MAC-PRY Cross-Layer Design for Secure Wireless Avionics Intra-Communications

Sámano‐Robles, Ramiro

doi:10.1109/est.2019.8806208

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…1. This generic network topology is used as reference for assessing spectrum requirements for all WAIC applications which make use of transceiver nodes installed within the aircraft structure and which are shielded to the outside [14].…”

Section: A Backgroundmentioning

confidence: 99%

Deep Reinforcement Learning for Power Controlled Channel Allocation in Wireless Avionics Intra-Communications

Zuo

et al. 2021

IEEE Access

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Wireless avionics intra-communications (WAIC) can play an important role in alleviate the issue of fuel consumption, stability and maintenance costs over traditional wired avionics systems. However, in order for WAIC system to coexist with other systems in aircraft, the transmitted power level of WAIC is strictly limited to 50 mW. Meanwhile, WAIC require extremely low outage probability for the safety-critical avionics applications. Hence, it is urgently needed to effectively allocate channels and utilize the limited power while ensuring the reliability and real-time performance of the WAIC system. In this paper, a deep reinforcement learning (DRL)-based power controlled channel allocation (DRL-PCCA) scheme for WAIC network is proposed, with physical layer of frequency hopping orthogonal frequency division multiplexing (FH-OFDM). First, we formulate a sub-bands allocation and power control optimization problem whose aim is to minimize the overall transmit power provided that all end nodes achieve their requested data rates and desired bit error rate (BER). However, the problem formulated is non-convex and NP-hard. To tackle this problem, we propose a DRL-based scheme, which can effectively solve the optimization problem of sequence decision making in complex environment by using neural networks to extract spatial correlation features of WAIC. In particular, a reliability framework for WAIC is presented to analyse the performance of the proposed scheme against the system requirements of the flight certification. Simulation results demonstrate that the performance of proposed DRL-based scheme is superior to the traditional power control and channel allocation scheme.INDEX TERMS channel allocation, deep reinforcement learning, UWB, WAIC, wireless communication.

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Section: A Backgroundmentioning

confidence: 99%

Deep Reinforcement Learning for Power Controlled Channel Allocation in Wireless Avionics Intra-Communications

Zuo

et al. 2021

IEEE Access

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“…By adopting cross-layer approach path selection in a router and router has features like, higher throughput, lesser delay and minimum loss rate [19]. A framework for security analysis through cross-layer design in Avionics inter communication systems and features associated to 5G technology have been presented using low latency and high reliability [20].…”

Section: Related Workmentioning

confidence: 99%

“…Nodes can monitor the behavior of neighboring nodes and find them [19] [20]. A node illegitimately creates multiple IDs and violates rules.…”

Section: B Types Of Attacks On Networked Protocol Stackmentioning

confidence: 99%

Cross-layer Planes Framework for Detection of Malicious Nodes in WSN

2019

IJITEE

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Cross-layer planes design is relatively new security approach for future technological era in which different parameters are analyzed across protocols stack, so that the internet connected exchange their information with utmost security. The traditional existing approaches operates at single layer security and across few cross layers on TCP/IP model. Hence intruder can monitor loop holes on victim nodes in Wireless Sensor Network (WSN), which is serious issue for sensitive data. For example, Intrusion Detection System (IDS) operates on network layer and identifies routing attacks, but it does not react to physical layer, MAC layer and transport layers anomalies. Cross-layer design among few layers can monitor and detect some intrusions but this consumes more energy at node and node will become inactive early in the network. Hence, in this article, we are proposing Cross-layer Planes Framework for Detecting Malicious Activities (CPFDMA) at different layers is proposed to secure the WSN as viable security framework is based on the Cross-layer planes which interact attributes in different layers of the protocol stack and monitor & analyze anomaly patterns, notifying them to avoid their malicious activities from the network

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