Resource-aware task scheduling by an adversarial bandit solver method in wireless sensor networks

Khan, Muhidul Islam

doi:10.1186/s13638-015-0515-y

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…This variant of standard SARSA(λ) [24] algorithm has some important features like cooperation by using a neighboring factor, a heuristic policy for exploration and exploitation, and a varying learning rate considering the visited state-action pair. Currently, we are applying the learning algorithm for the resource allocation of D2D users considering that the allocation of cellular users is performed prior to the allocation of D2D users.…”

Section: Cooperative Reinforcement Learning Algorithm For Resource Almentioning

confidence: 99%

Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

et al. 2017

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Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.

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Section: Cooperative Reinforcement Learning Algorithm For Resource Almentioning

confidence: 99%

Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

et al. 2017

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“…Comparatively, the resources have been allocated based on priority for sharing primary resources [24] between multiple networks in wireless medium. Also, the algorithm that has been integrated is premeditated by arranging the mechanisms that adhere to IEEE 802.15.6 standards.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Smart Grid Using Multimedia Sensor Networks with Effective Resource Allocation

Teekaraman

Kirpichnikova

Manoharan³

et al. 2022

Mathematical Problems in Engineering

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In recent days, for smart grid network updates, video sensors are used where each node needs to be compressed before transmission. This is very much useful for developing countries as in future the smart grid communication process will play a major role in entire society. Therefore, in this article, minimization of power, energy consumption, and delay for wireless video sensor networks have been computed by providing better quality of service at a similar period. Also, the problem of optimizing transmission and delivery rate has been studied. For solving the aforementioned problems, the intuitive migrant algorithm has been implemented for providing better energy consumption. Additionally, modified larvae optimization tool has been integrated for providing better convergence rate, where all the nodes will be compressed at a better rate with necessary quality of service. The simulation results show that by applying algorithms in two folds, and each video sensor node is compressed by satisfying necessary constraints with fast convergence rate.

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On a multisensor knowledge fusion heuristic for the Internet of Things

Martins

Souza

Santos

et al. 2021

Computer Communications

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Resource-aware task scheduling by an adversarial bandit solver method in wireless sensor networks

Cited by 5 publications

References 21 publications

Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Analysis of Smart Grid Using Multimedia Sensor Networks with Effective Resource Allocation

On a multisensor knowledge fusion heuristic for the Internet of Things

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