A <i>Q</i>-Learning-Based Topology-Aware Routing Protocol for Flying <i>Ad Hoc</i> Networks

Arafat, Muhammad Yeasir; Moh, Sangman

doi:10.1109/jiot.2021.3089759

Cited by 88 publications

(63 citation statements)

References 41 publications

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“…Aforementioned the limitations, several future developments of such communication models are possible if we employ an energy harvesting technique for the CUAV operation and transmit antenna selection method [40,41], allowing the CUAVbased entire system to optimally adjust the data rate and energy efficiency depending on the communication criteria. In addition, we investigate a Q-learning-based topologyaware routing protocol that provides dependable source-todestination pairings [42]. Moreover, we save our proposed system from malicious user attacks using a friend or foe detection technique with physical layer network coding [43].…”

Section: Discussionmentioning

confidence: 99%

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

et al. 2022

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Effective networking over wireless media has become extremely essential today as communication between massive Internet of things (IoT) devices is on an increase, thereby leading to a limited spectrum resource for utilisation. Specifically, for healthcare infrastructure in a remote or critical situation, providing uninterrupted communication between the macro base station and IoT devices or user nodes is imperative. However, owing to their limited spectral capacity, unmanned aerial vehicles (UAVs)-based networks can provide an efficient solution and utilise both licensed and unlicensed bands for communication among users or devices. In this paper, our focus is on cache-enabled cognitive networking for secondary users (SUs) that accredits precise communication delivery for critical healthcare systems that are performed by the cognitive UAV (CUAV). In addition, we develop a caching strategy wherein a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in its local and cloud storage by applying a non-orthogonal multiple-access method. In the downlink scenario, the CUAV proactively transmits the requested HP and MP information to the designated SUs considering this entire model over two states, namely effectual state and interference state, which we can realise by any presence or absence of interference. To maximise this system's energy efficiency, we formulate an optimisation problem to minimise the transmission power and satisfy the target performance in terms of throughput for SUs. We solve the optimisation issue using the Lagrangian approach and the Karush-Kuhn-Tucker conditions. In all simulations, the energy efficiency during the effectual state renders an average performance of approximately 400% better than that of the interference state.INDEX TERMS Caching, cognitive radio network, energy efficiency, human bond communication, nonorthogonal multiple access, optimisation, throughput, unmanned aerial vehicle. I. INTRODUCTION A. BACKGROUND AND MOTIVATIONSome of the key objectives of the potential universal communication environment are to enhance the standard of living by increasing convenience and enriching the experiences and circumstances of our daily activities. To realise this, several major technological advancements have centred on improving human interactions. The conventional approach to communication technology has always been to improve the information or signal so that the communication entities can manage them with the utmost efficiency. The limitation is that there is no transfer of information by such communication systems directly to the users' awareness that will allow for a more integrative sharing of sensory data. In relation to this, several articles have introduced the concept of human bond communication (HBC) to understand this dynamic growth

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Section: Discussionmentioning

confidence: 99%

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

et al. 2022

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“…The issue of connection management is related to the network routing protocol that will be used within the UAVN which is outside the scope of this paper. Several recent routing protocols for ad hoc networks, such as [19] and [20], proved their ability to efficiently connect mobile nodes to fixed infrastructure. Finally, the BC network will comprise the UAVs and GCSs which cooperate to generate and store the BC blocks.…”

Section: System Architecturementioning

confidence: 99%

PROACT: Parallel multi-miner proof of accumulated trust protocol for Internet of Drones

Mershad

2022

Vehicular Communications

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“…Arafat [28] proposed topology-aware routing based on Qlearning, where UAV nodes adaptively adjust their routing strategies based on obtaining information about their two-hop neighbor nodes, but it brings certain computational consumption and instability as the environment changes. Costa [29] proposed an improved Q-Learning algorithm to reduce network delay in scenarios with high-mobility, called Q-FANET.…”

Section: B Fanet Routing Protocolmentioning

confidence: 99%

Elastic Routing Mechanism for Flying Ad Hoc Network

Zhao

et al. 2022

IEEE Access

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Flying Ad-Hoc Network (FANET) is a hot topic in current research. The design of routing mechanism is challenging because when the scale of Unmanned Aerial Vehicle (UAV) nodes is large, vast amount of routing overhead may lead to network collapse. An elastic routing mechanism is proposed for large-scale small UAVs multitasking scenarios. Firstly, the New-Unifying Connected Dominating Set (N-UCDS) algorithm is proposed to construct a virtual backbone network based on the connected dominating set. The number of neighboring nodes, remaining energy and link duration are considered to influence the UAV network performance when electing backbone nodes. Secondly, by deploying and running the New Better Approach to Mobile Ad-Hoc Network-Advanced (NBATMAN-ADV) routing protocol on the backbone nodes, the link quality can be evaluated by using the received signal strength index and signal-to-noise ratio of the physical layer data. In this way, the change of the link can be quickly sensed while reducing the routing overhead. The simulation results show that the routing protocol proposed in this paper has significantly improved average packet delivery rate, end-to-end delay and received throughput compared with other traditional proactive routing protocols.INDEX TERMS Flying ad-hoc network; Elastic routing mechanism; Virtual backbone; N-UCDS algorithm; NBATMAN-ADV routing protocol.

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A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks

Cited by 88 publications

References 41 publications

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis

PROACT: Parallel multi-miner proof of accumulated trust protocol for Internet of Drones

Elastic Routing Mechanism for Flying Ad Hoc Network

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