks. Index-coding uses smart bit-shifting operations in order to encode short messages from a set of sensors to a sink using fewer transmissions. Our implementation in a real ZigBee testbed shows substantial enhancement of network performances and resiliency.

Wireless Sensor and Actuator Networks (WSAN) and permanent connections to the Internet converge to be an emerging and promising field: Machine-To-Machine (M2M) services. To take advantages of this new field, hardware and software infrastructure compliance must be verified. Services expected by M2M alter the organization of WSAN. The software design in this area can be divided into two main categories: a centralized approach (Orchestration) where a monolithic application collects data and sends orders, and a distributed approach (Choreography) in which nodes offer and use services in a collaborative way. In this paper, we study the impact of these two architectures over WSAN. First, a mathematical analysis shows the improvement offered by choreography, thanks to the use of shorter paths between nodes. Then, an application experiments these two architectural designs to measure the impact on a real testbed. Both the theoretical mathematical analysis and the real platform experiment gives better results for the Choreography in terms of network reliability and path length. Our work quantifies the benefits obtained and provides histograms and numerical results.

A wireless mesh network is characterized by dynamicity. It needs to be monitored permanently to make sure its properties remain within certain limits in order to provide Quality-of-Service to the end users or to identify possible faults. To establish in every moment what is the appropriate reporting interval of the measured information and the way it is disseminated are important tasks. It has to achieve information quickly enough to solve any issue but excessive as to affect the data traffic. The problem that arises is that the monitoring information needs to travel in the network along with the user traffic and thus, potentially causing congestion. Considering that a wireless mesh network has highly dynamic characteristics there is a need for a good understanding of the influences of disseminating monitoring information in the network along with user traffic. In this paper we provide an evaluation of the network performance while monitoring information is collected from network nodes. We study how different monitoring packet sizes and different reporting frequency of the information can impact the user traffic and compare these values to the case in which only user data travels across the network.

In wireless sensor networks several constraints decrease communications performances. In fact, channel randomness and energy restrictions make classical routing protocols inefficient. Therefore, the design of new routing protocols that cope with these constraints become mandatory. The main objective of this paper is to present a multi-objective routing algorithm RBCR that computes routing path based on the energy consumption and channel qualities. Additionally, the channel qualities are evaluated based on the presence of relay nodes. Compared to AODV and AODV associated to a cooperative MAC protocol, RBCR provides better performances in term of delivery ratio, power consumption and traffic load.

VANETs (Vehicular Ad-hoc NETworks) are a special case of MANETs (Mobile Ad-hoc NETworks). Their main feature is the high mobility range of nodes, which causes topology changes and frequent disconnections. Topology-based routing protocols have weak performances in such networks. This is why a new set of routing protocols, designated as geographic routing protocols, were designed to enhance performances and ensure a better scalability. These geographic protocols assume on one hand that all nodes must be aware about their position (by using a positioning system like GPS). On the other hand they also assume a certain knowledge about the position of the destination node and the position of their neighbors thanks to Location-based Services. In this paper, we compare three location-based services: Reactive Location Service (RLS), Grid Location Service (GLS) and Hierarchical Location Service (HLS) while coupled to the well known geographic routing protocol Greedy Perimeter Stateless Routing (GPSR). As far as we know, our work is the first that targets the performance evaluation of location-based services while coupled with a routing protocol. The simulations were performed using the NS-2 simulator on a realistic map about the city of Reims (France). Besides, a scalability study of GLS and HLS is presented. This study is based on three qualitative metrics (the location maintenance cost, the location query cost and the storage cost).

In this paper, we address the beacon congestion issue in Vehicular Ad Hoc Networks (VANETs) due to its devastating impact on the performance of ITS applications. The periodic beacon broadcast may consume a large part of the available bandwidth leading to an increasing number of collisions among MAC frames, especially in case of high vehicular density. This will severely affect the performance of the Intelligent Transportation Systems (ITS) safety based applications that require timely and reliable dissemination of the event-driven warning messages. To deal with this problem, we propose an original solution that consists of three phases as follows; priority assignment to the messages to be transmitted /forwarded according to two different metrics, congestion detection phase, and finally transmit power and beacon transmission rate adjustment to facilitate emergency messages spread within VANETs. Our solution outperforms the existing works since it doesn't alter the performance of the running ITS applications unless a VANET congestion state is detected. Moreover, it ensures that the most critical and nearest dangers are advertised prior to the farther and less damaging events. The simulation results show promising results and validate our solution.

Vehicular networks are a promising field in wireless networks enabling connection vehicles among themselves or between a vehicle and an infrastructure. These networks aim to offer several potential applications ranging from road safety applications and driver assistance to infotainment. However, these networks have limited coverage and capacity. Among the options that may help overcome these limitations, we can quote cooperative communications and analog network coding (ANC). The idea of this paper is to combine these two concepts in order to improve the vehicular network connectivity and capacity. The proposed solution is divided into three stages. The first stage aims at identifying the coding and relaying opportunities. The second stage uses a distributed scheme to select the Best Vehicular Relay among potential candidates when relaying is required, while the last stage performs our joint relaying and coding strategy on the received signals. In order to validate our approach, numerical analysis is performed to evaluate the performances in terms of raw Bit Error Rate (BER) and throughput. This confirmed our expectation showing that cooperative relaying achieves better performance than the direct transmission in terms of raw BER but decreases the throughput. However, by deploying analog network coding on the Best Vehicular Relay, the throughput is improved considerably at the price of a slight deterioration of the raw BER.

The relay-assisted Orthogonal Frequency-Division Multiple Access (OFDMA) cellular system is one of the most promising technology thanks to the enhancements of the system capacity and coverage it provides. However, some special features may lower its positive aspects. In multiple relay case, the asymmetric link problem in the two stages forming the relaying process may result in important resource wastage. Such resource wastage can even be more serious than in the case where only a single relay is used in the system. Only few works consider this resource wastage problem. In this paper, we first define a new term, channel holes, to denote the potential resource wastage in single and multiple relay systems. Then based on this new concept, we propose a novel resource management scheme for multiple relay systems, i.e. channel-hole based cooperative scheduling in multiple relay systems. In order to evaluate the performances of such resource management schemes, we build a system model and propose the corresponding analytical derivation, which helps analyzing the average queue length and the average packet delay. Simulations are used to validate and reinforce our theoretical analysis. Then using simulations, we compare our proposed scheduling algorithm to other algorithms from the literature. Hence, we show that our proposed algorithm performs better than others. In addition we also observe that our proposed scheduling algorithm performs a resource-aware relay selection that is more efficient than the one obtained using the ideal relay selection scheme.

Over the last decade cooperative communication in wireless sensor networks (WSN) received much attention. A lot of works have been done to propose a MAC layer that supports cooperative communication. However the impact of the association of a cooperative communication technique with a low power listening scheme was not studied in the literature. In this paper we propose CL-MAC, a Cooperative Low power mac protocol for WSNs. CL-MAC implements jointly Low Power Listening and cooperative communication. More precisely, we propose two variants of this protocol: a proactive version CL-MAC(P) and a reactive version CL-MAC(R). In order to evaluate the performances of the two proposed CL-MAC variants, we compare its to those of X-MAC. Simulation results proved that our protocol is able to enhance the use of the channel and to reach promising energy preservation especially in dense networks.

In this paper, we propose a newly distributed protocol called ADCD to manage information harvesting, and distribution in Vehicular Sensor Networks (VSN). The concept of ADCD is based on the characterization of sensed information (i.e. its importance, location and time of collection) and the diffusion of this information accordingly. Furthermore, ADCD uses an adaptive broadcasting strategy to avoid overwhelming users with messages for which they have no interests. Thanks to this adaptive broadcasting strategy, ADCD limits the generated overhead avoiding network congestions as well as long latency to deliver the harvested information, which are the main limitations of other existing protocols. Moreover, it is designed to be flexible regarding the use of roadside units or not, which is not the case in other schemes in the literature. To reach its objectives, ADCD operations are divided into three steps: (i) classification of data and the identification of their target area of diffusion, (ii) data-centric election of the set of broadcasters to avoid broadcasting redundancy, and (iii) iterative process for data dispatching in a targeted area. Performance evaluation shows that the ADCD protocol allows for mitigating the information redundancy and its delivery with an adequate latency while making the reception of interesting data for the drivers (related to their location) more adapted. Moreover, the ADCD protocol reduces the overhead by 90% compared to the classical broadcast and an adapted version of MobEyes. The ADCD overhead is kept stable whatever the vehicular density.

It has been analytically and empirically proved that Network Coding can significantly enhance wireless communications in terms of achievable throughput, data delivery and delay. While research in Network Coding has mainly ad dressed the problem of coding efficiency, buffer optimization and queuing, little attention has been paid to what we define as coding reliability, i.e., the ability for nodes to encode packets efficiently enough, so that a maximum number of its destinations can extract innovative data whatever the medium conditions can be. In this work, we investigate this concept in Wire less Sensor Networks (WSN) then we present Re-CoZi, a pack et transport mechanism which enables robust XOR Coding for WSN using echo-feedback packet reception and decoding acknowledgement. Our performance analysis shows that Re CoZi keeps the added value of NC in terms of bandwidth utilization and delay, while providing a more reliable coding.

The proliferation of low power networks like Wireless Sensor Networks (WSN) rose up new challenges. Power conservation and channel quality become the most important parameters. Obviously, hop count based routing protocols are no more adapted to such networks having power limitations and channel problems. Several alternatives were suggested to cope with these constraints. In MAC layer for example, cooperative protocols were designed to enhance the channel use: the neighbor nodes help the source to retransmit its packets. However, if the path proposed by the routing protocol contains poor channels, the cooperative communications will not save all the packets. Therefore, the design of new routing protocol becomes compulsory. In this paper we propose ECAR, a routing protocol that optimizes two objectives at the same time: energy and Channel State Information (CSI). Compared to AODV, ECAR provides considerable enhancements in delivery ratio, end-to-end delay and power consumption.

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.

ABSTRACT In this paper, we propose a newly distributed protocol called Advanced Diffusion of Classified Data (ADCD) to manage information harvesting and distribution in vehicular sensor networks. ADCD aims at reducing the generated overhead, avoiding network congestions as well as long latency to deliver the harvested information. The concept of ADCD is based on the characterization of sensed information (i.e., based on its importance, location, and time of collection) and the diffusion of this information accordingly. Furthermore, ADCD uses an adaptive broadcasting strategy to avoid overwhelming users with messages in which they have no interest. Also, we propose in this paper a new probabilistic model for ADCD based on Markov chain. This one aims to optimally tune the parameters of ADCD, such as the optimal number of broadcaster nodes. The analytical and simulation results based on different metrics, such as the overhead, the delivery ratio, the probability of a complete transmission, and the minimal number of hops, are presented. These results illustrate that ADCD allows mitigating the information redundancy and its delivery with an adequate latency while making the reception of interesting data for the drivers (related to their location) more adapted. Moreover, the ADCD protocol reduces the overhead by 90% compared with the classical broadcast and an adapted version of MobEyes. The ADCD overhead is kept stable whatever the vehicular density. Copyright © 2011 John Wiley & Sons, Ltd.

This paper describes CoZi, a new packet scheduling mechanism for large scale ZigBee networks. CoZi aims at enhancing the reliability of the data delivery and the bandwidth utilization of the network. Based on simple network coding, instead of the classic packet forwarding, our algorithm takes advantage of the shared nature of the wireless medium as well as the cluster-tree topology of IEEE 802.15.4 networks to increase the global throughput and to reduce transmissions in end-to-end and dissemination-based communications.

We propose WSC an event-centric data-dissemination scheme for event-centric wireless sensor networks (EC-WSN) based on a distributed network coding scheme. Nodes in this solution, do not only forward messages, but also perform linear random information coding in order to increase the throughput of the network, to reduce the number of transmissions, the end-to-end delay, and thus to increase the WSN lifetime, which is one of the key-issues in sensor networks architecture design.

In this paper, we present the results from a detailed energy measurement study of different TCP variants when used in Mobile Ad hoc Network environments. More precisely, we focus on the node-level cost of the TCP protocol; also know as the computational energy cost. In fact, the computational energy consumption is the most important part of TCP energy consumption. This is already proven in previous work and our results confirm this fact. Sometimes, the computational energy cost is three times that of the communication energy cost. The studied TCP variants, in this work, are TCP New-Reno, Vegas, SACK, and Westwood. In our analysis, we draw a breakdown of the energy cost of the main congestion control algorithm (i.e. slow start, fast retransmit/fast recovery, and congestion avoidance) used by these TCP variants. The computational energy cost is studied using a hybrid approach, simulation/emulation, using the SEDLANE emulation tool. This study takes into consideration different data packet loss models (congestion, link loss, wireless signal loss, interference) within such environments when different ad-hoc routing protocols (reactive and proactive) are used. The performed study gives a set of results that are of high interest for future improvements of TCP in MANETs. Among the obtained results, we show that the computational energy cost of TCP varies according to the type of data packet loss model it comes through: network congestion, interference, link loss, or signal loss. The results demonstrate that the link loss scenario is the most severe situation for TCP connections to face. In addition to that, we show that the Fast Retransmit/Fast Recovery phase has much less energy cost than both Slow Start and Congestion Avoidance phases, due to the fact that it sends more TCP data bytes in a shorter period of time. Finally, the computational energy cost is quantified and compared to the TCP end to end performance for each TCP variant showing the link between both.

Extensive research addressing IEEE 802.11 Distributed Coordination Function (DCF) and 802.11e Enhanced Distributed Channel Access (EDCA) performance analysis, by means of analytical models, exists in the literature. It started mainly with the famous Bianchi's model and still continues today as several aspects have not been yet covered. Indeed, the ultimate goal is to obtain the model that provides the most accurate prediction of the performance metrics such as throughputs and delays. Unfortunately, the currently proposed models, even if there are numerous, do not reach this accuracy and are still not sufficiently complete, in the sense that they omit some important EDCA features. In this paper, we propose a complete analytical model for EDCA under saturation conditions. Based on the proposed model, saturated throughput and access delay of each EDCA Access category are given. Simulation is also performed to demonstrate that the proposed model has better accuracy than others.

Supporting future large-scale vehicular networks is expected to require a combination of fixed roadside infrastructure and mobile in-vehicle technologies. The need for an infrastructure, however, considerably decreases the deployment area of VANET applications. In this paper, we propose a self-organizing mechanism to emulate a geo-localized virtual infrastructure (GVI). This latter is emulated by a bounded-size subset of vehicles currently populating the geographic region where the virtual infrastructure is to be deployed. An analytical model is proposed to study this mechanism. More precisely, this model is proposed to study the GVI in the frame of its main use: data dissemination in VANETs. Despite being simple, the proposed model can accurately predict the system performance such as the probability that a vehicle is informed, and the average number of duplicate messages received by a vehicle, and allows a careful investigation of the impact of vehicular traffic properties and system parameters on performance criteria. Analytical and simulation results show that the proposed GVI mechanism can periodically disseminate the data within an intersection area, efficiently utilize the limited bandwidth and ensure high delivery ratio.

In this paper, we present the results of a detailed measurement study of the computational energy cost of different TCP variants when used in mobile ad hoc networks. The studied TCP variants are TCP New-Reno, Vegas, SACK, and Westwood. We used a hybrid approach using simulation and emulation, through SEDLANE emulation tool. This study investigates different packet loss models (congestion, link loss, signal loss, interference) with different ad hoc routing protocols (reactive and proactive). The study demonstrates that the fast retransmit/fast recovery phase has a mush lower energy cost than both slow start and congestion avoidance phases, sending more TCP data bytes in a shorter period of time.

ldquoAlways Best Connectedrdquo (ABC) is a fundamental and challenging dimension of the fourth generation heterogeneous wireless networks. In order to enable the ABC, access network selection is obviously the key issue. In this paper, we analyze, adapt and consolidate the utility theory to define an appropriate decision mechanism in the frame of the access network selection. A thorough study of the existing proposed utility models is carried out and the limits of these methods are highlighted. Subsequently, we propose new single-criterion and multi-criteria utility forms to best capture the user satisfaction and sensitivity facing up to a bundle of access network characteristics. Mathematical proofs and numerical analysis confirm the suitability and the effectiveness of our proposed models.

The fundamental component for the success of VANET (vehicular ad hoc networks) applications is routing since it must efficiently handle rapid topology changes and a fragmented network. Current MANET (mobile ad hoc networks) routing protocols fail to fully address these specific needs especially in a city environments (nodes distribution, constrained but high mobility patterns, signal transmissions blocked by obstacles, etc.). In our current work, we propose an inter-vehicle ad-hoc routing protocol called GyTAR (improved greedy traffic aware routing protocol) suitable for city environments. GyTAR consists of two modules: (i) dynamic selection of the junctions through which a packet must pass to reach its destination, and (ii) an improved greedy strategy used to forward packets between two junctions. In this paper, we give detailed description of our approach and present its added value compared to other existing vehicular routing protocols. Simulation results show significant performance improvement in terms of packet delivery ratio, end-to-end delay, and routing overhead.

Ad hoc networks have gained place in the research area recently. There are many researches regarding different topics related to such networks, such as routing, media access control, security, scalability, and many others. There are two usual methods to test and evaluate ad hoc networks performance: simulations and real test-beds. A network emulator is a tradeoff between pure simulations and real test-beds. Here, we propose a multi-hop wireless ad hoc network emulator that uses the advantages of network simulator (NS-2) and traffic shaping tool (DummyNet), that we called SEDLANE [simple emulation of delay and loss for ad hoc networks environment]. SEDLANE is a network emulator that is based on TCP behaviour characteristics. Using SEDLANE, we can emulate a whole multihop ad hoc network through the data packet loss and round trip time (RTT) values over the TCP connection. SEDLANE helps testing and evaluating ad hoc network protocols using very simple and inexpensive test-bed configuration. The results confirm that; introducing SEDLANE within a simple configuration network (possibly 2 nodes) gives exactly the same results as those obtained when simulating such networks.

Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic information processing. This has widespread applications in the context of vehicular networks. The existing centralized approaches for traffic estimation are characterized by longer response times. They are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and infrastructure-free mechanism for road density estimation. The proposed solution is adaptive and scalable and targets city traffic environments. The approach is based on the distributed exchange and maintenance of traffic information between vehicles traversing the routes. The performance analysis of the proposed mechanism shows the accuracy of the algorithm for different traffic densities. It also gives insights into the promptness of information delivery in the mechanism based on delay analysis at road intersections. This promptness is a necessary condition to various applications requiring reliable decision making based on road traffic awareness.

TCP was mainly developed to be implemented within wired networks where the main cause for packet loss is network congestion. Conversely, in wireless ad hoc networks there are many other reasons to lose packets (such as fading, interference, multi-path routing, etc.). Reacting to these various loss types, TCP triggers its congestion control algorithm (i.e. considering all these losses as due to congestion). This, in some cases, might be an aggressive reaction leading to network performance degradation. On the other hand, since ad hoc nodes are battery operated, they need to be energy conserving so that battery life is maximized. In our work we analyzed the effect of TCP variants' congestion control algorithms on TCP performance (energy consumption and average goodput) in ad hoc networks. The study takes into consideration the different loss types that may occur in ad hoc environment; aiming to find the best adapted TCP variant for such networks. We intend to use the results of our current work to set design guidelines for specific TCP enhancements suited for ad hoc networks.

The client-server model is a commonly used model for distributed application programming. Most of group-collaboration applications, such as network gaming, rely on this model. We call a group-collaboration application an application where any participating entity can centralize the shared information and play the role of server. In wired networks, the choice of the participating entity playing the server role has a limited impact on the performances of the application, the station or the network. However, in mobile ad hoc networks (MANETs) an inadequate server choice can have a side effect on the network-, the application- or the wireless station-performances. The objective of our current work is to propose a novel and complete framework for server election and maintenance in ad hoc networks. This framework, called P-SEAN for policy-based server election in ad hoc networks, uses two concepts in order to perform the server election process: the serving ability degree and the situational server-election policy. Hence, the proposed framework implements the different situations for server election and maintenance in ad hoc networks as policy-rules (situational server-election policy). This election and maintenance are mainly based on factors such as connectivity, processing power, RAM capacity, remaining battery life, etc. These factors define the serving ability degree of each ad hoc node. The motivation behind using the policy-based networking paradigm is to render our framework extensible to incorporate additional application-specific criteria. In addition to these two main concepts, we also propose a complete architecture for a P-SEAN-enabled service and a lightweight protocol for server election and maintenance exchanges

TCP was mainly developed to be deployed within wired networks. Recently, many researches have studied its performance within mobile ad hoc networks (MANETs). These researches found that TCP performance are highly influenced by the characteristics of such networks. This is due to TCP's reliability mechanism and its inability to discriminate the packet loss cause. Indeed, unlike wired networks, where packet loss is mainly caused by network congestion, in MANETs we could have many other reasons to lose a data packet. However, TCP considers that all packet losses are due to network congestion. This enforces TCP to be aggressive in front of certain types of loss. Packet losses in MANETs can be either related to wireless communication environment (i.e. the effect of fading, interference, multipath routing, etc.) or to the dynamic nature of such networks (i.e. link failures, network partitioning). This latter could be due to the node mobility or to the node battery depletion. This could lead to frequent route re-computation within the network. In this work, we intend to study the effect of ad hoc routing protocols on TCP performance (energy consumption and average goodput) within MANETs. We consider studying different types of ad hoc routing protocols having different characteristics: reactive vs. proactive, distance vector vs. link state, and source routing. Our study results show that; DSDV as a proactive distance vector routing protocol leads to most accepted TCP performance results and this is confirmed at different mobility levels

In this paper, we are interested in improving TCP flow performance when a short loss of 802.11 signal leads to lose segments. A short analytical study and a set of simulations in common wireless environments with signal losses due to distance or interferences are made to highlight the distinct MAC and TCP loss-recovery levels and the lack of interactions between them. This study shows the interest, in case of signal failure, to adapt one of the MAC parameters, the retry limit (RL), to reduce the drop in performance due to the inappropriate triggering of TCP congestion control mechanisms. Starting from this, a MAC-layer LDA (loss differentiation algorithm) is proposed. This LDA scheme is based on the adaptation of the RL parameter depending on the quality of the 802.11 wireless channel. The evaluation of the proposed scheme shows the important gain in performance obtained compared to the case where the RL is configured statically

In this paper we present a complete analysis of dynamic resource allocation in policy-enabled networks. This analysis is carried out throughout both practical and analytical evaluations. Firstly, we present the details of our dynamic resource allocation architecture based on the policy-based management framework. Then, we evaluate, through extensive experimentations on a developed test-bed, the scalability of such architecture in a real environment. Finally, we develop an analytical model for the proposed architecture. This analytical evaluation allows us to confirm our practical analysis and to identify the weakness of such architectures

Controlling handoff drops in a cellular network is a very important QoS issue. In order to keep the handoff-dropping probability as low as possible, we have designed a new predictive scheme for radio resource allocation. This scheme consists in dimensioning the amount of resources to reserve for handoffs in each cell. The solution proposed is based on a reservation mechanism with a threshold estimated according to the usage history of the resources in the cell. This estimation is done using the Kalman filter, a powerful mathematical tool. Evaluation of the proposed scheme demonstrates a real improvement in the handoff-dropping probability compared to the static reservation scheme.

This paper presents a new binary shape sampling and coding method. Within the framework of the MPEG-4 standard, the binary shapes in video object plans have to be coded with a near to lossless approach in order to obtain a perfect spatial and temporal localization. For this purpose, a new sampling method based on the object skeleton is developed. The "r-sampling" is defined function of the local feature size (LFS) which is the distance between a shape point and the closest point on the medial axis of this shape. A weighting convex function /spl phi/ depending on the local curvature is also introduced to increase the local adaptiveness of the sampling ("the r&c-sampling"). This new approach is tested on contours presenting high level details. The first results obtained are satisfactory in terms of both the reduction of samples and the reconstruction error.

We investigate a downlink channel assignment scheme for multipoint communications and we propose a new simulcast-based dynamic channel assignment policy for both unicast and multicast connections in LEO satellite networks. In order to reduce co-channel interference power and to avoid intersymbol interference problem, our policy is combined with a power control algorithm. Performance evaluations show that our policy compared to other policies achieve bandwidth optimization and improves the quality of service since it reduces call blocking probabilities for both unicast and multicast connections.