Named Data Networking (NDN) is an evolution of IP networks with an architecture based on content names and centered around the consumer. It leverages inherent features such as in-network caching and Interest aggregation to optimize resource utilization. As NDN is increasingly applied in diverse domains, such as vehicular networks (VANETs), the Internet of Things (IoT), and e-health, support for Quality of Service (QoS) becomes critical to ensure the timely and prioritized delivery of Data, especially for real-time and mission-critical applications. However, conventional QoS mechanisms designed for IP networks are unsuitable for NDN due to fundamental differences in architecture: IP is session-based and end-to-end, whereas NDN is distributed, opportunistic, and content-oriented. To address this gap, we propose FQS-NDN, a novel Frequency-based QoS Strategy for NDN that prioritizes Interest and Data packets based on their generation and reception frequency. Our approach extends the default NDN forwarding behavior by introducing dynamic prioritization mechanisms that improve responsiveness and reduce latency in real-time scenarios. Preliminary evaluations with the ndnSIM simulator demonstrate the effectiveness of the proposed strategy in improving service differentiation and forwarding efficiency.

With the growing adoption of Artificial Intelligence (AI) in sensitive sectors such as healthcare and finance, protecting user privacy during data processing has become paramount. One promising approach is Fully Homomorphic Encryption (FHE), which offers a viable solution by allowing computations to be performed directly on encrypted data, thus safeguarding sensitive information. In this study, we investigate the practical application of the Cheon Kim Kim Song (CKKS) FHE scheme to perform inference with various machine learning models for heart disease detection. We evaluated five models: Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and a simple Neural Network, across multiple heart disease datasets. Our analysis compares their performance on both standard (plain-text) and encrypted data, using metrics including accuracy, precision, recall, and F1-score. Results demonstrate that encrypted models deliver predictive accuracy comparable to their standard counterparts, confirming the viability of privacypreserving inference with FHE despite the expected increase in computational time. Furthermore, our findings highlight up to $100 \%$ consistency between the predictions made on encrypted and plain-text inputs.

Named Data Networking (NDN), as an emerging architecture for evolving IP networks, focuses on content names rather than their origins. It distinguishes itself through key features such as one-Interest/one-Data transfer, in-network caching, Interest aggregation, and Data security. However, despite these advantages, NDN lacks a QoS mechanism to differentiate between data types, especially with the increasing heterogeneity and scale of network traffic. The rapid growth of applications such as IoT, VANETs, and e-health systems highlights the urgent need for effective QoS strategies. Existing state-of-the-art QoS strategies often fail to capture the dynamic nature of network traffic, while traditional IP-based QoS mechanisms cannot be directly applied to NDN due to its architectural specificity. To address this challenge and enable service differentiation, we propose MPQ-NDN: a QoS strategy based on dynamic packet prioritization according to their generation frequency, predicted using a Markov model. This prediction enables proactive adaptation to traffic variations and extends NDN forwarding with priority queues. Simulation results conducted in ndnSIM demonstrate that MPQ-NDN significantly improves network performance, responsiveness, and user satisfaction.

With the rapid advancement of cloud storage and the increasing use of connected devices, uploading data to the cloud results in the loss of physical control by data owners, making confidentiality and integrity entirely dependent on Cloud Service Providers (CSPs). This raises concerns about whether CSPs effectively safeguard outsourced data, as any malicious behavior can lead to data tampering or deviation. Traditional auditing schemes rely on Third Party Authorities (TPAs), which are not always trustworthy. Although various cloud data auditing mechanisms have been proposed, few effectively address the challenge of ensuring data integrity without relying on trusted third parties. To overcome this limitation, we propose a secure and efficient distributed blockchain-based data integrity auditing scheme. Specifically, our approach randomly assigns the audit task to a user selected from among the system participants via blockchain. Blockchain and InterPlanetary File System (IPFS) technologies are leveraged to enforce access control. Furthermore, the proposed scheme supports low-cost batch integrity verification without the need for a TPA. Theoretical analyses confirm that our solution ensures data traceability and auditability, and reduces reliance on third parties. Finally, our simulations show that proof generation remains under 0.7 seconds for 600 data blocks at 256-bit security, while verification costs remain negligible.

Abstract Cloud computing is one of the most cutting-edge technologies around the world that plays a major role in IT industries and personal use. Several sectors are showing efforts in adopting cloud computing to their services, considering the provided cost reduction and process efficiency. However, outsourcing users’ sensitive data increases the concerns regarding the security, privacy and integrity of stored data. Therefore, there is a need to build a trusting relationship between users and cloud systems. Hence, in this paper, we propose a privacy-preserving framework, called Hybrid and Secure Data Sharing Architecture (HSDSA), for secure data storage in cloud systems. The basic idea of HSDSA is to improve data security in a multi-cloud environment using a combination of cryptography techniques. These techniques ensure that the user has total control over the data generation and management of the decryption without relying on a trusted authority. HSDSA provides removal of centralized file storage distribution and ensures data integrity in the recovery process. We evaluate our contribution under Cloudera, and the results demonstrate the efficiency of HSDSA compared with the existing system.

Cache Pollution Attacks (CPA) are a growing concern in Named Data Networking (NDN) due to their potential to disrupt network services and compromise data integrity. While several defence mechanisms have been developed, they often struggle to keep up with the evolving nature of such attacks. This paper introduces a cutting-edge approach for detecting and mitigating CPA in NDN, utilizing Deep Reinforcement Learning (DRL). By employing a DRL framework, we leverage the power of deep neural networks to learn complex patterns within network traffic. Our DRL algorithm is designed to analyze the intricate dynamics of NDN environments and make informed decisions about cache management to protect against CPA. The agent’s learning process involves continuous interaction with the network, allowing it to adapt to CPA attack vectors and evolving NDN network conditions. The DRL-based mitigation mechanism is evaluated using the official NDNSim simulation environment. The results show that the DRL agent effectively identifies and mitigates CPA with high accuracy, thereby improving the Cache Hit Ratio, while incurring an acceptable increase in memory usage.

Vehicular Ad-Hoc Networks (VANETs) encounter various security threats, such as greedy behaviour attacks, with most existing research focusing on the CSMA/CD protocol. This paper investigates the TDMA protocol, specifically Distributed Time Division Multiple Access (DTMAC). In this paper, we focused on identifying and addressing four novel types of greedy actions that attackers can take advantage of, revealing vulnerabilities that have not been investigated before. To detect these behaviours, we propose a watchdog model designed to analyse network traffic, extract relevant features, and generate datasets at varying levels of network density. We use Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel to identify attackers in the network, employing Grid Search Cross-Validation (GSCV) for optimal results. The effectiveness of our proposed solution is evaluated through in-depth simulations using the NS2 simulator and Python. The results show that the proposed detection method can achieve a high detection rate with an accuracy attaining 95% in low density scenario and 80 % in high density scenario.

Summary Cloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to its centralized architecture, and lack of transparency and traceability, the results of the trust assessment cannot be fully recognized by all users. However, creating a trust‐based transaction environment has become its key factor. Blockchain, with its nature of decentralization and security, can be leveraged to address these challenges and build a distributed and decentralized trust architecture, due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. This article makes a comprehensive study of how blockchain is applied to deliver security services in the cloud computing model, focusing on up‐to‐date approaches, opportunities, and future directions. This survey also discusses the benefits of the technical fusion of blockchain and cloud. It provides a classification of proposed systems based on privacy, data sharing, authentication, and access control, as well as auditing and data integrity. Finally, the main conclusions of this study will be the challenges and future directions to stimulate further research in this promising field.

Vehicular platoon is a new Cooperative Intelligent Transportation Systems (C-ITS) application that enables automated vehicles to drive close together with short inter-vehicle distances. To effectively manage platoons, efficient beaconing process and platoon formation algorithm are crucial. These later rely on in-vehicle network protocols to regulate the speed of vehicles and control the platoon. Throughout these processes, an efficient and secure communication architecture is vital. However, it is important to note that the used V2V communication protocol is susceptible to several cyberattacks that can lead to potential disruptions or even completely disabling the communication system. This paper reviews the potential threats that can target vehicular platoons with an emphasis on Sybil attacks. We offer detailed explanations of the diverse methods through which Sybil attacks can be perpetrated in platoons. Additionally, we explore recent techniques for detecting and mitigating Sybil attacks, point out their limits, and identify open challenges to stimulate further research in this promising field and improve the overall security of the platoon system.

Abstract Named data networking (NDN) has emerged as a promising communication paradigm, proposed to deal with the shortcomings of the traditional IP‐based model. NDN introduces new name‐based routing, receiver‐based service, caching, and self‐certifying contents features that obviously improve data delivery efficiency and reliability. Moreover, NDN offers lightweight forwarding rules that suits constrained devices. These features makes NDN as highly promising communication model for the Internet of Things (IoT). On the other hand, one of the widely adopted networking specification for IoT is the IEEE 802.15.4 standard. This latter proposes interesting energy saving functionalities. To take full advantages of the two technologies, we propose an NDN over IEEE 802.15.4 communication solution that meets the requirements of low‐data rate and low‐power‐consumption monitor and control IoT applications. The proposed solution includes two modules: a reliable energy‐aware forwarding strategy that selects the next hop forwarder based on its residual energy level and a sleep mode scheduling algorithm that schedules the sleep/wake‐up mode according to the role of the node in the forwarding and the path repair processes. Extensive simulations and analyses have been conducted to confirm the viability and effectiveness of our proposal in terms of energy consumption, network lifetime, delivery ratio, retrieval delay, and scalability.

VANETs are characterized by the rapid changes in network topology due to their random movement patterns and their high-speed mobility. Hence, the support of efficient mobility management solutions become an important feature in VANET. Most of VANET applications need internet access almost everywhere and at any time without interruption. Thus, ensuring a seamless connection and enhanced throughput performance requires an improved handover strategy. In this paper, we introduce a new hand over optimization method for the 5G cellular network. A mobility prediction algorithm coupled with previous handover events logs was used to predict when and where the handover will occur in the network. In the proposed work, we aim to minimize the number of handover events without degrading network performance. A simulation-based performance study was conducted to evaluate the effectiveness of the proposed methods, and the results were compared to the 3GPP conventional handover solution. It was found that our proposed solution reduces the number of handover events without affecting the network quality.

The vehicular ad-hoc networks (VANET) has attracted the attention of both the industry and the academia researcher over the last decade. The concept of connecting vehicles to the Internet using the already deployed cellular network architecture has opened many avenues for research and development that are contributing significantly towards the Intelligent Transportation Systems (ITS). Almost every vehicle requires a seamless connectivity to the Internet without interruption. However, with the emergence of the 5G network and the Vehicle-to-infrastructure(V2I) concept, the design of efficient mobility management techniques that can handle the real-world mobility constraints in VANET becomes a critical task. In this paper, we propose a new handover algorithm that uses a Markov chain predictor to determine when and where a handover will be needed. The aim of the proposed solution is to reduce the number of unnecessary handover by maintaining the vehicle connectivity to the 5G base station as long as possible without degrading the network performance. Simulation studies were conducted to evaluate the performance of the proposed scheme. Our results show that the proposed handover algorithm greatly outperforms the conventional 3GPP handover algorithms.

Vehicular Ad hoc Networks (VANETs), while promising new approaches to improving road safety, must be protected from a variety of threats. Greedy behavior attacks at the level of the Medium Access (MAC) Layer can have devastating effects on the performance of a VANET. This kind of attack has been extensively studied in contention-based MAC protocols. Hence, in this work, we focus on studying the impact of such an attack on a contention-free MAC protocol called Distributed TDMA-based MAC Protocol DTMAC. We identify new vulnerabilities related to the MAC slot scheduling process that can affect the slot reservation process on the DTMAC protocol and we use simulations to evaluate their impact on network performance. Exploitation of these vulnerabilities would result in a severe waste of channel capacity where up to a third of the free slots could not be reserved in the presence of an attacker. Moreover, multiple attackers could cripple the channel and none could acquire a time slot.

Healthcare is among the sectors showing efforts in adopting cloud computing to its services considering the provided cost reduction and healthcare process efficiency. However, outsourcing patient’s sensitive data increases the concerns regarding security, privacy, and integrity of healthcare data. Therefore, there is a need for building a trust relationship between patients and e-health systems. In this paper, we propose a privacy-preserving framework, called Hybrid and Secure Data Sharing Architecture (HSDSA), to secure data storage in e-health systems. Our approach improves security in healthcare by maintaining the privacy and confidentiality of sensitive data and preventing threats. In fact, in the upload phase, Multi-cloud environment is used to store Rivest–Shamir–Adleman (RSA) encrypted medical records. We adopt a Shamir’s secret sharing approach for the distribution of shares to different independent cloud providers. In the retrieval phase, the reconstruction operation is based on the ( t , n ) strategy. To check the requester identity and to prove the hash possession, we used a zero-knowledge cryptography algorithm, namely the Schnorr algorithm. The patient has a total control over the generation and management of the decryption keys using Diffie-Hellman algorithm without relying on a trusted authority.

Recent advancements in avionics and electronics systems led to the increased use of Unmanned Aerial Vehicles (UAVs) in several military and civilian missions. One of the main advantages that makes UAVs attractive is their ability to reach remote regions that are inaccessible to human operators, i.e. provide new aerial perspective in visual surveillance. Autonomous visual surveillance systems require real time anomalies detection. However, there are many difficulties associated with automatic anomalies detection by an UAV, as there is a lack in the proposed contributions describing abnormal events detection in videos recorded by a drone. In this paper, we propose an anomaly detection approach in a surveillance mission where videos are acquired by an UAV. We combine deep features extracted using a pretrained Convolutional Neural Network (CNN) with an unsupervised classification method, namely One Class Support Vector Machine (OCSVM). The quantitative results obtained on the used dataset show that our proposed method achieves good results in comparison to existing technique with an Area Under Curve (AUC) of 0.93.

Power consumption and network lifetime optimization is an essential performance objective for the IoT networks. Since the devices in this type of networks are mostly battery powered with a limited battery life, energy saving strategies becomes vital for such systems operation. Typically, the standard sleep mode of the 802.15.4 protocol is used in order to reduce the power usage of the sensors in an IoT network. However, enabling the sleep mode function in an IoT device that utilizes the Named Data Networking (NDN) paradigm is challenging. In this paper, we propose a power saving extension for the Geographic Interest Forwarding (GIF) protocol. In the proposed E-GIF scheme, a cross-layer approach is used, which allows direct communication between protocols at nonadjacent layers. More precisely, the interactions between the MAC and the Routing layers are fully exploited in order to integrate the sleep mode into the forwarding decision of the sensor nodes that uses the NDN protocol stack. To validate the proposed scheme, we extend the ndnSIM simulator to support wireless multihop communication. Simulation experiments confirm the viability and the effectiveness of the proposal.

In this paper, we propose a Deep Neural Network model based on WiFi-fingerprinting to improve the accuracy of zone location in a multi-building, multi-floor indoor environment. The proposed model is presented as a Stacked AutoEncoder (SAE) to allow efficient reduction of the feature space in order to achieve robust and precise classification. The multi-label classification is used to simplify and reduce the complexity of the learning classification task during the training phase. To achieve a hierarchical classification, we applied an argmax function on the multi-label output to convert the multi-label classification into multi-class classification ones to estimate the building, the floor and the zone identifier. Experimental results show that the proposed model achieves an accuracy of 100% for building, 99.66% for floor and 83.47% for zone location with a test time that does not exceed 10.21s.

Unmanned Aerial Vehicles (UAVs) have known much popularity for dangerous missions for human operators or for applications which do not need human intervention (such as monitoring and surveillance of physical infrastructures and interest areas). They operate in frequency bands (IEEE L-Band, IEEE S-Band, and ISM band) shared with other users. Accordingly, these frequency bands have become overcrowded and UAVs may face the issue of spectrum scarcity. Furthermore, there are particular difficulties associated with aeronautical communication links. Cognitive radio (CR) has emerged as a promising strategy for resolving the problems caused by scarce spectrum. It checks the spectrum availability and allows the adjustment of the transmission parameters. The aim is to opportunistically use spectral bands with minimum interference to applications or other users. In this paper, we present a centralized CR based frequency allocation scheme for UAV-Ground Control Station (GCS) communication in surveillance applications within an urban environment. In the proposed model, the GCS monitors and allocates available WiMAX frequencies using CR and Software Defined Radio (SDR). If no WiMAX frequency is available at a given time, the Wi-Fi will be used. Therefore in the worst case, our approach will have the same performance as when the Wi-Fi is only used for UAV-GCS communication.

In recent years, a large number of researchers investigate the conception of systems that use a unique Unmanned Ariel Vehicles (UAV) or multiple independent UAVs to conduct civil or military missions, with minimal human intervention. In this paper we focus on using multiple UAVs to cooperatively monitor a crowded area. Communication in such UAVs network is an ongoing project. Due to the lack of proper communication standards and rules, designing a reliable communication model is essential for: (i) multi-UAV coordination, (ii) efficient bandwidth sharing according to data priority and urgency and (iii) avoiding useless transmission of the same data by multiple UAVs. To address the above challenges, we propose a centralized data-oriented communication architecture for crowd surveillance allocations using an UAV fleet. The Ground Control Station (GCS) is used as a central coordinator to manage bandwidth usage for the UAV fleet in its coverage area. To allow UAVs to send priority messages urgently to the GCS, we define two classes of urgent messages: critical state and important result. The class of the data as well as other relevant information about the detected event will be used by the GCS to authorize or not UAV data transmission and hence to optimize the bandwidth usage efficiency.

Recent advances in wireless communications and electronics have enabled the development of nano sensor nodes that are able to process and transmit sensed data. These tiny sensor nodes leverage the idea of nano sensor networks that are expected to find several applications such as health care monitoring, wildlife surveillance, etc. The networking of several nano-devices is still a major open issue. In fact, the very limited transmission ranges in the terahertz band renders direct communication between nano-devices unfeasible most of the time. Hence, multihop communication among nano-nodes is currently regarded as the viable solution for nano-network realization. However, the simplicity and energy constraints of nano-sensor nodes are far from the complexity of classical multi-hop routing and forwarding protocols. On the other hand, researches on dedicated communications protocols for Wireless Nano Sensor Networks (WNSNs) are still in their early stage. In this paper, we propose a geographic routing algorithm for efficient data dissemination in electromagnetic WNSN. We evaluate our solution in the context of a health-monitoring application where multiple nano-devices are deployed in an artery. We compare our proposal to the flooding scheme and results confirm that the geographic routing scheme is scalable and energy efficient without impairing the packet delivery ratio.

The transport model of the Named Data Networking (NDN) architecture introduces several new features, especially content can be retrieved from multiple sources and through multiple paths and may be stored in different in-network caches. These distinguished features make the traditional host-to-host TCP/IP congestion control schemes inconsistent with the NDN's communication model. In this paper, we describe and evaluate IRNA: a Hop-By-Hop Interest Rate Notification and Adjustment scheme. To address the above issues, IRNA controls the outgoing Interest queue occupancy at every NDN router. When the queue size exceeds or falls below a specified threshold, IRNA signals it towards downstream nodes by explicitly specifying the suitable Interest receiving rate. Consumers and routers along the path adjust their Interest sending rates according to the available rate specified in this notification. We evaluated IRNA through simulation and our results show that IRNA reacts quickly to congestion events while maintaining a proper bandwidth usage. Moreover, IRNA guarantees bandwidth fair sharing between consumers.

Named Data Networking (NDN) is an Information Centric Networks (ICN) solution that has recently attracted significant attention. NDN changes the Internet communication paradigm from the host-to-host IP model, to a name based communication model. In NDN, the requested Data can be retrieved from different sources and through multiple paths. This distinguished feature of NDN makes the traditional end-to-end congestion control schemes flagging with this new architecture. In this paper, we present a Hop-by-hop congestion control mechanism to regulate the Interest rate between the consumer and the congested router. Each NDN router continuously monitors its outgoing queues occupancy. When the queue size exceeds or falls below a specified threshold, an explicit notification is sent to downstream routers and consumers. The consumer and routers along the path then react by adjusting their Interest sending rates according to the available rate specified in the received notification. We prove the efficiency of the proposed solution and its ability to reduce the congestion impact and to maintain fairness per consumer. We highlight the advantages of our solution using different scenarios implemented in ndnSIM.

The need to locate objects and to be situated in the space, whether inside or outside, has long been the focus of a substantial amount of research. Especially in Wireless Sensor Networks, indoor localization has become an important issue in many fields of applications. In this paper, we propose an indoor location solution based on Support Vector Machine (SVM). SVM is a class of learning algorithms defined to resolve discrimination and regression problems. In fact, with many works, it turned out that it is very difficult to properly locate a target with only the RSSI measurements. Thus, the idea is to use multi-class SVM with RSSI measurements to propose a zoning localization approach. The performed experiments using different datasets, collected from two real world environments in both a hospital and a laboratory building, and the comparison with Artificial Neural Networks (ANN) confirm the effectiveness of our SVM-based localization proposal. Experimental results show that the system achieves a correct classification rate of around 90% with misclassification is in rooms where there is no wall separating them.

Named Data Networking (NDN) has emerged as a new and promising information-centric architecture for the future Internet that is also gaining momentum in Wireless Sensors Networks (WSNs) as an alternative paradigm to traditional IP. However, the main solutions in current works lack energy efficient design to meet the severely limited energy resources in WSNs. Moreover, the NDN architecture cannot natively allow devices to transmit unsolicited data, like alarms or status changes unless to properly modify the semantics of exchanged packets and the forwarding strategy. In this paper, we propose a Geographic Interest Forwarding scheme called GIF in short, for NDN-Based WSNs. In this proposal, we added support for push-based WSN traffic. We further extend the scheme with an efficient forwarding techniques in which several energy efficient mechanisms including flooding scope control, broadcast storm avoidance, packet suppression are designed to balance the energy consumption across the network. We extend the ndnSIM to support wireless multihop communication to validate the proposed scheme. Simulation experiments confirm the viability and effectiveness of the proposal.

Named Data Networking (NDN) is an Information-Centric Networking architecture that has recently attracted significant attention. NDN rethinks the Internet communication paradigm around the name of the data instead of its location. In Content-Oriented architectures, in-network caching enables data retrieval from different network nodes and may result in frequent data sources changes and wide RTT fluctuations during a flow. Since NDN architectures use Interest retransmission timer at the scale of network RTT estimations, this would generate erroneously premature timeouts events resulting in unnecessary Interests retransmission. In this paper, we tackle the Interest retransmission problem. We first analyze the impact of temporary caches on the number of timeouts events. Then we introduce “Source Change Notification” to address this issue. A preliminary evaluation has been conducted by means of the ndnSIM simulator. Our evaluation results show that our proposal succeeds in preventing unnecessary Interest retransmission when data providers change.

In a mobile ad hoc network, link failures and route changes occur frequently. Mistaking these events for congestion degrades TCP performance. Hence, TCP congestion control mechanisms should not react to such loss events. This paper deals with an enhancement of the congestion control mechanism, called TCP Adaptive RTO (TCP AR). This proposal relies on two basic concepts. First, it distinguishes routes failures from network congestion based on transport layer feedbacks. Second, during timeout, it adjusts the RTO's value to network conditions. We evaluate this new technique using different simulations scenarios, to compare its throughput gain, fairness and friendliness to those of TCP New Reno.