Power consumption model of sector breathing based congestion control in mobile network

According to Cooper＇s law, the number of conversations per location doubles every two and a half years. Therefore, congestion control has become a promising research area. Nowadays small cell deployment has become a solution to deal with congestion. If large numbers of small cells are allocated for congestion control, then two critical issues will arise： densification and interference management. In such a scenario sector breathing can offer low power congestion control by avoiding densification problem. This paper proposes a power consumption model for sector breathing based congestion control in a mobile network. With sector breathing, a congested cell is sectored at an angle of 60° or 120°. The congested sectors are then identified. The lightly loaded neighbour cells of the congested sector are sectored at 60°. The sectors of the lightly loaded cell capture the border region customers of the adjacent congested sector by increasing the coverage area. When the transmitter antenna of the adjacent lightly loaded sector expands coverage to provide service to the subscribers residing at the border region of the congested sector, the transmitter antenna of the congested sector reduces its coverage area. The simulation results indicate that sector breathing reduces the power transmission of the BS antennas by approximately 6-75% and 62-75% compared cell breathing and dense femtocell allocation based congestion controls respectively. Simulation results also show that sector breathing reduces the power consumption of the BS antennas by approximately 6-64% and 8240% compared with the cell breathing and dense femtocell allocation based congestion controls, respectively. Hence sector breathing is a green congestion control approach.

Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities globally.Recent research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral pathogenesis.However,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical interactions.This article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based tools.ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work.The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment analysis.Consequently,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

Group handoff management in low power microcell-femtocell network

This paper presents an analytical model of group based hand-off management based on bird flocking behavior. In the proposed scheme, a number of mobile devices form a group if these devices move together for a long time duration. Although call delivery or call generation are performed individually, hand-off is performed in a group. Dynamic group formation, group division and group merging methods are proposed in this paper. From the simulation results it is demonstrated that approximately 75%, 65% and 90% reduction in power, cost and latency consumption can be obtained respectively using group hand-off management. Thus the proposed scheme is referred as green, economic and fast hand-off strategy. In this paper instead of a macrocell network, a microcell-femtocell network is considered as the transmission power of a microcell or a femtoeell base station is much less than a macrocell base station. Simulation results present that the microcell-femtocell network achieves approximately 25-55% and 35-55% reduction in power transmission, and 50-65% and 15-45% reduction in path loss than only a macrocell network and macrocell-femtocell network respectively. Thus mierocell-femtocell network is a power-efficient network.

Nanoscale cryptographic architecture design using quantum-dot cellular automata

Quantum-dot cellular automata (QCA) based on cryptography is a new paradigm in the field of nanotechnology. The overall performance of QCA is high compared to traditional complementary metal-oxide semiconductor (CMOS) technology. To achieve data security during nanocommunication, a cryptography-based application is proposed. The devised circuit encrypts the input data and passes it to an output channel through a nanorouter cum data path selector, where the data is decrypted back to its original form. The results along with theoretical implication prove the accuracy of the circuit. Power dissipation and circuit complexity of the circuit have been analyzed.

Reversible binary subtractor design using quantum dot-cellular automata

In the field ofnanotechnology, quantum dot-cellular automata （QCA） is the promising archetype that can provide an alternative solution to conventional complementary metal oxide semiconductor （~MOS） circuit. QCA has high device density, high operating speed, and extremely low powex consumption. Reversible logic has widespread applications in QCA. Researchers have explored several designs of QCA-based reversible logic circuits, but still not much work has been reported on QCA-based reversible binary subtractors. The low power dissipation and high circuit density of QCA pledge the energy-efficient design of logic circuit at a nano-scale level. However, the necessity of too many logic gates and detrimental garbage outputs may limit the functionality of a QCA-based logic circuit. In this paper we describe the design and implementation of a DG gate in QCA. The universal nature of the DG gate has been established. The QCA building block of the DG gate is used to achieve new reversible binary subtractors. The proposed reversible subtractors have low quantum cost and garbage outputs compared to the existing reversible subtractors. The proposed circuits are designed and simulated using QCA Designer-2.0.3.

Quantum-dot cellular automata based reversible low power parity generator and parity checker design for nanocommunication

Quantum-dot cellular automata （QCA） is an emerging area of research in reversible computing. It can be used to design nanoscale circuits. In nanocommunication, the detection and correction of errors in a received message is a major factor. Besides, device density and power dissipation are the key issues in the nanocommunication architecture. For the first time, QCA-based designs of the reversible low-power odd parity generator and odd parity checker using the Feynman gate have been achieved in this study. Using the proposed parity generator and parity checker circuit, a nanocommunication architecture is pro- posed. The detection of errors in the received message during transmission is also explored. The proposed QCA Feynman gate outshines the existing ones in terms of area, cell count, and delay. The quantum costs of the proposed conventional reversible circuits and their QCA layouts are calculated and compared, which establishes that the proposed QCA circuits have very low quantum cost compared to conventional designs. The energy dissipation by the layouts is estimated, which ensures the possibility ofQCA nano-device serving as an alternative platform for the implementation of reversible circuits. The stability of the proposed circuits under thermal randomness is analyzed, showing the operational efficiency of the circuits. The simulation results of the proposed design are tested with theoretical values, showing the accuracy of the circuits. The proposed circuits can be used to design more complex low-power nanoscale lossless cation architecture such as nano-transmitters and nano-receivers

MedGini:Gini index based sustainable health monitoring system using dew computing

Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems.Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud.This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system.Wireless sensor nodes are used to monitor the biosignals dynamically.All accrued data is temporarily stored in the dew layer.It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data.Data synchronization plays an essential role in the cloud dew framework.We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud.Sometimes sensors produce erroneous data,which poses a significant threat to the sustainable health monitoring system.Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers.Further,we compared the proposed MedGini model with the existing models and analyzed the system performance.MedGini is found to outperform others concerning cost and power consumption.