Energy efficient cognitive body area network (CBAN) using lookup table and energy harvesting

Singh, Manwinder; Kumar, Manoj; Malhotra, Jyoteesh

doi:10.3233/jifs-169669

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…The proposed technique is simulated utilizing python based on several performance measures. Obtained outcomes of AGNN-ABM-MIMO-MCN technique analyzed with existing likes (MLAB-MMWM-MIMO) [21], (DL-ERN-MCN) [22] and (DLF-BSPC-MIMO) [23] techniques respectively; the simulation Parameter is given in table 1.…”

Section: Resultsmentioning

confidence: 99%

Alternating Graph-Regularized Neural Network for Adaptive Beamforming in 5G milli meter wave Massive MIMO Multicellular Networks

BOLLA,

SINGH

2023

Preprint

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The idea of ultra-dense networking (UDN) has been more and more popular in recent years as a potential way to increase energy efficiency (EE), spectral efficiency (SE) over tiny geographic areas. However, since channel state information (CSI) is necessary, deployment of large number of Access Points may not only significantly increase signaling burden but also take advantage of the pilot contamination effect. To overcome this issue, present an Alternating Graph-Regularized Neural Network for Adaptive Beamforming in 5G Milli Metre Wave Massive MIMO Multicellular Networks (AGNN-ABM-MIMO-MCN) is proposed. When each active base stations of evaluated orientations use huge multiple-input, multiple-output setups, here, beamforming is carried out with aid of preset set of configurations by appropriately producing higher directional beams on demand, can handle variety of traffic circumstances. Then, Beam forming approach depend on AGNN, which is trained to generate appropriate beam forming configuration. In general, AGNN classifier does not describe modifying optimization techniques to identify optimal parameters to beamforming configuration. Therefore, it is proposed to use Tyrannosaurus Optimization Algorithm (TOA) to optimize Alternating Graph-regularized Neural Network, which accurately generates the beamforming configuration. The proposed method is analyzed with different metrics likes Energy Efficiency, Spectral efficiency, Normalized Mean-Squared Error, Latency, Bit Error Rate, blocking probability and computational complexity. The simulation outcomes proves that the proposed technique attains provides 21.25%, 23.19% and 22.14% lower NMSE, 23.12%, 24.43% and 21.32% lower latency, 23.25%, 22.19% and 25.32% higher Spectral Efficiency while analyzed with existing techniques likes machine learning adaptive beamforming framework for 5G millimeter wave massive MIMO multi cellular networks (MLAB-MMWM-MIMO), deep learning-enabled relay node placement with selection framework in multicellular networks (DL-ERN-MCN) and deep learning framework for beam selection with power control in massive MIMO-millimeter-wave communications (DLF-BSPC-MIMO) respectively.

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Section: Resultsmentioning

confidence: 99%

Alternating Graph-Regularized Neural Network for Adaptive Beamforming in 5G milli meter wave Massive MIMO Multicellular Networks

BOLLA,

SINGH

2023

Preprint

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“…Walia et al [ 32 ] have emphasized on localization of dynamic wireless sensor networks. Singh et al [ 33 ] give a brief idea about the energy-efficient cognitive body area network. Hassan et al [ 34 ] proposed a survey for data sharing techniques for 5G.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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“…It looked at how much energy they used. [15] looked into the possibility of making a wireless data and power transmission system for a single person. The length of the power transfer and the power that was used to send it were both carefully thought out in order to come up with an energy-efficient resource allocation method that was limited by a delay.…”

Section: Figure 1 Energy Transfer From Transmitter To Receivermentioning

confidence: 99%

“…A model for deploying energy harvesting sources that takes into account how energy harvesting sources are spread out across the country is being worked on. In [15], you can find the best settings for small-cell access points to use when they're not connected and when they are connected. Wired communication systems have recently received significant scientific interest for a method of concurrently transferring data and collecting energy using radio frequency (RF) waves.…”

Section: Figure 1 Energy Transfer From Transmitter To Receivermentioning

confidence: 99%

Energy Harvesting Technique for Massive MIMO Wireless Communication Networks

Bolla

Singh

2022

J. Phys.: Conf. Ser.

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Massive For wireless data and energy transfer, MIMO, a key component of future 5G systems, is favourable. A big part of the reason for this is that it has the ability to narrow down the distribution of energy. The scope of something enormous Multiple-Input in 5G, harvesting may be used to boost system capacity and efficiency via multiple-output multiple access. This was followed by the introduction of the Simultaneous Wireless Information and Power Transfer (SWIPT) method, commonly known as energy harvesting optimization. Specific to each user’s needs, we connected a splitter on the receiver, and we measured their output using power beacons. In order to achieve the lowest possible communication rate, the user’s total gathered power is boosted. Simulations show that the proposed technique is more successful than the other options out there. There are two sources of energy for D2D transmitters: specialised beacon signals, and cellular users (CUs) radio frequency (RF) interference. In D2D transmitters, inverse power control techniques are used for power regulation. We employ Poisson Point Processes(PPP) to model the proposed hybrid network’s CU, D2D user (DU), and PB locations. Each slot in the time period is assigned a different beam forming weight. Using the alternating minimization technique, we provide a solution approach for a non-convex quadratic ally limited linear problem. Long-distance wireless power transmission requires the use of energy beam generation technologies in a large-scale MIMO system in order to make sure that the transmission is safe. In other words, this letter tries to maximise the energy efficiency of information transmission (bits per joule) while keeping the required quality of service (QoS), which is a delay constraint, in mind. This is done by maximising the transfer length and transmits power, among other things. In this case, we try to maximise the total amount of energy at the relays, use block diagonalization (BD) at the source, and try to minimise interference between the relay-destination channels. As a last step, we also run simulations of the power consumption of a generic circuit. As a supplement to this work, there are some basic online rules for all possible situations. Numerical results show that a near-optimal online technique can do the same job as its offline counterpart when the maximum power consumption of the circuit is taken into account.

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Energy efficient cognitive body area network (CBAN) using lookup table and energy harvesting

Cited by 16 publications

References 13 publications

Alternating Graph-Regularized Neural Network for Adaptive Beamforming in 5G milli meter wave Massive MIMO Multicellular Networks

Alternating Graph-Regularized Neural Network for Adaptive Beamforming in 5G milli meter wave Massive MIMO Multicellular Networks

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Energy Harvesting Technique for Massive MIMO Wireless Communication Networks

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