PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Abstract Crop yield prediction is a crucial area in agriculture that has a large impact on the economy of a country. This article proposes a crop yield prediction framework based on Internet of Things and edge computing. We have used a fifth generation network device referred to as femtolet as the edge device. The femtolet is a small cell base station that has high storage and high processing ability. The sensor nodes collect the soil and environmental data, and then the collected data is sent to the femtolet through the microcontrollers. The femtolet retrieves the weather‐related data from the cloud, and then processes the sensor data and weather‐related data using Bi‐LSTM. The femtolet after processing the data sends the generated results to the cloud. The user can access the results from the cloud to predict the suitable crop for his/her land. This is observed that the suggested framework provides better accuracy, precision, recall, and F1‐score compared to the state‐of‐the‐art crop yield prediction frameworks. This is also demonstrated that the use of femtolet reduces the latency by ˜25% than the conventional edge‐cloud framework.

The IEEE 802.11ah, also known as WiFi HaLow, is a scalable solution for medium-range communication in Internet of Things (IoT). While provisioning support for the IoT and machine-to-machine (M2M) communication, IEEE 802.11ah leverages various innovative medium access control (MAC) layer concepts, such as restricted access window (RAW), hierarchical association identification (AID), traffic indication map (TIM) segmentation, etc. This article presents a survey on various MAC protocols for IEEE 802.11ah. While discussing the essential features of IEEE 802.11ah, this survey points out various issues and limitations of such MAC protocols. Although there are some surveys available for MAC protocols of IEEE 802.11ah, they do not include a large number of schemes that have been recently proposed to solve different standardization and implementation-based issues. This article individually surveys issues and challenges in the different problem domains of the IEEE 802.11ah MAC protocol and analyzes the recently proposed solutions. Moreover, this article identifies various factors for further improvement of these protocols. Compared to other relevant surveys, this article emphasizes the issues and challenges to enable researchers to easily identify the problem domain.

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.

Summary Energy‐efficient smart retail system design is a challenging research area. In this paper, we propose an automated retail system using a femtolet‐based fog network. A femtolet is an indoor base station providing computation and storage. Femtolets in our system work as indoor base stations and maintain databases of the products located in their respective coverage areas. The femtolets switch to active or idle mode according to the user's presence in its coverage. A smart trolley is proposed for our retailing system, which guides the user to the particular product type selected by the user. The user, after entering the shopping mall, carries the smart trolley. The customer selects and purchases products using this trolley. On the basis of product purchasing, the respective databases maintained inside the femtolets are updated. An Android application for the proposed retailing is developed. We compare the power consumption and delay of the proposed retail system with the existing retail system. Simulation analyses illustrate that the proposed approach reduces power by approximately 89% and 94%, respectively, in comparison to the local cloud server–based and remote cloud server–based retail systems. Thus, we refer to the proposed system as a green retail system. The performance of the proposed system through experimental analysis is also evaluated.

Fast interactive response in mobile cloud computing is an emerging area of interest. Execution of applications inside the remote cloud increases the delay and affects the service quality. To avoid this difficulty cloudlet is introduced. Cloudlet provides the same service to the device as cloud at low latency but at high bandwidth. But selection of a cloudlet for offloading computation at low power is a major challenge if more than one cloudlet is available nearby. In this paper we have proposed a power and latency aware optimum cloudlet selection strategy for multi-cloudlet environment with the introduction of a proxy server. Theoretical analysis show that using the proposed approach the power and the latency consumption are reduced by approximately 29-32 and 33-36 percent respectively than offloading to the remote cloud. An experimental analysis of the proposed cloudlet selection scheme is performed using cloudlets and cloud servers located at our university laboratory. Theoretical and experimental results demonstrate that using the proposed strategy power and latency aware cloudlet selection can be performed. The proposed approach is compared with the existing methods on multi-cloudlet scenario to demonstrate that the proposed approach reduces the power consumption and the system response time.

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

In the field of nanotechnology, quantum dot-cellular automata (QCA) is the promising archetype that can provide an alternative solution to conventional complementary metal oxide semiconductor (CMOS) circuit. QCA has high device density, high operating speed, and extremely low power 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.

This study presents a frequency allocation strategy for macro‐femtocell and micro‐femtocell cluster‐based long‐term evaluation‐advanced network. In the proposed approach the network is divided into a number of clusters. Each cluster contains a macrocell or a number of microcells. Femtocells are deployed within a macrocell or a microcell. In the proposed strategy fractional frequency reuse is used. Each macrocell and microcell is divided into three regions. Different frequency sets are allocated for macrocell and femtocells in a macro‐femtocell cluster. Similarly in a micro‐femtocell cluster, different frequency sets are provided to a microcell and the femtocells contained in that microcell. For each region inside a macrocell or a microcell, the allotted frequency band is different from the adjacent region. The path loss model for the macro‐femtocell and micro‐femtocell cluster‐based network is developed in this study. The signal‐to‐interference‐plus‐noise ratio and spectral efficiency are determined. The simulation results show that using the proposed strategy signal‐to‐interference‐plus‐noise ratio can be increased to ∼62–92%. Simulation results present that macro‐femtocell and micro‐femtocell cluster‐based network reduces the power transmission to ∼48% than that of a macro‐femtocell network. Hence it is referred as a green mobile network.

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 proposed. 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 of QCA 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 nanocommunication architecture such as nano-transmitters and nano-receivers.