Mobile tourism applications have gained massive popularity, offering various services such as booking accommodations, navigating unfamiliar locations, and providing travel recommendations. These applications have revolutionized travel planning, making it easier and more convenient for tourists in smart cities. This work aims to enhance the tourist appeal of Morocco’s historical regions by developing a mobile application called E-Tourism, which leverages computer vision technology for historical site recognition in smart cities. The proposed application provides detailed descriptions and historical context for various heritage sites. Moreover, it utilizes a pre-trained lightweight neural network model to accurately recognize and provide information about historical landmarks, even when GPS fails to pinpoint users’ locations precisely. Users can scan landmarks with their device’s camera, and the application will display relevant information, enriching their visit with an educational experience.

Software-defined networking (SDN) enables dynamic, flexible, and programmatically efficient network design by separating the control plane from the data plane, revolutionizing network control and management. In response to the demands of large data centers, SDNs facilitate resource provision, traffic management, and network reconfiguration, while enhancing network virtualization and security. However, SDNs are vulnerable to conventional security threats. This paper proposes a comprehensive three-step approach for assessing security risks in SDN environments. Our methodology incorporates Multi-Criteria Decision Making (MCDM) techniques as follows: (1) Initial threat identification using Hesitant Fuzzy Sets-TOPSIS (HFS-TOPSIS), (2) Risk quantification via Hesitant Fuzzy Analytical Hierarchical Process (HF-AHP), and (3) Mitigation planning through Fault Tree Analysis (FTA). This structured approach ensures a thorough risk assessment and effective mitigation, enabling network administrators to identify potential risks and implement appropriate remedies before deploying SDN architectures. Our approach is compared with traditional risk-scoring methods to demonstrate its superior performance.

Software-defined Networks (SDNs) offer flexibility and programmability but introduce new attack surfaces, especially at the edge. Man-in-the-Middle (MitM) attacks on the control channel between the controller and edge devices pose a significant threat. This paper proposes a lightweight approach for detecting MitM attacks in SDN-based edge architectures. We evaluate several machine learning classifiers based on their effectiveness and efficiency for resource-constrained edge environments. Our approach analyzes real-time network traffic based on features sensitive to MitM activities. Through a simulated case study, we compare the performance of a rule-based baseline against various classifiers, including Gaussian Naive Bayes, Logistic Regression, Decision Tree, Random Forest, LightGBM, and XGBoost. The results demonstrate that the Decision Tree classifier achieves excellent performance, with 99% accuracy, precision, and recall, and critically, exhibits one of the lowest detection latencies (0.7 ms). This balance of high detection capability and low computational overhead positions Decision Trees as a highly suitable lightweight solution for real-time MitM attack detection in SDN edge architectures, outperforming simpler methods like Gaussian Naive Bayes and the rule-based baseline, and offering a more efficient alternative compared to complex ensemble models when latency is a primary concern. We also analyze the relative CPU and memory consumption, further supporting the practicality of Decision Trees for edge deployment.

Software-Defined Networking (SDN) introduces flexibility by decoupling control and data planes. However, this separation exposes the data plane to new threats, particularly malicious data sharing between network nodes. Existing trust mechanisms are often inadequate or too resource-intensive for edge environments. This paper proposes a novel framework to enhance trust and detect malicious data sharing within the SDN data plane. The approach combines a dynamic trust model-based on Ability, Benevolence, and Integrity (ABI)-with a lightweight deep learning anomaly detection system leveraging Autoencoders (AEs) and Graph Neural Networks (GNNs). Designed for integration with P4-programmable switches, the framework enables real-time analysis and response directly at the data plane. Key contributions include: (1) a tailored ABI-based trust model for SDN data planes, (2) a lightweight distributed detection architecture using AEs/GNNs, and (3) a P4-based testbed design for validation. This work aims to significantly improve trust establishment, accurately detect malicious data sharing, and minimize resource overhead, paving the way for more secure and resilient SDN data planes. The results demonstrate 97 % accuracy in detecting anomalies and highlight the critical challenges of classifying compromised nodes under class imbalance.

Privacy and false fall detection pose a significant challenge within the current camera-based human activity monitoring research. In response, we propose a solution that leverages the inherent relationship between binary fall detection and activity classification through hierarchical learning for low false fall classification. Our solution involves employing edge computing for data preprocessing, with a specific focus on extracting key points of the human body and motion features. Subsequently, the classification process based on multi-stream hierarchical learning takes place at the cloud level. This approach prevents sending raw RGB videos to the cloud to improve data privacy. Moreover, our network incorporates the hierarchical relationship between binary and multi-class classification using stream-to-stream skip connections and multi-level feature fusion. We created our Multiscale Convolutional Fusion Block (MSCFB) for multi-level feature extraction and fusion with an inner block skip connection. Additionally, we added an auxiliary fall detection loss to the first stream to regulate and control the feature extraction process for fall detection. Our network attained state-of-the-art performance in activity classification on the UP-Fall dataset, securing an F1-Score of 97.27%. Notably, there were no misclassifications between regular activities and fall sub-classes, leading to a perfect F1-Score of 100% for binary fall detection on this dataset. Furthermore, our fall detection network achieved state-of-the-art on the PRECIS HAR dataset with an F1-Score of 98.34%.

The Software-Defined Networking (SDN) paradigm has introduced heightened flexibility and scalability to network infrastructure management. However, the centralized control plane inherent in SDN architectures is susceptible to an array of security vulnerabilities, necessitating the development of efficient and real-time anomaly detection systems. This paper presents a novel integrated methodology for real-time anomaly detection within SDN architectures, capitalizing on the synergies between Security Information and Event Management (SIEM) systems and advanced machine learning techniques to bolster network security. The proposed framework operates by seamlessly collecting and analyzing live network traffic data, promptly pinpointing potential anomalies, and subsequently correlating these events via the SIEM system. To enhance accuracy while mitigating false positives, machine learning algorithms are harnessed to accurately categorize network traffic into benign and malicious activities, dynamically adapting to evolving threat landscapes. Empirical validation is conducted through an exhaustive dataset of real-world network traffic, encompassing an extensive array of attack scenarios. Findings vividly underscore the efficacy of the amalgamated SIEM and machine learning-driven anomaly detection system, yielding impressive detection accuracy while maintaining notably low rates of false positives. Noteworthy is the system's intrinsic adaptability to emergent threats, culminating in an elevated caliber of network security and fortitude within the SDN domain. This contribution significantly enriches the realm of real-time anomaly detection research, endowing SDN architectures with a pioneering strategy to counteract intricate cyber threats effectively.

Edge computing offloads the data processing capacity to the user side, provides flexible and efficient computing services for the development of the smart city, and brings many security challenges. To enhance the security of data sharing in edge computing, a secured blockchain shared data in Software Defined Networking System (BCDS-SDN) framework is proposed to ensure secured transactions between edge nodes and the Cloud, control access, and verify trusted edge from malicious nodes. The integration of SDN and blockchain concepts gives us the power to control and verify the identity of edge nodes to protect the infrastructure from many types of attacks, such as MitM, compromised channels, adding malicious nodes, and DDoS attacks. The combination of edge computing and Blockchain in a BCDS-SDN solution provides a vast scale of storage systems, database servers, and authenticated computation to the end in a secure manner. Our findings contribute toward more SDN-based security of data-sharing by providing a testbed-based proof of the efficiency and performance of Blockchain technology in ensuring the security of data-sharing in the edge-computing environment.

There is a need to improve fall detection systems to better discriminate falls from regular daily activities. Multi-sensor systems were proposed for this purpose. However, using wearable sensors may not be practical for elderly people. The use of surveillance cameras and advanced computer vision algorithms is thus an attractive solution. Indeed, raw RGB frames are rich in information including the human body shape and motions during activities. Yet, several disturbing features extracted from the raw frames may prevent distinguishing between normal activities and fall. To address this issue, we here leverage the human skeleton, and the binary history of motion during the activity. The former was analyzed with a Convolution Long Short Memory (ConvLSTM) to include spatial features during sequence learning. The latter was processed with a lightweight network for feature extraction. The outputs from both streams were merged to discriminate between fall and normal activities. Using the UP Fall dataset, our model detected falls with a single camera with a test accuracy of 100%, an improvement of 0.01% over current state-of-the-art methods.

This paper presents the findings of a field study conducted in Rabat, the capital of Morocco, utilizing the Wardriving technique to assess Wi-Fi network security. The study encompasses approximately 10,000 Wi-Fi networks situated in residential and administrative neighborhoods in Rabat. Through our comprehensive analysis, we observed that a substantial 89.42% of the networks use WPA2, suggesting that Wi-Fi security in Morocco compares favorably to that of developed countries. We also found that most networks don’t use default configurations, this indicates a proactive approach by network administrators to implement robust security measures. Moreover, our investigation revealed a balanced distribution of channels 1, 6, and 11, illustrating that network operators are mindful of potential interferences, particularly on channel 6, and have taken measures to mitigate such interferences effectively. Based on our results, we draw a positive conclusion that the Wi-Fi situation in the examined neighborhoods of Rabat is highly encouraging. The study highlights the efforts made by network administrators to secure their Wi-Fi infrastructures and optimize network performance, contributing to a safer and more reliable wireless environment for users. This research serves as a valuable reference for understanding the state of Wi-Fi security in Rabat and provides insights into the overall Wi-Fi landscape in the city. The data-driven conclusions can aid policymakers, businesses, and individuals in further enhancing Wi-Fi security practices to ensure the continued growth and stability of wireless connectivity in the region.

Vehicular Ad-hoc Networks (VANETs) have many promising applications such as improving vehicle driver's safety, and so, decreasing car deaths. In VANETs, the main communication way is broadcast. Such Broadcast needs to be done efficiently especially in congested areas to avoid the broadcast storm problem. A crashed vehicle disseminates a message about its incident to all nodes in the network. The vehicle nodes of the network have to disseminate in their turn the alert message carefully to maximize both reachability and saved rebroadcast (by reducing congestion) while minimizing the delay. This concern is demonstrated and modeled by decision hypothesis. In this paper, at first, we are keen on the Markov decision process (MDP), which is utilized to model and tackle such successive decision issues. We aim to optimize a utility for each vehicle relying upon an irregular domain and choices made by it. As the MDP formalism achieves its cutoff points when it is important to consider the cooperation between the several vehicles. We will begin utilizing the Graph-based MDP where the state and activity spaces are factorizable by factors. We then demonstrate that the transition functions and rewards are deteriorated into nearby functions, and the reliance relations between the vehicles are spoken to by a graph. To Figure the optimal strategy, we use Mean Field Approximation (MFA) for tackling GMDP issue.

Vehicular Ad-hoc Networks (VANETs) are a category of Mobile Ad-hoc Networks (MANETs) characterized by frequent topology changes. VANETs have many promising applications such as improving vehicle drivers' safety, and decreasing accidents. Vehicles communicate via Vehicle to Vehicle (V2V) and Vehicle to Roadside (V2R) mainly by broadcast, which needs to be efficient, especially in congested areas. An effective rebroadcasting protocol has to be designed carefully in order to maximize reachability while minimizing delay and the number of rebroadcasts. However, prior work typically minimizes either congestion or latency at the expense of reachability and the number of rebroadcasts. In this paper, we present the Nash Equilibrium Scheme (NES), a rebroadcast algorithm that outperforms prior work on reachability, number of rebroadcasts and delay, especially in congested areas. NES attains these goals by considering key environmental factors such as a number of received messages and distance to the event. We evaluate NES's performances through NS2 simulations.

Software defined networks represent a new centralized network abstraction that aims to ease configuration and facilitate applications and services deployment to manage the upper layers. However, SDN faces several challenges that slow down its implementation such as security which represents one of the top concerns of SDN experts. Indeed, SDN inherits all security matters from traditional networks and suffers from some additional vulnerability due to its centralized and unique architecture. Using traditional security devices and solutions to mitigate SDN threats can be very complicated and can negatively effect the networks performance. In this paper we propose a study that measures the impact of using some well-known security solution to mitigate intrusions on SDN's performances. We will also present an algorithm named KPG-MT adapted to SDN architecture that aims to mitigate threats such as a Man in the Middle, Deny of Services and malware-based attacks. An implementation of our algorithm based on multiple attacks' scenarios and mitigation processes will be made to prove the efficiency of the proposed framework.

Software defined networks (SDNs) represent new centralized network architecture that facilitates the deployment of services, applications and policies from the upper layers, relatively the management and control planes to the lower layers the data plane and the end user layer. SDNs give several advantages in terms of agility and flexibility, especially for mobile operators and for internet service providers. However, the implementation of these types of networks faces several technical challenges and security issues. In this paper we will focus on SDN's security issues and we will propose the implementation of a centralized security layer named AM-SecP. The proposed layer is linked vertically to all SDN layers which ease packets inspections and detecting intrusions. The purpose of this architecture is to stop and to detect malware infections, we do this by denying services and tunneling attacks without encumbering the networks by expensive operations and high calculation cost. The implementation of the proposed framework will be also made to demonstrate his feasibility and robustness.

Internet of Things is a large network of interconnected ”things”. IoT refers to the exchange of data and information from real world devices to the Internet. IoT technology will enable breakthrough applications in several areas such as e-Health, smart cities, transportation and industry, and includes multiple technologies, cloud computing, communication, etc. IoT reveals not only the possibilities of high efficiency and low cost, but also scalability. On the other hand, security issues exist and are a major concern for researchers. This article is a systematic literature review (SLR) where show the most basic and common architecture proposed for IoT with a security analysis of each layer. This SLR is also made to present IoT security solutions, as well as a comparison of these solutions. Finally we will present future research directions.

ABSTRACT This paper addresses the problem of localization in sensor networks where, initially, a certain number of sensors are aware of their positions (either by using GPS or by being hand‐placed) and are referred to as anchors. Our goal is to localize all sensors with high accuracy, while using a limited number of anchors. Sensors can be equipped with different technologies for signal and angle measurements. These measures can be altered by some errors because of the network environment that induces position inaccuracies. In this paper, we propose a family (AT‐Family) of three new distributed localization techniques in wireless sensor networks: free‐measurement (AT‐Free) where sensors have no capability of measure, signal‐measurement (AT‐Dist) where sensors can calculate distances, and angle‐measurement (AT‐Angle) where sensors can calculate angles. These methods determine the position of each sensor while indicating the accuracy of its position. They have two important properties: first, a sensor node can deduce if its estimated position is close to its real position and contribute to the positioning of others nodes; second, a sensor can eliminate wrong information received about its position. This last property allows to manage measure errors that are the main drawback of measure‐based methods such as AT‐Dist and AT‐Angle techniques. By varying the density and the error rate, simulations show that the three proposed techniques achieve good performances in term of high accuracy of localized nodes and less energy consuming while assuming presence of measure errors and considering low number of anchors. Copyright © 2012 John Wiley & Sons, Ltd.

Voice over IP (VOIP) is an application of great importance, particularly because of the significant revenue it can generate. In order for the Internet to constitute an attractive alternative to the traditional Public Switched Telephone Network (PSTN), it must provide high quality VOIP services. Given the importance of VOIP today, a new course was introduced as part of the undergraduate curriculum for the Information Technology students with specialization in Networking. The main focus of this course is on VOIP protocols and requirements. Laboratory experiments were designed so that students can obtain needed hands on experience to better realize the concepts being taught. This paper will provide details on the VOIP course material, structure with a focus on the designed hands on experiments the tools used, and assessment process.

This paper investigates the effects of power saving strategies on the performance of wireless local area networks (WLANs). More specifically, a power management model is formulated as an integer linear program that the network planner can use in order to achieve power savings while maintaining an acceptable quality of service (QoS), measured by the signal to interference ratio (SIR) for interference limited WLAN. Furthermore, through a network simulation implemented in NS-2, it is shown that the adaptive power saving scheme can guarantee the same average throughput as the non-adaptive counterpart, while significantly reducing the total transmitted power. Considering a realistic scenario, we show that, using the proposed power management model, one can save about 55% of the transmitted power while the SIR is increased by 6 dB thus improving the QoS. Also, using a simple experiment with two access points it is shown that, in the case of users within the overlap of the two coverage areas, the throughput remains constant when the transmit power is changed from a low value to a high value although a minor degradation of the average delay is noticed. As a conclusion, the commonly assumed fact that increasing the transmit power results in better network performance is not necessarily true and can result as shown in this paper in energy waste.

The work presented in this paper describes the evolution impact of mobile devices operating system's features through its increasing use for internet navigation. And thus, the growing risks of getting contaminated by hostile programs, which exploit the vulnerabilities of these systems and spreading to a large scale via services such as Bluetooth and MMS.

Discovering and accessing services while on the road is an important component in the architecture of future vehicular ad hoc networks, and for a successful deployment of services. Several studies have focused on the design and development of new routing and dissemination techniques that allow vehicles to communicate with each other and with road side units. However, detecting and reaching available services in a vehicular network remains problematic due to the amount of wireless traffic generated when service queries or advertisements are flooded across the network. In this paper, we propose a cooperative hybrid service discovery scheme for discovering services provided by mobile vehicles. This scheme is achieved through cooperating vehicles using store-and-forward approach and by sharing collected service information. We also propose to study the performance of the scheme by varying its degree of reactiveness and proactiveness. It is integrated with a caching mechanism which substantially improves the performance of the service discovery in terms of reduction of the traffic generated, and minimization of the response time while increasing the discovery success rate.

User profiling is commonly used to support personalization as well as to enhance usability of software systems by automatically adapting to the needs and preferences of their users. This paper presents an approach to dynamically update mobile users' profiles so that they can be provided with appropriate services while moving from one location to another. We propose an XML generic user profile structure that is dynamically updatable as the user experiences new services and that can be used by different software systems and applications. The proposed profile learning mechanism makes use of clustering users with a subset of similar attributes to predict appropriate services to a new user, and segmenting services with a subset of similar usage to discover and recommend new services that might be of interests to a mobile user. We demonstrated the feasibility of our approach by presenting a case study in tourism where tourists are assisted during their travels by providing them with services and information of interest. The tourist preferences and her/his current location are incorporated in the proactive formulation of suggestions on the tourist mobile devices about nearby points of interests (e.g. museums, restaurants...).

When GSM was introduced in the early 90's, it was considered an over specified system. Nowadays, it is obvious that the whole range of services is widely in use. In addition, performance is degrading due to the rapidly increasing number of mobile subscribers, numbering over 2.9 billion subscribers around the world. The performance of cellular networks is the most important issue concerning their operators. The main goal is to keep subscribers satisfied with the delivered quality of service (QoS). In order to achieve the best performance, service providers have to monitor and optimize their network continuously. A network management system (NMS) with an online database is responsible for the collection of data on live networks. For greater effectiveness, operators install systems that do a lot more than collect and store raw data. These systems are easier to use and take advantage of all the data provided by the NMS. In this paper, we summarize measurements that were carried out on an operative GSM-1900 network to evaluate the performance of the GSM's Air-Interface (Um), during eight months. In this paper we have established statistically the following facts: (i) The peak hour in North America is between 4:00 and 5:00 PM. (ii) During week days the duration of calls increases from Monday through Friday. (iii) Weekend traffic is different from week-days traffic. Using a regression analysis we forecast traffic over time. The presented KPIs along with the derived statistical facts are crucial for operators who are concerned with maintaining a reliable and stable network, while maintaining an acceptable QoS.

Radio Frequency Identification (RFID) has become a popular identification technology in a number of application areas such as supply chain management. A dedicated middleware solution is required to achieve the maximum benefits of RFID technology. The middleware components serve to abstract the communication between the middleware and the different types of sensing devices on one hand, and the middleware and backend applications on the other hand. FlexRFID is a simple and smart RFID middleware which provides device management and monitoring, data processing, filtering, and aggregation, rapid application development, as well as a policy based business rules layer which helps applying rules for accessing and configuring the services provided by the middleware. This layer serves to process some business intelligence rules locally so that the host system is offloaded from those mundane tasks. The paper shows that FlexRFID is a highly scalable and easily deployable middleware in the heterogeneous sites based on different standards and consisting of different hardware. Apart from these, FlexRFID incorporates the mechanisms for supervision, testing, and control of its components, plus handles the security and privacy issues that inhibit the adoption of RFID technology by applying the privacy policies in the business rules layer. FlexRFID middleware is controlling the data flows in and out as well as locally controlling some intelligence.

This paper investigates the impact of security on the performance of WLAN. More specifically, it analyzes the impact of applying encryption used by the well-known security protocol wired equivalent privacy (WEP) on the throughput over an infrastructure WLAN, IEEE 802.11g. This paper also addresses the different issues related to the security protocols currently used in WLAN IEEE 802.11g and demonstrates how these issues affect the final results of the experiments conducted. The main result is that the security adds moderate degradation on the throughput that may affect some applications over infrastructure WLANs.

E-learning is an emerging approach to education reflected in new learning techniques such as active and cooperative learning. Supporting communication technologies such as web-based tools, mobile devises and smart classroom used for interactive learning often come with side-effects that can be disruptive to the normal flow of the learning and the teaching processes. The use of technology van be hindered by the learning curve associated with the technology used during the class, the distraction from the initial goals of the course, the risk of mixing-up material related to the course, and concepts associated with the tools used in class, and finally, the time and effort overhead required from participants in preparing and using these tools. Therefore, it becomes crucial to continue benefiting from the advances in cooperative and communication tools, and in the same time minimizing the incurred overhead. In this paper, we propose a new classroom assessment tool: Active Class Probe (ACP) which can be used in an active and cooperative environment. ACP's main goal is designed such as to maintain focus on the initial course's learning goals, and minimize the external distraction incurred by its use in the classroom.