Smart agriculture is now at the heart of global concerns. In this paper, we propose a solution for predicting plant leaf diseases (such as corn common rust, corn northern leaf blight, and peach bacterial spot), recommending crops, and predicting soil types. To achieve this, we used Artificial Intelligence (AI) through various computer vision techniques and algorithms, particularly deep learning convolutional neural networks (CNNs), and also Internet of Things (IoT) technologies through a set of sensors.

With the growing impact of IoT and AI across various fields, their application in sign language recognition presents new opportunities for inclusive communication. This paper investigates different methodologies for recognizing Tunisian Sign Language by collecting and analyzing sign data. By applying advanced machine learning techniques to video-based sign recognition, this study aims to create a standardized and reliable dataset for the Tunisian signing dialect. Leveraging IoT and AI, this research enhances digital communication tools for the deaf community in Tunisia, ultimately contributing to their social integration and quality of life. The findings of our study pave the way for future advancements in AI-driven sign language recognition, fostering greater accessibility and inclusivity.E-Health, Tunisian sign language, Tunisian Dialect, Sign language recognition, Sign Language Translation, Deaf Communication, Artificial Intelligence (AI), Machine Learning, Deep Learning, Convolutional Neural Network(CNN).

The rapid advancement of digital technologies has led to the widespread adoption of Digital Twins, virtual replicas of physical systems that enable real-time monitoring, simulation, and predictive analysis. Although Digital Twins have been successfully implemented in manufacturing, healthcare, and smart cities, their application in smart agriculture represents unique challenges related to data security, interoperability, and standardization. This paper explores the structuring of Digital Twins in agriculture addressing key limitations in current implementations. A layered architecture is proposed to enhance data management, cybersecurity, and system integration, ensuring seamless interaction between IoT sensors, cloud computing, and AI-driven analytics. A classification model is introduced to categorize Digital Twins based on their functionalities and applications in smart agriculture. By overcoming existing challenges, this research aims to optimize resource management, improve decision-making, and promote sustainability in modern farming.

Adaptive aquaculture digital twins face cybersecurity challenges due to real-time synchronization, limited device resources, and heterogeneous networks. This paper introduces a multi-layered security framework combining ChaCha20-Poly1305 encryption on ESP32 edge nodes, secure LoRa transmission, and MQTT over TLS 1.3 for cloud communication. A supervisory digital twin monitors integrity and detects anomalies to strengthen resilience against cyber-physical threats. The framework is quantitatively assessed through a normalized security score (Stotal) based on NIST SP 800-53 and ENISA guidelines, achieving 9.0/10 across all layers, while BAN Logic verification ensures mutual authentication and replay protection. Results demonstrate that robust multilayer security can coexist with computational efficiency, enabling scalable and secure Agriculture 5.0 systems.

Recent advancements in 6G technologies have revolutionized vehicle-to-everything communications, accelerating the evolution of autonomous driving (AD) from a conceptual vision to an operational reality. In this paper, we propose an agile architecture integrated with an offloading framework specifically designed to support AD tasks by harnessing the potential of 6G, while adhering to ISO standards for Automotive Safety Integrity Level (ASIL). Unlike existing offloading approaches that rely on edge/cloud, the proposed ASIL-Centric Offloading Framework for Autonomous Driving (ACOFAD) introduces roadside units as additional remote computing nodes and incorporates unmanned aerial vehicles as communication relays to extend coverage in hard-to-reach or underserved areas. Offloading decisions are dynamically made based on node availability, link reliability, latency constraints, and task criticality. Simulation results demonstrate that ACOFAD achieves very low latency, evaluated to 5 ms and 0.5 s for 20 MB and 2GB payloads, respectively, enabling efficient offload of both safety-critical and non-critical tasks.

Path planning for search missions carried out by Unmanned Aerial Vehicles (UAVs) is a challenging problem. This is due to UAV limited energy budget and the importance of time for search operations. The objective of this study is to come up with an approach to minimize the total search time required to locate a specific target. To achieve this, we deployed a deep reinforcement learning (DRL) model based on the Proximal Policy Optimization (PPO) algorithm to solve the combinatorial optimization problem of UAV search path planning within a minimized search time. A smart reward formulation is designed to achieve the learning goal, fulfill the search requirement, and encourage the agent to select search paths that minimize search time. In addition, we employed Optuna hyperparameter optimization framework to systematically select optimal parameters for the PPO model. Most importantly, thanks to the state representation we considered, the model is generalized and adaptable to various search environments. The PPO model succeeds to compute an accurate search path to be followed by the UAV searcher. Results of the model are compared with results previously obtained with a linear program. We found that the PPO achieves almost the same expected search time, which proves the great relevance of the reward design and the hyperparameters selection we made.

Population growth, specific care for the aged or people suffering from chronic illnesses and the evolution of medical pathologies are some of the major challenges facing the healthcare sector. New technologies offer a solution to these challenges, enabling the development of preventive medicine. e-Health, a fast-growing field based on new information and communication technologies, is becoming crucial today to enhance patient healthcare. With e-Health services, patients can be better monitored and informed of the risks of some behaviors or illnesses. Simultaneously, Artificial Intelligence (AI) sector is emerging rapidly, offering applications that cover a wide range of human activities and bring significant improvements in all domains and especially in healthcare. AI plays today a vital role in e-Health, thanks to machine learning and deep learning techniques. In parallel to this progress, the Internet of Things (IoT) is witnessing a remarkable growth in advancing the e-Health sector thanks to its new technologies and cutting-edge devices. Together, AI and IoT form a fundamental basis for wellness, promoting connected health and personalized medicine. This paper represents a review of various research applying machine learning, deep learning, IoT techniques as well as a combination of these approaches for e-Health.

Livestock farming in agriculture has recently wit-nessed the surge of integrating various information and communication technologies for digital farming that improve the efficiency of resource use and increase the added value of agricul-tural products. In this context, this paper proposes a pioneering secured solution for cattle health monitoring. In particular, we focus on secured tracing of animals that have recently been in the vicinity of an infested one. We propose a new message structure that enables real-time detection based on the signal strength. Secured message exchange is ensured through Elliptic curve cryptography. Further, to avoid potential falsification of epidemic history, we develop a mechanism capable of detecting malicious messages wrongly claiming infection. Performance study shows that the proposed model satisfies all privacy requirements in the context of contact-tracing applications.

Delays in blockchain networks are mainly related to consensus protocols. Among these protocols, we focus on a specific family of protocols, where the mempool's role in the consensus mechanism is explicitly examined. A mempool is a temporary storage area for transactions waiting to be included in a block. This study investigates the round duration of two mempool-based protocols: one requiring a single quorum of messages and another demanding two. We perform the delay analysis with two approaches. First, we elaborate on a Markov chain to determine the distribution of the round durations. Second, we establish an analytical model of message delays while assuming an exponential distribution of message propagation delays. Finally, asymptotic analysis is conducted to estimate the time of quorum formation. We end the paper by comparing the simulation results with the theoretical ones. Results show that both results are very close. This research offers valuable insights into the performance characteristics of mempool-based consensus protocols, aiding in the design and optimization of blockchain systems.

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

The Internet of Things (IoT) allows multiple smart devices to communicate with each other with minimal human intervention. IoT is currently an increasingly popular area of research due to its rapid growth and use in many areas including health, transport, smart cities, smart homes and others. However, IOT face several security threats and challenges. The implementation of traditional security measures. To address these issues, it's necessary to ensure the security, confidentiality, integrity and availability of data in the IoT. The use of Intrusion Detection Systems (IDS) is one of the fundamental solutions available to tackle these challenges. In this study, our objective is to enhance the IoT security by proposing and implementing a new optimized intrusion detection system capable of improving detection accuracy and reducing the rate of false alerts. We define a hybrid technique that combines optimization with classification algorithms in machine learning as well as in deep learning. The main purpose of this hybrid approach is to boost accuracy, precision and performance of the IoT intrusion detection systems while reducing false alerts. To verify security and assess the validity of our proposal, we conduct experiments to validate its accuracy, precision, robustness and correctness.

This article investigates data dissemination delays in a Directed Acyclic Graph (DAG)-based Byzantine Fault Tolerant (BFT) blockchain. We focus particularly on the Narwhal protocol, a mempool-based approach for efficiently disseminating transactions and constructing a DAG. Narwhal is designed to work alongside a BFT consensus protocol like Tusk. Tusk then orders the transaction metadata based on the DAG information. Through an in-depth analysis of the protocol messages, we establish a mathematical model for message propagation delays. We start by considering a specific probability distribution for data network propagation delays: Gaussian Distribution. Then, we consider a general propagation delay distribution. Also, we assume large networks and apply some approximations, i.e., the Central Limit Theorem (CLT). Finally, we develop the Narwhal protocol and demonstrate that the simulated delaysarecompatible with the theoretical ones.

Mobile wireless sensor networks (WSNs) have achieved remarkable advancements across a spectrum of applications. However, the predominant focus in prior research has predominantly been on 2D planar domains, even though many practical scenarios demand the monitoring of three-dimensional spaces. One such instance is in the context of snow monitoring, a quintessential 3D-WSN application. Snowpack surveillance in mountainous terrains plays an integral role in supporting critical functions such as avalanche forecasting, water resource management, and climate control. The initial deployment of mobile 3D WSNs frequently exhibits inefficiencies in data collection due to incomplete area coverage and network connectivity issues. To address this challenge, we introduced a novel deployment strategy called 3D-DVFA-FSC in our prior research. This approach involves the distributed movement of mobile sensor nodes across 3D flat surfaces. Beyond deployment considerations, optimizing energy usage is of paramount importance, given that these nodes operate autonomously and possess finite energy sources. In this paper, our primary goal is to assess the energy consumption of 3D-FSC-DVFA and present an innovative solution to extend its operational lifespan through the implementation of energy harvesting techniques.

Nowadays, several applications target health monitoring through wbans based sensor networks and providing a good QoS is becoming a need more and more insisting in such applications. In this paper, we define HealthGlasses, a new WBAN based e-Health project aiming to ensure health monitoring through several sensors embedded in smart glasses. Defining an innovative way of healthcare, our project vision revolutionizes classical e-Health monitoring process. Through a scenario based on our project, we evaluate the performance of IEEE 802.15.6, the standard specially designed for WBANs, in wireless communication inside smart glasses equipped with healthcare sensors. Then, we optimize IEEE 802.15.6 features and parameters in order to guarantee reliable communication and therefore effective health monitoring in HealthGlasses.

The data storage service offered by cloud service providers is one of the most important markets today. For this reason, cloud computing has become indispensable for all information systems in the new era. However, there are several security risks associated with virtual data storage. Unauthorised access to data remains one of the most significant security risks. Reliable identification, robust authentication and fine-grained access for each client and service are the cornerstones of data security. This paper presents the importance of these models in data security. It also focuses on the proposal of an identification, authentication and access control (I4AS-cloud) module. It presents and discusses a taxonomy of authentication methods and access control models.

Technological progress translates into applications that improve our daily lives based on the collection of personal data. However, the intensive collection of this data, combined with the power of the analysis tools increases the risk of violation of users’ privacy. Web and mobile applications must be implemented with care to ensure that user expectations are met in terms of privacy and do not become tracking applications. This paper focuses on the concept of privacy preserving and the problem of tracking by highlighting the impact of tracking on social and psychological expectations. It further explores how users’ privacy could be exposed and how this risk of exposure could be mitigated using several methods and algorithms that ensure privacy.

This work exploits the potential of two important technologies which are UAVs (Unmanned Aerial Vehicles) and blockchain in the context of Agriculture 4.0. We propose a cattle health monitoring system based on UAVs that collect health measures from IoT devices equipping the animals. The main objectives of our system are twofold. First, the consumer will be aware of the quality of his/her food. Second, the national ecosystem (e.g. agriculture ministry, trade ministry) will get useful information about the quality and the number of cattle that can be put on the market. Thus, smart cattle management strategies could be undertaken afterward. The involved entities in such a system are multiple: the farmer, the veterinaries, the ministry of agriculture, etc… We first start by studying the system’s security by applying the FMEA risk assessment methodology. Our findings motivate us to integrate blockchain technology to manage the data collected as well as the attribution of the UAVs missions via a marketplace. Thanks to its properties, this technology ensures the transparent tracking of cattle status and fairness in the payment of the UAVs managed by private operators. Finally, we develop a proof of concept using the Sui blockchain platform.

The innovation introduced by connectivity brings about significant changes in the industrial environment leading to the fourth industrial revolution, known as Industry 4.0. However, the integration and connectivity between industrial systems have significantly increased the risks and cyberattack surfaces. Nowadays, Virtualization is added to the security field to provide maximum protection against toxic attacks at minimum costs. Combining paradigms such as Software Defined Networking (SDN), and Network Function Virtualization (NFV) can improve virtualization performance through Openness (unified control of heterogeneous hardware and software resources), Flexibility (remote management and rapid response to changing demands), and Scalability (a faster cycle of innovative services deployment). The present paper proposes a Virtualized Security for Industry 4.0 (ViSI4.0), based on both SDN and Network Security Function Virtualisation (NSFV), to prevent attacks on Cyber-Physical System (CPS). Since industrial devices are limited in memory and processing, vNSFs are deployed as Docker containers. We conducted experiments to evaluate the performances of IIoT applications when using virtualized security services. Results showed that many real-time IIoT applications are still within their latency tolerance range. However, the additional delays introduced by virtualization have an impact on IIoT applications with very strict delays.

With mobile cloud data storage, mobile users can offload personal and business data to the cloud for flexibility and save money. However, data outsourcing involves high levels of confidentiality and data protection risks. To address the aforementioned mobile cloud data storage problem, Attribute-Based Encryption (ABE) is proposed due to the flexibility of the encryption strategy and fine-grained access control. However, existing attribute-based multi-authority encryption schemes still require a trusted central authority to issue system parameters and generate secret user keys. In this article, we propose a multi-authority attribute-based access control system that ensures user anonymity, protects user identities from malicious permissions, and supports permission coexistence. Our system uses some location range restrictions with ABE as access policies, and subsequently grants authorization to users whose current location and time align with these policies. To verify security and assess the validity of our proposition, we employ the Scyther tool for verification and conduct experiments to validate its correctness.

Due to the constraints of wireless communications in general and wireless sensor networks in particular, the layered model is poor in mechanisms to support QoS (Quality of service) in such networks. However, the cross-layer model proposes different techniques and architectures allowing collaboration between layers to overcome the limitations of the layered model. In this context, we have proposed the HCQ (Hierarchical Cross-layer approach for QoS provisioning in WSN) model which uses a cross-layer architecture allowing the support of QoS in a monitoring application with different types of traffic. Indeed, our contribution involves three layers, namely, the application layer, the network layer, and the MAC (Medium Access Control) layer. Synchronization between the three layers is implemented allowing the same interpretation of the assigned priorities.

Collisions in multi-hop wireless networks down-grade the system's performance and affect its reliability. Therefore, most of the existing solutions aim to avoid or reduce the interference cases. Counter to all previous works, the newly proposed technique called Physical-layer Network Coding (PNC) transforms the collision's dilemma into an advantage in such a manner that multiple transmissions occur simultaneously and end nodes can extract the superimposed signals. Although there have been many theoretical studies on PN C efficiency, there have been few experiments with pure and heterogeneous PNC schematics. This paper presents the first heterogeneous PNC Software Defined Radio based implementation with a brief discussion of the main challenging related issues. Obtained results proved the efficacy of the proposed solution in indoor environment and serve as a proof for the feasibility of PNC in heterogeneous real-time applications,

Unmanned Aerial Vehicles (UAVs) are being extensively deployed in numerous Livestock Management applications such as cattle disease diagnosis, counting and behavioral monitoring from videos and images captured by drones. The paper focuses on one increasingly important family of applications, UAV-assisted cattle monitoring applications where the objective is to remotely acquire some health state information from IoT nodes attached to the herding cattle. Such livestock data acquisition applications have many challenges. One of these challenges, which is the focus of this paper, is the problem that the target cattle position may not be known precisely, and might be defined with a large area. To address this issue, we design a formulation of this UAV-cattle search path problem as a mathematical optimization problem and show how it can be derived from other well-known formulation and related literature. A Mixed-Integer linear Programming (MILP) formulation is introduced to minimize the expected search time while covering all the search area to efficiently locate the animal. This formulation exploits a cattle position probability distribution map. The results show that the suggested approach yields excellent results using the existing MILP solvers.

In the current era of agricultural robotization, it is necessary to use a suitable automated data collection system for a constant plant, animal and machine monitoring. In this context, Cloud Computing (CC) is a well-established paradigm for building service-centric farming applications. However, the huge amount of data has put an important burden on data centers and network bandwidth and pointed out issues that cloud-based applications face such as large latency, bottlenecks because of central processing, compromised security, and lack of offline processing. Fog Computing (FC) and Edge Computing (EC) are gaining exponential attention and becoming attractive solutions to bring CC processes within reach of users and address computation-intensive offloading and latency issues. These paradigms from Cloud to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations and challenges. This paper carries out a systematic review of the latest high-quality literatures and aims to identify similarities, differences and the main use cases in the mentioned paradigms particularly when using Unmanned Aerial Vehicles (UAVs).

Unmanned Aerial Vehicles (UAVs) and blockchain Technologies are relevant systems that have a significant performance in numerous sectors. In particular, applying these emerging technologies will affect positively the agricultural ecosystem. In this paper, we investigate the opportunities offered by UAV s and blockchain (BC) in the agricultural sector. We review recent research efforts in the subject with a synthesis illustrated by a classification table. Finally, open challenges and future directions for IoT-based agriculture applications are discussed.

Physical-layer network coding (PNC) can effectively improve wireless networks by extracting superimposed signals and transforming collision’s dilemma into an advantage. The proposition of higher layer methods (such as Data Link and Network Layer) explicitly designed for PNC can better enhance wireless network performances. Since the field of PNC is very vast and has several applications, the researchers of the higher layers find it very difficult to propose a suitable PNC system for their research, which has led to the scarcity of research works of this type. This article proposes a new classification method designed to help researchers choose the optimal method that suits their system. This paper is a complete guide for researchers of higher layers to discover the PNC field and improve wireless network performances.

The coronavirus pandemic continues to represent widespread panic, and global initiatives are focused on treating and limiting its spread. Contact tracing promises to help fight this pandemic through early detection of possible contagion events. In this context, several mobile applications have been proposed to try to identify the contact cases of a person who tested positive for COVID-19. In total, 32 countries have developed such applications and are using them as a strategy that may flatten the pandemic curve. These applications can also be adopted to combat future communicable diseases among humans. However, most of these applications do not fully respect the preservation of the privacy of users. That’s why, the adoption of these applications is being held back in many countries. Motivated by the need to protect user privacy, we analyze some of these applications. We demonstrate that they collect sensitive data which represents a violation of user privacy. At the end, we present several major recommendations that can serve as a reference, contributing to the development of privacy-preserving contact tracing applications.

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

In resource-constrained Wireless Body Area Networks (WBAN), one of the fundamental challenges is to provide sustainable e-health service. We here investigate time resource allocation for e-health applications with energy harvesting enabled sensor nodes to maximize the overall network lifetime. The allocation is processed by a central node that executes an allocation algorithm based on game theory. In particular, first-price sealed-bid auction-based (FPSBA) is formulated as a solution to achieve time allocation while ensuring service sustainability. The proposed solution prioritizes sensor nodes as bidders with a maximum instantaneous energy level to bid while offering the sensor nodes with low instantaneous energy level the opportunity to harvest more energy and increase their possibilities to provide the required energy to continuously submit their data.

New technologies, used in industry 4.0, have paved a way for a massive deployment of Cyber-Physical Systems (CPS). Today, we assist to new smart factory services and scenarios such as smart production, smart maintenance, smart logistic, etc. The security is one of the challenges that face the new industrial environment. Indeed, smart factory applications present many vulnerabilities that have been investigated in the literature. In this paper, we identify the IIoT applications and we present a state of the art of security issues in the industry 4.0.

Thanks to advanced techniques such as carrier aggregation (CA), alongside with the emergence of the LTE on the 5Ghz band (LTE-U), it has become possible to boost the performance of existing cellular networks. However, LTE-U threatens the performance of co-existing Wi-Fi systems operating over the 5Ghz bands. We will investigate in this paper, an effective coexistence mechanism, that aims at creating a common ground between LTE-U's QOS satisfaction, and the WIFI's performance. We will use a poly-game generator approach, where a set of games is predicted by a neural network, to solve the issue.

A significant proportion of elderly population suffers from age-related health issues and different chronic diseases which leads to a progressive decline in physical and cognitive skills which prevents them to live independently in their home and to perform basic Activities of Daily Living (ADL). In this context, the integration of information and communication technologies (ICT) has resulted in a rapid emergence of pervasive healthcare systems such as Health Smart Homes which aim to monitor and evaluate the person's health condition and their behavior in performing ADL and to avoid, as long as possible, the delays of recourse to healthcare institutions (e.g. nursing homes and hospitals). In this research, we propose a predictive model for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. We focus on keeping track of the evolution of the dependency level and detecting the loss of autonomy for elderly person using a Hidden Markov Model based approach. We were interested in including blood pressure in this predictive process as it is considered the primary risk factor for cardiovascular disease, stroke, kidney failure and many other diseases. Our simulation was applied on a empirical dataset that concerned 3046 old persons monitored over 9 years. The results show that our model accurately evaluates person's dependency, follows its evolution of autonomy over the time and thus predicts moments of important changes.

Due to the rapid increase in mobile usage within cloud computing, a new computing paradigm has emerged which is called mobile cloud computing (MCC). In here, some new classes of privacy and security challenges are introduced. In this work, we purpose this survey to present the main privacy and security challenges in this domain, which have grown much interest among the research community. However, many researchers have proposed and identified corresponding security solutions to address these challenges.

The objective of health monitoring systems is to provide sustainable and high-quality service to subscribers, hence requiring that the system run without interruption for a sufficiently long time. In this paper, we investigate lifetime maximization in Wireless Body Sensor Networks (WBSN) to provide sustainable e-health service with energy harvesting enabled nodes. Maximizing the lifetime is equivalent to minimizing the energy budget within the whole network. To this aim, we optimize the time resource allocation for all involved sensor nodes in order to maintain a sufficient energy level for longer time after each packet transmission. Specifically, we model the energy harvesting process as a discrete-time Markov chain and evaluate the instantaneous energy budget in order to select the node that will occupy a specific time slot. The energy budget is presented as the net energy, which we quantify through the gap between the consumed and harvested energies. Therefore, to achieve sustainability, the idea of the proposed algorithm focuses on prioritizing nodes with a minimum net energy level while offering nodes with high net energy level the opportunity to harvest more energy and increase their capabilities to provide the required energy to continuously submit their data. The proposed algorithm is evaluated in terms of sensor uninterrupted lifetime and packet loss due to energy run out through simulations. The results demonstrate longer uninterrupted lifetime compared to the considered benchmark and low packet loss probability to subscribers.

Cloud Computing is the most promising paradigm in recent times. It offers a cost-efficient service to individual and industries. However, outsourcing sensitive data to entrusted Cloud servers presents a brake to Cloud migration. Consequently, improving the security of data access is the most critical task. As an efficient cryptographic technique, Ciphertext Policy Attribute Based Encryption(CP-ABE) develops and implements fine-grained, flexible and scalable access control model. However, existing CP-ABE based approaches suffer from some limitations namely revocation, data owner overhead and computational cost. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated RS-CPABE. We applied splitting algorithm. We execute symmetric encryption with Advanced Encryption Standard (AES)in large data size and asymmetric encryption with CP-ABE in constant key length. We re-encrypt in case of revocation one single slice. To prove the proposed model, we expose security and performance evaluation.

Today, the number of applications developed for a smart transportation is constantly increasing. PUVEC (Urban Platform for Connected Electric Vehicles) is a context aware application that aims to solve Electric Vehicles (EV) issues, such as battery autonomy, by providing EV drivers with an energy map. In the framework of PUVEC, data are collected periodically from the sensors embedded in the vehicle and sent to the cloud. In this paper, we are interested in the intra-vehicle wireless sensors communication submitted to meet new security challenges and requirements. In particular, security mechanisms should be adapted to the driving dynamic context. We propose a mathematical model to describe an adaptive security for the Intra-Electric Vehicle wireless sensor network. This approach relies on a set of strategies depending on the state of charge, the nearest available charging station and the traffic conditions. Simulation experiments are conducted to evaluate the proposed approach in terms of trade-off between security and energy.

The unlicensed band was found to be useful in terms of users applications QoS Regarding the downlink throughput. The unlicensed band, once used exclusively by the military and medical industry, will soon be exploited for the public interests. The large bandwidth guaranteed by the 5GHZ frequency band will help reduce the data transmission latency. It is for these reasons that the LTE-U (Long Term Evolution on Unlicensed band) was developed and will play a key role in the wireless networks of the 2020's. Its deployment, however, will be at the expense of other existent wireless networks technologies, specifically the Wifi. Indeed once the Wifi networks deployed alongside with the LTE-U, their users applications will be constantly interrupted, since the LTE-U is a dominant technology Regarding the channel access. This issue will cause a degradation of a large part of those deployed users in the heterogeneous networks regarding the QoS. Which is why it is important to figure out a solution that allows Wifi-exploiting applications in heterogeneous networks to serve their users with a minimum value of QoS. In this publication, we will detail the important steps towards a coexistence between the Wifi and the LTE-U, using the cooperative game theory, and more specifically the canonical coalition game, for a fairer channels/sub-channels allocation. The paper also solves the time allocation problem between the access points and the small cells, thanks to the bankruptcy game, which is an entitlement problem involving the allocation of a given amount of a perfectly divisible time among the wireless nodes. The publication will also show that the proposed solution provides a higher average user's throughput than the Bargaining Game solution, whether the user is in the Wifi or the LTE-U network.

Access control models for the Internet of Things (IoT) proposed in the literature are based on centralized architecture and raise security issues due to the spontaneous and dynamic interaction between IoT devices. In addition to the scalability and lightweight features, the need of secure and distributed access control architecture to overcome the single point failure problem of a centralized entity becomes a big challenge. This can be done through the Blockchain technology which is used recently to provide access control services. Exploiting this technology to manage access IoT devices in term of distribution, heterogeneity, scalability, fault tolerance capability, security and privacy are promising. In this paper, a comprehensive review of the existing access control models based on Blockchain is presented and discussed with comparison and analysis.

The Internet of Things is becoming increasingly widespread, and with this growth arise new concerns and challenges such as IoT synchronization protocols and power consumption. Despite the advancement of research, it is challenging to find an IoT constrained device operating system (OS) that can fill all IoT needs. The purpose of this paper is to implement the synchronization protocol SiSP under IoT OSs and to compare these selected OSs according to their power consumption. Study results show that RIOT OS is a better choice than FreeRTOS due to its efficient power consumption.

Internet of Things (IoT) is a novel networking paradigm that contributes greatly in improving daily life. However, it includes a great number of interactive nodes that generate, aggregate and exchange critical data. Thus, security and privacy challenges must be handled. Named Data Networking (NDN) represents a promising future networking paradigm fitting perfectly with the requirements of IoT applications and especially those related to security and privacy. In this paper, we leverage the basic feats of NDN vision for designing a robust privacy preserving NDN-based e-health IoT system (PP-NDNoT). It ensures security and fulfills content and contextual privacy requirements. Indeed, the security and privacy level is fixed according to the patient's state. To prove the robustness and effectiveness of our proposal, an extensive security analysis and performance evaluation are reported.

Remote control the smart home eases the burden of the user's daily tasks. Indeed, he can program smart products from anywhere in the world according to his needs. However, the sensor devices as well as the user mobile device communicate via an insecure communication channel. Thus, many attacks are possible, such as privileged-insider attack, impersonation attack, denial of service attack, and the mobile device stolen attack. In the literature, most of the current schemes for smart home remote control are not secure quite regard the aforementioned attacks. As well, they are not lightweight enough at the sensor device side. In this paper, we propose a remote user authentication scheme for a smart home environment. For the computation of a robust session key, we propose to use Elliptic Curve Cryptography. The solution is lightweight for resource-constrained devices with limited resources as the gateway node will assist in deriving the session key to the sensor device. The security of the proposed solution is proved using a formal security evaluation via the Scyther tool. Also, a performance evaluation is performed to show the effectiveness of our solution.

Access Control models are required to secure Cloud Computing from unauthorized access. As an efficient cryptographic tool, Ciphertext Policy Attribute Based Encryption offers a fine-grained access to user assets. But, revocation operation presents a major brake in the implementation of this algorithm. However, existing approaches either cannot deal with this problem or have an important computation and/ or storage overheads. In this paper, we propose a sliced revocable solution resolving the aforementioned issues abbreviated MRS-CPABE. It is an efficient solution in case of lazy or complete revocation. We apply information dispersal algorithm and we introduce a match then decrypt process. To assess the perfromance of our solution, we perform a security evaluation with Scyther tool and a formal validation to prove that our proposal can withstand various known attacks. Then, we expose the experiments simulation results.

The increasing proliferation of Cloud Services (CSs) has made the reliable CS selection problem a major challenge. To tackle this problem, this article introduces a new trust model called Chain Augmented Naïve Bayes-based Trust Model (CAN-TM). This model leverages the correlation that may exist among QoS attributes to solve many issues in reliable CS selection challenge, such as predicting missing assessments and improving accuracy of trust computing. This is achieved by combining both the n-gram Markov model and the Naïve Bayes model. Experiments are conducted to validate that our proposed CAN-TM outperforms state-of-the-art approaches.

Wireless sensor networks (WSNs) are composed of collaborating sensor nodes that are able to sense the environment in order to monitor physical phenomena and to track moving targets. Sensor deployment is a crucial issue in a WSN in order to optimize coverage and connectivity of sensors with an optimized number of sensor nodes. To reach this goal, sensor nodes must be placed at well-defined positions that will guarantee full area coverage. Previous researches were mainly interested in Two Dimensional plane deployment or in Three Dimensional volume deployment. But recently, some researchers focused their studies on sensor deployment in 3D surfaces, trying to achieve a high coverage rate while maintaining the network connectivity. In this paper, we are interested in a fully distributed deployment of WSN in several 3D terrain configurations using autonomous and mobile nodes. Our goal is to ensure full 3D surfaces coverage and maintain network connectivity while minimizing energy consumption. So, we propose 3D-IDVFA-TC, a distributed deployment algorithm based on an improved virtual forces strategy to move sensor nodes over 3D smooth, undulating and rough surfaces and in order to 1) provide 3D terrain full coverage 2) ensure network connectivity and 3) eliminate the nodes' oscillations problem which is the main drawback of virtual forces approach. Simulation results show that 3D-IDVFA-TC provides a full coverage rate regardless of the 3D surface shape while maintaining network connectivity and reduces the nodes' energy consumption when the full coverage is reached.

The standard IEEE 802.11p was proposed to support the rapid exchange of data between the vehicles themselves and between the vehicles and the RSU (Road Side Unit). Besides, the Medium Access Control (MAC) protocols in Vehicular Ad-hoc Network (VANET), presented in the literature, have many weaknesses in supporting real time traffic. Indeed, some of them combine the advantage of TDMA with CSMA/CA to organize the channel access, therefore collisions may occur when safety messages are transmitted. In the context of hard real time, meeting deadlines requires prior knowledge of the possible communications between vehicles. Thus, we introduce an extension of the Enhanced Distributed Channel Access (EDCA) defined in IEEE 802.11p combined with TDMA, called Qos-aware Centralized Hybrid MAC protocol (QCHMAC). To evaluate our proposed protocol, we implemented it in ns-3 and we used the simulator SUMO to generate vehicular mobility traces. The simulation results reveal that QCH-MAC significantly outperforms pure TDMA in terms of transmission delay, packet loss rate and network throughput.

E-health represents one of the most promising Internet of Things (IoT) applications. Indeed, it enhances the traditional medical services by monitoring periodically patient's health information. Due to the sensitivity of exchanged medical data, strong and efficient security and privacy protocols must be deployed. However, existing privacy preserving approaches either cannot achieve a high level of privacy, or have an important computation and/ or storage overheads. In this paper, we propose a privacy-preserving e-health system, achieving patient privacy and better meeting the IoT environment requirements. This system includes a new cryptographic scheme called KE-IBE which relies on Identity-Based Cryptography (IBC) and resolves its key escrow issue. To prove that the proposed protocol can withstand various known attacks, we perform an informal security analysis. To assess the performance of our proposal, we implement it and expose the experiments simulation results. This evaluation shows that the proposed system is highly efficient in comparison to related approaches found in literature and that it achieves privacy with an acceptable running time.

WSNs are used in many application fields thanks to their low cost and ease of deployment. They are generally used to control hostile and inaccessible areas. Thus, they require a QoS support. This paper presents a routing protocol, called Delay and energy efficient proactive routing protocol for event-driven WSNs (DEEP). The later is a hierarchical solution that uses a shortest path routing tree and a clustering algorithm to perform aggregation. This protocol aims to create a trade-off between the minimization of the end-to-end delay and the energy consumption as QoS metrics. The simulation shows good results in terms of average delay, energy consumption, and packet delivery rate.

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU's throughput by up to 9 times and 22% in comparison to these both schemes.

E-health IoT application has a great potential in improving daily life. However, it includes a huge number of communicating nodes exchanging sensitive data, which rises both security and privacy challenges. Named Data Networking (NDN) represents a promising future network architecture, dealing with such challenges. It fits perfectly with all IoT applications and specifically E-health. In addition, it supports natively several security and privacy aspects. In this paper, we propose a pioneering privacy preserving E-health solution over NDN. This solution ensures security and achieves all privacy requirements. It relies mainly on the improvement and the adaptation of an existing privacy attempt for NDN, named ANDa̅NA. We prove the robustness of our proposal using a security analysis and we demonstrate its effectiveness through performance evaluation. The simulation results reveal that our proposal has an acceptable transmission delay and involves a negligible overhead.

In metropolises, air pollution is constantly increasing due to multiple factors, including high vehicles mobility and manufactures emissions. Nowadays, the urban environment management and control are among the most important issues of network technologies. Compared to the old environmental monitoring methods, the new network technologies provide several advantages such as flexibility, low latency, and real time control. Therefore, to better control the environment, we present an Alternative Data Gathering strategy for the collection of air pollutants data. Our strategy is based on applying LoRa and LoRaWAN protocols as well as on using NS3 simulator. The experiments showed that our strategy reveals better results in term of the Packet Delivery Ratio.

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

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

Unmanned Aerial Vehicles (UAVs) are becoming an attractive solution for road traffic monitoring because of their mobility, low cost, and broad view range. Up to now, existing traffic monitoring systems based on UAVs only use one UAV with fixed trajectory to extract information about vehicles. In this paper, we propose a road traffic monitoring system using multiple UAVs. We develop a method to generate adaptive UAVs trajectories, which is based on the tracking of moving points in the UAV field of view. Also we generate UAVs trajectories using mobility models that are usually used to model vehicles mobility. UAVs monitor the traffic on a city road, they are responsible for collecting and sending, in real time, vehicle information to a traffic processing center for traffic regulation purposes. We show that the performance of our system is better than the performance of the fixed UAV trajectory traffic monitoring system in terms of coverage rates and events detection rates.

The use of Electric Vehicle (EV) is growing rapidly due to its environmental benefits. However, the major problem of these vehicles is their limited battery, the lack of charging stations and the re-charge time. Introducing Information and Communication Technologies, in the field of EV, will improve energy efficiency, energy consumption predictions, availability of charging stations, etc. The Internet of Vehicles based only on Electric Vehicles (IoEV) is a complex system. It is composed of vehicles, humans, sensors, road infrastructure and charging stations. All these entities communicate using several communication technologies (ZigBee, 802.11p, cellular networks, etc). IoEV is therefore vulnerable to significant attacks such as DoS, false data injection, modification. Hence, security is a crucial factor for the development and the wide deployment of Internet of Electric Vehicles (IoEV). In this paper, we present an overview of security issues of the IoEV architecture and we highlight open issues that make the IoEV security a challenging research area in the future.

Today, Cloud Computing architectures offer a cost-efficient services delivered for business, scientific and legal organizations. These structures are used as a guideline to understand the Cloud component, services, actors and interactions. However, few Cloud Computing architectures are proposed and used as references to build a Cloud infrastructure. Most of these models does not treat and burst security layers despite the fact that it presents the most important concerns in the Cloud. The aim of this paper is to propose a new end-to-end reference architecture based on a novel classification of security issues in Cloud Computing environment. To do so, we are going to explore the fundamental concepts of Cloud. We will study and propose a novel actor-based classification of security issues. We will model a Cloud reference architecture from security perspective and we will study in depth security and defense in the Cloud provider tier. We will add a management layer in order to refine our design. To the best of our knowledge, our proposal provides the first Cloud Computing reference architecture actor-based from both security and management perspectives in research field.

E-health represents an attractive application for the Internet of Things. It improves the quality of medical services by allowing periodic patient diagnostics. As it collects and sends sensitive biomedical information, both content and contextual privacy requirements must be satisfied. Content privacy ensures the protection of patient information against inappropriate disclosure. Contextual privacy is about the context of the communication. Several privacy-preserving approaches have been proposed. However, they only consider content privacy requirement. To fill this gap, we propose in this paper a new privacy preserving scheme adapted to e-Health systems, satisfying all privacy requirements, as well as communication security and authentication. Content privacy is based on cryptographic primitives. Contextual privacy relies on the onion routing concept, fake messages injection and a multicast strategy. To validate our proposal, a security and performance analysis are reported. The security analysis confirms its safety and proves its resistance to a set of security attacks. The performance analysis highlights the trade-off between privacy and efficiency.

Software Defined Networking (SDN) and Named-Data Networking (NDN) are two new paradigms that aim at changing the way we design and architect computer networks. The NDN architecture was first proposed to solve the problems of current Internet Protocol (IP) communication. However, there is an important disadvantages in the existing NDN, which are the very large size of the NDN forwarding table and the huge overhead cost of the forwarding process. This may result in not only a slow delivery of data, but also in a lot of network traffics. For this reason, this architecture cannot be appropriately used in a large-scale network.To solve this problem, we proposed a novel forwarding Strategy for Named Data Networking based on SDN and bloom Filter (NDN-SF) in order to improve the efficiency of content delivery and enhance the consumption of network resources. Our experimental results showed that latency and bandwidth consumption are reasonably low in our method NDN-SF. They also demonstrated that the developed scheme is more efficient than the existing forwarding strategies in NDN.

The Internet of things (IoT) is pervading our lives to allow a comfortable and smarter human living space by connecting surrounding human things to the Internet. But, it reveals sensitive and private data as well as human appliances to intruders. To solve these problems, security solutions based on cryptography might be used. But, these solutions are based on encryption keys that must be managed securely and properly while taking into account the IoT characteristics. In this paper, we investigate the LoRaWAN IoT architecture. We evaluate its security by gathering possible attacks that can be launched on it, using the Scyther tool. Then, we propose an enhanced version of the LoRaWAN architecture that solves those attacks. We evaluate the proposed solution in terms of security and key management requirements and compare it to existing solutions.

As it refers to the ever-growing network of physical objects that feature an IP address for internet connectivity, the Internet of Things (IoT) is becoming very challenging for research community and domain. Operating systems for IoT devices play a vital role in this regard. These low-end devices can not use traditional operating system (OS) such as Windows or Linux due to stringent resource constraints. A lot of efforts have been made to design operating systems for these concerned devices. The main contribution of the paper is the comparative study of the most recent operating systems for low-end IoT devices. The comparison will focus on the major OS keys as architecture, scheduling, real-time capabilities, programming model, memory footprint, network connectivity, hardware support and energy efficiency. We highlight these OS keys values at the selected OSs: FreeRTOS, Mbed, Contiki, TinyOS and RIOT.

Wireless sensor networks (WSNs) are considered as the key technology for IoT (Internet of Things) applications thanks to their robustness and their deployment ease. The IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for multi-hop WSNs. However, RPL poorly adapts to sensor nodes' movement which rapidly alters the network performance. Therefore, we propose in this paper, a Bayesian model to accurately predict the sensor nodes' speed distributions. Then, we introduce the Mobility based Braided Multipath RPL (MBM-RPL) to support mobility over RPL. MBM-RPL establishes a primary path based on a new routing metric that exploits the predicted sensor nodes' speed values. An alternative path is established to prevent links expiration along the primary path. To evaluate the performance of MBM-RPL, we first validate the accuracy of the Bayesian model using the Cooja simulator. Then, we compare the performance of MBM-RPL with other RPL based approaches in terms of packet loss rate (PLR) and average transmission delay (ADT).

This paper introduces an approach that handleswith the trustworthy cloud service selection issue in Cloudcomputing environments. Despite the fact that most of theexisting trust systems consider several QoS attributes for trustcomputing, none of them did consider the correlation that mayexist among these attributes. However, we demonstrate in thispaper that the integration of correlation between QoS attributesin trust computing significantly helps to solve many issues suchas predicting missing assessment, detecting malicious feedbacksand improving the accuracy of trust values. The main goal ofthis paper is to show the significance of correlation betweenthose attributes for trust computing. To do that, we proposecombining both the popular Naïve Bayes model and the ngramMarkov model to design a more efficient trust model forcloud environments. Our proposed trust model also takes intoaccount the user's requirements, aggregates both the qualitativeand quantitative assessment, and considers several sources whencomputing cloud services' trust values. Experimental results showthat our proposed approach outperforms the traditional Naïve Bayes trust models.

Exploiting multi-user diversity (MUD) means to assign the resources to the user experiencing the best channel conditions. When multicast communication is considered, MUD means that a sender can serve the intended receiver users using one or many data transmission rates. In fact, MUD-based multicast schemes aiming to maximize the multicast throughput (e.g. DOMS and MOST) or to minimize the multicast delay (e.g. EMTT) are shown to significantly outperform other multicast schemes. In this paper, we suggest a distributed, stateless and multi-user diversity-based multicast scheme; the Multicast scheme for Opportunistic Throughput subject to Delay (MOTD), which is utilized to study the intrinsic trade-off between the multicast throughput and the multicast delay in multi-rate networks. Then, we study the performance of the previously mentioned multicast schemes and we compare them to MOTD. The evaluated features are the throughput and the transmission delay. Mainly, MOTD registers very good throughput values that are constrained by moderated transmission delays.

Distributed Denial of Service (DDoS) attacks represent major risks for the current cloud computing architecture. The rate of DDoS attacks in cloud is growing because of the essential characteristics of cloud computing. In this paper, we propose to use Software Defined Network (SDN) architecture and Fast Entropy approach in order to secure cloud computing environment from DDoS attacks in real time. Thus, we exploited the centralized control and programmable characteristics of SDN architecture to supervise cloud traffics. The provided architecture collects and analyzes flow tables from switches. It also detects DDoS attacks by the controller using Fast Entropy algorithms. Through the performed experimental tests, we evaluated the performance of our solution in defending Cloud computing infrastructure against Distributed Denial of Service attacks.

This paper addresses the users' need to trust the commercial cloud providers and the security issues related to data storage in a cloud storage service. The cloud storage is one of the prominent services offered in cloud computing. Data stored over cloud in the plain text format represents a security threat. Thus, in this paper, we propose a method relying on the Identity Based Cryptography (IBE) for cloud storage that allows user to store and access the data securely. It also guarantees that only the authenticated client can access the data. This method provides security and privacy for data stored in public servers.

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

We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.

The social internet of things (SIOT) is a thriving research field that emerged after the integration of social networking concepts in the internet of things. It resulted in the appearance of new and more powerful applications. Indeed, trust management system (TMS) has been considered as an effective security mechanism in the Internet of things. Thus, many research works have been carried to propose trust evaluation and trust prediction methods. In the traditional trust management system, historical behavior data are taken into account to predict the trust value of the network entity, while the context of the network entity is rarely considered. The novelty of our approach can be summed up in three aspects: i) designing a highly scalable trust model, ii) the ability of the system to provide the most trustworthy service provider which takes into account the dynamic aspect of the internet of things, such as context, the capacity object and the social relationship between objects, iii) and the application of decision tree to analyze first the relationship between the different components of the network and the object behavior, and to improve then the decision making.

The smart grid, new generation of the electric grid, aims to improve the energy production, delivery and customer usages. To reach this goal, smart devices, smart meters, etc. must exchange information with the control center in a bidirectional way. The Home Area Network (HAN) manages communications among the smart meter and smart devices inside the home. The ZigBee Alliance developed a profile for the energy HAN called the ZigBee Smart Energy Profile (SEP). While the HAN is vulnerable to several types of attacks (eavesdropping, spoofing, DoS, etc.), SEP defined security mechanisms to join the network and to protect the traffic. In this paper, we identify vulnerabilities of SEP security mechanisms and we propose improvements to the SEP join process and key refreshment. We conducted simulations to evaluate the latency for energy real time applications while using SEP and the success rate for SEP and the enhanced SEP.

To enhance the security of spectrum sensing in centralized cognitive radio networks, we introduce a novel trust game-based model. We aim at encouraging the secondary users to send correctly their local sensing outcomes to the data fusion center. Our proposal ensures both the attacks detection and the punishment of malicious users. Indeed, our proposed punishment function intends to exempt the malicious secondary users with faulty spectrum sensing from participating in the spectrum sensing phase. Extensive simulations illustrate that the proposed game-based model outperforms the AND-rule and OR-rule models in terms of correct decision, missed-detection, false alarm, error probability and throughput.

Wireless Sensor Networks (WSNs) are used in a wide range of applications due to their monitoring and tracking abilities. Depending on the applications goals, sensor nodes are deployed either in a two dimensional (2D) area or in a three-dimensional (3D) area. In addition, WSN deployment can be either in a distributed or a centralized manner. In this paper, we are interested in a fully distributed deployment of WSN in several 3D-flat-surface configurations using autonomous and mobile nodes. Our goal is to ensure full 3D flat surfaces coverage and maintain network connectivity for these surfaces. To reach our goal we propose 3D-DVFA-FSC, a distributed deployment algorithm based on virtual forces strategy to move sensor nodes over different 3D-flat-surface shapes. Simulation results show that 3D-DVFA-FSC provides a full coverage rate regardless of the 3D-flat-surface configuration while maintaining network connectivity.

Vulnerability(s) and attack(s) can occur on the proxy or during translation protocol of secure transport protocols in Constrained Application Protocol (CoAP). Existing security countermeasure deploys Datagram Transport Security layer (DTLS) and Transport Security layer (TLS) between client and server as IoT (Internet of Things) communicating entities. Proxy plays the role of interface between client and Server. It can also decrypt the received message and encrypt data according to the used security transport protocol of the other side. The vulnerability appears during this phase, especially, where the proxy is not confident or supervised by an illegitimate entity. Consequently, passing through the proxy communication node, security services like confidentiality and integrity can easily be compromised. Exploiting advantages of studied cryptographic algorithms, we focus on our customized security objectives regarding proxy element and DTLS-TLS translation. We detail, in this paper, the algorithm and the sequence diagram of secure communication of our proposal adapted for CoAP architecture. As an encryption strategy, we follow the cryptographic envelope principle based on ID-KEM and Three-pass Protocol. As a hypothesis, we assumed that the communication deploys our recent IDMS (Identity management System) contribution for IoT, relying on the EAP_OAuth2.0 (Extensible Authentication Protocol and Open Authorization Protocol) protocols via DTLS, as the starting phase in order to keep authentication and authorization services. Finally, we describe the security validation, present our perspectives and conclude our work.

Collision probability in Vehicular Ad-hoc Network (VANETs) has been widely studied because it is an essential issue for any performance evolution, such as delay evaluation or throughput. Indeed, EDCA (Enhanced Distributed Channel Access) represents the distributed channel access mechanism in IEEE 802.11p standard. In this paper, we present a new model to estimate collision probability based on the existing model. We use the simulation to study the impact of mobility on EDCA and to compare our proposed approach with those introduced in literature.

In this paper, we present a solution for Unmanned Aerial Vehicle (UAV) energy saving problem while ensuring a continuous tracking of a mobile target. Our tracking algorithm is based on three zones, and according to the target placement in those zones, the UAV will do a specific type of actions. We proposed an additional zone called the authorized zone. In this zone, the UAV keeps a fixed velocity and a fixed altitude, and this contributes to the limitation of the energy consumption while also maintaining a minimal altitude. We also proposed an adapted criterion which considers the velocity, altitude and acceleration changes to evaluate the energy consumption. The idea here is to limit the number of the UAV adjustments to reduce the energy consumption and still keeping the target in its camera field of view.

This paper addresses the channel occupation and the channel selection problem for the Long Term Evolution Unlicensed (LTE-U) technology when coexisting with the WiFi system on the unlicensed spectrum (typically the 5 GHz band). For instance, LTE is a communication standard developed by the 3GPP corporation based on the GSM/EDGE and the UMTP/HSPA technologies and conceived for high-spaced wireless communications. If we focus on LTE-U, it is an extension of the LTE-A (LTE-Advanced) scheme in unlicensed bands. In this paper, we will discuss the coexistence matters between the WiFi and LTE-U technologies. Therefore, we review the different solutions discussed in literature that addressed the coexistence issue. These works are either based on fair spectrum allocation between LTE-U and WiFi technologies or on the game theory paradigm where multi channel access and inter-dependance scenarios are discussed. As a future work, we intend to further investigate the coexistence issues between LTE-U and WiFi systems, especially in the case of inter-dependance scenarios and other configurations including hidden/exposed terminal problem in WiFi networks.

Internet of Things (IoT) raises many security challenges in relation with the different applications that can be deployed over these environments. IoT access control systems must respond to the new IoT requirements such as scalability, dynamicity, real-time interaction and resources constraint. The goal of this paper is to propose an approach based on Fog and Distributed Hash Table (DHT) toward access control for the Internet of Things. To evaluate the performances of our access solution, we used NS-3 and SUMO. The preliminary obtained results show acceptable overhead for the considered Intelligent Transport System (ITS) scenario.

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

Spectrum sensing security in cooperative cognitive radio networks with continuously mobile secondary users becomes a critical challenge. Thus, we propose a trust game-based model to ensure the spectrum detection while the mobility of the SUs is taken into account. Our proposal ensures both the attacks detection and the punishment of mobile malicious users launching the Spectrum Sensing Data Falsification (SSDF) attacks. Extensive simulations prove that the proposed model outperforms the AND-rule, OR-rule and Game-Based Secure Sensing (GSS) models in terms of correct decision probability, throughput and error probability with the random and linear mobility models and under four types of SSDF attacks.

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

Vehicular Ad-Hoc Networks (VANETs) have become an emerging technology due to the variety of their applications in Intelligent Transportation Systems (ITS). By creating a vehicular network, each vehicle can exchange information to inform drivers in other vehicles about the current status of the traffic flow or a dangerous situation. Multi-hop communications is an effective method that can be used for information exchange over distances greater than the transmission range of the transmitting vehicle. However, it is a great challenge to ensure a stable multi-hop communication link with a low delivery delay due to the high mobility of the vehicles involved. The goal of this paper is to design a TDMA aware Routing Protocol for Multi-hop wireless vehicular ad hoc networks (TRPM) in order to provide the ability to transmit/receive packets over long distances. The proposed routing scheme is based on a medium access control protocol, in which the intermediate vehicles are selected based on the TDMA scheduling. The simulation results reveal that our routing protocol significantly outperforms other protocols in terms of average end-to-end delay, average number of relay vehicles and the average delivery ratio.

The Internet of Things (IoT) is based on the idea that things surrounding the human living space can be connected to the Internet, allowing smarter living environments and more comfortable life. However, adoption of the IoT might not be approved unless security issues are solved. In this paper, we propose a new Dirichlet based trust management system for the IoT called DTMS-IoT. This system detects nodes malicious behaviour which permits to mitigate both on-off attacks and dishonest recommendations. It also uses service levels and things capacities to reinforce security. As in many trust management systems introduced in the literature, the computation of trust in our solution is based on direct observations and recommendations. The novelty of our approach can be summed up in two aspects. The first aspect concerns recommendations that we do not consider unless direct observations are not credible. However the second aspect tries to prevent On-Off attacks using the precaution factor and mitigates dishonest recommendations based on k-means selection algorithm and guarantor things. The effectiveness of the proposed system is proved by simulation against on-off attacks and good/bad mouthing attacks.

WSNs are composed of sensors that collect, process, and send data to a sink. To communicate, sensors use a shared medium. Thus, a robust MAC layer protocol has to be implemented for transmission management. This paper presents a QoS based WSNs MAC protocol that ensures service differentiation and less energy consumption. It is based on a duty-cycle approach that combines TDMA and CSMA/CA schemes. The proposed Priority-MAC protocol introduces also an EDF queue scheduling policy that aims to give priority to the urgent traffic taking into consideration the packet deadline. Simulations were conducted to evaluate the performances of our solution. Results have shown better performances in terms of energy consumption.

Privacy is considered as one of the hottest issues in nowadays communication research areas. It focuses on protecting the content of the transmitted data and on preserving the contextual information such as the identity of the communicating entities. This paper presents an overview of research done in this area in order to build a foundation that helps the research community to understand more the privacy concept and issues. We start by defining the privacy and classifying its requirements in two categories: content-oriented privacy and contextual privacy. We concentrate particularly on contextual privacy by identifying its properties. We also expose a taxonomy according to the type of transferred messages. An analysis of privacy protocols and systems described in the literature is then performed, followed by a description of privacy attacks. Based on the compromised privacy requirements, we propose a new expressive and precise classification of attacks. It is more adapted to privacy context and helps to identify areas of remaining weaknesses that require additional work. Moreover, we describe various attacks against privacy. Finally, we highlight the future research directions in this area.

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

IEEE 802.15.6 standard defines several service differentiation mechanisms with the aim of providing good QoS to different Wireless Body Area Network (WBAN) based applications including medical, entertainment, military, and security. The purpose of this paper is to improve IEEE 802.15.6 slotted CSMA/CA efficiency, making it more suitable for healthcare applications. To do so, we propose an enhancement to the 802.15.6 backoff assignment function. Then, we compare the native and improved channel access mechanisms in terms of reliability and throughput. Simulation results demonstrate that our proposal allows WBAN-based applications to benefit optimally and get better QoS than 802.15.6 features.

The ICN (Information Centric Networking) approach, totally centered on the content, was proposed to reconstruct the model of Internet communication. It introduces new concepts such as naming, forwarding and caching. The ICN paradigm is explored by several research projects to present a new Internet communication architecture. In this work, we focus on NDN (Named Data Networking) project in order to analyze the impact of Interest traffic generation and Forwarding Strategies for three different Cache Replacement Mechanisms: LFU, LRU, and FIFO. In this paper, we evaluate the effects of these three mechanisms on hit ratios. Our experimental results show that the interest traffic generation has a positive effect on network performance. However, the choice of forwarding strategy has a positive influence on the network performance.

This paper introduces a new trust management system (TMS) for the cloud computing domain. The wide majority of these systems do not fulfill the requirements of today's cloud environments which are characterized by heterogeneity in providers, capabilities and users. As such, they are limited to few aspects of trust. In fact, they are bound to a single function or a direct interaction with the cloud provider for trust computing. They do not take into account users' requirements and their preferences' goals. They do not consider either both the qualitative and quantitative assessment. In this paper, we propose a new hybrid TMS which addresses the shortcomings of prior trust systems and focuses on the relevant features of cloud environments. Simulation results reveal that our proposed TMS performs better than similar trust systems and accurately calculates the trust values of cloud services.

The Internet of things (IoT) interconnects every object of our living space to the Internet. Its adoption relies on the settling down of robust security solutions, especially key management ones. In this paper, we address key management protocols in the Internet of things (IoT). Since the IoT interconnects heterogeneous nodes, key management protocols used in the Internet might not be adapted for this type of networks and lightweight protocols are needed for constrained nodes. Thus, it is prime to design a key management solution that takes into account resources constrained nodes and exploits the heterogeneity of objects to help in key computation. We investigate a variety of key management protocols proposed for the IoT. We analyse the advantages and disadvantages of each protocol and give a performance comparison in terms of key management requirements.

The interest for wireless sensor networks (WSNs) is continuously growing especially with the emergence of applications such as smart grids, smart cities, e-health, etc. where billions of objets (sensors) will be permanently connected to the Internet. In this context, the IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for the next generation multi-hop WSNs. The RPL scheme fits the sensor nodes characteristics since the protocol was originally designed for energy efficiency. Nevertheless, in the case of sensor nodes' movement, RPL poorly adapts to such scenarios which rapidly alters the network performance. In this paper, we investigate the problem of mobility support over RPL. Therefore, we propose a new Sensor Nodes' Speed Classifier (SNSC) to predict the sensor nodes' movement in the network. Then, we exploit the speed values-predicted by the SNSC model-to enhance the behavior of the native RPL in the mobility context. Our modified RPL scheme is called Mobility Prediction based RPL (MP-RPL). To evaluate the performance of MP-RPL, we first validate the SNSC model using the Cooja/Contiki simulation environment. Then, we compare MP-RPL to the native RPL in terms of Packet Loss Rate (PLR) and Packet Delivery Delay (PDD).

In this paper, we present a hierarchical routing algorithm based on a bio-inspired heuristic named Cuckoo Search (CS) approach. In our proposed scheme, the network area is divided into a logical virtual grid. Nodes are deployed randomly and organized as static clusters where each cell represents one cluster. After the cluster heads are elected, data is collected, aggregated and forwarded to the base station using the cuckoo search approach. Extensive simulations showed the effectiveness of our data dissemination mechanism. They confirm that our proposed scheme outperforms other conventional existing techniques such as LEACH and M-GEAR in terms of energy saving.

The smart grid is a complex network that aims to manage the electricity usage and reduce consumption. It is composed of a large number of intelligent devices and systems that exchange sensitive data via several networks (HAN, NAN, FAN). The Neighborhood Area Network covers communications between smart meters and the collector that transmits aggregate data to the control center. The NAN network is vulnerable to several types of attacks (Eavesdropping, spoofing, replay) and, security is essential to protect sensitive information in the NAN. In this paper, we propose to use the IPsec protocol to secure the traffic exchanged between smart meters and the control center. Simulation experiments are conducted to evaluate the latencies for real time NAN applications in urban and rural scenarios.

Prolonging the network lifetime is a challenging problem in wireless sensor networks. Using additional mobile agents able to gather data from static sensor nodes reveals to be one of the convenient solutions in order to conserve static sensor nodes energy. In this paper, we focus on a conflictual situation between two Data Collectors (DCs) tending to gather maximum sensed data and deliver them to a sink node before a required deadline. A non-cooperative game is used to model this behavior and evaluate the performance of the gathering scheme when two DCs are competing with each other to gain more sensed data. Under this scheme, aggressive behavior is discouraged since each data gathering operation incurs sensor nodes energy decrease and data delivery delay increase.

In this paper, we investigate intelligent handoff decisions in femtocell networks, with the goal of achieving a seamless femtocell-to-femtocell handoff procedure that minimizes unnecessary handoffs and provides a good quality of connection to mobile users. To this aim, we propose a promising handoff scheme called Optimized Handoff based on Mobility Prediction and Call Admission Control (OHMP-CAC). Our proposal is based on a next cell prediction module using an efficient Hidden Markov Model (HMM) predictor on one hand, and a Call Admission Control (CAC) algorithm with service differentiation for applications with and without QoS constraints, on the other hand. To gauge the effectiveness of our proposal, we conducted extensive simulations based on real human mobility traces. Results show that OHMP-CAC outperforms existing approaches in terms of the number of handoffs, the dwell time, and the handoff decision time, while providing a connection with excellent quality of signal to femtocell user equipments (UE).

We define in this paper a new 4D-Markov chain model for both evaluating analytically and improving efficiently IEEE 802.15.6 CSMA/CA mechanism. Considering non-ideal channel, we propound an enhanced version of IEEE 802.15.6 retransmission process. We investigate the performances of this WBANs (Wireless Body Area Networks) standard in terms of reliability, throughput and energy consumption in a non saturated sensor network. Simulations revealed that the recommended improvements enhance the QoS in IEEE 802.15.6-based WBAN for different application profiles.

Introducing new methods to enhance health services for elderly persons has become the main objective of Smart Home researchers. One of the most important issues that they have dealt with is the application of forecasting models used to predict states of elderly persons. In fact, our approach focuses on some critical health parameters (blood pressure, pulse, etc) over 40 days. In this paper, we have applied the forecasting Grey Model GM(1,1) on data collected in the smart home MavHome Project. We have also compared the system performances obtained to predict sensor datawhile using the Grey model with those provided while applying the Box-Jenkins ARIMA as a conventional forecasting model. The simulation results show that the Grey Model is more efficient than the Box-Jenkins ARIMA as it has resulted in more accurate forecasting values.

The IEEE 802.15.4 standard is considered as the most notorious MAC layer for Wireless Sensor Networks (WSNs) in both single hop and multi-hop contexts. For instance, in a multi-hop environment, the unslotted IEEE 802.15.4 is adopted as the medium access scheme. Several works evaluated the performance of the unslotted IEEE 802.15.4 in terms of average delay, exact average energy consumption, reliability, etc. But, none of the existing studies derived analytical energy consumption bounds of this MAC layer. In this paper, we propose an energy consumption analysis of the unslotted CSMA/CA based on a discrete Markov chain modeling of the backoff process. Therefore, we derive approximated average energy consumption and energy consumption bounds of beaconless IEEE 802.15.4. The probabilistic energy consumption bounds are computed based on generalized transition state diagrams. The bounds are expressed as a function of the probability that a packet energy consumption exceeds a certain value. Finally, we validate the analytical results by simulation using the Omnet simulator.

Wireless Sensor Networks (WSNs) become one of the most common technologies that can be deployed in various domains. However, the networks suffer from many problems caused by the limited sensor resources and the harsh environments where these networks are deployed. Many algorithms have been proposed to manage data routing while respecting the specificity of such networks. One of the known proposed routing protocols is RPL (IPv6 Routing Protocol for Low power and Lossy networks). Nevertheless this protocol is designed with consideration of the limited sensor energy, it cannot be integrated in may WSN applications. In fact, RPL, as it has been proposed, assume that the sensor nodes are static and don't manage any type of mobility. In this paper, we propose a new approach, called BMP-RPL (Bayesian model Mobility Prediction RPL for wireless sensor networks), that aims to adapt native RPL to nodes' mobility scenarios. This approach is based on nodes' identification and velocity prediction as well as the estimation of the link duration. Thus, we introduce a new metric which constructs routes according to the node status and within information lost. The performance of our approach is proved through simulation.

A Vehicular Ad-Hoc NETwork (VANET) consists of a set of vehicles moving along roads, which can communicate with each other through ad hoc wireless devices. VANETs have attracted a great deal of attention in the research community in recent years, with the main focus being on their safety applications. One of the major challenges of vehicular networks is designing an efficient Medium Access Control (MAC) protocol which can cope with the hidden node problem, the high speed of the nodes, the frequent changes in topology, the lack of an infrastructure, and various QoS requirements. Motivated by this observation, we present a fully distributed and location-based TDMA scheduling scheme for VANETs, named DTMAC. The main goal of this work is to propose a MAC protocol that can provide a reliable broadcast service with bounded access delay, while reducing access collisions and merging collisions with various vehicle densities without having to use expensive and complex spectrum mechanisms such as CDMA or OFDMA. An analytical model of the average access collision probability has been derived, which can be used to evaluate the performance of DTMAC and validate the simulation results under different traffic conditions. The simulation results reveal that DTMAC significantly outperforms VeMAC in terms of transmission collisions and broadcast coverage.

In the context of multi-rate communication, the throughput-based multicast routing that aims at maximizing the end-to-end throughput is very different from the commonly adopted delay-based multicast routing that aims at minimizing the end-to-end delay. Definitely, the delay-based approach is not suitable for applications that need to transfer large amounts of data (e.g. grid applications). In view of that, we introduce in this paper the first distributed routing strategy that allows high-throughput multicast in multi-rate IEEE802.11 networks. The new mesh-based routing, called the Protocol for End-to-end Multicast Session Efficiency (PEMSE), exploits the rate-diversity (MUD) and on the broadcast-nature of the wireless medium (WBA). It aims at: i) considering the effect of the number of transmissions/retransmissions, required to exploit the MUD, on the end-to-end multicast throughput and ii) establishing the routing structure that guarantees the best end-to-end multicast throughput. Simulations prove that PEMSE achieves considerable throughput improvement compared to the PUMA [11] routing protocol. They also show that PEMSE attains reasonable delays and delivery ratios.

Internet of Things viewed as Future Internet aims to provide the capacities for each physical entity to be connected to the Internet and to establish its requirements services or to be attached to a distributed system computing. Therefore, each object must have its identity (ies) that permits accomplishing the authentication and authorization phases before beginning every secure transaction. Hence, the number of identities increase at least according to the number of connected object. Today, several challenges such as security, privacy and identities management must be taken into account. In this paper, we propose a taxonomy of identities management systems (IDMS) in Internet of Things (IOT) relying on the viewpoint of paradigms and models. We present the core components of an Identity management system. Discuss the different types of IDMS paradigms such as User Centric, Application and Network Centric. Also, we present a comparative analysis of Identity management models: - user model, service provider (SP) model, Combining user and SP model in the form of Taxonomy and we discussed the Related Works of IDMS of IOT.

In the recent few years, Wireless Body Area networks (WBANs) showed what can be done remotely to greatly improve healthcare systems and facilitate the life to elderly. One of the recent ehealth projects is CANet which aims at embedding a WBAN into a cane to monitor elderly/patients. Our main goal in this paper is to evaluate the performances of the emerging standard IEEE 802.15.6 when applied on different sensors from CANet eHealth project. At this end, we defined a small scenario extracted from CANet, and we assigned IEEE 802.15.6 priorities to the selected cane sensors according to their inherent characteristics. We considered further the mandatory RAP period of IEEE 802.15.6 superframe under the beacon period with superframes mode since it supports both normal and urgent traffic. Our results showed that the contention access behavior of this considered model of simulation depends on several constraints (including the nature of the studied application and the traffic types and frequency). This would be necessarily taken into account to get the most advantage of all features offered by WBANs standard IEEE 802.15.6.

Vehicular Ad hoc NETworks (VANETs) are a major component recently used in the development of Intelligent Transportation Systems (ITSs). VANETs have a highly dynamic and portioned network topology due to the constant and rapid movement of vehicles. Currently, clustering algorithms are widely used as the control schemes to make VANET topology less dynamic for Medium Access Control (MAC), routing and security protocols. An efficient clustering algorithm must take into account all the necessary information related to node mobility. In this paper, we propose an Adaptive Weighted Clustering Protocol (AWCP), specially designed for vehicular networks, which takes the highway ID, direction of vehicles, position, speed and the number of neighboring vehicles into account in order to enhance the stability of the network topology. However, the multiple control parameters of our AWCP, make parameter tuning a nontrivial problem. In order to optimize the protocol, we define a multi-objective problem whose inputs are the AWCP's parameters and whose objectives are: providing stable cluster structures, maximizing data delivery rate, and reducing the clustering overhead. We address this multi-objective problem with the Non-dominated Sorted Genetic Algorithm version 2 (NSGA-II). We evaluate and compare its performance with other multi-objective optimization techniques: Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Differential Evolution (MODE). The experiments reveal that NSGA-II improves the results of MOPSO and MODE in terms of spacing, spread, ratio of non-dominated solutions, and inverse generational distance, which are the performance metrics used for comparison.

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

In this paper, we propose a new approach for optimizing the handoff decision in femtocell networks using Hidden Markov Model. To do so, we formulate the handoff problem as an optimization problem whose objective is to find the best Femtocell Access Point (FAP) assignment strategy that minimizes the number of unnecessary handoffs while maintaining a good quality of wireless communications. We have used Hidden Markov Model prediction tools to predict the target FAP by observing the geographic positions of the mobile. To evaluate the effectiveness of our proposal, we conducted extensive simulations. Results show that our proposed approach minimizes the number of handoffs by up to 7 times and enhances the dwell time in the FAP by up to 42% in comparison with others handoff decision making strategies commonly used in related cellular modeling works.

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

Wireless Sensor Networks (WSN) consist of nodes with limited power deployed in the area of interest. Nodes cooperate to collect, transmit and forward data to a base station. In WSN, clustering and scheduling techniques ensure collecting data in an energy efficient manner. In this paper, we present a Power Aware Scheduling and Clustering algorithm based on Ant Colony Optimization (PASC-ACO). In the proposed approach, energy is saved by scheduling some nodes in the active state to generate data and keep network connectivity, while putting others in the sleep state. Then, ACO algorithm is used for routing data packets in the network in order to minimize the energy wasted in transferring the redundant data sent by sensors in a densely deployed network. ACO scheme can play a significant role in the enhancement of network lifetime by selecting the optimum path to reach the base station. The PASC-ACO algorithm was studied by simulation for various network scenarios. The results confirm that our proposed scheme outperforms other existing techniques such as LEACH, M-GEAR and our pervious work PASC. PASC-ACO achieves better performances in terms of lifetime by balancing the energy load among all the nodes.

In many applications such as precision agriculture (fruit tree plantation, olive groves) or environmental monitoring, wireless sensors are, very often, randomly scattered in the 3D area of interest. Such applications require full three-dimensional coverage. Undoubtedly, an initial random deployment does not achieve neither full coverage of the 3D area of interest, nor network connectivity. Thus, a redeployment algorithm has to be introduced in order to ensure these two goals. Our contribution is the design of 3D-DVFA, a distributed deployment algorithm based on virtual forces in three dimensional wireless sensor networks where sensor nodes are assumed to be mobile and autonomous. We simulate its behavior with NS3. The extensive simulations results demonstrate the effectiveness of the 3D algorithm proposed that provides full 3D area coverage and network connectivity.

Vehicular ad hoc networks (VANETs) where vehicles act as mobile nodes is an instance of Mobile Ad hoc NET-works (MANETs), which are essentially developed for intelligent transportation systems. A challenging problem when designing communication protocols in VANETs is coping with high vehicle mobility, which causes frequent changes in the network topology and leads to frequent breaks in communication. The clustering technique is being developed to reduce the impact of mobility between neighboring vehicles. In this paper, we propose an Adaptive Weighted Cluster Protocol for VANETs, which is a road map dependent and uses road IDs and movement direction in order to make the clusters structure as stable as possible. The experimental results reveal that AWCP outperforms four other most commonly used clustering protocols in terms of control packet overhead, the packet delivery ratio, and the average cluster lifetime, which are the most usual metrics used for comparing performance.

In this paper, we introduce a new routing strategy that allows high-throughput and channel-aware multi-rate multicast in wireless networks. This new routing strategy, based on the so-called End-to-end Multicast Session Efficiency (EMSE), is founded on the multi-user diversity (MUD) and the wireless broadcast advantage (WBA) features. It aims at: i) joining users that provide the best-received multicast throughput, ii) guaranteeing the use of homogenous capacities, iii) considering the effect of the transmission/retransmission number needed to exploit the multi-rate diversity on the routing throughput and iv) constructing the multicast structure composed of paths that give the best end-to-end throughput. Compared to other routing strategies and multicast schemes, the new routing algorithms show considerable improvement in terms of the average throughput and an important reduction in the relays number needed to convey the multicast data.

In this paper we propose a cluster-based multichannel scheduling algorithm for intra-cluster communications in Cognitive Radio Sensor Networks (CRSNs) to avoid collisions between neighbor clusters. Since nodes having neighbor nodes out of their clusters may cause collisions between neighbor clusters, when transmitting in the same time slot on the same channel, the scheduling of each cluster cannot be established independently. We propose to fix in each cluster the channels used by nodes having conflicting nodes out of their clusters. This channel assignment is done by the cluster heads in a distributed way but according to the decreasing priority order of the clusters. Then, each cluster head executes the scheduling algorithm independently. We evaluate the performance of this algorithm in terms of total number of time slots needed by all the nodes to transmit all their packets to the sink, energy consumption and transmission time interval.

This paper describes an energy efficient scheduling algorithm with adaptive fault tolerance for clustered mobile wireless sensor networks. This algorithm equalizes the cluster lifetime by making the total energy stored in the clusters proportional to their power consumption. It is proposed to relocate, in an optimized way, redundant sensors using cascaded movement to achieve optimal connectivity and coverage of the network. Simulation results confirm that our proposed scheme outperforms other existing techniques such as LEACH and M-GEAR and show that it achieves better performances in terms of lifetime by balancing the energy load among all the nodes.

Current Wireless Sensor Networks (WSNs) are deployed over unlicensed frequency bands that face an increased level of interference from various wireless systems. Cognitive Radio Sensor Networks (CRSNs) overcome this problem by allowing sensor nodes to access new spectrum bands to minimize interferences. In this paper, we focus on the MultiChannel Time Slot Assignment problem (MC-TSA) in CRSNs. Each sensor node is assigned the number of time slots it needs to transfer its own data as well as the data received from its children in the rooting tree rooted at the sink without interfering with other secondary users. Besides, sensor nodes cannot transmit on a channel occupied by a primary user. Our objective is to increase the network throughput offered to sensor nodes. We start by formulating the MC-TSA problem as an Integer Linear Program where the goal is to minimize the number of slots in the schedule. We then propose an Opportunistic centralized TIme slot assignment in Cognitive Radio sensor networks (OTICOR). We evaluate its performance in terms of number of slots and throughput.

Cars cruising for parking adds a non negligible amount to traffic congestion and CO2pollution. Hence, good parking management policies are required to reduce such discomfort. In this paper, we address this problem from two sides. First, we consider how local parking authorities called parking coordinators (PC) can optimize the distribution of the slots they manage through a full cooperation between them. Second, we model the problem as a congestion game, where vehicles act as players who will eventually choose the best parking garage for them, while minimizing the whole network cost. We study the effectiveness of both schemes (i.e, PC-aware and game theoretic approaches) in various contexts and compare them with the reference centralized model as well as a greedy approach. Simulation results show that our proposals provide high request satisfaction ratio, close to the optimal baseline approach and outperform the greedy method up to 30%, while ensuring a fair distribution of slots through the network.

Next generation Networks (NGN) are required to support the seamless delivery of voice, video and data with high quality. One of the most important key features of NGN is Quality of Service (QoS), which has been a focal point of NGN research, development and standardization. In this work, we propose and demonstrate an efficient NGN QoS-Aware resource and admission control and management methodology which guarantees the QoS requirements. The advantages of the proposed method, in terms of QoS gains, are demonstrated through modeling of the main QoS parameters. Simulation results are derived to evaluate the performance of the proposed method in terms of supporting the QoS and improving the transport network scalability.

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

This paper investigates the design of effective routing metrics in the purpose of network resources optimization and the satisfaction of users QoS requirements. Using several real experiments, we point out the shortcoming of the Expected Transmission Count (ETX) metric for eventual optimizations towards a more efficient routing. Experiments were carried out into an heterogeneous IEEE 802.11n based network running with OLSR routing protocol and have shown that ETX presents several shortcoming resulting in inaccurate estimation of the link quality and then of the routing decision. This paper presents improvements of the ETX metric based on link availibility for accurately finding high-throughput paths in multihop wireless mesh networks.

Using wireless body sensors in medical field is becoming an efficient, low cost and non invasive way for continuously monitoring patients' vital signs. New MAC protocols have to be designed to handle the intrinsic constraints of Wireless Body Area Networks (WBANs) especially insuring low latency for alarms management and high reliability. In this paper, we propose a new access method in WBANs for alarms management: E-Health Access Channel Mechanism (E-HACM). E-HACM is intended to be used in e-health applications designed to monitor patients' vital signs by fixing on them body sensors for periodic measurements and to detect critical cases that require real-time delivery and no data loss. Simulation results show that our proposed mechanism outperforms IEEE 802.15.4 in terms of delay, energy consumption and packet loss ratio.

This paper provides a description of a wireless mesh network testbed setup and a measurement-based performance evaluation of the Optimized Link State Routing (OLSR) protocol [7] under three different routing metrics. The considered metrics include hop-count, ETX and ETT. The network performances are evaluated in an indoor testbed formed by heterogeneous MIMO devices. A part of our tests was about the impact of 802.11n features on the network performances showing the importance of lower layers consideration. Our measurements point out the shortcoming of each metric and eventual optimizations towards a more efficient routing. Experimental results show that OLSR-ETT outperforms OLSR-ETX and OLSR-hopcount significantly in terms of packet loss, end-to-end delay, and efficiency.

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

In the last few years, femtocells have gained a great deal of interest as an emerging wireless and mobile access technology to improve indoor coverage and network capacity. In such an environment, mobility management is one of the major concerns that may limit the wide deployment and adoption of such networks. In this paper, we investigate the handover procedure for the two-tier macro/femto LTE networks. An optimized handover algorithm with an efficient call admission control has been proposed and described. Our proposed scheme is mainly designed to reduce the number of unnecessary handovers and to maintain the communication quality during the handover. The choice of the femtocell target takes into account the direction of the mobile user, its velocity and the quality of the signal. Performance evaluation results show that our algorithm minimizes both the number of hand-in and the handover drop rate. Besides, the signal quality in terms of SINR after the hand-in is maintained higher than a fixed threshold, which maximizes the sojourn time of the mobile user within the selected femtocell.

We propose a new technique to relocate a set of redundant sensor nodes from their initial deployment area called ROI (region of interest) to a new location outside the ROI called COI (center of interest) where a new event happened (e.g., to measure the soil contamination level in the case of a chemical spill disaster). Relocating mobile sensor nodes from their initial deployment area ROI requires covering that area at a given center of interest while maintaining connectivity with the initial region of interest. Our proposed technique to relocate redundant mobile sensors nodes towards a new target position is a four-phase solution that includes the clustering phase and the cell head election, the evaluation of the number of nodes to be relocated, the propagation of the COI position, and finally the election of the redundant nodes and their relocation. A performance evaluation of our novel relocation approach shows that the consumed energy and the relocation time increase as the distance separating the initial ROI from the COI increases regardless of the size or the density of the initial deployment area.

Satisfying the Quality of Service (QoS) requirements is one of the main objectives in the design and implementation of IP-based Next Generation Networks (NGN). This paper formalizes an efficient NGN resource-based call admission control (CAC) method which guarantees the QoS requirements expressed in terms of per-service traffic flow throughput, latency, jitter, loss and authorized waiting delay. The proposed method delivers high quality session-based services compliant with the available capacity over an NGN network based IP/MPLS transport infrastructure. The effects of the proposed method on the QoS provision are demonstrated through modeling of the main QoS parameters. Numerical simulations, which illustrate the proposed method benefits in terms of QoS guarantees and network scalability, are also presented.

We address QoS guarantees in a Bluetooth piconet by considering mainly two constraints: the packet deadline and its priority. We evaluate the performances of four intra piconet scheduling schemes: one round robin (1-RR), exhaustive round robin (ERR), limited round robin (LRR) and deficit round robin (DRR), each one combined with our previously proposed local scheduling scheme FP/EDF. We show that DRR gives better performances than the three other schemes. Thus, we propose a new DRR based scheme called PBDRR that takes into consideration the priority of packets. Results have shown that PBDRR performs better than DRR in terms of miss probability.

We address analytic performance evaluation of a bluetooth piconet by considering the M/G/1 queue with batch arrivals and vacation times. We present a probabilistic approach to support diverse traffics with QoS guarantees. We consider mainly two constraints: the packet deadline and its priority. We focus on two new scheduling techniques that we have proposed in our previous works. The first technique combines FIFO and the class based priority queuing (PQ) while the second combines EDF (Earliest deadline First) and PQ. We focus our interest to providing Quality of Service (QoS) guarantees to various classes of soft real-time applications by using a probabilistic approach. Here, we compute the end-to-end delay distribution composed by the uplink and downlink waiting times. The end-to-end delay is used to obtain the probability that the response time doesn't exceed a given deadline. Then, we propose a probabilistic admission control procedure for real time flows. We validated the obtained theoretical results through simulations using an extension of the network simulator NS2 called Bluehoc.

We focus on applications with quantitative QoS (quality of service) requirements in their end-to- end response time. Two types of quantitative QoS guarantees can be delivered by a network: deterministic and probabilistic. The deterministic approach is based on a worst case analysis. The probabilistic approach uses a mathematical model to obtain the probability of the response time exceeding a given value. We assume that flows are scheduled according to non-preemptive FP/FIFO. The packet with the highest fixed priority is scheduled first. If two packets share the same fixed priority, the packet that arrives first on the node considered is scheduled first. We compare deterministic and probabilistic QoS guarantees and introduce the concept of p-schedulability: the QoS requested by all flows is met with probability p.

In this paper, we propose a real time scheduling policy over 802.11 DCF protocol called Deadline Monotonic (DM). We evaluate the performance of this policy for a simple scenario where two stations with different delay constraints contend for the channel. For this scenario a Markov chain based analytical model is proposed. From the mathematical model, we derive expressions of the average medium access delay called service time. Analytical results are validated by simulation results using the ns-2 network simulator.

In this paper, we focus on the quantitative quality of service (QoS) guarantee in a differentiated services (DiffServ) domain that can be granted to an expedited forwarding (EF) flow in terms of end-to-end delay. This study shows that delays much smaller than the deterministic bound can be guaranteed with probabilities close to one. An admission control derived from these results is then proposed, providing a probabilistic QoS guarantee to EF flows.

In a real-time distributed transactional system, customers generate transactions, which should be executed on different servers. Transactions are scheduled according to the (earliest deadline first) EDF discipline. To get a global view of the system, the consensus algorithm has been considered. In this paper, a mathematical model is developed to obtain the average stay time of a transaction within the system. This model introduces a bulk arrival M/G/1 station with K classes of customers where bulks are considered according to the FIFO discipline, customers are scheduled according to EDF within a group, and the algorithms operation is taken into account according to the head of line discipline. The response time distribution is also computed. This has allowed us to determine the optimal minimal interconsensus for a given consensus duration, the deadline miss probability and the minimum value to attribute to a transaction deadline in order to guarantee a given probability that the transaction does not miss its deadline.

In a real-time distributed transactional system, customers generate transactions, which should be scheduled to be executed on different servers. The transactions must be executed before their deadlines. To schedule these transactions the circulating multisequencer and the consensus algorithms have been considered to obtain a global view of the system. Mathematical models are developed to obtain the average stay time of a transaction within the system. The response time distribution is also computed. This allowed us to determine the minimum relative deadline, to affect to a generated transaction, to guarantee a given probability p that the transaction does not miss its deadline. This study shows that the circulating multisequencer algorithm presents better results.