M. SAHRAOUI Mohamed

MCA

Directory of teachers

Department

Informatics Department

Research Interests

Wireless netwoks peer to peer networks

Contact Info

University of M'Sila, Algeria

Email : mohamed.sahraoui@univ-msila.dz

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Recent Publications

2024-05-28

Double firefly based efficient clustering for large-scale wireless sensor networks

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate.
Citation

M. SAHRAOUI Mohamed, Saad Harous, , (2024-05-28), "Double firefly based efficient clustering for large-scale wireless sensor networks", [national] The Journal of Supercomputing , Springer

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2023-07-15

A Firefly Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

The fundamental metric token by clustering algorithms in Wireless Sensor Networks (WSN) is energy enhancement to maximize network lifetime. One of the crucial issues is network coverage in order to use all of the network’s resources, which increases the lifetime of the network. Moreover, load balancing techniques play an essential role in improving network lifetime due to their efficient way of distributing the load between nodes. The goal of this work is to assemble these two approaches in clustered WSN in order to improve resources utilization and increase network lifetime. Thus, we present a new clustering algorithm named Firefly optimization based Adaptive Clustering for Energy Efficiency (FACEE) which uses a novel clustering based firefly optimization algorithm for coverage improvement and load balancing. The simulation results indicate that our proposed algorithm can significantly improve the network lifetime as well as the delivery rate.
Citation

M. SAHRAOUI Mohamed, Abd Elmalik Taleb-Ahmed, , (2023-07-15), "A Firefly Algorithm for Energy Efficient Clustering in Wireless Sensor Networks", [national] Communications in Computer and Information Science , Springer

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2022

A new Deep Learning Algorithm for Face Emotional Recognition

Automatic recognition of human emotions has received increasing interest from researchers in the field of computer vision, which has led to the proposal of several methods.
Many of them relied on handcrafted features and traditional fusion and classification techniques. The use of deep learning techniques to automatically extract powerful features from multimedia information as well as their use for merging and classification are new trends that researchers are currently pursuing. In this work, we define a new accurate facial expression detection algorithm based on a deep learning method, specifically on an intentional convolutional neural network capable of focusing on important parts of the face in an image or video database through the use of more layers. As a result, our proposed algorithm significantly improves the accuracy rate compared to previously proposed models in several datasets.
Citation

M. SAHRAOUI Mohamed, Djamel Amer Ouali, Abdeslem Achour, , (2022), "A new Deep Learning Algorithm for Face Emotional Recognition", [international] 1st International Conference on Autonomous Systems and their Applications , El-Tarf, Algeria

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Firefly optimization based adaptive clustering for coverage improvement and load balance in wireless sensor networks

The fundamental metric token by clustering algorithms in Wireless Sensor Networks (WSN) is energy enhancement to maximize network lifetime. One of the crucial issues is the network coverage in order to use all of the network's resources, which increases the lifetime of the network. Moreover, load balancing techniques play an important role in improving network lifetime due to their ecient way of distributing the load among the nodes. The objective of this work is to assemble these two approaches in clustered WSN in order to improve resources
utilization and increase the network lifetime. Thus, we present a new clustering algorithm named Firey optimization based Adaptive Clustering for Energy Eciency (FACEE) which uses a novel clustering based on the rey optimization algorithm for coverage improvement and load balancing. The simulation results show that our proposed algorithm can signicantly improve the network lifetime as well as the delivery rate.
Citation

M. SAHRAOUI Mohamed, abdelmalik taleb-ahmed, , (2022), "Firefly optimization based adaptive clustering for coverage improvement and load balance in wireless sensor networks", [international] 5th Artificial Intelligence Doctoral symposium , Algers, Algeria

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Schedule based Cooperative Multi-agent Reinforcement Learning for Multi-channel communication in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become an important component in the Internet of things (IoT) field. In WSNs, multi-channel protocols have been developed to overcome some limitations related to the throughput and delivery rate which have become necessary for many IoT applications that require sufficient bandwidth to transmit a large amount of data. However, the requirement of frequent negotiation for channel assignment in distributed multi-channel protocols incurs an extra-large communication overhead which results in a reduction of the network lifetime. To deal with this requirement in an energy-efficient way is a challenging task. Hence, the Reinforcement Learning (RL) approach for channel assignment is used to overcome this problem. Nevertheless, the use of the RL approach requires a number of iterations to obtain the best solution which in turn creates a communication overhead and time-wasting. In this paper, a Self-schedule based Cooperative multi-agent Reinforcement Learning for Channel Assignment (SCRL CA) approach is proposed to improve the network lifetime and performance. The proposal addresses both regular traffic scheduling and assignment of the available orthogonal channels in an energy-efficient way. We solve the cooperation between the RL agents problem by using the self-schedule method to accelerate the RL iterations, reduce the communication overhead and balance the energy consumption in the route selection process. Therefore, two algorithms are proposed, the first one is for the Static channel assignment (SSCRL CA) while the second one is for the Dynamic channel assignment (DSCRL CA). The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance through the two algorithms.
Citation

M. SAHRAOUI Mohamed, azeddine bilami, abdelmalik taleb-ahmed, , (2022), "Schedule based Cooperative Multi-agent Reinforcement Learning for Multi-channel communication in Wireless Sensor Networks", [national] Wireless Personal Communications , Springer

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2021

Heuristically accelerated reinforcement learning for channel assignment in wireless sensor Networks

In wireless sensor networks (WSNs), multi-channel communication represents an attractive field due to its advantage in improving throughput and delivery rate. However, the major challenge that faces WSNs is the energy constraint. To overcome the channel assignment problem in an energy-efficient way, reinforcement learning (RL) approach is used. Though, RL requires several iterations to obtain the best solution, creating a communication overhead and time-wasting. In this paper, a heuristically accelerated reinforcement learning approach for channel assignment (HARL CA) in WSNs is proposed to reduce the learning iterations. The proposal considers the selected channel by the neighboring sender nodes as external information, used to accelerate the learning process and to avoid collisions, while the bandwidth of the used channel is regarded as an important factor in the scheduling process to increase the delivery rate. The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance.
Citation

M. SAHRAOUI Mohamed, bilami azeddine, abdelmalik taleb-ahmed, , (2021), "Heuristically accelerated reinforcement learning for channel assignment in wireless sensor Networks", [national] International Journal of Sensor Networks , Inderscience

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2018

optimal converage based multi-channel improvement on LEACH protocol for wireless sensor networks

Wireless sensor networks present many challenges that need to overcome in order to achieve better performance. The main three metrics that determine this performance are energy consumption, throughput, and latency, while the major challenge that faces WSNs is the drain of energy. For this reason, various protocols have been proposed for energy efficiency in WSNs. The Low Energy Adaptive Clustering Hierarchy (LEACH) protocol puts forward an algorithm where sensor nodes are built into clusters to fuse data before transmitting to Base Station (BS). Many improved versions of LEACH are presented in the literature to improve energy efficiency but no one takes into account nether coverage problem nor multi-channel technology. This paper presents an improvement of LEACH based on multi-channel technology, called Multi-Channel Low-Energy Adaptive Clustering Hierarchy (MC LEACH), which balances coverage distribution of network by means of constructing balanced disjoints zones. Simulation results show that MC-LEACH can improve system lifetime very considerably.
Citation

M. SAHRAOUI Mohamed, Bilami ezzeddine, abdelmalik taleb ahmed, , (2018), "optimal converage based multi-channel improvement on LEACH protocol for wireless sensor networks", [international] The third International Symposium on informatics and its Applications , Msila

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