Enabling connectivity for tactical networks in mountainous areas by aerial relays

Al-Shehri, Salman M.; Loskot, Pavel; Hirsch, Michael J.

doi:10.1007/s11235-018-0532-3

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…A directed communication graph, G(V, E), can be used to model the wireless network deployment. An edge (i, j) ∈ E is a directed link from device i to device j. E is the set of all possible links in the network and devices n i ∈ V in the network are represented with the set of vertices, V. The haversine distance, considering the earth's curvature, between device i and device j is represented by d(i, j) [10]. The transmission range of a device determines the presence of a communication link between any two devices.…”

Section: Network Modelmentioning

confidence: 99%

“…In [9] the performance metric is chosen as coverage. Although some studies such as [10] took the effect of propagation loss and the characteristics of a real environment, there is a lack of studies that take the performance evaluation problem of a cooperative wireless network in terms of overall communication activity in an aggregated manner.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive methodology to evaluate the performance of a cooperative wireless network

Kalaycıoğlu¹,

Akbulut²,

Yilmaz³

et al. 2019

Turk J Elec Eng & Comp Sci

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Performance evaluation of a cooperative wireless communication network depends on several parameters.Many of the studies, in fact, take a limited number of performance metrics individually to reach a conclusion about the performance of a network. Besides, in general, some performance metrics might be in conflict. Thus, ignoring the combined effect of all performance criteria in the network may lead to an incorrect conception of the overall communication performance of the network. Therefore, in this study, a comprehensive evaluation methodology in terms of various metrics is proposed to calculate the performance of a cooperative wireless communication network deployment using a real terrain map. The proposed methodology, based on desirability functions, is discussed in terms of signal quality, signal quality with hopping, average number of hopping, hopping duty, and deployment altitude. In addition to individual scores for each performance criterion, an overall performance score is also obtained. Furthermore, the effects of the desirability function's ambition factor, terrain characteristics, and connectivity requirement rate on the score are also presented. The simulation results show that the desirability function-based performance evaluation approach could support a flexible evaluation as well as provide an agreement on the conflicting performance metrics.

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Section: Network Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive methodology to evaluate the performance of a cooperative wireless network

Kalaycıoğlu¹,

Akbulut²,

Yilmaz³

et al. 2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Although, maintaining the required QoS is challenging because radio coverage varies due to obstacles in the signal propagation path and high user density significantly reduces network capabilities. These limitations can be combated by deploying unmanned aerial vehicles (UAVs) having the ability to establish line of sight (LoS) dominant air to ground channel in a controllable manner [2]. Specifically, the UAV-enabled wireless sensor network (WSN) utilizes UAVs as computing hub and mobile data collectors to perform several analyses on the data, collected from spatially separated sensor nodes (SNs) and communicating devices [3].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network

Mondal

Hossain

2021

Preprint

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Due to their high maneuverability, flexible deployment, and line of sight (LoS) transmission, unmanned aerial vehicles (UAVs) could be an alternative option for reliable device-to-device (D2D) communication when a direct link is not available between source and destination devices due to obstacles in the signal propagation path. Therefore, in this paper, we have proposed a UAVs-supported self-organized device-to-device (USSD2D) network where multiple UAVs are employed as aerial relays. We have developed a novel optimization framework that maximizes the total instantaneous transmission rate of the network by jointly optimizing the deployed location of UAVs, device association, and UAVs’ channel selection while ensuring that every device should achieve a given signal to interference noise ratio (SINR) constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed into a Markov decision process (MDP) where UAVs learn the system parameters according to the current state and corresponding action aiming to maximize the generated reward under the current policy. Finally, we conceive SARSA, a low complexity iterative algorithm for updating the current policy in the case of randomly deployed device pairs which achieves a good computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on the optimal deployment of UAVs. The proposed methodology improves the total instantaneous transmission rate of the network by 75.37%, 52.08%, and 14.77% respectively as compared with RS-FORD, ES-FIRD, and AOIV schemes.

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“…as well as providing range-extension [7]- [9] and dataferrying [10]- [12] services in tactical military networks.…”

Section: Introductionmentioning

confidence: 99%

An Analytic Latency Model for a Next-Hop Data-Ferrying Swarm on Random Geometric Graphs

et al. 2020

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Next-hop data-ferrying is a data-driven approach to ferrying data between graph components of a disconnected network. In contrast to pre-planned routing methods like the Traveling Salesman tour, only the next-hop is planned, allowing the ferry to be reactive to the data flows within the network. When multiple ferries are used, explicit coordination between them is difficult due to the data-driven approach of the algorithm, and the distributed and disconnected nature of the problem. However, coordinated behavior can still be produced through swarm intelligent means and while this is a useful pragmatic result, mathematical models describing system properties are difficult to develop. This generally results in algorithm correctness being demonstrated through simulation. In this article, we describe the development of a mathematical model of such a next-hop ferrying swarm. We show that the closed-form expression for user data latency is a close match to simulation results for Random Geometric Graphs under the assumption of a zero expected degree. In non-zero degree graphs, a near constant offset between the model and simulation is observed.INDEX TERMS Data ferrying, emergent behavior, MANET, random geometric graph.

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Enabling connectivity for tactical networks in mountainous areas by aerial relays

Cited by 7 publications

References 15 publications

A comprehensive methodology to evaluate the performance of a cooperative wireless network

A comprehensive methodology to evaluate the performance of a cooperative wireless network

Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network

An Analytic Latency Model for a Next-Hop Data-Ferrying Swarm on Random Geometric Graphs

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