Recent interest has been generated in multimodal biometrics technology due to its potential to increase recognition rates by overcoming some of the fundamental limitations of single biometric modalities. A typical biometric recognition system will consist of components for sensing, feature extraction, and matching. The system’s robustness is dependent on the accuracy with which relevant data can be gathered from certain biometric features. This study presents a novel face-iris trait feature extraction approach for use in multimodal biometric systems. Among the state-of-the-art algorithms for biometric based iris/face recognition, image processing algorithms with deep learning will have an edge as they are very solid. The iris authentication fits the complex mathematical patterns of the irises which are drastically particular for each. A comprehensive look on biometric authentication located that the fake rejection price of iris authentication is only 1.8% which is the bottom and highest accuracy of 93.78%.

Recent interest has been generated in multimodal biometrics technology due to its potential to increase recognition rates by over- coming some of the fundamental limitations of single biometric modalities. The importance of different data compression algo- rithms is highlighted in the process of storing big data in data warehouses and archives and in reducing the volume of big data during its transmission through communication channels, so many compression algorithms have emerged that are designed to process different data. Images are used in different fields in our daily lives, such as social networks, medical diagnoses, remote sensing, and other fields, and since the size and accuracy of images are constantly evolving, the issue of storing and transferring images requires a compressing process to reduce their size. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies

Fog Computing is a term made by Cisco that insinuates extending cloud computing to the edge of a network. Fog computing supports the operation of Fog/cloud, storage, and networking services between end devices and conveyed processing data centers. While depending on the fog network will enhance the performance by eliminating the upper layer between IoT devices and Cloud servers by making users and devices closer to the servers. But as users become closer to the servers and data centers, attackers also become closer too, this will make the fog layer subjected more to attack and the data centers will be dangerous. Now fog computing faces new-fangled security and assurance defies other than those procured from cloud computing. In this paperwork, we will list the types of attacks that affect the Fog network and we discuss the available solution, moreover, we will illustrate our new method to protect this layer from the attacks which will introduce a new security layer between IoT devices and fog network to detect and prevent the attacks from reaching the fog layer, in addition, we will protect the fog layer from blocking when an attack is presented.

To allow further automatic big data management, and to reduce data resource allocation problems, we propose an approach for embedded systems reconfiguration. The reconfiguration of embedded systems consists of adding new servers and clusters for exchanging the big data. For that, we propose a methodology based on an automatic programming approach to deploy new data servers based on NoSQL performances. These servers will be interlinked to get generated data from an embedded system. This will increase the number of satisfied transactions and queries with an optimal data access latency. Moreover, to evaluate the performance of our model, we propose a new definition based on committing full queries and the ability of all the servers to manage and process the generated big data with the constraint of time. To test and validate the proposed approach, a software tool named SFRBDM (Software for Real-time Big Data Management) has been developed. This software tool allows users to simulate the functioning of embedded systems, inject, generate data, and ensure recoveries between data servers and clusters. SFRBDM also allows calculating a better resource allocation of the simulated embedded databases with a higher performance based on NoSQL. The experimental study performed over multiple servers, with different structures and sizes, validates the efficiency of our proposed approach.

Today NoSQL databases have become unavoidable that more and more companies are turning to these solutions. The variety of non-relational databases resides in their data storage structure to face the big amount of data and their modeling as well. Hence, our paper presents a unified schema modeling for NoSQL solutions based on a mapping approach. Indeed, NoSQL refers to a family of database management systems (DBMS) that departs from the traditional paradigm of relational databases. These systems remain efficient in a continuous scalability case. Among the NoSQL solutions our paper deals with Columns, Document and Graph oriented databases to test the feasibility of our proposed approach.

The Resource Provisioning (RP) and Task Scheduling (TS) issues has become an attractive paradigms in cloud industry, this is due to the increasing demand for the services provided by virtual machines that are structured by physical servers owned by the data centers of cloud service providers (CSPs). In this paper, we propose a new model based on multi-agent system for the RP and TS reducing the cost of energy using Deep Reinforcement Learning DRL. A Quantile Regression Deep Q Network (QR-DQN) algorithm generates an appropriate policy and the optimal long-term decisions. A set of experiments show the efficiency of our proposed scheduling approach and the performance of our task allocation method..

In this study, Artificial Neural Networks (ANN) were used to model parameters of scheduling of reconfigurable embedded systems containing resource constraints for applications running in real-time. The main goal is to implement a neural network based approach for real-time scheduling in order to handle real-time constraints in execution scenarios. Many techniques have been proposed for both the planning of tasks and reducing energy consumption. This paper presents a new hybrid contribution that handles the real-time scheduling of embedded systems by keeping energy consumption at a low power depending on the combination of Dynamic Voltage Scaling (DVS) and the energy Priority Earlier Deadline First (PEDF) algorithm. Indeed, in our original proposed approach, an other combination of DVS and time feedback can be used to scale the frequency by dynamically adjusting the operating voltage. The originality of our algorithm appears by allowing medium priority tasks to be executed more quickly before their deadline while decreasing their ability to be send again to the waiting list in order to ensure the execution of a task with the lowest voltage possible.

Communication security has taken vital role with the advancement in digital communication. The universal use of internet for communication has increased the attacks to users. The security of information is the present issue related to privacy and safety during storage and communication. This paper deals with the proposition of a multilayered secure channel to transfer sensitive data/video over an unreliable network. The secret video is first encrypted using the NOLSB algorithm. The cipher video produced is hidden in a video file with more size. This video file is in turn encrypted following the (m,k) firm technique to maximize resource utilization and to optimize the bandwidth. Then video shares are sent over many channels in the network in order to assure security. This method guarantee that, even if some shares got lost over network, video files could be recovered at the end receiver without need to resend the video file by the sender.

This paper work deals with the presentation of an hybrid genetic approach which allocates and schedules, on a multiprocessor architecture, a system of real-time tasks while balancing the load on the processors. In addition, this hybrid heuristic approach takes into account the safety critical applications which is the focus of our work. We have carried out a performance analysis showing that the proposed hybrid genetic approach has results better than the classical GAs in terms of load balancing, minimum response time and good flexibility.

Nowadays, scientific progress in multiple disciplines gave rise to conducting large scale scientific experiments and applications known as High Performance Computing (HPC). These HPC applications are commonly structured as workflows of heavy tasks with large data size and intricate dependencies which are typically performed in large-scale distributed systems (LSDS) such as clusters, Grids and recently Cloud infrastructures. In fact, workflow scheduling in distributed systems, especially in Clouds, is proved to be an NP hard problem. Our main target in this paper is to design a workflow scheduling approach based on the Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II) in hybrid distributed systems by optimizing the Makespan and cost. In this work, we also studied the improvement of the Makespan-Cost trade-off with the scalability concept in the Cloud with our designed approach. For that, we proposed different scenarios dealing with the provisioning strategy of processing nodes alongside an existing resource pool. Conducted experiments show the advantage of Cloud infrastructures against other distributed systems and allow investigating the different factors in correlation with resources provisioning process.

Wireless sensor networks (WSNs) consists of a large number of sensor nodes to monitor physical or environmental conditions. Each sensor node has specific tasks to do with its neighbours. On the other hand, if node does not perform it, it is considered as misbehaviour node and often interrupt the normal functionality of a WSN. In order to keep functional WSNs, it is necessary to identify the misbehaviour of the node, which will save the network from internal attacks. So, an effective security mechanism is essential. The work presented in this paper is concerned with introduced trust management based Watermarking-LEACH to prevent at the same time internal and external attacks. This leads to introduce a new trust model into Watermarking-LEACH schema to infer the total trust and security with the energy-efficiency. To this end, the implementation and simulation of T-W-LEACH approach is done with the MATLAB tool simulator and the evaluation of security and energy is made to perform a comparative with TBE- LEACH a Trust based energy efficient routing in LEACH for wireless sensor networks.

The huge amount of exchanged data in the internet between entities and the quick development of edge computing has increased users frustration about the future generations. We expect a future where transactions will be based on clouds and virtual machines, gathered from sensors and IoT devices and processed by different artificial intelligence algorithms or agents. The speed and backlash change in the world has driven researchers to work on privacy and security for entities’ information and transactions. Different concepts arise and still in their early stage as digital identity, self sovereign identity, global unique identifier and identity of things (IDoT). Therefore, motivated by the recent explosion of interest around Blockchains, we examine whether they make a good fit for the Identity Internet of Things (IDoT) sector. Blockchain a major distributed peer-to-peer network where non-trusting members can interact together without a need for a trusted third party, it actually make available many advantages to providers and consumers and solve data protection features lifelong a transaction existing. As being immutable, transparent and trustful platform the Blockchain allows managing identities and privacy of its nodes information. Our contribution is a new architecture eventually using a public Blockchain and creating a smart identity wallet. It contains standard node data and will also integrate proactive data behavior and the reactive one. The main target is to protect users from sybil attack at early stage. We will define a new digital wallet based on entities behavior in order to prevent 51% attack and Sybil attack

In nowadays, robotics database management systems are increasing. These systems ensure good storage of data and with big data analytic, a new approach demands new structures and methods for collecting, recording, and analyzing enterprise data. This paper work deals with the NoSQL databases which are the secret of the continual progression data that new data management solutions have been emerged. They crossed several areas as personalization, profile management, big data in real-time, content management, catalogue, view of customers, mobile applications, internet of things, digital communication and fraud detection. Machine learning, for example, thrives on more data, so smart machines can learn more and faster, the Robotics are our use of case to focus on our Test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of Robotics we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Wireless sensor networks are must important nowadays and with rapid propagation. Also, when the accuracy of data is most importantly in the Internet of Things, detecting sensor data with faulty readings is an important issue of secure communication and power consumption. So, requirement of energy-efficiency and integrity of information is mandatory. Thus, cryptography is an effective technique that provides integrity to sensed data. In contrast, we can’t simply transferred traditional cryptographic techniques to WSNs due to sensors constraints. This paper, deals with the proposition of a novel energy-efficient and data integrity version of LEACH based routing protocol on Watermarking for wireless sensor networks because LEACH routing protocol does not take the security aspect into consideration and the secure improved LEACH works are based only on cryptographic techniques. Our hybrid proposed approach based on the Watermarking-LEACH achieves not only data integrity but also Energy-Efficient. It is the first schema that attempts to add security based on watermarking to LEACH routing protocol. To this end, we simulated and evaluated our WSN before and after falsification and we implemented our Watermarking-LEACH approach to evaluate the effectiveness in terms of integrity by evaluating the false negative rate (FNR) and in terms of energy-efficient through computing the energy consumption and compared its performance against other works by evaluating remaining energy metric across using the COOJA Simulator.

This paper gives insights into the most important existing problems of security and privacy of the Cloud Computing (CC), Internet of Things (IoT) and Cloud of Things (CoT) concepts especially confidentiality issue. With the evolution of ubiquitous computing, everything is connected everywhere, therefore these concepts have been widely studied in the literature. However, intrusions and vulnerabilities will be more recurrent due to the systems complexity and the difficulty to control each access attempt. To tackle this issue, researchers have been focused on various approaches enforcing security and privacy. In the present paper, risk factors and solutions regarding these technologies are reviewed then current and future trends are discussed.

This paper work deals with the implementation of a neural networks based approach for real-time scheduling of embedded systems composed by Operating Systems (OS) tasks in order to handle real-time constraints in execution scenarios.In our approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption.In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage.In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running.Indeed, we present in this paper a new hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm.Experimental results illustrate the efficiency of our original proposed approach.

While most embedded systems are designed for real-time applications, they suffer from resource constraints. Many techniques have been proposed for real-time task scheduling to reduce energy consumption. A combination of Dynamic Voltage Scaling (DVS) and feedback scheduling can be used to scale dynamically the frequency by adjusting the operating voltage, and to improve the run-time reliability of embedded systems. We present in this paper a novel hybrid contribution that handles real-time scheduling of embedded systems and low power consumption based on the combination of DVS and Neural Feedback Scheduling NFS with the priority-energy earliest-deadline-first (PEDF) algorithm.

Wireless Sensor Networks (WSNs) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This paper work presents an intelligent hybrid WSN application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety and to avoid the denial of service (DoS) attacks. Since its operates on a wireless network, an adversary can always perform a DoS attack by jamming the radio channel with a strong signal. The main contribution of our work is to use a hybrid approach that handles the problem of jamming. The proposed solution improves security by protecting against DoS and returns near-optimal solutions. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety.

This paper deals with the problem of scheduling multiprocessor real-time tasks by a hybrid genetic based scheduling algorithm. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at run-time. We propose a hybrid genetic based scheduling approach that automatically checks the systems feasibility after any reconfiguration scenario was applied on an embedded system. Indeed, if the system is unfeasible, the proposed approach operates directly in a highly dynamic and unpredictable environment and improves a rescheduling performance. This proposed approach which is based on a genetic algorithm (GA) combined with a tabu search (TS) algorithm is implemented which can find an optimized scheduling strategy to reschedule the embedded system after any system disturbance was happened. We mean by a system disturbance any automatic reconfiguration which is assumed to be applied at run-time: Addition-Removal of tasks or just modifications of their temporal parameters: WCET and/or deadlines. An example used as a benchmark is given, and the experimental results demonstrate the effectiveness of the proposed genetic based scheduling approach over others such as a classical genetic algorithm approach.

Several real-time embedded system must be dynamically reconfigured to account for hardware/software faults and/or maintain acceptable performances. Depending on the run-time environment, some reconfigurations might be unfeasible, i.e., they violate some real-time constraints of the system. In this paper, we deal with the development of dynamically reconfigurable embedded systems in terms of the production of execution schedules of system tasks (feasible configuration) under hard real-time constraints. More specifically, we propose an approach that starts from a set of reconfigurations to construct a Software Product Line that can be reused in a predictive and organized way to derive real-time embedded systems. To make sure that the SPL offers various feasible reconfigurations, we define an intelligent agent that automatically checks the system's feasibility after a reconfiguration scenario is applied on a multiprocessor embedded system. This agent dynamically determines precious technical solutions to define a new product whenever a reconfiguration is unfeasible. The set of products thus defined by the agent can then be unified into an SPL. The originality of our approach is its capacity to extract, from the unfeasible configurations of an embedded system, an SPL design enriched with real-time constraints and modeled with a UML Marte profile. The SPL design can assist in the comprehension, reconfiguration as well as evolution of the SPL in order to satisfy real-time requirements and to obtain a feasible system under normal and overload conditions.

The paper deals with the real-time scheduling of reconfigurable embedded systems which can change their behavior at run-time by adding, removing, or also updating OS tasks according to external events or also user requirements. We propose a new approach that checks the system's feasibility of the tasks that we assume periodic and a periodic while minimizing their response times. An agent-based architecture is proposed to provide run-time technical solutions for users in order to reach again the system's feasibility after any reconfiguration scenario. The effectiveness and the performance of the designed approach is evaluated through simulation studies.