Learning automata as a utility for power management in smart grids

Misra, Sudip; Krishna, P. Venkata; Saritha, V.; Obaidat, Mohammad S.

doi:10.1109/mcom.2013.6400445

Cited by 72 publications

(28 citation statements)

References 12 publications

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“…On obtaining the routes we can perform the estimation of bandwidth (Reddy and Krishna, 2012;Reddy et al, 2014;Krishna et al, 2009;Misra et al, 2010;Krishna et al, 2012;Misra et al, 2013;Misra et al, 2014) for the specific routes in order to discover the optimal path with more bandwidth availability. The MLM layer then compares the required bandwidth of the application to the available bandwidth of different routes.…”

Section: Mlm (Path Delay Bandwidth Estimation)mentioning

confidence: 99%

“…On finding the node the corresponding mesh router responds by sending its own router ID in the NSREP (Node Search Reply) packet to the BR. The packets are then routed to that BR (Reddy and Krishna, 2012;Reddy et al, 2014;Krishna et al, 2009;Misra et al, 2010;Krishna et al, 2012;Misra et al, 2013;Misra et al, 2014) by utilising the optimal path (Reddy and Krishna, 2012) through shared database . The packets are further forwarded towards the destination mobile nodes after they reach the respective BRs.…”

Section: Nlm (Node Identification Route Table)mentioning

confidence: 99%

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Energy aware cross layer-based connection admission control mechanism for wireless mesh networks

Reddy

Krishna

2016

IJSGGC

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This paper studies the allocation scheme of bandwidth for the purpose of a quality end-to-end communication in multi-hop and multi-radio wireless mesh networks. In wireless mesh networks the role of bandwidth in providing QoS is pivotal. In view of the limited availability, efficient allocation of bandwidth becomes necessary. We proposed a scheme for connection admission control based on energy aware cross layer mechanism, to address the problems relating to the allocation of bandwidth for real-time and non-real-time traffic utilising a shared database. The shared database contains the energy available at the node, routing information, prediction of congestion and issues of congestion control in order to maximise the throughput of the network. The performance of the proposed model is evaluated in comparison with the other models utilising cross layer techniques. The design of the proposed network developed on the basis of energy aware cross layer optimisation, which yields considerable enhancement in the throughput of the network and these results can be crucial in the practical design of the network. Keywords: energy; cross layer optimisation; bandwidth allocation; congestion control.Reference to this paper should be made as follows: Reddy, P. and Krishna, P.V. (2016) 'Energy aware cross layer-based connection admission control mechanism for wireless mesh networks', Int.

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Section: Mlm (Path Delay Bandwidth Estimation)mentioning

confidence: 99%

Section: Nlm (Node Identification Route Table)mentioning

confidence: 99%

Energy aware cross layer-based connection admission control mechanism for wireless mesh networks

Reddy

Krishna

2016

IJSGGC

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“…α n (·) = tan −1 ν y n (·) ν x n (·) (8) where | ν n (·)| and α n (·) are the magnitude and the angle of direction of the velocity of each PHEV n ∈ N (·).…”

Section: Mobility Model For Cloud-based Mobile Smart Gridmentioning

confidence: 99%

Game-Theoretic Distributed Virtual Energy Cloud Topology Control for Mobile Smart Grid

Mondal

Misra

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

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In this paper, the problem of energy distribution using virtual energy-cloud to the plug-in hybrid electric vehicles (PHEVs) is studied as a single leader multiple follower noncooperative Stackelberg game. In this game, the energy-cloud service provider acts as the leader, and decides the price to be paid by each PHEV according to its usage. On the other hand, the PHEVs act as the followers, and need to decide the amount of energy to be consumed based on their requirements. Using variational inequality, it is shown that the proposed scheme, virtual energy cloud topology control (VELD), has a generalized Nash equilibrium, which is also socially optimal. The proposed scheme, VELD, which enables the energy-cloud service provider and the PHEVs to reach the equilibrium state, is evaluated theoretically as well as through simulations. Using the proposed scheme, VELD, the PHEVs consume up to 47.49−52.96% higher amount of energy, while paying 5.52% less per unit energy, which, in turn, increases the utilization of the generated energy by the micro-grids.

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“…The application of the SPL to learn such a meta-parameter is straightforward. 5) Applications of the SPL: LA have been utilized in many real-life applications including power management in smart grids [27], distributed channel selection [60], solving the minimum weight connected dominating set [58], multiclass classification [1], power control [62], service selection [61], solving a large class of wireless networks related problems [33], a general class of stochastic decentralized games [57], adaptive control of antennas in wireless push networks [32], and in optimal sensor placement [8]. Without belaboring the point, the reader will see that in the light of the above, the SPL has applications in all these areas where LA have been utilized.…”

mentioning

confidence: 99%

A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme

Yazidi

Granmo

Oommen

et al. 2014

IEEE Trans. Cybern.

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Learning automata as a utility for power management in smart grids

Cited by 72 publications

References 12 publications

Energy aware cross layer-based connection admission control mechanism for wireless mesh networks

Energy aware cross layer-based connection admission control mechanism for wireless mesh networks

Game-Theoretic Distributed Virtual Energy Cloud Topology Control for Mobile Smart Grid

A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme

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