Acknowledgment-based punishment and stimulation scheme for mobile ad hoc network

Bounouni, Mahdi; Bouallouche-Medjkoune, Louiza

doi:10.1007/s11227-018-2434-2

Cited by 12 publications

(16 citation statements)

References 22 publications

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“…If a source node does not receive an acknowledgment message, it is taken as a misbehavior node [16,17]. In 2018, Mahdi Bounouni et al proposed an acknowledgment-based method to discover malicious and selfish nodes [18]. The proposed approach consists of four models for punishing malicious nodes and stimulating selfish nodes to cooperate with other nodes.…”

Section: -Related Workmentioning

confidence: 99%

“…Credit based approaches trade data packets between nodes in the business network; the lack of encouragement and punishment of cooperation and selfish nodes is the disadvantage of these categories [13][14][15]. Acknowledgment messages are sent from destination nodes in acknowledgment based methods in the different approaches presented in this group due to the transmission of authentication packets, network traffic, and the average end to end delay to the result in increased data packets and network efficiency have decreased [16][17][18]. In the game theory methods, each node plays a role as players in the game, which are interacting with each other and design the game and its profits to send the data packets.…”

Section: -Introductionmentioning

confidence: 99%

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Selfish Node detection based on GA and Learning Automata in IoT

Nobahary¹,

Garakani²,

Khademzadeh³

et al. 2020

Preprint

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It is critical to increasing the network throughput on the internet of things with short-range nodes. Nodes prevent to cooperate with other nodes in the network are known as selfish nodes. Previous studies have done on the selfish nodes detection that leads to increase throughput and reduce the end to end delay. The proposed method for discovering the selfish node is based on genetic algorithm and learning automata. It consists of three phases of setup and clustering, the best routing selection based on genetic algorithm, and finally, the learning and update phase. For appropriate network performance, the clustering algorithm implemented in the first phase. Nodes are working together to send the data packet to the destination in the second phase, and the neighbor node selected for forwarding the data packet in which that node has a high value of fitness function, among others. In the third phase, each node monitors the performance of its neighbor nodes in forwarding the data packet and uses the learning automata system to identify the selfish nodes. By preventing to cooperate selfish nodes and decreasing the probability selection of selfish nodes, it increases the throughput in the network. The results of the simulation show that the detection accuracy of selfish nodes in comparison with the existing methods average 12%, and the false positive rate has decreased by 5%.

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Section: -Related Workmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

Selfish Node detection based on GA and Learning Automata in IoT

Nobahary¹,

Garakani²,

Khademzadeh³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Each of the three EAACK sections uses a digitally signed digital signature and retrieves the message. Bounouni et al proposed an approach consists of four models [26]. The monitoring model is responsible for controlling the sending of routing packets and data packets by using the acknowledgment packet.…”

Section: Related Workmentioning

confidence: 99%

“…In these methods, the throughput is low; it suffers from the collusion of the selfish and malicious nodes; it has high overhead (communicative, data packet, etc.) and end-to-end delay increases in the network due to the high traffic generated by the acknowledgment messages [23][24][25][26]. Another method to detect selfish nodes is game theory-based methods.…”

Section: Introductionmentioning

confidence: 99%

Selfish node detection based on hierarchical game theory in IoT

Nobahary

Garakani

Khademzadeh

et al. 2019

J Wireless Com Network

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Cooperation between nodes is an effective technology for network throughput in the Internet of Things. The nodes that do not cooperate with other nodes in the network are called selfish and malicious nodes. Selfish nodes use the facilities of other nodes of the network for raising their interests. But malicious nodes tend to damage the facilities of the network and abuse it. According to reviews of the previous studies, in this paper, a mechanism is proposed for detecting the selfish and malicious nodes based on reputation and game theory. The proposed method includes three phases of setup and clustering, sending data and playing the multi-person game, and update and detecting the selfish and malicious nodes. The process of setup and clustering algorithm are run in the first phase. In the second phase, the nodes of each cluster cooperate with each other in order to execute an infinite repeated game while forwarding their own or neighbor nodes' data packets. In the third phase, each node monitors the operation of its neighbor nodes for sending the data packets, and the process of cooperation is analyzed for determining the selfish or malicious nodes which forwarded the data packets with delay or even not sent them. The other nodes reduce the reputation of the nodes which does not cooperate with them, and they do not cooperate with the selfish and malicious nodes, as punishment. So, selfish and malicious nodes are stimulated to cooperate. The results of simulation suggest that the detection accuracy of the selfish and malicious nodes has been increased by an average of 12% compared with the existing methods, and the false-positive rate has been decreased by 8%.

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“…Otherwise, the communication is done based on the cooperation of intermediate nodes (Multi-hop communication). Then, to deliver correctly data packets, the cooperation of intermediate nodes is essential and critical [1][2][3], which is not easy to guarantee due to the specific's characteristics of MANET, such as the open wireless medium and dynamic topology. In a real world, nodes could adopt a malicious behavior, being unwilling to drop data packet destined to be forwarded in order to disrupt the well-functioning of network operations.…”

Section: Introductionmentioning

confidence: 99%

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

Bounouni

Bouallouche-Medjkoune

Beraza

et al. 2021

Preprint

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In this paper, we propose a new reputation approach, called I-WG (improved Watchdog). The aims is to eliminate selective dropping attack that occurs when malicious nodes drop packets at low rate to damage the network, while at the same time to avoid to be detected. The proposed approach is structured around four modules. The monitoring module overhears the forwarding activities of neighbors nodes using the promiscuous mode. The reputation module evaluates the nodes reputation values. We have have proposed a new reputation method that enable nodes to evaluate their neighbors in multiple monitoring sessions. Thus, the computed reputation value is used to determine the increment and decrement reputation rate. The exclusion module is responsible for excluding nodes with reputation values below the reputation threshold. The route selection module make restriction about discovered forwarding routes. Only forwarding routes satisfying the route incorporation threshold are accepted. The simulation results demonstrate that I-WG improves the success rate and reduces the number of packets dropped by malicious nodes, while increases the end-to-end delay.

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Acknowledgment-based punishment and stimulation scheme for mobile ad hoc network

Cited by 12 publications

References 22 publications

Selfish Node detection based on GA and Learning Automata in IoT

Selfish Node detection based on GA and Learning Automata in IoT

Selfish node detection based on hierarchical game theory in IoT

Eliminating Selective Dropping Attack in Mobile Ad Hoc Network

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