Recently, the rapid expansion of the Internet of Things (IoT) has opened up new possibilities and introduced significant security challenges. This evolution enhances everyday life but also increases risks in various domestic and industrial contexts due to growing threats such as cyberattacks and intrusions. To protect both domestic activities and industrial infrastructures, it is imperative to address these challenges. This study enhances security in IoT and IIoT by exploring machine learning-based intrusion detection techniques. The primary goal is to strengthen system protection and ensure the continuity of essential operations. Utilizing the N-BaIoT dataset, designed to simulate realistic IoT attack scenarios, we evaluated the effectiveness of various multiclass classification methods, including PCA dimensionality reduction. After extensive data preprocessing and the application of several classifiers such as KNN, Random Forest, Naive Bayes, Decision Tree, Extra Trees, and XGBoost, we built an effective IoT IDS. The Extra Trees algorithm, in combination with PCA, showed the best performance, achieving an impressive 99.94% accuracy. This underscores the effectiveness of machine learning in detecting and mitigating IoT and IIoT cyber threats and highlights the importance of selecting appropriate methods for optimal results in complex security environments.

The blockchain oracle problem is a central challenge in the integration of blockchain in decentralized systems. Ensuring that off-chain data fed into smart contracts is reliable is a problem, and relying on a single oracle introduces a single point of failure. To address this, several decentralized oracle designs have been proposed, including those based on threshold signature schemes. In such systems, a data feed is accepted only if a minimum number of oracles sign it. While this improves robustness, it introduces coordination issues: signing incurs a cost, and individual oracles may prefer to free-ride, expecting others to sign. In this work, we model oracle participation as a discrete public goods game and analyze the conditions under which signing is a rational strategy in equilibrium. We characterize the set of pure and symmetric mixed-strategy Nash equilibria and study how key system parameters, such as the number of oracles, the cost-to-reward ratio, and the signature threshold, affect participation incentives. Our results show that system parameters can give rise to multiple symmetric mixedstrategy equilibria, but that such equilibria disappear when the signing cost reaches as little as 27.5% of the reward.

Coflow scheduling and reducer placement are key to minimizing job completion times in data-parallel clusters. The RPC framework jointly addresses these tasks but assumes all coflows have equal importance, neglecting priority differentiation in practical workloads. This paper extends RPC by introducing a weighted scheduling mechanism that computes a score for each coflow based on its waiting time and priority, enabling priorityaware placement and bandwidth allocation. An efficient online algorithm minimizes these scores to favor high-priority coflows. Simulation results show that our approach significantly improves completion times for critical coflows and enhances overall fairness compared to baseline RPC.

Research on underwater wireless sensor networks (UWSNs) has been significant for applications such as forecasting adversity and disaster, hydrological and military surveillance, seepage monitoring, and underwater triangulation. These networks however, face challenges like significant delay spread, soaring interference, noise, jarring environments, poor connectivity, and restricted battery life. They also cause significant problems in terms of energy efficiency and network longevity. Nodes in UWSNs are subject to additional limitations, including fluctuating ambient conditions, large propagation delays, and limited energy supplies. Designing routing protocols for UWSNs is a promising solution to overcome these issues. The Adaptive Fuzzy Energyefficient Clustering and Energy Optimization (AFECEO) protocol, proposed especially for UWSNs, is thoroughly evaluated in this study in comparison to six popular clustering protocols: GEC, LEACH, PEGASIS, DCHS, DEEC, and LGCA. The fuzzy logic-based adaptive clustering process used by AFECEO dynamically chooses cluster heads by taking into account variables including distance to the sink, node residual energy, and underwater communication difficulties such as acoustic signal attenuation. According to simulation data, under various underwater settings, AFECEO performs better than its competitors in a number of critical performance parameters, such as average remaining energy, dead node count, and network longevity. Notably, AFECEO outperforms conventional protocols in terms of residual energy by up to 45% and dead node reduction by 60 %, guaranteeing improved energy optimization and dependable data transfer in UWSNs. This study demonstrates how well AFECEO works as a reliable option for energy-efficient communication in submerged settings, opening the door for more advanced monitoring and exploration uses.

Integrating blockchain technology into the Internet of Things (IoT) has revolutionized industries, enabling decentralized and reliable management of systems, while improving both efficiency and security. However, a key challenge for blockchain-based IoT solutions is ensuring the accuracy of data fed into the blockchain, known as the “blockchain oracle problem.” This work addresses this challenge by proposing the BIVO system (blockchain information verification oracles), a blockchain-based decentralized oracle for IoT networks. The system utilizes a reputation and voting mechanism suitable for both crowdsourced and semi-controlled environments. We also model the weighted voting mechanism as a stochastic game and conduct stress tests to analyze the system’s expected accuracy and cumulative payoffs under various conditions. Our findings indicate that the system achieves higher accuracy compared to nonweighted voting approaches. In semi-controlled environments, the system demonstrates resilience against up to 64% of adversarial nodes. However, under the worst conditions, malicious nodes need to control no more than 36% of the network to benefit from malicious behavior. Additionally, we implemented a prototype of the BIVO system and deployed it on both a local blockchain simulator and the public Ethereum testnet Sepolia to evaluate the cost and feasibility of blockchain integration.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in various applications, especially with the emergence of 6G systems and networks. However, their widespread adoption has also led to concerns regarding security vulnerabilities, making the development of reliable intrusion detection systems (IDS) essential for ensuring UAVs safety and mission success. This paper presents a new IDS for UAV networks. A binary-tuple representation was used for encoding class labels, along with a deep learning-based approach employed for classification. The proposed system enhances the intrusion detection by capturing complex class relationships and temporal network patterns. Moreover, a cross-correlation study between common features of different UAVs was conducted to discard correlated features that might mislead the classification of the proposed IDS. The full study was carried out using the UAV-IDS-2020 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results highlighted the effectiveness of the proposed multiclass classifier model with an accuracy of 95%.

The rapid expansion of Internet of Things (IoT) devices has led to a paradigm shift in the cyber-security landscape, with new security challenges. intrusion detection is considered one of the most challenging tasks in order to protect IoT networks from multiple attacks. This paper tries to provide a comprehensive survey of the different methods and techniques that are employed in intrusion detection in IoT networks, especially for rare attacks. Through this study, we show the importance of rare attacks and give a summary of existing stations. Finally, we discuss the challenges and open issues of intrusion detection in IoT networks and present the implications of the proposed solutions. We believe that the survey can help researchers in the field of IoT security develop more effective and secure techniques,

The success of a team or a fencer requires the development of a strong strategy. This is the subject of a research project whose focus is on the performance of the team and/or the fencer. This study uses game theory to create a fencing strategy before an assault and to follow the instructions in the strategy at the assault. The game theory analysis shows that pure Nash equilibria exist, so the mixed strategy equilibrium analysis provides an optimal fencing strategy. Fencers can follow this strategy to achieve favorable results before the attack, and make necessary adjustments to the attack depending on the other player and the evolution of the situation at the assault. The results suggest that game theory can provide fencing coach and athletes with effective instructions for executing their game plan throughout preparation and during the bout.

With the rapid growth in the Internet of Things (IoT), current cybersecurity threats are growing to levels at which traditional intrusion detection systems (IDSs) cannot suffice. The purpose of this paper, therefore, is to evaluate the efficacy of ML techniques in enhancing IDS to adapt to emerging sophisticated and dynamic modern cyber threats. We adopted four supervised ML models: Decision Tree, Random Forest, Naive Bayes, and Gradient Boost, all of which describe the CSE-CIC-IDS2018 dataset representing different network attack situations. This reflects today's cyber threats. We have analyzed both binary and multiclass classification tasks to understand what kind of cyber threat and how many any model was best suitable for. This implies that ML-supported IDS can effectively improve the detection of not only generic but also specific cyber threats, therefore enhancing security. It is in line with this that the current paper outlines the strengths and weaknesses of the discussed models to produce an insight and judgment of their practical implementation in real-world scalability. The outcomes came into view that ML-based IDS gives resilient, adaptable, and proactive solutions to cybersecurity over IoT networks.

Intrusion Detection Systems (IDSs) have played a significant role in the detection and prevention of cyber-attacks in traditional computing systems. It is not surprising that this technology is now being applied to secure Internet of Things (IoT) networks against cyber threats. However, the limited computational resources available on IoT devices pose a challenge for deploying conventional computing-based IDSs. IDSs designed for IoT environments must demonstrate high classification performance, and utilize low-complexity models. Developing intrusion detection models in the field of IoT has seen significant advancements. However, achieving a balance between high classification performance and reduced complexity remains a challenging endeavor. In this research, we present an effective IDS model that addresses this issue by combining a lightweight Convolutional Neural Network (CNN) with bidirectional Long Short-Term Memory (BiLSTM). Additionally, we employ feature selection techniques to minimize the number of features inputted into the model, thereby reducing its complexity. This approach renders the proposed model highly suitable for resource-constrained IoT devices, ensuring it meets their computation capability requirements. Creating a model that meets the demands of IoT devices and attains enhanced precision is a challenging task. However, our suggested model outperforms previous works in the literature by attaining a remarkable accuracy rate of 97.90% within a prediction time of 1.1 seconds for binary classification. Furthermore, it achieves an accuracy rate of 97.09% within a prediction time of 2.10 seconds for multiclassification.

Wireless communication systems rely on channel estimation and equalization to ensure reliable and efficient data transmission. However, with the increasing demand for high connectivity in massive IoT networks, these processes are facing significant challenges. The complexity and intensive computation required for channel estimation and equalization results in high communication latency and power consumption, which can ultimately prevent the transceiver from restoring the originally transmitted signal. In this paper, we propose a novel approach to simplify wireless communication systems by using a conditional generative adversarial network (cGAN) model to replace both channel estimation and equalization blocks. We formulate the data recovery task as a translation from received data to the corresponding transmitted signal and introduce the concept of Rx-to-Tx translation based on a cGAN, which was initially developed for image-to-image translation. Our preliminary results demonstrate the feasibility and effectiveness of this approach, particularly for digital modulations. By carefully tuning the model's hyperparameters, we achieve the theoretical symbol error rate (SER) of QAMs in a Rayleigh propagation channel. Our proposed approach has the potential to significantly reduce the computational complexity and overhead typically associated with traditional channel estimation and equalization blocks. This can lead to more efficient and cost-effective wireless communication systems.

In the last few years, there has been a massive increase in Internet of Things (IoT) devices and the data generated from these appliances. Devices involved in IoT networks can be challenging because of their resource-constrained nature, and security integration's on these devices are frequently disregarded. This results in attackers targeting more IoT devices. Thus, as the number of possible attacks on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to deal with these attacks effectively. This paper presents a hybrid deep learning-based approach, a one- dimensional convolutional neural network, and long short-term memory (1D CNN-LSTM) algorithm, for anomaly detection that harnesses the power of the IoT, providing qualities to efficiently examine all traffic across the IoT. The comprehensive study was conducted utilizing the Bot-IoT dataset extracted from real network traffic, consisting of benign and malicious variants. Then, the anomaly detection including binary and multi-decision categories has been performed. The experimental results highlighted the superiority of the proposed model with an accuracy of 99.20% and lower false alarm with 0.80% compared to single CNN-based IDS.

A resource-constrained unmanned aerial vehicle (UAV) can be used as a flying LoRa gateway (GW) to move inside the target area for efficient data collection and LoRa resource management. In this work, we propose deep reinforcement learning (DRL) to optimize the energy efficiency (EE) in wireless LoRa networks composed of LoRa end devices (EDs) and a flying GW to extend the network lifetime. The trained DRL agent can efficiently allocate the spreading factors (SFs) and transmission powers (TPs) to EDs while considering the air-to-ground wireless link and the availability of SFs. In addition, we allow the flying GW to adjust its optimal policy onboard and perform online resource allocation. This is accomplished through retraining the DRL agent using reduced action space. Simulation results demonstrate that our proposed DRL-based online resource allocation scheme can achieve higher EE in LoRa networks over three benchmark schemes.

The e-mobility infrastructure faces several challenges that hinder the general adoption of electric vehicles (EV). Indeed, the management system requires multiple actors to jointly act on different interdependent processes which makes it complex, time-consuming and inefficient. Moreover, the current state of the infrastructure raises several security and privacy concerns that render it non-compliant with security regulations and privacy laws. In this work, we propose a blockchain-based solution that allows a more efficient, secure and privacy-preserving management of the EV infrastructure. We used hybrid smart contracts and blockchain oracles to feed data to the blockchain in a secure and trusted manner.

In this paper, we propose a model for Delay Tolerant Networks (DTNs) based coalition and stable structure of all relay nodes to deliver a complete message from one source to one destination using the Epidemic Forwarding Policy. The message is viewed as a series of uniformly sized chunks that are produced by a fixed source. For a message to be considered successfully delivered, all of its chunks must arrive at the fixed destination within the validated time. The Age of Information (AoI) provides a deadline by which all message chunks must reach the destination to be considered timely. Mobile relays within the network will facilitate the transfer of chunks using an insensitive reward mechanism. We propose a distributed coalition algorithm that aims to establish a state of stability among all participating relays in the game. Through this algorithm, we were able to determine that the formation of stable coalitions results in higher payoffs for relay nodes compared to acting alone, as shown in our performance evaluation results.

This paper describes a new preprocessing method using Markovian modification to distinguish preprocessed mammograms (healthy and pathological), and these techniques are very necessary to find the direction of mammograms so that we can eliminate noise, improve image quality and obtaining more appropriate images than the original images as well as the disappearance of misplaced discrete points. Markovian image segmentation was performed to extract the region of the blocks if the blocks ended up being divided into groups of pixels that are homogeneous with the image with respect to certain criteria, based on the neighborhood system, using a relaxation approach that It is maintained by iterative conditional patterns (ICM), relying on an energy function adapted to the irregular neighborhood models of the image, and with the energy criterion of the image groups, the neighborhood system, and some cluster groups. These preprocessed mammograms are analyzed using co-occurrence matrices from which Haralick traits are extracted. The peculiarity of this approach is that it selects features from among the most selective mammograms based on the type of contact chosen for preprocessing. The most discriminating features are selected according to a supervised scheme that makes it possible to represent mammograms in a relatively small space where they can be discriminated with high reliability. To test this method and verify its validity, we use mammograms of normal and sick cases from the Reference Center for Reproductive Health in Kenitra, Morocco (CRSRKM).

This survey article explores recent advancements in data transfers, coflow scheduling, and reducer placement techniques for optimizing the performance of data-intensive applications in computer clusters. These techniques address the challenges of managing data transfers, optimizing resource allocation, and minimizing Coflow Completion Time (CCT). The surveyed research papers present innovative approaches such as intelligent data transfer scheduling, network-aware algorithms, near-optimal heuristics, and leveraging inter-flow relationships. These techniques aim to improve job completion time, enhance resource utilization, and minimize interference in datacenter networks. By summarizing these advancements, this survey article provides a comprehensive overview of the latest research in the field. The findings highlight the significance of these techniques in improving the performance and efficiency of data-intensive applications in computer clusters, and also identifies open challenges and future directions, stimulating further research and development in this area.

Over years, the Industrial Internet of Things (IIoT) has evolved rapidly, offering increased connectivity and benefits in terms of efficiency and productivity as well as significant business opportunities. It is used in various fields such as transportation, production, supply chain management, the oil and gas sector, mining and metallurgy, energy services, aviation, etc. However, this increased connectivity also exposes industrial systems to greater security risks, including intrusion attempts and cyberattacks. The multitude of sensors present in these networks generates a considerable amount of data, attracting the attention of cybercriminals worldwide. To protect Industrial Internet of Things networks, applications against these attacks and intrusion detection systems play a crucial role. However, they have limitations in terms of detection accuracy and false alert management. By using machine learning and deep learning techniques, it is possible to mitigate the multiple security threats and enhance the ability of intrusion detection systems to identify complex attacks patterns and to adapt configurations against new threats. Indeed, such artificial intelligence methods can analyze vast amounts of real-time data, detect anomalies, identify known attack signatures and even predict potential attacks. This article constitutes a bibliographical overview of artificial intelligence based intrusion detection approaches as well as the different datasets on which they have been tested. Additionally, it aims to identify current limitations and challenges in ongoing and existing researches and solutions, while providing some directions for further scientific works.

During the last decade, the integration of artificial intelligence (AI) and the use of intrusion detection systems (IDSs) in the Internet of Things(IoT) networks have brought a new dimension to technological progress. Deep learning (DL) and machine learning (ML)-based IDS are vulnerable to adversarial perturbations. However, anomaly detection methods suffer from unbalanced and missing sample data, thus causing IDS training to be complicated. In this paper, we propose using conditional generative adversarial networks (cGANs) to enhance the training process by handling the unbalanced data and coping with the lack of specifics class samples, which may succeed in evading our Convolutional Neural Network-Long Short-Term Memory (CNNLSTM) based-IDS model. We evaluated our proposed IDS model before and after applying the adversarial training using the Bot-IoT dataset. Promising results showed that the accuracy of detecting Theft attacks could be increased by 40%. To the best of our knowledge, we are the first to suggest the combination of cGAN and CNNLSTM based-IDS system to enhance its performance.

Selfish mining is an attack on blockchain networks, where a minority mining pool deviates from the original mining protocol and keeps some blocks private. The goal of the attacking pool is to waste the computational power of the other miners and increase their revenue. In this paper, we use a new approach to analyze the profitability of such attacks. Using game theory, we model the interactions between pools to derive the utility of mining strategies. We simulate the game for a Bitcoin blockchain and analyze the profitability of an attack, in terms of the monetary award instead of the relative revenue. We express the utility to include the cost of a strategy and revisit existing selfish mining strategies to discuss possible outcomes of the game. Depending on the game parameterization, we highlight scenarios where the system could be compromised. To the best of our knowledge, this is the first work that models the selfish mining attack as a stochastic game.

With considerable demand for higher throughput, greater capacity, and lower latency for consumers, the Internet of Things (IoT) network is anticipated to meet the desired security and privacy requirements. This study provides high transaction throughput on critical IoT applications, particularly Bitcoin security against double-spending attacks. To this end, we investigated the signaling game approach to model the interaction between two miners while considering players behavior (malicious or honest miners) and the incoming transaction throughput. To the best of our knowledge, this is the first work that exploits the signaling game to cover the incoming transactions randomness waiting for validation, which influences the honest miners behavior. With extensive simulations, we show that our proposed signaling game reduces the impact of double-spending attacks on IoT transactions. The results also illustrate the benefit of using the signaling game to model the interaction between two miners while handling the incomplete information of the incoming transactions and the type of miners.

Several anomaly detection systems prototypes are deployed to set up real-world solutions exclusively dedicated to the banking industry due to the high potentials of attacks. Exchanging highly security-sensitive data over a network between nodes requires high-security levels of IoT devices, representing a big challenge. Thus, many systems were developed to detect and predict any malicious activity. In our digital world, it is very challenging to master the fact that we, as a whole internet community, create an uncountable number of bytes of data every 24 hours. This truth leads researchers to explore and discover new technologies to handle massive data by ensuring individuals security and privacy. Inspired by this, our work supports researchers in this field by providing a selective overview of the most relevant findings investigating and proposing solutions on Intrusion Detection Systems (IDS) over the Internet of Thing (IoT). Furthermore, the Blockchain integration as the principal registry for safe data storage is well explained and detailed while covering security qualities that analyze and classify different confronted open challenges in this path.

In the new cellular systems (5G), the approach of caching content in the small Base Stations (sBS) is considered to be a suitable approach to improve the efficiency and to reduce the user perceived latency content delivery. Proactively serving estimated users demands, via caching at sBS is crucial due to storage limitations. But, it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, human behavior is predictable, and contents popularity are subject to fluctuations since mobile users with different interests connect to the caching entity over time and in different places. In this paper, we focus on the prediction of popularity evolution of video contents/files, based on the observation of past solicitations. We propose the FORECASTING schemes to manage this problem based on the time series model Seasonal AutoRegressive Integrated Moving Average (SARIMA) to interpret the temporal influence. The scheme is based on two algorithms in static and dynamic cases to manage future cache decisions. Several numerical results are given with comments that confirm the proposed idea.

Underwater Sensor Network (UWSN) suffers from the limited batteries life of sensor nodes. Thus, some nodes will disappear from the network topology during the communication process which leads to isolated nodes and important buffered packets will be discarded. Traditional greedy forwarding protocol used in UWS N s are based on the selection of the nearest next-hop forwarder from the destination, that's the nearest one from the sea surface relaying the source and destination. By this, some nodes are selected by multiple source nodes, so their energy risk to be drained. In order to overcome this problem, we enhance this protocol by distributing the forwarding task between multiple next-hop forwarders. Also, this protocol is based on depth adjustment to solve the problem of isolated nodes. The source packet is splitted and each sub-packet is transmitted to a single upper neighbor node. Otherwise, multiple data channels are used to avoid collision between source nodes selecting the same next-hop node. Numerical results show significant improvement in greedy forwarding protocol performance.

Macro-small cells networks, which include a multiple small cells under the macro cell area, provide an attractive solution for meeting capacity requirements of the network and achieve higher user data rate transmission. The deployment of small cells also is an effective approach to reuse the spectrum that provide an increasing spatial reuse of bandwidth. In this study, we propose a game model to manage the macro user association in the heterogeneous network architecture composed of one single macro base station and a set of small-cells operating in the same spectrum. We construct an utility gain framework to allow macro base station to encourage macro users to play the same strategy (defined as a recommended small base station that offers a high throughput to the macro user with low interference, because the macro base station is considered as a controller of the current users under different small base stations) in order to help them to get an acceptable throughput. We obtain the best distribution of macro users equipment among small-cells and macro base station. The proposed game model is done in the dynamic and randomly environment. We use the Combined fully distributed payoff and strategy learning algorithm to prove in one side the existence of Nash equilibrium and to accelerate the convergence in the other side.

Nowadays, information security is extremely critical issues for every organization to protect information from the useless data on the manipulation of network traffic or intrusion. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviors because its capable of detecting attacks in several available environments. Thereafter, many researches concentrate on developing new algorithms to treat the Dataset by different way. In this work, we suggest a new proposed PCA-fuzzy Clustering-KNN method that means ensemble of Analysis of Principal Component and Fuzzy Clustering with K-Nearest Neighbor feature selection technics. However, we perform two main class classifications to construct our suggested model. Then, to check the robustness of model we used as well-known Dataset NSL-KDD used for analysis of anomaly. This Dataset is based on benchmark data used for intrusion detection, KDDCup 1999. Therefore, we analyse NSL-KDD Dataset using PCA-fuzzy Clustering-KNN analytic and try to define the performance of incident using machine learning algorithms, the algorithm learns what type of attacks are found in which classes in order to improve the classification accuracy and reduce high false alarm rate and detects the maximum of detection rate from Dataset as shown by the numerical results.

Recently, the problem of the intrusion detection has been largely studied by the computer and networks security communities. Then, the Intrusion Detection System (IDS) becomes a interest topic in research and in particular in machine learning and data mining. In order to improve the classification accuracy and to reduce high false alarm rate from the classical data base like KDD99 or others. In this work, we present a state of the art about this topic and we use classification algorithms such as Linear discriminant analysis (LDA) and Principal Component Analysis (PCA) to identify the intrusion and classification anomaly. The experiments of the IDS are performed with NSL-KDD data set and we try to improve the existing classification methods.

Today's mobile users want faster data and more reliable services. The next generation of wireless networks 5G promises to deal with this, and more. In this context, to enable ultra-short response times, fast relocation of service instances between edge nodes and reduce migration time its required to cope with user mobility to guarantee the (QoE). In this new paradigm called 5G cellular systems, the technique of content caching in small base stations (SBS) is considered to be a suitable approach to improve the efficiency and to alleviate the backhaul burden and reduce user perceived latency in wireless content delivery. Proactively serving predictable user demands, via caching at base stations (BS) and users' devices is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations since mobile users with different interests connect to the caching entity over time. In this paper we focus on the prediction of popularity evolution of video contents. Based on the observation of past solicitations of individual video contents. The popularity prediction in this proactive approach relies on AR (Auto-Regressive), MA (Moving-Average) and Exponential smoothing techniques to complete a proposal caching Algorithm to manage cache decisions.

Recently an extensive deployment of small cell Networks (SCNs) called two tier-heterogeneous networks have been proposed, especially for dense urban zone. Therefore, Macro Cell Networks containing a hyper density of SCNs distributed randomly within it will emerge the field. The benefits of two tier Heterogeneous Networks (HetNets) are realized through the traffic flooding via small cells. To enhance the performance of the two-tier cellular networks and offer the best quality service for Macro user, the cell association problem is modeled using the partially observable Markov decision process (POMDP). POMDP works based on a set of beliefs states that the macro-user get considering the impact of the base station's (BS) best channel. The macro-user has to make decisions based on its channel state partially observable and the shared information in order to associate with BS, the association decisions are determined based on the calculated SINR. The numerical results of the proposed solution are given with comments and they show the acceptable system performance.

Users interact in the social network by exchanging useful information. The huge amount of traffic generated requires the design of an accurate model for good management. Hence, knowing the consumers' access patterns is of a great interest for content owners. We propose a study of the access and sharing patterns based on the content's popularity. We suppose that popularity is inferred by the observation of the number of views. Our goal is twofold. On the one hand, we aim to help users select the optimal action that allows content owners to decide to change or not the used social network to another to increase their profits and, on the other hand, to assist consumers, according to their area of interest, to decide to access or not a posted content. We establish a threshold structure of the optimal policies based on a trade‐off study between profits (money, access to useful information, etc.) and costs (time spent to edit or consult content, etc.). The experimental results of the proposed analytical model show that both owners and consumers maximize their utility by choosing the best strategies.

Recently Underwater wireless Sensor Networks (UWSNs) have been suggested as a powerful technology for many civilian and military applications, such that tactical surveillance. Geographic routing that uses the position information of nodes to route the packet toward a destination is preferable for UWSNs. In this paper, we propose a New Greedy Forwarding (NGF) strategy using splitting mechanism based on Chinese remainder theorem(CRT) for UWSNs. In the approach, source node reduced the number of bits transmitted using the proposed splitting mechanism based on CRT if there are more than two nodes participate in the forwarding of one packet. This strategy distribute the forwarding task between more nodes instead of selecting one node as next-hop, that reduce the energy consumption per node and maintain node communication for a long time. Thus resulting the increase of network life time and decrease the number of isolated/void nodes. We use topology control through depth adjustment to cope with the problem of isolated and void nodes appeared in geographic routing protocols. Simulation results shows that with the anycast greedy forwarding strategy the network life time is about 500 rounds whereas it is about 1000 rounds using the new greedy forwarding strategy, which means the new strategy increase the network life time and increase the network performance in energy saving.

Vehicular Ad-hoc NETwork (VANET) is a particular type of the Mobile Ad-hoc NETworks (MANET) and is developed to provide communications in a group of vehicles in range of each other and between vehicles and fixed equipments (Road Side Unit) within a communication range, usually called equipment of the road. This network is very sensible to safety problem. In this work, a new mechanism is proposed to study the safety problem in VANET networks. This mechanism focuses on denial of service (DoS) attacks on the physical and MAC layers in IEEE standard 802.11p. Our proposed solution is used to detect and to identify DoS attacks by using the values of packet delivery ratio (PDR) metric. Simulation results show the acceptable performance.

Nowadays, Small-Cells are widely being deployed to assist and improve performance of mobile networks. Indeed, they are a promising solution to improve coverage and to offload data traffic in mobile networks. In this paper, we propose a signaling-less architecture of the heterogeneous network composed of one single Macro Base Station and a Single Small-Cell. First, we construct a game theoretic framework for channel-state independent interaction. We present many conditions for the existence of Pure Nash equilibrium. Next, and in order to capture the continuous change of the channel state, we build a random matrix game where the channel state is considered to be random (potentially ruled by some given distribution). A characterization of Nash equilibrium is provided in terms of pure strategies and mixed strategies. Convergence to Nash equilibrium is furthermore guaranteed using a variant of the well-known Combined fully distributed payoff and strategy learning. Our algorithm converges faster (only 10–20 iterations are required to converge to Nash equilibrium) and only need a limited amount of local information. This is quite promising since it says that our scheme is almost applicable for all environments (fast fading included).

Macro network alone cannot support the ever growing demand of the bandwidth hungry applications for the indoor users. Without any assistance, Macro network poses serious concerns for maintaining a good quality of service. Therefore, femtocells provide an attractive solution for meeting capacity requirements of the network. The access control mechanisms of the femtocell plays a crucial role in mitigating cross-tier interference and avoiding additional handover attempts. In this paper, we propose an utility gain framework to permit to the wireless service provider to encourage femto holders located within its coverage to share their resources with macro users. We suppose that femtocells adopt the hybrid access mode. Consequently, each femtocell will reserve a fraction of resource to macro users and will get a gain from the wireless service provider. Our goal is to improve the overall performance and to promote the hybrid access mode. For this, we model the system as a game, we prove the existence of Nash equilibrium and we propose two learning algorithms for the femto holders and the wireless service providers to choose the best strategy allowing them to reach a win-win situation. Numerical results show that both wireless service provider and femto holders benefit from the proposed learning gain mechanism to improve their utilities.

Recently Underwater Sensor Networks (UWSNs) have been suggested as a powerful technology for many civilian and military applications, such as tactical surveillance. The most important issue in these networks is the communication, mainly due to the presence of fading, multi-path and refractive properties of the sound channel, this necessitate the development of precise underwater channel model for each application and provide an efficient routing protocol that consider the energy constraint of underwater nodes and resolve the problem of disconnected nodes. In this work, we study the impact of two topology control methods, that are used to resolve the problem of void/isolated nodes appeared in geographic routing protocols, in network performance. Simulation results of topology control through Depth adjustment DA and Transmission Power Controls TPC showed a significant reduction of the number of void/isolated nodes.

The trust is one of the crucial and important factors for decision making in online social network. In this paper, we compute explicitly the level of trust, distrust and uncertainty between two users using social interactions. The study of social interactions is fundamental to understanding the dynamics of relationships between users and their behaviors in the social network. We use later this formulation to infer trust between a user and a stranger who has no direct interaction with this latter. Finally, we use the Dempster-Shafer theory to combine the different views of different users to infer trust between a user and a stranger who is connected through neighbors by introducing the friendship, family and professional relationship. The numerical study of the proposed model shows its acceptable performance.

The battery duration is one of the most important concerns in mobile WiMAX systems. The consumed energy of mobile nodes depends mainly on the selected modulation level, and on the operator policy. On the one hand, when a high AMC level (such as 64QAM) is opted by the Base Station (BS), fewer time slots (burst profiles) are consumed while the corresponding mobile transmits its data, however this leads to a higher amount of consumed energy. In this work, we study IEEE 802.16e mobile WiMAX network at the active mode in order to maximize the transmission rate at the uplink sub-frame space taking into account acceptable energy consumption and efficient burst profiles. We propose a mechanism to achieve this aim based on which the base station chooses an adequate modulation level. In addition, resource assignment in the mobile node is performed based on a tradeoff between the burst profile, minimal energy and an acceptable QoS. The numerical study of the proposed mechanism shows its acceptable performance.

International audience

The Adaptive Modulation and Coding (AMC) scheme which handles user’s mobility issue plays a significant role to improve the desired quality of service in High Speed Downlink Packet Access (HSDPA) networks. We develop a resource allocation which maintains constant bit rate for real-time (RT) and non-

The capacity of CDMA wireless network is usually studied considering two classes of services: real-time and best-effort. In this paper, we are interested in analyzing sharing between three classes of services: real-time (RT), non-real-time (NRT) and best-effort (BE). A classical approach which is widely used in wireless networks is based on adaptively deciding how many channels to allocate to calls of a given class. The rational behind our idea is that the NRT class (e.g. FTP) requires a minimum transmission rate. The capacity allocated to NRT traffic includes a fixed portion of bandwidth as well as a dynamic part which is shared with RT service. In contrast, BE applications can adapt their transmission rate to the network’s available resources. Hence, the best-effort applications can use only the unused resources of the NRT band. Using a spectral analysis approach, we compute the steady-state distribution of the calls number for those different classes which allows us to provide explicitly the performance measure. The QoS parameters of interest are primarily the blocking probability for both RT calls and NRT calls, and expected sojourn times for both NRT calls and BE calls. We finally provide numerical study to show the benefit of our capacity allocation method by providing a desired quality level of service for NRT services, and we propose some CAC policies for NRT and BE services.