Protecting user rights and enabling cross-border interoperability require the existence of private and secure management of personal identity data in digital environments. To address this need in the context of electronic health cards for digital identification and healthcare services, this paper proposes a novel architecture that integrates self-sovereign identity (SSI), verifiable credentials (VC), and zero- knowledge proofs (ZKP). With SSI, users maintain full control over their data, storing and sharing identity credentials through digital wallets using decentralized identifiers (DIDs). VC enables trusted organizations to issue authenticated, tamper-proof digital certificates, while ZKPs allow individuals to prove identity-related claims without disclosing unnecessary personal details. The combination of these technologies enables a framework that ensures a tradeoff between user privacy and security processes. The findings of the tests achieved indicate that our framework, compared to conventional models, optimizes the interoperability and privacy of e-health card systems. Additionally, the suggested solution supports recent data protection regulations and demonstrates strong potential for global adoption.

ABSTRACT Smart contracts are self‐executing programs that operate on a blockchain network. They are designed to automate transaction execution without the need for intermediaries. Once deployed in the blockchain network, smart contracts cannot be altered. However, this immutability can also lead to security risks. If a smart contract contains a vulnerability upon deployment, it cannot be corrected, leaving the code vulnerable. Therefore, incorporating security considerations during the design phase of smart contract development is crucial. Access control is a key security concept that must be integrated into the design of smart contracts to prevent unauthorized access to critical functions or data. In this paper, we introduce a Model‐Driven Architecture (MDA) approach to design access control for smart contracts, and we validate, using Ethereum, the proposed approach using Smart Contract Security Verification Standard (SCSVS) rules.

Disasters and emergencies management requires four main phases. Mitigation phase is the first phase of the crisis management cycle. It basically aims to mitigate the danger or even to avoid the crisis. Understanding and anticipating crisis occurrence are key activities to succeed this phase. Snow avalanches are one of the natural disasters that affects several countries in the world. In this paper, we study and discuss the impact of land characteristics and snow stability on avalanche occurrence and severity level. We introduce a Machine learning framework for avalanches forecasting and we use a real dataset to determine prominent factors that can predict the class/severity of an avalanche. To achieve our goal, we apply a Multi-Layer Perceptron (MLP) and we determine a feature subset which achieves the highest accuracy

Emergency and crisis management is a constantly changing field that needs a lot of attention. In order to save lives and lessen the effects of crises, numerous response organizations work together to ensure the successful execution of various phases. Organizations and managers must act quickly and wisely due to the urgency of the situation. The gathered data and available expertise play a major role in how well these decisions turn out. The quality and applicability of the information gathered, which comes from various sources like satellite imagery, local sensors, and social media produced by the local population, is intricately linked to the decision-making process. Achieving effective crisis management requires the capacity to swiftly analyze and interpret the data at hand, accurately assess the circumstance, and render well-informed decisions based on knowledge and accepted practices. The communication, representation, and integration of heterogeneous information are difficult tasks for these processes. Ontologies play a critical role in facilitating decision-making processes in the fields of artificial intelligence and computer science. By offering a formal framework and a common vocabulary for representing knowledge within particular domains, they act as priceless tools. In this article, we propose a comprehensive crisis management ontology that effectively encompasses the fundamental concepts associated with this field. This ontology facilitates the efficient management, organization, and comprehension of information, leading to improved streamlining of processes.

The performance of irregular scientific applications can be easily affected by an uneven distribution of loads between computing resources. In this context, load balancing is one of the most important solutions to improve resource utilization. Yet many heuristic and metaheuristic algorithms have been proposed in the literature for dynamic load balancing, choosing the best performing load balancing algorithm for a given application is not a trivial task especially in situations where applications have a dynamic behavior.This paper proposes a new approach of Load Balancing using Deep Reinforcement Learning. The proposed agent schedules tasks to the cluster’s node using state of the art scheduling algorithms. Based on the user’s tasks and the available nodes, the agent must choose an efficient scheduling algorithm as an action from the action space and use it to schedule the pending tasks to cluster’s nodes. The agent’s action space is composed of state of the art scheduling algorithms. This approach optimizes both the average execution time and the resource utilization while keeping a low Imbalance Degree in the cluster.

Snow avalanches is considered among the most dangerous natural disasters that cause material damage but also dozens of deaths every year. The concerned countries have allocated important resources to manage this disaster. Space-time avalanche observation and prediction can help save people's lives and minimize damages. In this context, this article provides a comprehensive insight on existing avalanche forecasting systems. We review observed natural phenomenons and parameters that most existing systems use, then we discuss existing methodologies for snowpack modeling. We establish a comparative study on most important methodologies and tools for avalanche prediction. Finally, we give some recommendations and discussion for building an operational prediction system.

Network interface selection (NIS) aims at choosing the best connection of the user equipment (UE) to the best available wireless network at any time and everywhere, satisfying the user’s and/or the service requirements in terms of quality of service (QoS). Although multi-attribute decision-making approaches(MADM) are commonly used to solve NIS problem, they suffer from couple of weaknesses: i) the rank abnormality that changes the alternatives’ ranking after discovering new networks or removing an existing one from the HWN environment, and ii) they always select the alternative having the highest ranking score without considering the user’s and/or service requirements. In this paper, we introduce a novel approach based on combining the Combined Compromise Solution (COCOSO) approach with utility function, called COCOSO-Based Utility function (COCOSO-UF), to overcome the COCOSO limits. We remove the rank reversal problem and we rank available networks based on the user or/and application needs. The obtained experimental results showed that our approach can avoid the rank reversal problem and satisfy the user or/and application QoS requirements.

Access control in Blockchain context is a crucial step. During this process it is important to verify that users have the right roles to get access to the protected resources. To model its access control, the Ethereum Blockchain is based on RBAC model. The whole process is written in a smart Contract imported from The OpenZepplin Library [1]. However, the automatic generation of the access control policy still missing. This automation can widely enhance and simplify the implementation of access control for developers of blockchain based applications. In this paper, we propose a new model for automatically generating the access control smart contract. Our approach is based on the Model Driven Engineering (MDE).

The network interface selection (NIS) is an important function that should be executed to connect the user's equipment to the best available network anytime and anywhere by meeting the user/application QoS requirements. Multi-attribute decision-making approaches (MADM) are commonly applied to model and solve NIS problems. Although they can rank networks quickly with a high precision, they suffer from two drawbacks. Firstl, the rank reversal problem (RRP). Second, The selection of the highest-ranking score network without considering user and/or service requirements. In this paper, we introduce a novel approach to solve the above-cited drawbacks. We model the weighting techniques as a multi-objective problem. Then, we apply an extended version of the Whale optimization algorithm to obtain the decision criteria weights according to the traffic classes and the chosen MADM approaches. The obtained simulations clearly show that applying our approach with MADM approaches reduces the RRP and satisfies the user/service requirements.

When medical information and documents are exchanged outside of the secure medical facilities networks, patient' privacy is at risk. This can compromise biomedical studies, patients privacy, and all other medical stakeholders' data. Therefore, blockchain is more and more adopted as infrastructure for Electronic Medical Records Management Systems (EMRMS) to address secure data sharing, access management and critical data handling issues. In this paper, we consider the problem of reducing transactions scheduling delays in blockchains as a main issue impacting the performances of EMRMS. We suggest a novel approach that aims to design a structured and weighted queue of transactions request that can promote differentiated level of service for requested transactions. A logical scheduling strategy based on a hybridization of the Genetic Algorithm and the Variable Neigh-borhood Search (VNS) algorithm for scheduling transactions is also proposed.

In this paper, we introduce a novel scheduling mechanism for cognitive radio sensor networks. Our approach aims at defining an optimized time slot and channel assignment that exploits spatial reuse with respect to all types of nodes communications interferences. We identify first all types of nodes conflicts, then, we solve the spatial dependencies by the mean of the Maximum Matching graph theory problem. We use after a First-price sealed-bid auction (FPSB) to perform channels and time slots assignment to completely independent cognitive sensor nodes. Our approach outperforms existing solution for scheduling in CRSN, by reducing the total length of communications and maximizing the stability factor.

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.

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.

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.

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.

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.

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.

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.

A mobile ad hoc network (MANET) is a collection of autonomous mobiles nodes forming a temporary network without using any existing infrastructure or centralized administration. MANETs are characterized by a dynamic topology which makes routing a critical task. Conventional routing protocols are mainly based on shortest paths computation which could be not effective facing the mobility of nodes. Therefore, finding stable paths in MANETS is a more reliable strategy to improve routing. In this paper, we study the stability of links and paths in MANETs. At that end, we define a generic and simple model for links and paths stability in MANETs, then, we investigate, using the new defined metrics, the effect of mobility under various scenarios. Our study showed that our model is perfectly capable of predicting the behavior of links and paths and that proactive routing always constructs low stability paths even under slow moving nodes.

In this paper, we introduce a new fault tolerant initial deployment schema. We aim at maximizing WSN lifetime by considering the energy consumption of nodes led by their communication activities. We defined a circular multi-layer model in which a number of nodes are placed in each layer with regards to the overall energy consumption. To that end, we define first a simple worst case model for nodes energy consumption taking into account their involvement in the routing function. From this model, we derive the optimal number of nodes that should be placed in every layer of the network to achieve energy consumption balancing among all layers. Extensive simulations show that the proposed deployment scheme outperforms random and uniform grid deployment models in terms of connectivity, network lifetime and energy consumption.

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.

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 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.

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.

This paper describes a new approach for routing in Mobile Ad Hoc Networks that is based on the concept of polymorphic behavior. This approach combines the benefits of proactive routing and probabilistic routing in time in a periodic manner in order to get the best of each approach at the appropriate