The evolution of 5G and Beyond (B5G) networks requires innovative solutions to address the growing complexity of network management and enhance Energy Efficiency (EE). Sleep Mode (SM) has emerged as an essential strategy for minimizing energy consumption in densely deployed networks, particularly in Base Stations (BS), significantly improving Energy Saving (ES) in 5G/B5G network. By dynamically deactivating underutilized BS components, SM can be effectively applied across various 5G infrastructures. In addition, the application of Artificial Intelligence (AI) and Self Organizing Networks (SON) provides advanced tools for automatic and intelligent system reconfiguration, further enhancing ES in B5G networks. This paper explores ES approaches centered on SM, providing an overview of their application in 5G architectures and the role of AI algorithms and the SON paradigm in improving ES. Finally, we discuss potential ES advancements for future networks, including the transition to 6G, highlighting the importance of intelligent and adaptive energy management in next-generation networks.

Unmanned Aerial Vehicles (UAVs) are at the center of a number of mission-critical applications, however, the largescale deployment of these vehicles is accompanied by cybersecurity risks that can compromise the operational safety and data integrity. A machine learning-based network intrusion detection system implemented on UAV-GCS communications along with the UAV Network Intrusion Detection Dataset (UAV-NIDD) is what we are suggesting. Our method treats the class imbalance problem with the CRISP-DM approach, which utilizes such advanced resampling methods as Borderline-SMOTE. The ensemble voting classifier of ours combines Random Forest, Decision Tree, and XGBoost models to reach the highest possible level of detection performance. We base our approach on the UAV-NIDD Scenario 1 experimental evaluation, where our method achieves a macroaveraged F1-score of 0.95 on a $20 \%$ hold-out test set for 11 types of UAV network intrusion as the result. This research marks the first comprehensive machine learning study that used the UAVNIDD dataset, showing the value of the systematic data science methodologies in solving cybersecurity problems and putting the spotlight on the use of UAV-specific datasets as a source of security solutions.

This paper focuses on integrating security measures into the design of RFID-based IoT inventory systems, with a focus on RFID authentication, alarm messaging, and geofencing tracking. Our main contribution is a novel RFID-based IoT system featuring enhanced sensing capabilities and a robust authentication protocol. Our proposed solution includes a holistic architecture utilizing MQTT for efficient communication and cloud-based storage. Our proposed collaborative mutual authentication scheme is rigorously evaluated for security informally and formally using Proverif.

The increasing global automotive fleet brings different adverse effects like pollution, congestion, and inefficient land use. In response, smart parking emerges as a multifaceted solution, that significantly enhances urban life by increasing time efficiency, boosting revenue generation, and providing a framework for better urban planning. Our system harnesses the capabilities of Tiny Machine Learning (TinyML) and IoT cameras to effectively tackle urban parking challenges. Key features encompass accurate license plate identification, a reservation mobile app, and real-time slot calculation. Our TinyML integration ensures a lightweight and energy-efficient implementation, promoting efficiency in resource-constrained environments. Results showcase high accuracy in slot computation and license plate recognition, signaling a significant stride towards efficient urban living with global applicability.

Unmanned Aerial Vehicles (UAVs) industry has experienced rapid growth and widespread adoption across various sectors. This increased affordability of UAVs has led to their extensive use, making them valuable assets for critical missions. However, the growing reliance on UAVs has also exposed them to various attacks. Multiple researchers have proposed frameworks based on different technologies to address these issues. Among these, AI-based frameworks and intrusion detection systems have emerged as prominent solutions for detecting UAV attacks. Due to the existing vulnerabilities, it is crucial to implement additional security measures to protect UAV processes. In this regard, a responsive Security Operations Center (SOC) offers a suitable solution to bridge this gap. This paper explores the need for a SOC specifically tailored for UAVs, focusing on cyber awareness and control measures. We emphasize the importance of control mechanisms within a SOC to ensure the protection of UAVs. We propose a responsive security solution for UAVs by integrating a SOC within an AI-based framework. The detection and mitigations expected through combining SOC in an AI-based IDS are promising. They indicate the effectiveness of the chosen techniques, which guarantees UAVs security.

The advent of 5G and beyond networks is envisioned to support lower latency, higher data rates, and wider connectivity than previous cellular network generations. However, given the denser deployment of base stations (BSs) to accommodate such improvements, this results inevitably in a significant and unsustainable increase in the network’s energy consumption. Sleep Control (SC), which allows switching off some BS hardware components during light-traffic time, is considered a viable solution for greener and more energy-efficient Radio Access Networks (RAN). However, the optimization of SC is a highly challenging large-scale network combinatorial problem that depends on dynamic wireless channel conditions and varying traffic demands with stringent Quality-of-Service (QoS) requirements. Driven by the benefits and efficiency of Deep Reinforcement Learning (DRL), which has been successfully applied to multiple wireless network optimization problems, this paper investigates DRL approaches addressing sleep control in 5G and beyond RAN. To this end, we propose a taxonomy to classify the related literature. Then, we provide an overview of the different components of the Markov Decision Process (MDP) modeling the sequential decision-making of sleep control and the applied DRL algorithms. Finally, we highlight the main challenges in existing works and suggest novel strategies to address them.

Industry 4.0 is going through a transitional period via the radically automotive transformations. In particular, unmanned aerial vehicles have significantly contributed to the development of intelligent and connected transportation systems. Thus, the continuous development using diverse technologies to achieve a variety of high-performance services raised the security concerns regarding communicating entities. Thus, being managed by networked controllers, UAVs uses controller area networks (CAN) protocol to broadcast information in a bus. However, this protocol is used as a de facto standard which does not have sufficient security features that raise the security risks. This issue caught the attention of the automotive industry researchers and several studies have attempted to improve the security of the CAN protocol attack detection. However, the proposed studies established general perspective solution and did not pay attention to UAVCAN attack detection. To alleviate these concerns, this paper proposed a dynamic intrusion detection frameworks (DIDF) for UAVCAN. The proposed UAVCAN DIDF scheme adopts an artificial intelligence (AI) based model to achieve high detection performance. We performed experiments using public UAVCAN dataset to evaluate our detection system. The experimental results demonstrate that UAVCAN DIDF has significantly reached a high detection rate with a high true positive and a low false negative rate. The simulation results are encouraging and demonstrate the effectiveness of UAVCAN DIDF.

The Industrial Internet of Things (IIoT) has completely changed how industrial processes are carried out, resulting in higher production and efficiency. Strong intrusion detection systems (IDS) must be implemented inside IIoT systems because of the increasing risk of security attacks brought on by increased connection and communication channels. In this work, we provide a combined strategy for successful IDS in IIoT systems based on data mining and machine learning/deep learning processes. Our suggested approach integrates a number of strategies, including anomaly detection, feature selection, and ensemble learning, to precisely identify and categorize distinct sorts of intrusion attempts. We conduct extensive trials on publicly accessible datasets (Edge IIoT) to show the efficacy of our method, and the results outperform current state-of-the-art methods.

Considering the stringent delay requirements in some use cases of V2X networks, relying only on cloud computing to execute some tasks of vehicles is sometimes infeasible. Meantime, increasing physically the number of onboard resources will lead to an increase in the cost of vehicles during the manufacturing process. Currently, a base station (BS) equipped with an edge server is adopted to shift computing capabilities close to vehicles in order to fulfil time-sensitive application requirements. However, in bursty traffic or disaster caused by some social events, Non-Line-of-Sight (NLoS) communication will be created and the BS will not be able to serve efficiently the vehicles in such conditions. In this context, considering their flexibility and the Line-of-Sight (LoS) communication that they provide, unmanned aerial vehicles (UAVs) can be used to address the above-mentioned issue. More specifically, by dispatching edge server-mounted UAVs, they can assist the BS to fulfil the requirements of delay-sensitive use cases in V2X. Therefore, the resources equipped by UAVs and the BS need to be efficiently managed. Since using a centralized system will result in (1) high energy consumption at the UAVs level and high delay due to the communication between the edge servers and the centralized server, and (2) poor scalability, we formulate this problem as multi-agent learning and solve it with a deep deterministic policy gradient (DDPG) with the aim of maximizing the number of offloaded tasks while fulfiling the quality-of-service (QoS) of such tasks.

Health monitoring systems have achieved great popularity and great importance, especially with the presence of pandemics, large numbers of patients, and a lack of health staff. The presence of sensors on the patient's body to measure blood pressure, body temperature, and heart rate, in addition to room temperature and humidity, constantly supports the specialized medical staff in measuring these indicators. The advantage of these devices is that they are with the patient all the time, and a nurse cannot accompany a patient for this period. In this paper, a health care system is designed and implemented to measure vital signs and room environment temperature and humidity. ESP32 receives the vital signs data from the patient and his room. This information has been sent to the Raspberry Pi 4 where the information was compared to the information provided by the doctor to ensure obtaining the alarm when the measure has a large difference. The system obtains two types of alarms; the first is a medical alarm that accrues when vital signs are high or low from the normal measurements. This alarm calls the medical staff, while the second alarm occurs during a hardware malfunction. The second type of alarm call the technical staff. Testing the system shows that the two types of alarm have been recognized on their occurrence. All these measurements and alarms have been stored in the cloud for patient health monitoring.

In the realm of personalized healthcare, ensuring patient adherence to prescribed pharmacological regimens is identified as a crucial factor in achieving positive health outcomes. This research study presents a special methodology that utilizes Multilayer Perceptron (MLP) neural networks to properly predict patient medication adherence. The methodology places a specific focus on achieving high levels of precision. The data preprocessing methods involved removing duplicate columns, identifying and eliminating outliers, and normalizing the dataset by z-score normalization. The preliminary results of this inquiry exhibit a significant achievement, as indicated by a maximum validation accuracy of 0.894%. The shown accuracy in predicting medication adherence underscores the promise of the multilayer perceptron (MLP)-based approach in tailoring healthcare treatments to address the specific needs of individual patients optimizing medication adherence forecasts, healthcare practitioners can improve their allocation of resources and interventions, resulting in better patient outcomes and lower healthcare costs. Further exploration and refinement of this approach hold promise for enhancing the effectiveness of personalized treatment and contributing to the well-being of patients on a global scale.

Blockchain is emerging as one of the most promising and resourceful security technologies for cloud infrastructures. In a distributed database system, blockchain is used to store, read, and validate transactions. It can improve security, trustworthiness, and privacy by using an unchallengeable, shared distributed ledger on cloud nodes. Cloud-based healthcare systems (CHS) are vulnerable to various threats and attacks such as identity theft, medical fraud, insurance fraud, and alteration of critical patient data. Secure retrieval, access, and storage of data on CHS are necessary to protect critical medical data. Accordingly, the integrated cloud and BlockChain (ICBC) architecture emerge as a potential solution for shaping the next era of a healthcare system while providing efficient, secure, and effective patient care. In this context, this paper presents an in-depth exploration of advanced approaches to securing cloud-based healthcare data management systems using blockchain technologies. It provides a taxonomy and highlights the benefits and limitations of the approaches examined.

The concept of structural health monitoring (SHM), which ensures maintenance and conservation of the built environment, is progressively growing in importance. SHM offers the building's historical and cultural value in addition to its safety. Nowadays days, Wireless Sensor Networks (WSN) are frequently employed for SHM and offer a strong contender to address a number of problems, including sensor location. A sensor placement approach is therefore needed considering fragility and significance of the historic structures. In this paper, we propose sensors placement methods applied on the historical monument Aqueduct of Carthage of Tunisia. Our method is based on the Finite Element Modeling (FEM) to carry out the mesh model of the structure arches and to identify two types of the arch zones; stressed and unstressed zones. Based on FEM results, we determine the optimal sensor positions to maximize the covered surface, given a limited number of sensor.

With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learning-based intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection.

During this last decade, Unmanned Aerial Vehicles (UAVs) are being useful in complex missions and critical scenarios in particular for hostile areas supervision. The integration between Space-Air-Ground Networks (SAGIN) is gaining more attention especially with the future generation of cellular networks (6G). In this context, mainly, we focus on the integration between aerial and terrestrial networks. The aerial network corresponds to the use of the multi-UAVs network, called Flying Ad Hoc Networks (FANETs), and the terrestrial networks correspond to the use of the Ground Control Station (GCS), Wireless Sensor Network (WSN), Internet of Things (IoT), cellular networks and cloud computing. Moreover, we propose a novel architecture named Multi-UAVs-based SDN, IoT, and Cloud Architecture (MUSICA), in which we use Software Defined Network (SDN) controller to manage the integration between terrestrial and aerial networks and we deploy cloud storage and computing resources. The detailed functional components of the proposed MUSICA architecture and the data flow between its different components are discussed and the benefits of MUSICA for scalable land supervision are pinpointed.

Agriculture, encompassing industrialization, security, traceability, and sustainable resource management, is critical to the survival of humans. As resources dwindle, it is critical to develop strategies to assist in preserving agriculture. The development of the Internet of Things (IoT), Artificial intelligence, UAVs, and Blockchain technologies as new sectors has the potential to significantly improve the status of the Agricultural domain. In this context, this study does a comprehensive assessment of the literature to analyse the most recent breakthroughs in schemes that can innovate the agriculture domain. Following the determination of the fundamental needs in smart agriculture, several solutions and projects are highlighted. Furthermore, the present investigation will help in the identification of new avenues for future research related to the employment of AI, UAVs, and BC in agriculture.

Internet of Things (IoT) devices face significant challenges due to their limited capabilities and battery life especially in hazardous regions. Furthermore, the availability of telecommunication infrastructures in these areas is insufficient or event non-existent. As a result, transmitting the data collected by IoT devices becomes a formidable undertaking. Moreover, the limited lifespan of IoT devices, which rely on batteries for operation, exacerbates the situation. Recent research has explored the potential of unmanned aerial vehicles (UAVs) as effective means to facilitate air-ground communications and gather data from IoT devices deployed in harsh environments. However, the ability of UAVs to provide sustainable and reliable services is hindered by their dependence on limited battery power. Therefore, the UAV mobile edge/fog computing paradigm offers low latency communication, computation and storage capabilities for offloading intensive tasks of IoT devices. In addition, the utilization of current technology, such as Wireless Power Transfer (WPT), presents itself as a viable means to facilitate the capability of unmanned aerial vehicles (UAVs) to acquire power without the need for physical connection through ground base stations (GBS). Furthermore, this technology can also be employed to wirelessly replenish the energy of Internet of Things (IoT) devices situated in distant regions. In this study, we examine a scenario where UAVs providing offloading and energy services to UEs, making binary decisions for data offloading and continuous decisions for energy allocation. Using a federated deep reinforcement learning (FDRL) approach, we address privacy concerns and data leakage. The network simulation aims to determine the optimal number of UAVs a single fog node can serve and assesses the maximum UEs each UAV can handle. Our proposed framework’s performance is compared to single-agent and traditional central training, using mean reward as a metric. We evaluate mean delay over training iterations, comparing with central learning, and assess mean energy consumption against traditional central learning.

During this last decade, Unmanned Aerial Vehicles (UAVs) are being useful in complex missions and critical sce-narios. In this paper, we propose novel architecture for data gathering and storage in which data is collected from IoT devices using cooperative UAVs. The main purpose of our scheme is to ensure secure data acquisition and storage using the BlockChain (BC) technology. The performance of the proposed scheme is analyzed via experimental evaluation.

The Internet of Things (IoT) enables the interconnection of devices that collect massive amounts of data, making IoT security requirements crucial and secured through encryption. However, traditional encryption protocols are no longer suitable for all IoT scenarios. We propose a new lightweight encryption method optimized for patient information protection in healthcare, considering the limited capacity of portable medical devices. Our method has been experimentally demonstrated to be effective, with a largest entropy of 7.9917 in medical images (computed tomography) and average coding and decoding times of 3.8952 sec and 3.0584 sec, respectively. The encoded image exhibits an even distribution of pixels and lower correlation coefficients between neighboring pixels, supporting the effectiveness of our less complex method compared to current state-of-the-art methods.

The Industrial Internet of Things (IIoT) widespread adoption has prompted multiple breaches on IIoT devices by attackers. Thus this threatens the security of data for the end user. Recurrent neural networks (RNNs) have been used for intruder detection in IIoT because IIoT traffics are created consecutively, but they are unable to represent long traffic sequences and cannot be parallelized either. In order to solve these problems in this paper we have introduced the Attention technique which is applied to the encoding layer. We also used a federated learning (FL) approach to reduce the communication overhead of collecting data from each worker node and storing it in the cloud server in the case of a centralized model, thus preserving network scalability. With the use of the Edge-IIoT dataset, we test our suggested methodology. The outcomes of our FL experiment enable the system to scale.

A resilient and guaranteed Unmanned Aerial Vehicles (UAVs) security framework should be designed to be secure against different types of attacks and faults. Recent developments have seen a proliferation of methods for improving UAVs security. Although, many studies proposed different approaches using artificial intelligence to enhance their security. Unfortunately, no study yet worked on an examination of an hybrid framework on UAVs faults and attacks and different architectures. Hence, our article aims to provide a prior detection results by proposing an hybrid adaptive framework for faults and attacks detection for UAVs applied on centralized and decentralized architectures. Our framework is based on two entry flows for faults and attacks in order to learn high-level features automatically from data. We validated our framework using deep-learning architectures. Finally, the empirical results show that our framework reached over 85% and 96,7% in term of accuracy for UAVs faults and attacks, respectively.

The Internet of Vehicles (IoV) is an Internet of Things (IoT) application that offers several utilities such as traffic analysis, safe driving, road optimization, and travel comfort. Software-Defined Networking (SDN) technology has been shown to provide various benefits to support the IoV. However, the construction of IoV makes it a complex system posing several challenges among which the important ones are security and privacy of data. Intrusion Detection Systems (IDSs) have been proposed in the IoV to identify cyber attacks and protect private data. Recently work has started to implement IDSs based on Federated learning as collaborative IDSs have proved effective security of IoV. In another hand, trust management has revolutionized the IoV filed, providing decision-making support to secure the network. Stating that an SDN-driven IoV architecture in which nodes trustworthiness gets assessed can provide a promising framework for IDS, we propose in this paper a Federated learning-based IDS for the IoV under the SDN structure. We integrate trust metrics to assist in securing the IoV network. Simulation experiments are conducted to validate the proposal.

The Internet of things (IoT) growth has created a large spectrum of heterogeneous devices where these devices produce a considerable amount of data every second. The produced data could be sensitive and even critical. Hence, one of the biggest IoT concerns is to ensure data privacy and security against threats and attacks. In this paper, we investigate numerous security-related threats and issues. Furthermore, we discuss and categorize conventional IoT attacks. Consequently, in order to cope with the dangerous circumstances, we present an evaluation of the latest proposed countermeasures.

We are now in the early stage of the Fifth Generation (5G) commercialization, and its improved capabilities and unique features will revolutionize the present wireless network. The 3rd Generation Partnership Project (3GPP) is currently working on the 5G New Radio, also known as the worldwide standardization of 5G, which can operate across a wide variety of frequency bands from minus than 6GHz to mmWave (100GHz). Nonetheless, 5G will not be able to satisfy all requirements for the foreseeable future. Hence, Beyond 5G (B5G) and Sixth Generation (6G) have emerged as concepts representing Next Generation Wireless Networks (NGWNs). B5G and 6G networks will probably integrate Artificial intelligence-based services, tactile services, hybrid access, quantum computing, edge computing, and optical wireless communication. This paper describes potential background concepts relating to Network Slicing and artificial intelligence. We present the literature review by studying the impact of Artificial Intelligence with its promising methods, training models, and architectures to be integrated into B5G Network slicing to perform Resource Management.

The Beyond 5G (B5G) networks vision afford wireless access to a vertical market with varying Quality of Service (QoS) requirements. Network Slicing (NS) is one of the key features that offers the opportunity to have a logical network that affects the market model of these verticals. Accordingly, Network Slicing opens the door to new market players including the Infrastructure Provider (InP) and the Virtual Network Operator (VNO). The InP is the owner of the infrastructure that supports several types of slices. The VNO will buy network resources (i.e., bandwidth) from the InP in order to provide a particular service to its users. Consequently, deciding on the optimal resources allocation among VNO users while maximizing the revenue of the InP has become a fundamental issue that needs to be solved. Previous works have presented optimal solutions for resources allocation and focused mainly on a single scenario based on pricing mechanisms. In this paper, we propose an inter-slice resources allocation based on multiple sets, namely customer residential profile, industry coverage, and business area. We propose an Integer Linear Programming (ILP) formulation to the problem, describe an admission control scheme and devise a greedy-based heuristic to solve the problem.

Summary Water pipeline monitoring system becomes a relevant solution to cope with various pipeline hydraulic failures in order to save the environment from water losses. In this respect, cognitive water distribution system (WDS) combines Internet of Things (IoT) technology with Big Data generated by various connected objects and devices for reliable structural health monitoring of pipelines. Accordingly, designing a scalable WDS with smart leak detection and localization requires a serious study and an adequate planning. In this paper, we suggest a cognitive IoT‐based architecture with adequate data collection based on the Apache Spark framework. Furthermore, our objective is to elaborate a hybrid mechanism that defines and performs accurate leakage identification and localization by taking into account both the steady‐state and transient behavior of water. The bio‐inspired sensitivity analysis of the water pressure profile based on the genetic algorithm ensures the effectiveness and the accuracy of the proposed monitoring solution.

We are now in the first phase of the Fifth Generation (5G) network, and its full potential is still a long way to reach. Network operators and manufacturers are preparing to release new 5G end-users services and products. Each service must be performant to meet the need of users. Network Slicing (NS) is now one of the most popular 5G technologies in the research community. For network softwarization, Network Slicing affords flexibility and scalability with embedded Quality of Experience (QoE) and Quality of Services (QoS) features. In this paper, we explore the latest developments in related research fields. We summarize the 3GPP network slicing evolution, the Radio Access Network, and Core Network architectures. We describe background concepts correlated with inter-slice and intra-slice, as well as their management and orchestration architectures. We extend our discussion to the taxonomy of NGWN resource allocation via a deep comparison of the optimization algorithms. Finally, we enrich our study by including open issues and some recommendations for the future.

The last decade has witnessed a growth related to the use of cooperative Unmanned Aerial Vehicles (UAVs) in several complex military and civilian applications. Among the cooperative-based UAV applications, this paper considers the protection of Convoy of Ground Mobile Vehicles (CGMV). The sketched problem is a canonical problem that requires coordinating a team of UAVs including their movements, tasks, and balancing resources. In this regard, we propose an efficient task allocation scheme. Besides that, the suggested approach is based on a hybrid strategy combining the circular path and the path ahead protection strategies involving two UAV sets. The first UAV set performs a circular path encircling all the vehicles in the convoy and protects them from the potential threats on the two lateral sides. The second UAV set explores the path ahead of the convoy. The conceived convoy protection scheme, called CGMVP-CUAVs, is validated by simulation using the NetLogo3D environment. Different experiments were conducted to analyze the efficiency of the proposed approach. Simulation results demonstrate the robustness of our strategy.

During the pandemic of Corona-virus Disease 2019 (COVID-19), the whole world was confronted by a particularly high death toll and infection rate. Research has shown that air pollution plays a considerable part in the spread of certain illnesses and diseases. In the case of the COVID-19 pandemic, research has shown that increased air pollution has a negative effect on people’s well-being and plays a role in the quick spread of the disease. Air pollution by itself affects the respiratory system of individuals which is aggravated, in addition, by a COVID19 infection. Some efforts have been made to use emerging technologies to combat the virus and its subsequent aerosol aspects to reduce transmission. In this context, we present an IoT system for Air Quality (AQ) monitoring and prediction using deep learning for data analysis and Augmented Reality (AR) for data visualization. The proposed system shows great potential for using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) units as a framework for leveraging knowledge from time-series data of AQ. Moreover, integrating AR visualization for the proposed IoT system enables intuitive interaction between users and IoT devices and further improves visualization of AQ data which effectively contributes to easily conducting a deeper analysis of data and makes faster decisions.

The advent in Wireless Body Area Networks (WBANs) provides promising services for remote diagnostic and monitoring. The security requirements in this context are challenging and evolving. The WBAN should support these security requirements to continuously provide efficient services to patients. Trust management is one of the interesting security mechanisms that can be enforced to enhance the WBAN security. In this paper, we propose a new cluster-based approach to manage trust in WBAN. The clusters are defined based on the direct and indirect exchanges between nodes. Our approach is hybrid: it calculates the direct and the indirect trust based on the priority of forwarded messages, the feedbacks of different exchanges, and the credibility coefficient. The simulation results show the efficiency of our model against three different attacks: the on-off attack, the collusion attack, and the newcomer attack.

5G network is considered as an Heterogeneous networks (HetNets) able to support a multitude of new services, where performance requirements will be extremely polarized. In this context, several key issues for 5G communications should be considered to satisfy Quality of Service (QoS) requirements. Vehicle-to-Everything (V2X) user selection and resource allocation are considered as an important key issues for Internet of Things (IoT) communications. In this paper, we propose an Adaptive V2X users selection and resource allocation for Ultra-Dense 5G HetNet network (AU-HetNet). AU-HetNet consists on two stages; Vehicle Selection Stage (VSS) and Adaptive Resource Allocation Stage (ARAS). The first stage defines the selected vehicle that will be served based on an utility function results to designate the set of candidate vehicles and a maximum throughput of each vehicle. The second stage concerns the adaptive resource allocation model which is based on QoS priority in order to privilege real time and non-real-time traffics. Our work aims to enhance system model performances in terms of resource utilization ratio, dropped request probability, and total average throughput.

In several countries, the connected systems and especially the Internet of Medical Things (IoMT) based systems has been deployed with other advanced technologies to counter and to mitigate the spread of the coronavirus disease 2019 known as (COVID-19) and to smooth the severity of the pandemic. Accordingly, many IoMT-based systems merged with the use of blockchain, artificial intelligence and big data analytics proposed and adopted by diverse countries and governments to counter COVID-19 pandemic. In this context, this paper highlights the needs, the architecture, the design guidelines and the provided services related to our proposed system named (CMK-COVID: Connected Medical Kiosks to Counter COVID-19). Besides that, this paper offers useful insights into the literature related IoMT-based solutions countering COVID-19.

Even though several systems for Air Quality (AQ) monitoring have been in existence for over a decade, a research model for Knowledge Management (KM) of AQ data has to be created in order to enhance the decision-making and organize the air quality data collected from the Internet of Things (IoT) consumer devices. This model should be made more performant by ensuring greater flexibility and interoperability between devices and emerging technologies. In this context, we propose an approach for representing Data WareHouse (DWH) schema based on an ontology that captures the multidimensional knowledge of tools, techniques, and technologies used for novel AQ systems. This enhances decision-making by coping with potential problems such as data sources heterogeneity and covering the various phases of the decision-making life cycle.

Summary Network slicing (NS) presents the key enabler of cellular network improvements. It allows enhancing the performance of diverse requirements supported for verticals industries. The concept of NS was carefully studied over the previous few years, and the primary operational principles were developed. However, there is an important need for more investigations on studying NS to enable further development. This article offers a deep study related to the NS principle, the recent standardization process for Third Generation Partnership Project and Fifth Generation Public Private Partnership, the diverse broad use cases, NS key concepts, NS architectures, and NS management and orchestration. Besides, it discusses radio access network slicing and sharing, the algorithms, the projects, and the NS practical experience and practices. Finally, this article proposes and highlights a possible solution to several open research issues.

Vehicular fog computing is an extension of fog computing in the context of vehicular networks. Vehicles are used to serve mobile users at the edge of networks. The vehicular fog is characterized by its dynamicity due to the mobility of vehicles. The change of vehicular fog members over time causes the variation of the available resources. So, the selection of the suitable fog node (i.e. vehicle) to execute a task is challenged. In some cases, the users' requests can be redirected to the cloud to be processed if no suitable vehicle is found to fulfill local processing. To this aim, we suggest a multi-objective based vehicular fog resource allocation scheme. A software-defined networking (SDN) controller is deployed to manage the allocation of the vehicular fog resources based on the information gathered from vehicles and the received requests. The proposed problem is solved using non-dominated sorting genetic algorithm (NSGA-II).

Nowadays, remote healthcare monitoring systems (RHMS) are attracting patients, doctors and caregivers. RHMS reduces the number of unessential hospitalizations by providing the required healthcare services for patients at home. Furthermore, continuous health monitoring using RHMS is a hopeful solution for elderly people suffering from chronic diseases. RHMS is in general three tiers architecture where the first tier uses intelligent wearable sensors to gather physiological signs. The majority of wearable sensors constructors commercialized sensing devices with Bluetooth Low Energy (BLE) communication interfaces, which lead to the development of diverse RHMS deploying BLE communication interfaces for physiological patient data gathering. In this paper, we introduce the basic concepts related to RHMS design and development. Besides that, we focus our investigation on the BLE communication protocol used in the healthcare context and its configuration to sense several physiological data. Also, we highlight the different steps enabling reading sensed data on mobile application.

The average of global life expectancy at birth was 72 years in 2016 [ 1 ], however, the global healthy life expectancy at birth was only 63.3 years in the same year, 2016 [ 2 ]. Living a long life is not any more as challenging as assuring active and associated life [ 25 ]. We propose in this paper an IoT based holistic remote health monitoring system for chronically ill and elderly patients. It supports smart clinical decision help and prediction. The patient heterogeneous vital signs and contexts gathered from wore and surrounding sensors are semantically simplified and modeled via a validated ontology composed by FOAF (Friend of a Friend), SSN (Semantic Sensors Network)/SOSA (Sensor, Observation, Sample and Actuator) and ICNP (International Classification Nursing Practices) ontologies. The reasoner engine is based on a scalable set of inference rules cohesively integrated with a ML (Machine Learning) algorithm to ensure predictive analytic and preventive personalized health services. Experimental results prove the efficiency of the proposed system.

The majority of the Internet-of-things (IoT)-based health monitoring systems adopt ontologies to represent and interoperate the huge quantity of data collected. Classical ontologies cannot appropriately treat imprecise and ambiguous knowledge. The integration of Fuzzy logic theory with ontology can effectively resolve knowledge problems with uncertainty. It considerably raises the accuracy and the precision of healthcare decisions. This paper presents a fuzzy-ontology based system using the internet of things and aims to ensure continues monitoring of diabetic patients. It mainly describes the ontology-based model and the semantic fuzzy decision-making mechanism. The system is evaluated using semantic querying. The results indicate its feasibility for effective remote continuous monitoring for diabetes.

Nowadays, Unmanned Aerial Vehicles (UAV) are being widely deployed due to new technology advancements. Therefore, UAVs used for diverse applications and for various contexts and scenarios to provide to humans advanced services and to support them in hazardous areas and difficult environments. Accordingly, UAVs and swarms' UAVs are widely deployed for smart agriculture, public safety, logistics inspection and supervision. Furthermore, UAVs used to increase the performance and to boost the coverage of existing cellular systems. However, UAVs communication systems could be attacked. In this context, this paper focus on assessing vulnerabilities and enhancing the security of the communication links between UAVs and the ground control Station (GCS). Accordingly, several mechanisms and UAV security issues were investigated. Furthermore, we have identified the MAVLINK protocol vulnerabilities by implementing different scenarios of attacks.

The Internet of Things (IoT) has the potentialities to advance the concretisation of the smart world through a wide interaction between the human society with its physical environment. This interaction is enabled by Wireless Sensor Network (WSN) used to identify and to interpret contexts. However, the IoT and WSN have intrinsically challenges, particularly Network Lifetime (NL) maximisation. In this context, researchers have developed power-aware mechanisms for WSN and IoT. These mechanisms delay the battery depletion of a sensor node and extend the NL to ensure continuous full operational network activity. Accordingly, in this paper, we review and discuss various recent contributions related to nature inspired power-aware wake-up scheduling approaches. Furthermore, we present a comprehensive taxonomy of the various nature-inspired wake-up schedule mechanisms. Besides this paper provides for the reviewed approaches usability contexts.

Internet of Vehicle (IoV) has the ability to achieve an integrated intelligent transportation system in order to control and impact traffics, road, safety, accidents, and driving experiences through technology by data sharing and exploration between vehicles, things, and human. Accordingly, as the number of Intelligent and Connected Vehicles (ICV) keeps increasing, a new generation of IoV solutions based on intelligent reasoning known as Cognitive IoV (CIoV) faces various types of attacks related to security and privacy risks and vulnerabilities. Hence, the improvement of existing security methods in the IoV ecosystem is required. Therefore, recent advanced Deep Learning (DL) methods of data exploration are used for detecting and learning normal or abnormal behavior among the CIoV and make intelligent decisions on its own against attacks and threats. In this context, we aim to review the advances in issues of security and privacy in cognition and computing intelligence associated with you. In addition, we discuss the opportunities and challenges involved in applying DL methods to CIoV security. Furthermore, we highlight possible threats and attacks to develop enhanced security methods for IoV systems.

Nowadays, the vehicular ad hoc networks (VANETs) received attention. However, due to the dynamic characteristics of VANETs, its network transmission efficiency is low. To resolve this issue, many investigations and contributions suggest the integration of Information Centric Networks (ICN) into VANETs. In this paper, we propose a new approach named VC-ICN integrating a clustering protocol and ICN into vehicular network. The proposed approach allows vehicles to enhance their content delivery ratio. The simulation results for real context prove the accomplishment of VC-ICN in information-centric vehicular network context.

Currently, Unmanned Aerial Vehicles (UAVs) are gaining a lot of attention due to their different potentialities and use cases. We can use UAVs to track intruders, to capture images and videos in harsh areas and to boost the coverage of existing cellular systems. For all these context and scenario security of the communication system between UAV and its ground control station (GCS) is primordial. The standardized point-to-point communication protocol between UAV and the GCS named MAVLINK has several vulnerabilities. MAVLINK used to carry telemetry, to control, and to command small UAVs. UAV-GCS communication link could be attacked. In this context, this paper proposes MAV-DTLS mechanism to enhance the security of the communication links between UAVs and the GCS. We have described the main issues of our proposed solution that empowers secure communication between UAV and GCS. The obtained result proof that MAV-DTLS resist to the different studied attacks (DOS, GPS spoofing, MItM, Data modification).

Pipeline Systems are extensively used to transport and distribute natural gas, water, oil, sewage etc. Due to the aging of these systems, leaks and pipe bursts occur frequently. Therefore, the necessity of continuous monitoring of such systems is required in order to provide early detection of a sudden problem such as leaks, before they attain the magnitude of a major disaster. Wireless Sensor Networks (WSNs) which consist of low-power consumption, low-cost and multi-functional sensor nodes for environmental conditions monitoring, present as a suitable technology to achieve this goal. As nodes in WSNs are powered by a battery, efficient energy consumption is an important factor to enable the network to operate as long as possible. At the same time, network throughput is another important metric for maintaining the Quality of Service of the network (QoS) which need suitable optimization. In this paper, considering these two objectives functions, we propose a novel optimization model based on Multi-objective Chaotic Ant Swarm Optimization (MCASO) approach aiming to optimize WSN energy efficiency and to enhance the network throughput. A K-Means++ algorithm is used to perform the clustering process while MCASO is applied during the optimization phase. The obtained simulation results confirm the enhancement of the network lifetime and the maintaining of the QoS.

The challenge with self-driving cars is to create a model that converts sensors data (such as cameras or proximity sensors) into actions. This way the car can react to its changing environment and make the right decisions. In the literature, Neural Networks is the most promising technique used to parse these sensors data. A well trained and designed neural network can take the sensors values and output the right actions. In this paper, we introduce a Way to train Efficiently Neural Networks with Genetic principles, called WENNG. Moreover, we propose a comparative study between all the variations of WENNG to highlight the best-performing ones. To evaluate our WENNG training variation, we implement two well known neural network algorithms: the FullyConnected one and the NEAT algorithm. Through extensive simulations, we demonstrate that the Natural Selection WENNG outperforms the Greedy WENNG at training the genetic neural networks with a low mutation rate. Finally, we show that an IMproved version of NEAT called IMNEAT, minimizes twice the number of generations to reach the maximum fitness value compared to the traditional NEAT algorithm.

Internet of Things (IoT) communications are considered as a key technology to enhance 4G Long Term Evolution-Advanced (LTE-A), and to maintain its dominance in 5G systems. In order to support uplink traffic, several enhancements are needed to bear IoT applications in 5G systems. The scheduling mechanism is still considered an area of concern of several researchers since it greatly contributes to this aim. In this paper, we propose a Dynamic Borrowing Scheduler (DBS) for IoT communications based on QoS (Quality of Service) requirements. The proposed scheduler considers two types of IoT traffics; Machine-to-Machine (M-M) and Human-to-Human (H-H) communications by guaranteeing the network performance and avoiding ineffective exploitation of available resources. The simulation results prove that the proposed DBS operates efficiently in terms of maximizing the bandwidth utilisation rate which is an important challenge of the 5G radio resource allocation.

Even though several architectures have been in existence for over a decade, a new research model has to be created in order to solve the requirements (volume, velocity, and variety) and constraints that affect the intelligence of smart environments. Thus, the merge of computing technologies has made the collection of Big Data (BD) possible from the Internet of Things (IoT) devices. Data do not include only the information about the environments, but also the daily changes of information and so forth. In this context, We propose new business information architecture for BD and IoT (BDIoT). The proposed architecture uses context-aware computing and combines the Data WareHhouse (DWH) and Data Lake (DL) in order to benefit computing mechanisms. The BDIoT architecture is validated through use case related to E-health service (Alzheimer's disease).

Building a new Business Intelligence (BI) architectures for big data and Internet of Things (IoT) is a complex task. It aims to satisfy the needs of decision makers by taking into consideration several constraints and requirements (volume, velocity, and variety). This can influence the decision of choosing the most appropriate tools, techniques, and technologies in the data flow management for IoT solutions. With the aim of helping the decision makers to control their analytical needs and to achieve the business goals, we propose a Computer-Aided Software Engineering (CASE) tool that supports the design of BI for IoT architecture (BIIoT) based on knowledge. It recommends existing technologies which fit well with the scalability of the business requirements and verifies the data flow in the proposed architecture. Thus, the BIIoT tool is validated through a use case related to agriculture service provisioning.

Continuous health monitoring is a hopeful solution that can efficiently provide health-related services to elderly people suffering from chronic diseases. The emergence of the Internet of Things (IoT) technologies have led to their adoption in the development of new healthcare systems for efficient healthcare monitoring, diagnosis and treatment. This paper presents a healthcare-IoT based system where an ontology is proposed to provide semantic interoperability among heterogeneous devices and users in healthcare domain. Our work consists on integrating existing ontologies related to health, IoT domain and time, instantiating classes, and establishing reasoning rules. The model created has been validated by semantic querying. The results show the feasibility and efficiency of the proposed ontology and its capability to grow into a more understanding and specialized ontology for health monitoring and treatment.

Big data and the Internet of Things (IoT) are considered as the main paradigms when defining new information architecture projects. Accordingly, technologies that make up these solutions could have an important role to play in business information architecture. Solutions that have approached big data and the IoT as unique technology initiatives, struggle in finding value in such efforts and in the technology itself. A connection to the requirements (volume, velocity, and variety) is mandatory to reach the potential business goals. In this context, we propose a new architecture for Cognitive Internet of Things (CIoT) and big data. The proposed architecture benefits computing mechanisms by combining the data WareHouse (DWH) and Data Lake (DL), and defining a tool for heterogeneous data collection.

Wireless Sensor Networks (WSN) are widely deployed to maintain Structural Health Monitoring of Water Pipeline System (WPS). Accordingly, it is imperatively important to ensure reliable communication between sensor nodes deployed in harsh environment to allow a continuous data collection and processing. In this context, we propose and implement an energy efficient solution that enables a seamless interconnection between sensor nodes, and trusty data transmission in order to maximize the network lifetime. After a clustering step, a Data Redundancy Elimination technique is applied to remove redundant data at each cluster head. This operation is followed by a data fusion algorithm based on Dempster-Shafer evidence theory at the Base Station. This scheme is proposed with aim of reducing the size of data carried by the network and consequently save on energy consumption. This results in improved WSN lifetime and more accurate WPS systems.

The ability to perceive and understand surrounding road-users behaviors is crucial for self-driving vehicles to correctly plan reliable reactions. Computer vision that relies mostly on machine learning techniques enables autonomous vehicles to perform several required tasks such as pedestrian detection. Furthermore, within a fully autonomous driving environment, driverless vehicle has to communicate and share perceived data with its neighboring vehicles for more safe navigation. In this context, our paper proposes a warning notification diffusion solution related to real-time pedestrian presence detection, through an inter-vehicle communication system. To achieve this purpose, pedestrian and vehicle recognition is required. Thus, we implemented intended detectors. We used Histogram of Oriented Gradients (HOG) descriptor with the linear Support Vector Machine (SVM) classifier for the pedestrian detector, and Haar feature-based cascade classifier to reach vehicle detection. The performance evaluation of our solution leads to fairly good detection accuracy around 90% for pedestrian and 88% for vehicle.

In Vehicular Ad hoc Networks (VANET), each node can communicate with the other ones in single-hop or multi-hop manner. VANET users require increasingly multimedia services (i.e video streaming) which now represents an important part of the data traffic between those users and the internet. Actually, VANET users tend to pay more attention to Broadband mobile wireless systems for having internet connection to watch video from the internet. Whereas, due to the limited bandwidth to the Internet, users may experience low video resolution and bad transmission quality. In addition, high mobility and dynamic network topology may create an unstable and unreliable communication among VANET users. Thus, guaranteeing quality of service in this kind of communication networks is known to be very challenging. With the objective of providing high-quality video transmission, we propose in this paper, a new multisegment cooperative forwarding strategy for video streaming for VANET users. The new transmission scheme takes into account the speed of the vehicle, its position (direction) and the distance to the access point. To evaluate the performance of the proposed scheme, extensive simulations are conducted using myEvalvidSVC, NS-2 and VanetMobiSim. Simulation results show that the proposed transmission scheme enables efficient and reliable video transmission over VANETs.

Water Pipeline Monitoring System (WPMS) is extremely important considering the several pipeline damages and the various hydraulic failures that cause a critical water loss. In this context, Cognitive Water Distribution System integrates Internet of Things (IoT) technology, based on smart sensors, actuators and connected objects, with a reliable Big Data processing for smart and robust Structural Health Monitoring (SHM) of pipelines. In this paper, we propose a cognitive IoT-based architecture where we used Apache Spark framework to maintain a real time processing of the large amount of collected data. This efficient processing of measured data and its correspondent calculated values simplify the transient simulations and leak detection and make it faster and easier.

Hydraulic failures in Water Pipeline System (WPS) can cause catastrophic environmental hazards. Wireless Sensor Networks (WSN) are greatly deployed to maintain a Structural Health Monitoring of pipeline and supervise the WPS. Since, its implementation increases significantly, its energy consumption represents a critical challenge that should be imperatively investigated in order to ensure an efficient and seamless interconnection between sensor nodes. In this context, the data aggregation techniques are well-designed and various smart algorithms are developed to reduce the quantity of transmitted data and to minimize the energy consumption. In this paper, we combine between data aggregation and bio-inspired clustering algorithm in order to improve the WSN Lifetime.

Internet of things has the ability to control and impact our physical world through technology by combining data collected from sensors and databases along with mining computational intelligence and analytics within IoT platforms. This is leading to a new generation of IoT solutions based on cognition and intelligent reasoning and known as cognitive IoT that will to paving the way to the deployment of new powerful tools. However, this cognition is susceptible to heavy risks related to security and privacy risks and vulnerabilities. In this context, this study will identify cognition and computing intelligence associated to IoT and highlights possible threats and attacks.

Vehicular networking is an enabling technology that promises a huge advancement in the field of Intelligent Transport System (ITS)and mobile networks. Vehicular Ad hoc NetworK (VANET) is emerging quickly as a new architecture that is based on V2X communications. Vehicles are equipped with Electronic Control Units (ECUs) which are interconnected via various intra-vehicle networks (e.g., LIN, CAN, Ethernet, MOST, and FlexRay). Accordingly, a communication gateway is required to facilitate interfacing between the different vehicular environment components. To the best of our knowledge, contributions in this area are very limited. In this context, this paper proposes a new In-car gateway architecture for the Intra and Inter vehicular networks in order to perform the required specifications for V2X communications.

Nowadays, Unmanned Aerial Vehicles (UAVs) are widely deployed in many contexts (civilian and military). Real experiments using UAVs are costly; therefore, the performances of UAVs systems should be analyzed before their deployments. Accordingly, researchers and software engineers developed several simulations tools, environments and frameworks for UAV systems evaluation. In this context, this study highlights and identifies the most suitable simulators for UAVs flights and UAVs managements. This paper details the requirements, the goals, the strengths and the weakness of each studied tools. This investigation helps researchers to identify and to select the adequate UAVs performances analysis tools that satisfy their needs.

Water Pipeline Monitoring System (WPMS) is tremendously important considering the huge amount of water loss caused by leakages and other possible hydraulic failures. Accordingly, to design an accurate Water System management represents a critical task that imposes a serious study and an adequate planning especially in industrial domain. Internet of Things (IoT) technology based on intelligent sensors and physical objects networks is implemented to supervise the Water Distribution System and to cope with water wastage during the supply process. In this area, a great range of industrial IoT (IIoT) methods have been, recently, developed and deployed. In this paper, we propose an IIoT-based Water Distribution Monitoring System approach to detect and locate leaks in a Water Pipeline System. An assessment phase is proposed to evaluate its performance.

The Internet has evolved to become a crucial part of our daily life, professional operation and society. However, the current design is facing big challenges to satisfy users' requirements with the emergent technologies such as Cloud and Fog computing, Big Data, video streaming, etc.Software Defined Networking (SDN) and Information-Centric Networking (ICN) are considered to be the promising paradigms to build the Future Internet and to exceed current issues. This paper initially provides a background on SDN and ICN. Then, we reviewed the existing works on the combination of such paradigms. We investigated the impact of this approach on the innovative and emerging applications.

The reduction of operational expenditure has become a major concern for telecommunication operators and Internet service providers. In this paper, we propose an energy aware routing (EAR) in Carrier Ethernet networks operating with Shortest Path Bridging (SPB) protocol with equal cost multi-path (ECMP). Since traffic load has no influence on power consumption of Carrier Ethernet network elements, the conventional solution to reduce power consumption is to find the maximal set of network elements that can be turned off on so that the network performance is not deteriorated. To tackle this optimization problem, we propose an exact method based on Mixed Integer Linear Programming (MILP) formulation, called SPB energy-aware routing (SPB-EAR). Since SPB-EAR is proved to be NP-hard, we present two heuristics algorithm suitable for large-sized networks, called Green SPB (G-SPB) and Fast Greedy SPB (FG-SPB). In this work, we consider that a connection between two nodes is represented by bundled link consisting of multiple cables. Experimentations on four realistic network topologies show that G-SPB and FG-SPB can save almost as much power consumption as SPB-EAR.

MPEG Dynamic Adaptive Streaming over HTTP (DASH) has become the most used technology of video delivery nowadays. Considering the video segment more important than its location, new internet architecture such as Content Centric Network (CCN) is proposed to enhance DASH streaming. This architecture with its in-network caching salient feature improves Quality of Experience (QoE) from consumer side. It reduces delays and increases throughput by providing the requested video segment from a near point to the end user. However, there are oscillations issues induced by caching with DASH. In this paper, we propose a new Network-Assisted Strategy (NAS) based-on traffic shaping and request prediction with the aim of improving DASH flows investigating new internet architecture CCN.

Underground Pipeline Monitoring System is a challenging task that requires a serious study and a deep investigation. Accordingly, monitoring pipelines in industrial domain for a reliable water or gas transmission is vital considering the huge amount of economic and environmental losses caused by leakages. Hybrid monitoring techniques are employed to offer a robust approach for leak detection that overcomes the challenges of traditional methods and to enhance the localization process. In this paper, hybrid mechanism based on Real Time Transient Modeling and Wave Propagation Method is implemented to detect and locate the position of the leak in a water pipeline. A mathematical model is carried out to solve the transient based leak detection model and different scenarios are developed to estimate the relationship between the pressure fluctuation and leak position. The obtained results approve the potentiality of the proposed technique.

Although H.264 standard is the most efficient video codec, it is sensitive to transmission error over noisy channels. This issue is achieved by several error resilience/concealment techniques, to deal with packet losses. Whereas, these typical techniques are not suited for streaming video transmission which requires high quality. In fact, since the H.264 standard provides different type of frame, these techniques don't give high priority to I frames. Moreover, video transmission over error prone channel has increased greatly. These facts provided the necessary to propose a new technique to deal with errors in video streaming over noisy channels. In this paper, we propose a novel technique, named novel robust decoding algorithm-aware channel conditions for video streaming (denoted as RDVS). The main idea of RDVS is to estimate CAVLC sequence by exploiting the bitstream structure and the semantic property. Through simulations, we show that the RDVS outperforms the H.264 standard.

Content-centric networks (CCNs) have received a lot of interest as one of the major innovative future Internet paradigms. The CCN key feature is built around the named content which can be requested, replayed, routed by name, and stored through in-network caching. Software defined networking (SDN) represents a new concept that can leverage the research and innovation enabling migration to future Internet. This stream of thought can be explored through certain research projects. In this survey, we present the basic concept of each paradigm. We introduce a thorough description of CCN mechanisms as well as their related studies. Therefore, we highlight the benefit of combining CCN networks with SDN and OpenFlow. Then, we analyze some of the most prominent approaches combining the CCN and the OpenFlow.

Content-Centric Networking (CCN) has emerged as a future Internet communication model by replacing host addresses with named contents. Over the last years, caching at routers in CCN is attracting a lot of interest. Specially, caching of video traffic is emerging as a serious concern for CCN deployment due to the special characteristics of video streaming such as large content size and low latency requirements. In this paper, we propose a hierarchical parent-child caching strategy called H-CS in order to ensure low access latency and to reduce load on the network. Basically, each router is assigned a level-based caching indicator to guarantee higher probability content storage as closer as possible to the requesters. Performance evaluation demonstrates the effectiveness of the proposed H-CS scheme using ns-3 based NDN (named data netwok) simulator (ndnSIM). In fact, simulations show how H-CS functionality can be used to achieve efficient and reliable video dissemination including the support of delay tolerant delivery.

Healthcare monitoring systems (HMS) are becoming emerging and revolutionary technology for ambulatory and bedridden patients. They are also expected to offer home assistance to elderly persons while providing qualitative and low cost services. As Wireless Body Area Networks (WBANs) are designed to be part of monitoring systems, a WBAN based HMS might implement Quality of service (QoS) tools to guarantee some physiological data requirements such as data loss and delay as healthcare applications are time-sensitive and loss-sensitive. This paper focuses on mitigation of delay at the coordinator of a WBAN based HMS for emergency and medical traffic flows by proposing a new scheduling strategy named PWRR (Priority-Weighted Round Robin). The PWRR as a combination of a priority scheduling and a weighted round robin utilizes the user priorities of physiological data within the WBAN to determine how to schedule and send them off the WBAN. Analytical and simulation results show that PWRR strategy offers much improvements in terms of delay of emergency and medical data flows at the coordinator compared to the FIFO (first in fist out) strategy.

Energy optimization has become a crucial issue in the realm of ICT. This paper addresses the problem of energy consumption in a Metro Ethernet network. Ethernet technology deployments have been increasing tremendously because of their simplicity and low cost. However, much research remains to be conducted to address energy efficiency in Ethernet networks. In this paper, we propose a novel Energy Aware Forwarding Strategy for Metro Ethernet networks based on a modification of the Internet Energy Aware Routing (EAR) algorithm. Our contribution identifies the set of links to turn off and maintain links with minimum energy impact on the active state. Our proposed algorithm could be a superior choice for use in networks with low saturation, as it involves a tradeoff between maintaining good network performance and minimizing the active links in the network. Performance evaluation shows that, at medium load traffic, energy savings of 60% can be achieved. At high loads, energy savings of 40% can be achieved without affecting the network performance.

Nowadays, the deployment of wireless sensors on patients becomes more and more popular. This promotes researchers and application developers to focus on issues related to Wireless Body Area Networks (WBANs). Applications associated to WBANs include healthcare, personal assistance, and entertainment where sensor nodes collect information and issue them to a central data server. To meet the new requirements of WBANs, the design of an efficient MAC protocol to handle the diversity of data traffic becomes necessary. In this paper, a Priority MAC (PMAC) protocol is introduced where priority is given to life critical traffics. A QoS architecture containing PMAC protocol is proposed and implemented. Simulation results show that our proposed architecture overcomes other architectures based on IEEE 802.15.4 and IEEE 802.15.6 in terms of energy consumption and Packet Delivery Ratio (PDR).

This paper presents an effective cross layer data dissemination approach for healthcare Wireless Body Area Networks (WBANs). This approach guarantees an efficient medium access and route establishment while taking into account the traffic types, energy saving and scheduling issues. Simulation results prove the efficiency of our proposed approach in terms of energy consumption compared to IEEE 802.15.4 and IEEE 802.15.6 standards.

Monitoring systems using Wireless Body Area Networks (WBANs) are becoming more and more popular and widely used to respond to the increasing demand in real time healthcare applications. When operating in group, the WBANed people could form an ad-hoc network remotely monitored via Internet, help each other to relay their data and thus prolong their lifetime. In fact, if a BAN coordinator reaches critical energy level or gets battery depletion or else connection loss, the all WBAN will cope with the data transmission interruption. Fortunately, WBANs cooperation is a promising solution to overcome this issue. In this paper we propose a routing protocol intending to help a group of WBANs to cooperate for relaying packets according to energy and connection issues. The main goal is to provide to WBANs sensors a technique to deliver their data even when the coordinator's battery is very low or depleted or else the connection to the access point is lost or weak while balancing energy consumption between cooperating coordinators. Therefore, the energy threshold technique is used. Simulation results show interesting performances in terms of lifetime about 30% and data delivery about 20% in average.

Video streaming (like YouTube) services and related applications become more and more widespread. Therefore, video streaming delivery over a mobile ad-hoc network (MANET) becomes a necessity as an important content delivery infrastructure between the user (content consumer) and the content storage node. Furthermore, user mobility impacts the quality of the delivered video and hence new concepts should be considered. Accordingly, the innovative concept on network coding (NC) emerges as a promising approach for improving the video transmission quality mainly in multicast environment. In this paper, we focus on Quality of Service (QoS) improvement for video streaming over MANET using random network coding. Basically, we consider video coded by H264/SVC codec that generates packets with different priorities and uses the IEEE 802.11e MAC for traffic differentiation. A successful transmission of high priority packets leads to enhance the video transmission quality. Accordingly, we propose a transmission scheme to protect high priority packets from being lost. Our approach, named Multicast Scalable Video Transmission using Classification-Scheduling Algorithms and Network Coding over MANET (and denoted MSVT_CSA_NC), adopts a cross layer solution between the H.264/SVC codec, the network and MAC layers. Moreover, our delivery mechanisms based on random network coding ensure high throughput and low network load over MANET. Simulation results confirm the substantial performance improvement brought by our approach.

The convergence of telecommunication technologies and the miniaturization of the electromechanical allowed an evolution proved in the world of wireless networks. This evolution is characterized by the emergence of a new generation of wireless personnel networks termed wireless body area networks (WBAN) that manages the human body functions. However, this technology has many requirements yet to be resolved. Security is the main challenge for this type of networks. In this paper, we propose a secret key exchange protocol using physiological signals in wireless body area networks (SKEP). This scheme allows a secure inter-sensor communication basing on physiological signals of the human body using cubic spline interpolation technique. Security analysis prove that our protocol guarantee data confidentiality and integrity in comparison with the previous protocol termed an efficient and secure key agreement scheme using physiological signals in body area networks.

In recent years, Wireless Body Area Network (WBAN) has emerged as a new technology which allows human body monitoring. Because of their enormous benefits, the use of this type of wireless networks is surprisingly increasing day by day and it has a strong presence in several domains. However, WBAN has many challenges yet to be resolved. Security is the most crucial issue. In this paper, we propose a novel key agreement scheme termed Physiological-Feature-based Key Agreement (PFKA) as a biometric based security system. The proposed scheme allows sensors belonging the same WBAN to agree on a symmetric cryptographic key generated from overlapping physiological signal features. On one hand, security analysis shows that our protocol can guarantee data confidentiality, authenticity and integrity. In the other hand, performance analysis show that our scheme reduces communication overhead in comparison with the other proposal protocol termed Secure and Efficient Ordered-Physiological-Feature-based Key Agreement for Wireless Body Area Networks (OPFKA).

QoS requirement is a key issue in wireless communication. Today, researchers are giving considerable interest to multicast video transmission. The legacy IEEE 802.11 WLAN standard is one of the most widely adopted technologies for delivering wireless traffic; whereas, QoS is not yet envisaged in this standard. The IEEE 802.11e (EDCA) standard is defined as the best solution to this issue. Yet, EDCA mechanism performs poorly and continues to have serious issues due to adverse condition of the wireless channel. Besides, EDCA cannot satisfy the high bandwidth that a video application requires. Recently, the 802.11aa is addressing the multicast limitation of audio and video transport stream; because EDCA needs improvements to support differentiation between video streams. The question is how to receive efficient multicast streaming video when the source node delivers simultaneously multicast and unicast flows?. This situation encourage us to design a new cross-layer scheme, based on the 802.11aa, to allow efficient and robust multicast transmission over WLAN. Our approach is called Cross-layer scheme-based Mapping, Scheduling and dynamic Arbitration Algorithms for Multicast Scalable Video Coding CMSAA-MSVC. We analyze the performance of the proposed architecture and evaluate it via simulations. Simulation results show that our approach performs better compared to the traditional EDCA mechanism and recent work in terms of E2ED and PSNR.

Wireless Body Area Network (WBAN) consists of a set of sensor nodes deployed on or implanted in the body and these nodes send sensed physiological data to the personal assistant. Some of these sensor nodes can be located far from the personal assistant or due to body posture the link between the node and the personal assistant is obstructed and so require an intermediate node to help relay their data. As defined in the IEEE 802.15.6 standard, a node can initiate the two-hop extension cooperative communication to relay other nodes data. However, it is impossible to accept relaying for more than one node at the same time. In addition, when a relay node has data to send too, it has to choose either to leave the relaying mode or to maintain it. In this paper we propose a Decode and Merge technique that maintains the relaying mode by merging frames from relayed and relaying nodes. By doing so, a MAC format resizing is required. Apart from maintaining cooperative communication, this technique increases the general throughput without increasing the energy consumption, management and control flows. Furthermore, it increases the ability to resist against interference.

Network management is a challenging problem of wide impact with many enterprises suffering significant financial losses. The Software Defined Networks (SDN) approach is a new paradigm that enables the management of networks with low cost and complexity. The goal of SDN is to make sure that all control-level logical decisions are taken at a central way, as compared to traditional networking, wherein control-level decisions are taken locally and intelligence is distributed in each switch. The aim of this paper is to present a routing solution based on SDN architecture implemented in OpenFlow environment and providing the shortest path routing. The simulations have been carried out in an emulation environment based on Linux and POX controller.

Elliptic Curve Digital Signature Algorithm (ECDSA) provides several security services for resource-constrained embedded devices. It can be the target of attacks as Side-channel attacks. The ECDSA level security can be enhanced by tuning several parameters as key size and the security level of each ECDSA elementary modules such as point multiplication, hash function and pseudo random number generators (PRNG). This paper presents conception and hardware implementation of ECDSA taking in consideration requirements related to correlation between key size and security level according to academic and private organizations. In this work, we have considered a key size equal to 233 bit, Montgomery point multiplication technique and hashing functions SHA-224. The ECDSA design is implemented on a reconfigurable hardware platform (Xilinx xc6vlx760-2ff1760). We used the hardware description language VHDL for compartmental validation. The implementation results illustrate security evaluation and hardware performances in terms of time computation and area occupation.

Quality of service (QoS) is still a crucial issue that deals with the IEEE 802.16 network performance. Scheduling is a key component of the MAC IEEE 802.16 layer which ensures QoS for different service classes. In this paper, we give a detailed simulation study for some scheduling algorithms by evaluating their performance in order to support the QoS classes. We propose a new WiMAX scheduling algorithm for real time applications, called minimum Delay maximum Signal to Interference Ratio (mDmSIR) which is suitable for real time Polling Service (rtPS) class. Our idea is to take into account the maximum latency required by the rtPS connections and their radio states. Our proposed scheme could be the better choice for variable-size real-time connections as it is a tradeoff between maintaining a higher throughput and minimizing the mean average delay. It gives a better results that show an improvement in term of delay. The simulation is carried out via the Network Simulator NS-2.34.

Bus-multiplexed structure of electronic systems in electric vehicle has many advantages. The most important is the connective reduction (size and length of wires) that reduces the electromagnetic interference, which is agreed with EMC (electromagnetic compatibility). In addition, only two wires are required to manage a different system, which necessitates an exchange of date between them. This can only done by networking, using a controller area network (CAN) bus. After a brief introduction of the need for the CAN network in embedded system particularly in electric vehicle application, we describe necessary acquired data in electric vehicle system. Then we present the new developed unit band on CAN network, for date control and acquisition in different levels: conception, structure and links with other systems.