Mobility as an Alternative Communication Channel: A Survey

Baron, Benjamin; Spathis, Prométhée; Amorim, Marcelo Dias de; Viniotis, Yannis; Ammar, Mostafa

doi:10.1109/comst.2018.2841192

Cited by 15 publications

(10 citation statements)

References 89 publications

(199 reference statements)

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“…All in all, the upper bound G U pper meets the slight restrictions of (16). Moreover, the lower bound G Lower satisfies the severe restrictions.…”

Section: A Maximum Throughput Algorithm For Optimal Positionmentioning

confidence: 82%

“…B. Baron et al [16] presented that the mobility of these storage-capable entities can create a communication channel that could help overcome the limitations or deficiencies of traditional data network scenarios and enhance throughput. By studying the per-session throughput of applications with loose latency constraints, M. Grossglauser et al [17] find that user mobility makes the topology vary with packet delivery time scale.…”

Section: B User Cooperative Mobility Approachmentioning

confidence: 99%

“…Proposition 5: The optimal locations M * (ρ m , Θ m ) of movable nodes m i ∈ M for the best throughput T h * sys must be located at the scope of the upper bound G U pper and lower bound G Lower areas. Proof 5: As stated in (16), the limitation factors indicate that the range of domain G should guarantee the transmission protocol regular operation. From the physical interference perspective, the SINR for fixed and movable nodes should be larger than the threshold β.…”

Section: A Maximum Throughput Algorithm For Optimal Positionmentioning

confidence: 99%

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An Optimal Location Allocation by Multi-User Cooperative Mobility for Maximizing Throughput in MANETs

Xie

Murase

2020

IEEE Access

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“…All in all, the upper bound G U pper meets the slight restrictions of (16). Moreover, the lower bound G Lower satisfies the severe restrictions.…”

Section: A Maximum Throughput Algorithm For Optimal Positionmentioning

confidence: 82%

Section: B User Cooperative Mobility Approachmentioning

confidence: 99%

Section: A Maximum Throughput Algorithm For Optimal Positionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Location Allocation by Multi-User Cooperative Mobility for Maximizing Throughput in MANETs

Xie

Murase

2020

IEEE Access

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“…References [21] and [22], A. Chaintreau et al and B. Baron et al analyze the impact of human mobility on opportunistic data forwarding and note that the use of mobility to transport data helps to achieve better throughput.…”

Section: B User Cooperative Mobility Approachmentioning

confidence: 99%

Multiple User Cooperative Mobility in Mobile Ad Hoc Networks: An Interaction Position Game

Xie

Murase

2020

IEEE Access

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Mobile ad hoc networks (MANETs) have been widely used by individuals for residential or commercial usage in small-scale areas depending on fifth-generation (5G) and device-to-device (D2D) advancements. MANET demands extremely high system throughput, low latency, and best quality of service (QoS) maintenance. The current user mobility approaches only analyze the single movable user and use intuitive methods but cannot fundamentally solve multiple user cooperative mobility in practical applications. In this paper, we first jointly consider multiple user mobility and different geographical places and distances among all users. Then, we propose an interaction position game (IPG) to achieve high throughput and decrease computational cost. In this game, cooperative behaviors among movable users are proposed instead of assumed selfishness, as in traditional game models. In addition, we also present the simulated annealing (SA) algorithm to solve this NP-hard problem as a performance comparison. Finally, we evaluate the performance of this proposed game in various position cases. The results show that this game method improves the maximum throughput ratio evidently by 57.35% and 27.27% compared with the conventional intuitive method and SA algorithm, respectively. Compared with the exhaustive algorithm, this game reduces the computation cost by 82.79%. INDEX TERMS Mobile ad hoc networks, system throughput, multiple user cooperative mobility, interaction position game, cooperative behaviors, simulated annealing.

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“…The concept of communication-aware trajectory planning for robots has recently attracted an increasing amount of attention within the robotics, control and communications communities. This interdisciplinary research field aims at improving robots' wireless communications performance by controlling their position [16]- [18]. In this context, various problems have been considered: the utilisation of a robot to physically transfer data from the source to the destination [16]; the optimization of a robot's trajectory taking into Daniel Bonilla Licea is with the International University of Rabat,…”

Section: Introductionmentioning

confidence: 99%

UAV Trajectory Planning for Delay Tolerant Communications

Licea

Bonilla

Ghogho

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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In this paper, we address the problem of optimizing a communication-aware trajectory for a quadrotor that must transfer periodically (with fixed period T) a maximum amount of data from a source node (SN) to a destination node (DN). The communications aspect is mathematically stated by linking the bit rate to the channel capacity concept from information theory. The trajectory is optimized using a parametric approach using Fourier series in order to reduce the computational load of the optimization process. We show that the proposed trajectory results in a large increase of the amount of transferred data, and can be easily tracked by the quadrotor. Index Terms-Trajectory planning, data ferry, delay tolerant communications. NOTATION A zero dimension subspace is denoted by {0}, and the identity operator is denoted by I, or In when its size, n×n, is specified. For a full row (column) rank matrix X ∈ R n×m , its right (left) inverses are denoted by X r (X), i.e. XX r = In (X X = Im). If X is square and invertible, its inverse is denoted by X −1. x T stands for the transpose of the matrix (or vector) x. s stands for the complex variable of Laplace transform. Given a pair of mappings (A, B), such that A : R n → R n and B : R m → R n , C (A, B) stands for the controllability matrix C (A, B) = B A B • • • A (n−1) B. Ψm(ξ) ∈ R n•m×m [ξ] stands for: Imξ Imξ 2 • • • Imξ n T. Further, c(ϑ) and s(ϑ) stand for cosine and sine of a given angle ϑ, respectively; and s −1 (ϑ) stands for arcsin(ϑ). The ceiling and the floor functions are denoted as • and • respectively.x stands for the estimate of the variable x; E[•] stands for the statistical expectation operator. A vector function f (ε, t) ∈ R n is said to be O(ε) over an interval [t1, t2] if there exist positive constants K and ε * such that f (ε, t) ≤ Kε for all ε ∈ [0, ε * ] and for all t ∈ [t1, t2], see [4]. CONFIDENTIAL. Limited circulation. For review only.

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Mobility as an Alternative Communication Channel: A Survey

Cited by 15 publications

References 89 publications

An Optimal Location Allocation by Multi-User Cooperative Mobility for Maximizing Throughput in MANETs

An Optimal Location Allocation by Multi-User Cooperative Mobility for Maximizing Throughput in MANETs

Multiple User Cooperative Mobility in Mobile Ad Hoc Networks: An Interaction Position Game

UAV Trajectory Planning for Delay Tolerant Communications

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