Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks

Aslam, Nelofar; Xia, Kewen; Haider, Muhammad Tafseer; Hadi, Muhammad Usman

doi:10.3390/fi9040054

Cited by 20 publications

(11 citation statements)

References 38 publications

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“…Inspired by the effects of extending network lifetime, some works have connected the multi-hop routing strategy with charging technology. Aslam [20] attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimizes energy consumption and prolongs the overall network lifetime of WSN. Tang [21] proposed an optimization algorithm from the aspects of both charging and routing processes.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks

Guo

Tang

et al. 2019

Applied Sciences

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Recent progress in wireless charging technologies has greatly promoted the development of rechargeable wireless sensor networks (RWSN). The network lifetime of RWSN can be commonly extended through routing strategy and wireless charging technology. However, the node accepts the relay request of its neighbor unconditionally, and it cannot remove its overload on its own in a timely manner in traditional routing strategies. The energy balancing efficiency of the network may be limited by this passive mechanism, which poses a great challenge to obtaining optimal joint efficiency of routing and charging strategies. In this paper, we propose an autonomous load regulation mechanism-based energy balanced routing algorithm (ALRMR) for RWSN. In addition to an efficient framework of joint wireless energy transfer and multi-hop routing where the routing strategy is adapted to the charging scheme, an innovative load regulation mechanism is proposed. Under this mechanism, each node can actively adjust its own load by controlling its relay radius. The simulation demonstrates the advantages of our algorithm for energy balance efficiency and improving the network lifetime through the charging scheme and the innovative mechanism.

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Section: Related Workmentioning

confidence: 99%

Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks

Guo

Tang

et al. 2019

Applied Sciences

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“…A combination of double exponential smoothing (DES) and SVM for network equipment failure prediction has been employed in [20]. In [17,[21][22], an ANN is trained to learn historical fault patterns in networks and is subsequently used for detecting significant network faults with much better accuracies and proactive reaction times as compared to traditional threshold-based fault detection methods. Figure 1 presents the summary of the applications of ML methods in optical communication systems.…”

Section: Machine Learning Applications In Optical Communication Smentioning

confidence: 99%

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

Hadi¹

2019

Preprint

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Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

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“…They deployed the shortest path model to enhance the vacation time of the network and grid clustering to balance the energy consumption [15].…”

Section: Prior Knowledgementioning

confidence: 99%

Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

Ali

et al. 2018

Information

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Nowadays, wireless power transfer is ubiquitously used in wireless rechargeable sensor networks (WSNs). Currently, the energy limitation is a grave concern issue for WSNs. However, lifetime enhancement of sensor networks is a challenging task need to be resolved. For addressing this issue, a wireless charging vehicle is an emerging technology to expand the overall network efficiency. The present study focuses on the enhancement of overall network lifetime of the rechargeable wireless sensor network. To resolve the issues mentioned above, we propose swarm intelligence based hard clustering approach using fireworks algorithm with the adaptive transfer function (FWA-ATF). In this work, the virtual clustering method has been applied in the routing process which utilizes the firework optimization algorithm. Still now, an FWA-ATF algorithm yet not applied by any researcher for RWSN. Furthermore, the validation study of the proposed method using the artificial neural network (ANN) backpropagation algorithm incorporated in the present study. Different algorithms are applied to evaluate the performance of proposed technique that gives the best results in this mechanism. Numerical results indicate that our method outperforms existing methods and yield performance up to 80% regarding energy consumption and vacation time of wireless charging vehicle.

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Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks

Cited by 20 publications

References 38 publications

Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks

Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN

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