Publications

@conference{Azzouz20221534,

title = {Energy-Aware Cluster Head selection protocol with Balanced Fuzzy C-mean Clustering in WSN},

author = {I. Azzouz and B. Boussaid and A. Zouinkhi and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143748653\&doi=10.1109%2fSSD54932.2022.9955909\&partnerID=40\&md5=85cce57f420b26546a3dd3379a6017dd},

doi = {10.1109/SSD54932.2022.9955909},

isbn = {9781665471084},

year  = {2022},

date = {2022-01-01},

journal = {2022 19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022},

pages = {1534-1539},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The Energy saving in wireless sensor network has become one of the ascending issue. To manage the remaining energy in nodes and extend the life time of the network, clustering is one of the efficient solution. Structuring homogenous and stable clusters as well as choosing the optimal cluster head is the key of an energy efficient clustering. This paper deals with an Energy Aware Cluster head selection protocol based on balanced Fuzzy c-mean clustering approach. This approach proposes uniform and stable cluster formation using Balanced Fuzzy c-mean algorithm with modified centroid. As for Cluster Head selection, three parameters are considered such as Residual energy of the node, Distance from the Base Station and Distance-Ratio. Simulation results on Matlab of the proposed approach are compared to basic energy efficient approaches such as LEACH, C-LEACH and TSI-LEACH. © 2022 IEEE.},

note = {cited By 1},

keywords = {Balanced fuzzy; Cluster-head selections; Clusterings; Energy aware; Energy aware clustering; Eu-clidean distance; Fuzzy C-Means clustering; Fuzzy-c means; Ratio-distance; Stable clusters, Clustering algorithms; Energy efficiency; Leaching; MATLAB; Power management (telecommunication); Routing protocols, Sensor nodes},

pubstate = {published},

tppubtype = {conference}

}

@conference{Neji2020338,

title = {T-LEACH: Threshold sensitive Low Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks},

author = {W. Neji and S. B. Othman and H. Sakli},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100495167\&doi=10.1109%2fSTA50679.2020.9329354\&partnerID=40\&md5=96bba821dfd0387f4eaea822a7b09a92},

doi = {10.1109/STA50679.2020.9329354},

isbn = {9781728188157},

year  = {2020},

date = {2020-01-01},

journal = {Proceedings - STA 2020: 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering},

pages = {338-342},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {A Wireless Sensor Network (WSNs) consists of spatially distributed autonomous sensor nodes to monitor physical or environmental conditions. The major advantage of WSN is that it can be installed in harsh environment such as in volcanic eruption, seismic regions, battlefield and forest, etc. The sensor nodes are generally battery-powered devices, the key task in WSN is to reduce the energy consumption of nodes so that the lifetime of the network can be augmented. Energy efficiency and information gathering is a major concern in many applications of WSNs. Many techniques have been developed till now in order to achieve an energy efficient network. Hierarchical clustering is an effective method to save energy in WSNs. Some of the most common energy-efficiency sensor networks protocols is Low Energy Adaptive Clustering Hierarchy (LEACH) as source. In this paper, we propose Threshold sensitive Low Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks (T-LEACH). This last considers the node's heterogeneity of nodes and residual energy for choosing the optimal cluster head (CH). The simulation results have clearly shown that T-LEACH reduces the node's energy consumption, improves the network lifetime and packet transfer ratio. © 2020 IEEE.},

note = {cited By 4},

keywords = {Automation; Energy efficiency; Energy utilization; Hierarchical clustering; Leaching; Low power electronics; Process control; Volcanoes, Autonomous sensors; Battery powered devices; Energy efficient networks; Environmental conditions; Information gathering; Low energy adaptive clustering hierarchies (LEACH); Low-energy adaptive clustering hierarchies; Wireless sensor network (WSNs), Sensor nodes},

pubstate = {published},

tppubtype = {conference}

}

@conference{Trab2013237,

title = {Energy minimization algorithm based on Bayesian approach for fault tolerant detection in Wireless Sensor Network},

author = {S. Trab and B. Boussaid and A. Zouinkhi and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899072736\&doi=10.1109%2fSTA.2013.6783137\&partnerID=40\&md5=79d84a1dc1459675fc18af59b9cd0f2a},

doi = {10.1109/STA.2013.6783137},

year  = {2013},

date = {2013-01-01},

journal = {14th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2013},

pages = {237-242},

publisher = {IEEE Computer Society},

abstract = {Wireless Sensor Networks (WSNs) have been a significant system considerably exploited in numerous fields including medical, environmental, military and household applications. Yet, its high energy consumption and its potential fault vulnerability still their major drawback. Both of these issues require the implementation of fault distributed detection methods, capable of distinguishing faulty nodes from normal ones while minimizing energy. Thus, several approaches are proposed to detect these distributed errors, mentioning the Bayesian approach insuring the best choice of a proper sensor neighbourhood size n for best fault detection as well the energy conservation. In this paper, we define an energy minimization algorithm based on the Bayesian approach which ensures preventive sensor nodes pre-deployment against faults distribution. The Bayesian approach represents a study oscillating between Theory of Signal Detection (TSD), fault tolerance and energy minimization by determining the optimal parameters required to achieve this fault tolerance detection. This method includes the error probability due to the sensor itself which is included in the fault detection scheme thus the network will be more accurate. The experimental simulations will join a perceptive study allowing to the best choice of algorithm inputs then we will prouve that the proposed algorithm is able to provide a better detection performance while optimizing energy in WSN. © 2013 IEEE.},

note = {cited By 2},

keywords = {Algorithms; Automation; Bayesian networks; Control; Energy utilization; Fault detection; Fault tolerance; Military applications; Optimization; Process control; Sensors; Wireless sensor networks, Bayesian approaches; Distributed fault detections; Energy minimization; Energy minimization algorithms; Experimental simulations; Fault-tolerant detections; High energy consumption; Wireless sensor network (WSNs), Sensor nodes},

pubstate = {published},

tppubtype = {conference}

}