Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach Using the Multi-Relay Channel Model

Chattopadhyay, Arpan; Sinha, Abhishek; Coupechoux, Marceau; Kumar, Anurag

doi:10.1109/tmc.2016.2561271

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…The local search approximation algorithm and relay location selection algorithm were used to save the number of relay nodes. The literature [12] considers the situation of high attenuation and low attenuation of the channel, and the relay node deployment is regarded as a Markov decision process. The proposed method makes the relay nodes evenly distributed in the case of high attenuation, and makes the relay nodes mostly close to the source node in the case of low attenuation.…”

Section: The Related Workmentioning

confidence: 99%

Relay Node Placement Based on Optimal Transmission Distance in Two-Tiered Sensor Network

Mao

Jiang

2020

IEEE Access

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With the gradual expansion of the application field in wireless sensor networks, the problems of the energy consumption has received more and more attention. In wireless sensor networks, the location of sensor nodes and gateway nodes are usually fixed. To ensure that the energy consumption of the sensor network is minimized during data transmission, it is necessary to optimize the placement of relay nodes. This paper proposes the solution of relay nodes placement based on optimal transmission distance, which can minimize the energy consumption and extend the lifetime of the entire network during the transmission process. This scheme starts with a one-dimensional queue network and calculates the optimal distance for each hop transmission to minimize the energy consumption of the one-dimensional queue network. Due to the poor connectivity of the one-dimensional queue network, the concept of the optimal transmission distance is introduced into two-dimensional network, so that the placement scheme can be divided into the symmetrical relay node placement and the asymmetric relay node placement. Finally, the energy consumption of the network is minimized by properly selecting relay nodes. The simulation show that the proposed relay node placement scheme can reduce the energy consumption of the sensor network and extend the network life cycle.

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Section: The Related Workmentioning

confidence: 99%

Relay Node Placement Based on Optimal Transmission Distance in Two-Tiered Sensor Network

Mao

Jiang

2020

IEEE Access

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“…⌊N 2⌋ using forward propagation based on (24). 10 Send a message from UAV ⌊N 2⌋ to UAV ⌊N 2⌋+1 and measure SIR ⌊N 2⌋+1 . V. NUMERICAL RESULTS Similar to [8], we consider f c = 2GHz, C LoS = 10 0.01 , C NLoS = 10 2.1 , and η NLoS = µ NLoS .…”

Section: B Position Planning For a Given Number Of Uavsmentioning

confidence: 99%

“…Nevertheless, none of the aforementioned works consider the placement of UAV(s) in the presence of a source of interference, which is the main motivation for our work. Also, there are some similar works in the literature of sensor networks, among which the most relevant ones are [9], [10]. In [9], the two-dimensional (2-D) placement of relays is investigated aiming to increase the achievable transmission rate.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], the two-dimensional (2-D) placement of relays is investigated aiming to increase the achievable transmission rate. In [10], the impromptu (as-yougo) placement of the relay nodes between a pair of source and sink node is addressed considering that the distance between those nodes may be a random variable, where the space is restricted to be one-dimensional (1-D). Nonetheless, none of these works consider a source of interference in the network.…”

Section: Introductionmentioning

confidence: 99%

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Interference Avoidance Position Planning in UAV-Assisted Wireless Communication

Hosseinalipour

Rahmati

Dai

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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We consider unmanned aerial vehicle (UAV)-assisted wireless communication employing UAVs as relay nodes to increase the throughput between a pair of transmitter and receiver. We focus on developing effective methods to position the UAV(s) in the sky in the presence of a major source of interference, the existence of which makes the problem non-trivial. First, we consider utilizing a single UAV, for which we develop a theoretical framework to determine its optimal position aiming to maximize the SIR of the system. To this end, we investigate the problem for three practical scenarios, in which the position of the UAV is: (i) vertically fixed, horizontally adjustable; (ii) horizontally fixed, vertically adjustable; (iii) both horizontally and vertically adjustable. Afterward, we consider employing multiple UAVs, for which we propose a cost-effective method that simultaneously minimizes the number of required UAVs and determines their optimal positions so as to guarantee a certain SIR of the system. We further develop a distributed placement algorithm, which can increase the SIR of the system given an arbitrary number of UAVs. Numerical simulations are provided to evaluate the performance of our proposed methods.

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“…We also developed, in [20], as-you-go deployment algorithms for deploying a multi-relay line network, so that the end-to-end achievable rate is maximized; but it was done for an information-theoretic, full-duplex, multi-relay channel model where the nodes carry out decode-and-forward relaying. However, devices with such sophisticated relaying capability is not yet available for full commercial use.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Stochastic Approximation Based Learning Algorithms for As-You-Go Deployment of Wireless Relay Networks Along a Line

Chattopadhyay

Ghosh

Kumar

2018

IEEE Trans. on Mobile Comput.

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We are motivated by the need, in emergency situations, for impromptu (or "as-you-go") deployment of multihop wireless networks, by human agents or robots (e.g., unmanned aerial vehicles (UAVs)); the agent moves along a line, makes wireless link quality measurements at regular intervals, and makes on-line placement decisions using these measurements. As a first step we have formulated such deployment along a line as a sequential decision problem. In our earlier work, reported in [1], we proposed two possible deployment approaches: (i) the pure as-you-go approach where the deployment agent can only move forward, and (ii) the explore-forward approach where the deployment agent explores a few successive steps and then selects the best relay placement location among them. The latter was shown to provide better performance (in terms of network cost, network performance and power expenditure), but at the expense of more measurements and deployment time, which makes explore-forward impractical for quick deployment by an energy constrained agent such as a UAV. Further, since in emergency situations the terrain would be unknown, the deployment algorithm should not require a-priori knowledge of the parameters of the wireless propagation model. In [1] we, therefore, developed learning algorithms for the explore-forward approach.The current paper fills in an important gap by providing deploy-and-learn algorithms for the pure as-you-go approach. We formulate the sequential relay deployment problem as an average cost Markov decision process (MDP), which trades off among power consumption, link outage probabilities, and the number of relay nodes in the deployed network. While the pure as-you-go deployment problem was previously formulated as a discounted cost MDP (see [1]), the discounted cost MDP formulation was not amenable for learning algorithms that are proposed in this paper. In this paper, first we show structural results for the optimal policy corresponding to the average cost MDP, and provide new insights into the optimal policy. Next, by exploiting the special structure of the average cost optimality equation and by using the theory of asynchronous stochastic approximation (in single and two timescale), we develop two learning algorithms that asymptotically converge to the set of optimal policies as deployment progresses. Numerical results show reasonably fast speed of convergence, and hence the model-free algorithms can be useful for practical, fast deployment of emergency wireless networks.Index Terms-Wireless networks, impromptu network deployment, as-you-go relay placement, relay placement by UAV, Markov decision process, stochastic approximation. !

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Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach Using the Multi-Relay Channel Model

Cited by 9 publications

References 29 publications

Relay Node Placement Based on Optimal Transmission Distance in Two-Tiered Sensor Network

Relay Node Placement Based on Optimal Transmission Distance in Two-Tiered Sensor Network

Interference Avoidance Position Planning in UAV-Assisted Wireless Communication

Asynchronous Stochastic Approximation Based Learning Algorithms for As-You-Go Deployment of Wireless Relay Networks Along a Line

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