TEEM: Trust-based Energy-Efficient Distributed Monitoring for Mobile Ad-hoc Networks

Lupia, Andrea; Kerrache, Chaker Abdelaziz; Rango, Floriano De; Calafate, Carlos T.; Cano, Juan-Carlos; Manzoni, Pietro

doi:10.1109/wd.2017.7918128

Cited by 10 publications

(15 citation statements)

References 5 publications

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“…To identify malicious nodes, a trust management protocol, QoS trust with some social trust, is used. In this method, a false positive rate and energy consumption are high [35]. Lupia et al proposed a method called TEEM that used a time division-based monitoring method to get high security levels.…”

Section: Related Workmentioning

confidence: 99%

A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

Nobahary

Babaie

2018

Applied Computer Systems

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Ad-hoc networks are a set of mobile nodes that are connected via a wireless channel. Some of the nodes in this network behave selfishly and do not send data to other nodes so that in order to increase network performance these nodes must be identified. A credit-based algorithm is proposed to detect the selfish nodes. Three watchdog nodes are selected to monitor suspicious nodes in each cluster. The cluster head nodes detect the existence of selfish nodes by controlling general features of network, such as delay, the total number of sent packets, the total number of received packets, throughput, and network traffic. The watchdog nodes send their comment on selfishness or cooperation of the node to the cluster head. Cluster head makes decisions with a majority vote on a suspicious node. The simulation results show that the rate of detection accuracy and the life time of network are considerably high and the false alarm rate and energy consumption are low comparing to that of similar methods.

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Section: Related Workmentioning

confidence: 99%

A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

Nobahary

Babaie

2018

Applied Computer Systems

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“…TEEM is a trust-based approach to detect malicious and selfish nodes in mobile ad hoc networks and wireless sensor networks, which is usually dependent on the watchdog approach, although such monitoring devices have more energy consumption [31]. is method is based on the time division of the monitoring strategy to achieve high-security levels.…”

Section: Related Workmentioning

confidence: 99%

Selfish node Detection Based on Fuzzy Logic and Harris Hawks Optimization Algorithm in IoT Networks

Akhbari¹,

Ghaffari

2021

Security and Communication Networks

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The Internet of things describes a network of physical things for example, “things” that are connected with the sensors, software, and other technologies to connect and exchange data with other devices and systems via the Internet. In this type of network, the nodes communicate with each other because of the low radio range by step by step with the help of each other until they reach their destination, but there are nodes in the network that do not cooperate with other nodes in the network, which are called “selfish nodes”. In this paper, we try to detect selfish nodes based on a hybrid approach to increase the performance of our network. The proposed method consists of three stages: in the first stage, with the help of the Harris hawk operation, we try to set up the cluster and select head cluster; in the second stage, the sink investigates the existence or nonexistence of selfish nodes in the network by considering the general parameters of the network; and in the event of a selfish node in the network, it informs the head clusters to check the cluster members and recognize the selfish node. In the third stage, with the help of fuzzy logic, the amount of reputation of each of the nodes has been realized, and finally, with the help of fusion of head clusters and fuzzy logic, each node is decided to be cooperate or selfish nodes, and in case of head clusters and fuzzy logic in some cases, the opportunity node will be reestablished to participate in network activities otherwise the node will be isolated. The results show that the accuracy of selfish node detection has increased by an average of 12% and the false positive rate is 8% in comparison to existing methods.

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“…The security can become a critical issue in e-Health applications in which this protocol is used [24,25]. Moreover, the security management can use a lot of energy and this can significantly reduce the nodes lifetime [26,27].…”

Section: A An Iot Lightweight Protocol: Mqttmentioning

confidence: 99%

A Distributed Mitigation Strategy against DoS attacks in Edge Computing

Potrino

Rango

Fazio

2019

2019 Wireless Telecommunications Symposium (WTS)

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Internet of Things (IoT) is a platform where every day devices become smarter, every day processing becomes intelligent, and every day communication becomes informative. Numerous challenges prevent to secure IoT devices and their end-to-end communication in an IoT environment. In fact, the IoT security is still an open challenge. The purpose of this work is to examine a distributed strategy for mitigating Denial of Service (DoS) attacks against the fog node in an edge computing context in which the nodes exchange messages through Message Queue Telemetry Transport (MQTT) protocol. The proposed strategy is based on a dynamic message sending frequency of the lightweight nodes. It is also mitigated data tampering and eavesdropping by using Elliptic Curve Cryptography (ECC).

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TEEM: Trust-based Energy-Efficient Distributed Monitoring for Mobile Ad-hoc Networks

Cited by 10 publications

References 5 publications

A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network

Selfish node Detection Based on Fuzzy Logic and Harris Hawks Optimization Algorithm in IoT Networks

A Distributed Mitigation Strategy against DoS attacks in Edge Computing

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