A Hybrid Localization Approach for UAV based on AODV and RSSI in FANETs

Halder, Tamesh; Das, Amit Kr.; Banerjee, Sagnik; Sarkar, Sajal; Basak, Arindam; Ray, Arundhati Mishra; Chakravarty, Debashish

doi:10.1109/igarss46834.2022.9884700

Cited by 2 publications

(1 citation statement)

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“…The MAC protocol is modeled using the IEEE 802.11a standard and uses a receiver signal strength indicator (RSSI) threshold of −80 dbm for communication range to make it more practical. The main reason for considering RSSI is that the value of RSSI fluctuations obtained has taken into consideration its effect on changes in channel conditions, including multi-path fading [ 41 ].…”

Section: Performance Evaluationmentioning

confidence: 99%

DLSMR: Deep Learning-Based Secure Multicast Routing Protocol against Wormhole Attack in Flying Ad Hoc Networks with Cell-Free Massive Multiple-Input Multiple-Output

Pramitarini,

Perdana,

Shim

et al. 2023

Sensors

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The network area is extended from ground to air. In order to efficiently manage various kinds of nodes, new network paradigms are needed such as cell-free massive multiple-input multiple-output (CF-mMIMO). Additionally, security is also considered as one of the important quality-of-services (QoS) parameters in future networks. Thus, in this paper, we propose a novel deep learning-based secure multicast routing protocol (DLSMR) in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). We consider the problem of wormhole attacks in the multicast routing process. To tackle this problem, we propose the DLSMR protocol, which utilizes a deep learning (DL) approach to predict the secure and unsecured route based on node ID, distance, destination sequence, hop count, and energy to avoid wormhole attacks. This work also addresses key concerns in FANETs such as security, scalability, and stability. The main contributions of this paper are as follows: (1) We propose a deep learning-based secure multicast routing protocol (DLSMR) to establish a high-stability multicast tree and improve security performance against wormhole attacks. In more detail, the DLSMR protocol predicts whether the route is secure based on network information such as node ID, distance, destination sequence, hop count, and remaining energy or not. (2) To improve the node connectivity and manage multicast members, we propose a top-down particle swarm optimization-based clustering (TD-PSO) protocol to maximize the cost function considering node degree, cosine similarity, cosine distance, and cluster head energy to guarantee convergence to the global optima. Thus, the TD-PSO protocol provides more strong connectivity. (3) Performance evaluations verify the proposed routing protocol establishes a secure route by avoiding wormhole attacks as well as by providing strong connectivity. The TD-PSO clustering supports connectivity to enhance network performance. In addition, we exploit the impact of the mobility model on the network metrics such as packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses.

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Section: Performance Evaluationmentioning

confidence: 99%