An optimized deep learning-based trust mechanism In VANET for selfish node detection

Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

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

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.

Detection and Isolation of Selfish Nodes in MANET Using Collaborative Contact-Based Watchdog With Chimp-AODV

Mobile Ad-hoc Network (MANET) is one of the most important self-configuring and independent wireless network. Numerous intermediate nodes are used among MANET to interchange the information without the requirement of any centralized infrastructure. But some nodes act selfishly and utilize the resources only for their own purposes and do not share with the neighbors. This selfish nodes might delay or drop the packet and do not perform routing. Though watchdog is a well-known selfish node detection technique, it causes false negatives and false positives that can affect the performance in terms of precision and speed. To eliminate the drawbacks of existing approaches in selfish node detection, this paper integrates both Ad-hoc On-Demand Distance Vector (AODV) protocol incorporated with chimp optimization algorithm and Collaborative Contact based Watchdog to propose a novel technique called Chimp-CoCoWa-AODV in order to improve the performance of MANET. The main role of chimp optimization algorithm in AODV is to undergo optimal route selection process. The performance of the proposed Chimp-CoCoWa-AODV approach is compared with existing approaches in terms of average routing load, Average Packet Delivery Fraction (PDF), Average End-to-end Delay (EED), Average Throughput, Total packet drop in the application layer, and maliciously dropped packet in the routing layer. The simulation results shows that the proposed approach is effective with 82% PDF and 7.4 ms EED at 50 nodes in detection and isolation of selfish nodes in MANET even in the presence of malicious node.

PUSR: Position Update Secure Routing protocol for MANET

Mobile ad hoc network is a self-organizing wireless network, which is formed by the wireless mobile devices without any centralized infrastructure. The mobile nodes in the network are mainly affected by the inherent characteristics such as unpredictable network topology and open wireless medium. Especially, the presence of selfish nodes in the network creates the packet loss and affects an entire communication system. In this paper, the hash function with position updating algorithm is proposed in the Ad hoc On-Demand Distance Vector (AODV) routing protocol for improving the security against the selfish nodes. The AODV routing protocol is used to transmit the data packets from the source to the destination. Therefore, the Prevention of Selfish Node using Hash Function (PSNHF) with position update algorithm is proposed for minimizing the packet loss through the network. The performances of proposed AODV-PSNHF method are analysed in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), packet loss and normalized routing load. In addition, the AODV-PSNHF method is compared with the existing trust-aware ad-hoc routing protocol (T2AR). The selfish node identification using hash function and positioning update using AODV-PSNHF method provides reliable and secure data transmission under selfish nodes and shows better performance in terms of throughput, packet delivery ratio and packet loss. For 2% of malicious nodes the PDR of the AODV-PSNHF method is 89%, it is 9% higher when compared to the T2AR protocol whose PDR% is only 80% for the same.

Enhancing the Performance of Dynamic Source Routing Protocol by Modifying the Algorithm in Mobile ad-hoc Network

In our day-to-day life, wireless technology plays a great role, because users move from place to place across the globe, it’s important to think about the type of network we can deploy quickly. Collection of two or more node forms A mobile ad-hoc network (MANET) without the aid of any centralized infrastructure. There are independent nodes that communicate without a wire in a mobile ad hoc network. Due to the behavior of nodes in MANET the time when node joining/leaving the network is unknown. This study focuses on improve performance of DSR protocol affected due to selfish nodes. So, identify path without any selfish node is the main issue for delivering a packet by selecting a reserved path. The algorithm counts the number of failed route request messages to aware the selfish node that causes failure on NS-2 simulator tools. The node is assigned to indicating the selfishness status of the node during route discovery. In this study the researcher conducts performance evaluation of existing DSR and modified one is assessed by using packet delivery ratio, and endto-end delay metrics and achieved very good result.