Moufida Maimour
@univ-lorraine.fr
Lorraine University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Aya Sakhri, Arsalan Ahmed, Moufida Maimour, Mehdi Kherbache, Eric Rondeau, and Noureddine Doghmane
Elsevier BV
Moufida Maimour, Arsalan Ahmed, and Eric Rondeau
Elsevier BV
Ahcen Aliouat, Nasreddine Kouadria, Moufida Maimour, and Saliha Harize
Springer Science and Business Media LLC
Mehdi Kherbache, Arsalan Ahmed, Moufida Maimour, and Eric Rondeau
Elsevier BV
Mohamed Madani Hafidi, Meriem Djezzar, Mounir Hemam, Fatima Zahra Amara, and Moufida Maimour
Emerald
Purpose This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required. Design/methodology/approach This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems. Findings Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models. Originality/value This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.
Mehdi Kherbache, Moufida Maimour, and Eric Rondeau
Elsevier BV
Mehdi Kherbache, Otabek Sobirov, Moufida Maimour, Eric Rondeau, and Abderrezak Benyahia
Elsevier BV
Ahcen Aliouat, Nasreddine Kouadria, Moufida Maimour, Saliha Harize, and Noureddine Doghmane
Elsevier BV
Ahcen Aliouat, Nasreddine Kouadria, Saliha Harize, and Moufida Maimour
Institute of Electrical and Electronics Engineers (IEEE)
Moving object detection (MOD) has become a popular topic in video analysis due to its use in several applications, including video coding in wireless surveillance. However, implementing MOD in constrained sensors is challenging due to their high complexity and energy consumption. Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems. In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed. The algorithm ensures a trade-off between detection accuracy and computational complexity. To this end, we propose constructing an activity map by measuring each block activity between successive frames. The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-the-art techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.
Aya Sakhri, Oussama Hadji, Chakir Bouarrouguen, Moufida Maimour, Nasreddine Kouadria, Abderrezak Benyahia, Eric Rondeau, Noureddine Doghmane, and Saliha Harize
Elsevier BV
Souhila Kettouche, Moufida Maimour, and Lakhdar Derdouri
Springer Science and Business Media LLC
Aya Sakhri, Moufida Maimour, Eric Rondeau, Noureddine Doghmane, and Saliha Harize
Elsevier BV
Mehdi Kherbache, Otabek Sobirov, Moufida Maimour, Eric Rondeau, and Abderrezak Benyahia
Elsevier BV
Mehdi Kherbache, Moufida Maimour, and Eric Rondeau
Elsevier BV
Ahcen Aliouat, Nasreddine Kouadria, Moufida Maimour, and Saliha Harize
IEEE
In this work, we propose a fast and efficient Region-of-Interest based video coding strategy for surveillance systems involving low bitrate. The proposed algorithm is based on a combination of three major techniques, namely, edge detection, frame differencing and sum of absolute differences. We improve the algorithm accuracy through the use of morphological operations. A thresholding is performed to classify the frame blocks into moving and non-moving blocks. This allows to compress and sent to the destination only moving blocks in an object-based video coding scenario. The obtained results prove the efficiency of our proposal in terms of accurate detection, data reduction and bitrate saving.
Moufida Maimour, Aya Sakhri, Eric Rondeau, Mohamed Omar Chida, Chaima Tounsi-Omezzine, and Celine Zhang
IEEE
Multimedia Internet of Things (IoMT) is witnessing explosive growth due to its applications in multiple areas. To cope with limited resources of low-power and lossy networks (LLN), it is common that: (i) images are captured with a degraded quality due to limited camera capabilities, (ii) a low-cost lossy compression is applied to reduce the amount of data to deliver which introduces additional distortion and (iii) transmissions are prone to losses that induce holes in the images, further degrading their quality and making them difficult to use. In this work, we propose a complete efficient encoding-transmission-reconstruction chain. In addition to the use of a low complexity image compression method, an appropriate packetization scheme is proposed. At the destination, more powerful resources are leveraged to apply deep learning models to compensate for the distortion caused by the adopted lossy compression as well as to fill in the holes induced by packet losses. The obtained results show the effectiveness of our proposal.
Oussama Hadji, Ouahab Kadri, Moufida Maimour, Eric Rondeau, and Abderrezak Benyahia
IEEE
Wetlands or humid zones are one of the most vital areas where many species of birds that maintain the balance of ecological systems. Due to global warming and change climate, rare species are threatened with extinction. It is important to preserve track and monitor these species. Most wild life monitoring systems are expensive or sub-optimal in terms of performance, deployment and network overloading. In this paper, we explore the possibility of using image processing techniques to reduce the large amount of data transmitted in traditional audio/video streaming monitoring systems. We used the region of interest technique to convey only the occurrence of a moving object. Feature extraction and matching techniques are used to deal with the redundancy and counting problem. We believe that our results show the viability of employing and using these techniques to reduce the amount of data transmitted in wild life monitoring systems.
Ahcen Aliouat, Nasreddine Kouadria, Saliha Harize, and Moufida Maimour
Springer International Publishing
Mehdi Kherbache, Moufida Maimour, and Eric Rondeau
IEEE
Digital Twins are starting to revolutionize many industries in the last decade providing a plethora of benefits to optimize the performance of industrial systems. They aim to create a continuously synchronized model of the physical system which enables rapid adaptation to dynamics, mainly unpredicted and undesirable changes. A wide range of industrial fields have already benefited from digital twins technology such as aerospace, manufacturing, healthcare, city management and maritime and shipping. Furthermore, recent research works are starting to study the integration of digital twins for computer networks to allow more innovation and intelligent management. One of the basic building blocks of digital twins technology is the Internet of Things where wireless sensors and actuators are deployed to ensure the interaction between the physical and digital worlds. This type of network is complex to manage due to its severe constraints especially when it controls critical industrial applications, resulting in the Industrial Internet of Things (IIoT). We believe that the optimization of the IIoT will lead to efficient integration of Digital Twins in Industry 4.0. In this paper, we design a Network Digital Twin for the IIoT where sensors, actuators and communication infrastructure are replicated in the digital twin to enable intelligent real-time management of such networks. This way, new networking services such as predictive maintenance, network diagnosis, resource allocation, energy optimization with other intelligent services can be efficiently integrated and exploited in the network life-cycle. We validate the proposed architecture by providing a promising prototype implementation that should unleash the full potential of the network digital twin.
Sihem Benkhaled, Mounir Hemam, Meriem Djezzar, and Moufida Maimour
Slovenian Association Informatika
Fatima Zahra Amara, Mounir Hemam, Meriem Djezzar, and Moufida Maimour
Springer International Publishing
Mehdi Kherbache, Moufida Maimour, and Eric Rondeau
MDPI AG
The Industrial Internet of Things (IIoT) is known to be a complex system because of its severe constraints as it controls critical applications. It is difficult to manage such networks and keep control of all the variables impacting their operation during their whole lifecycle. Meanwhile, Digital Twinning technology has been increasingly used to optimize the performances of industrial systems and has been ranked as one of the top ten most promising technological trends in the next decade. Many Digital Twins of industrial systems exist nowadays but only few are destined to networks. In this paper, we propose a holistic digital twinning architecture for the IIoT where the network is integrated along with the other industrial components of the system. To do so, the concept of Network Digital Twin is introduced. The main motivation is to permit a closed-loop network management across the whole network lifecycle, from the design to the service phase. Our architecture leverages the Software Defined Networking (SDN) paradigm as an expression of network softwarization. Mainly, the SDN controller allows for setting up the connection between each Digital Twin of the industrial system and its physical counterpart. We validate the feasibility of the proposed architecture in the process of choosing the most suitable communication mechanism that satisfies the real-time requirements of a Flexible Production System.
Moufida Maimour
Wiley
Multipath routing holds a great potential to enhance transmission reliability in Industrial Wireless Sensor Networks (IWSN). This may be heavily impeded by the phenomena of interpath interference especially for high data rate applications. The aim of this paper is to assess the impact of a set of interference aware metrics on the performance of an iterative multipath routing where paths are built one after the other. A path is chosen based on its estimated interference with already built ones. We consider both active and passive metrics and show that the latter are more preferred in the context of IWSN characterized by their limited resources. Among passive monitoring metrics, we show that the best metric is not necessarily the one that is the most costly to estimate.