Enhanced multi-route ad hoc on-demand distance vector routing

Shamsoshoara, Alireza; Darmani, Yousef

doi:10.1109/iraniancee.2015.7146282

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…The channel coefficients are defined as follows: i) h P T,U i refers to the channel parameters between the PU's transmitter and i th UAV; ii) h U i ,P R denotes the parameters between the i th UAV and the PU's receiver; iii) h S,U i and h U i ,F , respectively denote the channel coefficients between the Source and the i th UAV, and between the i th UAV and the fusion center. For the sake of simplicity, the instant Channel State Information (CSI) are assumed to be available for all UAVs following similar works in [30,32,5,3,26] The source of the noise at the receivers is considered as a symmetric normally distributed random variable, denoted by z ∼ CN (0, σ 2 ). Many works such as [24,20,26] optimized the power consumption and nodes' lifetime in this area.…”

Section: System Modelmentioning

confidence: 99%

Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

Shamsoshoara

Khaledi

Afghah

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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In this paper, we develop a distributed mechanism for spectrum sharing among a network of unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide a practical solution for situations where the UAV network may need external spectrum when dealing with congested spectrum or need to change its operational frequency due to security threats. Here we study a scenario where the UAV network performs a remote sensing mission. In this model, the UAVs are categorized to two clusters of relaying and sensing UAVs. The relay UAVs provide a relaying service for a licensed network to obtain spectrum access for the rest of UAVs that perform the sensing task. We develop a distributed mechanism in which the UAVs locally decide whether they need to participate in relaying or sensing considering the fact that communications among UAVs may not be feasible or reliable. The UAVs learn the optimal task allocation using a distributed reinforcement learning algorithm. Convergence of the algorithm is discussed and simulation results are presented for different scenarios to verify the convergence 1 .

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Section: System Modelmentioning

confidence: 99%

Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

Shamsoshoara

Khaledi

Afghah

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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“…AODV is a single path distance vector routing protocol in ad hoc wireless networks. This protocol mixes the route discovery mechanism in DSR with the approach of destination sequence number in DSDV [4]. Basically when there is one node that wants to communicate with another node that is not in range, it finds a route through the other nodes.…”

Section: Overview Of Ad Hoc On-demand Distance Vector Routing Protocolmentioning

confidence: 99%

“…The increasing development in wireless local area networks has opened new limits in the field of telecommunication. MANET a composed of nodes that can communicate with each other without knowing their position [4]. There is no base station in these networks and nodes communicate in multi-hop pattern.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Approach of Detection and Prevention of Black Hole Attack on AODV over MANET

Gojiya¹,

Nayak²,

Patel³

2016

IJCA

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Mobile Ad-hoc network is a situate of portable nodes that correspond with wireless links and communication acknowledged out without any central control or fixed communication. The dynamic topology of MANETs allows nodes to join and leave the network at any point of time .this generic characteristic of MANET has rendered it vulnerable to security attacks. The black hole attack is one of such security risks. In this attack, a malicious node fallaciously advertise shortest path to the destination node with an intension to disrupt the communication In this paper, we intend a solution to the black hole attack in one of the most prominent routing algorithm, ad-hoc on demand distance vector (AODV) routing, for the MANETs. The anticipated scheme uses Watchdog mechanism to detect malicious node with usage of local information of intermediate node and propagates the information of black hole node to all other node in network the simulation results show the efficiency of anticipated scheme in presences of black hole node.

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“…In the literature, many papers including [19], [20] address power optimization; while we assume that the transmission power is arbitrary but constant for all transmitters.…”

Section: Introductionmentioning

confidence: 99%

A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks

Shamsoshoara

Afghah

Razi

et al. 2019

2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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In this paper, we study the problem of spectrum scarcity in a network of unmanned aerial vehicles (UAVs) during mission-critical applications such as disaster monitoring and public safety missions, where the pre-allocated spectrum is not sufficient to offer a high data transmission rate for real-time video-streaming. In such scenarios, the UAV network can lease part of the spectrum of a terrestrial licensed network in exchange for providing relaying service. In order to optimize the performance of the UAV network and prolong its lifetime, some of the UAVs will function as a relay for the primary network while the rest of the UAVs carry out their sensing tasks. Here, we propose a team reinforcement learning algorithm performed by the UAV's controller unit to determine the optimum allocation of sensing and relaying tasks among the UAVs as well as their relocation strategy at each time. We analyze the convergence of our algorithm and present simulation results to evaluate the system throughput in different scenarios.

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Enhanced multi-route ad hoc on-demand distance vector routing

Cited by 9 publications

References 11 publications

Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

An Enhanced Approach of Detection and Prevention of Black Hole Attack on AODV over MANET

A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks

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