A clustering method for wireless sensors networks

Fouchal, Said; Monnet, Quentin; Mansouri, Djamel; Mokdad, Lynda; Ioualalen, Malika

doi:10.1109/iscc.2012.6249414

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…There is a number of proposals derived from LEACH, to improve either its efficiency [25], [26] or its security [27]. Other example clustering algorithms include HEED [28] or FFUCA [29].…”

Section: Clustering Algorithms and Energy Preservationmentioning

confidence: 99%

Fair Election of Monitoring Nodes in WSNs

Monnet¹,

Hammal²,

Mokdad³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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In this era of big data, of quantified self and of smart cities, wireless sensor networks are meant to be used every day, for all sort of applications. Made of tiny sensors, they collect data and communicate through wireless technologies. Because they may take part in sensitive or military applications, security is an essential matter in such networks. Confidentiality and authenticity can be ensured by the use of dedicated mechanisms. Focusing on availability, we propose here a new practical approach to protect the network against denial of service attacks thanks to the use of traffic monitoring agents called cNodes. The approach uses a fair election process of cNodes in accordance with classical criteria related to residual energies and the presence of compromised nodes which may have greedy or jamming behaviors. Results obtained from simulations show that this method is effective both in terms of detection and of energy conservation.

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“…There is a number of proposals derived from LEACH, to improve either its efficiency [25], [26] or its security [27]. Other example clustering algorithms include HEED [28] or FFUCA [29].…”

Section: Clustering Algorithms and Energy Preservationmentioning

confidence: 99%

Fair Election of Monitoring Nodes in WSNs

Monnet¹,

Hammal²,

Mokdad³

et al. 2014

2015 IEEE Global Communications Conference (GLOBECOM)

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“…They can be repartee into three categories namely [9]:-Hierarchical clustering algorithms -Clustering algorithms based on heuristics and Partitional clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

Detecting DoS attacks in WSN based on clustering technique

Mansouri¹,

Mokdad²,

Ben‐Othman³

et al. 2013

2013 IEEE Wireless Communications and Networking Conference (WCNC)

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WIth the facility of deployment, Wireless Sensor Networks becomes very popular but have special characteristics such as limited battery, limited processing power, and limited storage that makes the energy consumption saving a real challenge. Add to this and due to their distributed deployment, these networks are exposed to denial of service attacks such as jamming and greedy attacks. In all cases these attacks tackle the energy consumption in order to degrade the overall Quality of Service (QoS). In this paper, we propose an energy-preserving solution to detect compromised nodes in WSNs. The proposed method is based on hierarchical clustering technique which elect Controlled nodes (Cnode) that analyze the traffic inside a cluster and to send warnings to the cluster-head (CH) whenever an abnormal behavior is detected. The proposed method is dynamic as the Cnodes are periodically elected among ordinary nodes on each atomic cluster. Such a solution results in a better energy balance while maintaining good detection coverage as it is based on the distance between nodes, the output throughput and delay between packets transmission.

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“…In a hierarchical WSN, the network is divided into several clusters. The partition is done according to a clustering algorithm such as LEACH , HEED , or one based on ultra‐metric properties . In each cluster, a single common node is designated and becomes a cluster head (CH), responsible for directly collecting data from the other nodes in the cluster.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the analysis of the respective performances of the three mechanisms is used as a basis to establish recommendations regarding the use cases of those methods. [10,11]. In each cluster, a single common node is designated and becomes a cluster head (CH), responsible for directly collecting data from the other nodes in the cluster.…”

mentioning

confidence: 99%

DoS detection in WSNs: Energy‐efficient methods for selecting monitoring nodes

Monnet

Mokdad

Ballarini

et al. 2017

Concurrency and Computation

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Summary The use of wireless sensor networks (WSNs) has increased rapidly over the last years. Due to their low resources, sensors come along with new issues regarding network security and energy consumption. Focusing on the network availability, previous studies proposed to protect clustered network against denial of service attacks with the use of traffic monitoring agents on some nodes. Those control nodes have to analyze the traffic inside a cluster and to send warnings to the cluster head whenever an abnormal behavior (e.g., high packets throughput or non‐retransmission of packets) is detected. But if the control nodes (cNodes) die out of exhaustion, they leave the network unprotected. To better fight against attacks, we try to enhance this solution by renewing periodically the election process. Furthermore, we propose three energy‐aware and secure methods to designate the cNodes in a hierarchically clustered WSN. The first one is a simple self‐election process where nodes randomly designate themselves. It leads to a better load balancing than a static method (i.e., with no renewal), but we argue that we can obtain better results by considering the remaining energy of the nodes at cNodes selection time. Hence, the second algorithm is purely based on the residual energy of the sensors. We discuss limitations of this deterministic process concerning security and cluster coverage and suggest workarounds. These improvements lead us to the third mechanism. It is based on residual energy too, but it includes a democratic election process in which nodes in the cluster vote to optimize the cNode role attribution. Results obtained from simulation experiments with the ns‐2 tool are provided to analyze the energy repartition in the network and to compare the three selection algorithms. All experimental outcomes show improvements of the load balancing in the network, while maintaining good detection coverage, in regard to static selection. Furthermore, the analysis of the respective performances of the three mechanisms is used as a basis to establish recommendations regarding the use cases of those methods. Copyright © 2017 John Wiley & Sons, Ltd.

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A clustering method for wireless sensors networks

Cited by 6 publications

References 18 publications

Fair Election of Monitoring Nodes in WSNs

Fair Election of Monitoring Nodes in WSNs

Detecting DoS attacks in WSN based on clustering technique

DoS detection in WSNs: Energy‐efficient methods for selecting monitoring nodes

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