Showing papers by "Jaypee Institute of Information Technology published in 2020"

A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact

Abstract: The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.

A blockchain-based framework for lightweight data sharing and energy trading in V2G network

Abstract: The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2G network is required. Existing studies use traditional blockchain as to achieve this requirement. Blockchain-enabled V2G networks require a high computation power and are not suitable for micro-transactions due to the mining reward being higher than the transaction value itself. Moreover, the transaction throughput in the generic blockchain is too low to support the increasing number of frequent transactions in V2G networks. To address these challenges, in this paper, a lightweight blockchain-based protocol called Directed Acyclic Graph-based V2G network (DV2G) is proposed. Here blockchain refers to any Distributed Ledger Technology (DLT) and not just the bitcoin chain of blocks. A tangle data structure is used to record the transactions in the network in a secure and scalable manner. A game theory model is used to perform negotiation between the grid and vehicles at an optimized cost. The proposed model does not require the heavy computation associated to the addition of the transactions to the data structure and does not require any fees to post the transaction. The proposed model is shown to be highly scalable and supports the micro-transactions required in V2G networks.

A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes

Abstract: Blockchain technology is found to have its applicability in almost every domain because of its advantages such as crypto-security, transparency, immutability, decentralized data network In present times, a smart healthcare system with a blockchain data network and healthcare 40 processes provides transparency, easy and faster accessibility, security, efficiency, etc Healthcare 40 trends include industry 40 processes such as the internet of things (IoT), industrial IoT (IIoT), cognitive computing, artificial intelligence, cloud computing, fog computing, edge computing, etc The goal of this work is to design a smart healthcare system and it is found to be possible through integration and interoperability of Blockchain 30 and Healthcare 40 in consideration with healthcare ground-realities Here, healthcare 40 processes used for data accessibility are targeted to be validated through statistical simulation-optimization methods and algorithms The blockchain is implemented in the Ethereum network, and with associated programming languages, tools, and techniques such as solidity, web3js, Athena, etc Further, this work prepares a comparative and comprehensive survey of state-of-the-art blockchain-based smart healthcare systems The comprehensive survey includes methodology, applications, requirements, outcomes, future directions, etc A list of groups, organizations, and enterprises are prepared that are working in electronic health records (EHR), electronic medical records (EMR) or electronic personal records (EPR) mainly, and a comparative analysis is drawn concerning adopting the blockchain technology in their processes This work has explored optimization algorithms applicable to Healthcare 40 trends and improves the performance of blockchain-based decentralized applications for the smart healthcare system Further, smart contracts and their designs are prepared for the proposed system to expedite the trust-building and payment systems This work has considered simulation and implementation to validate the proposed approach Simulation results show that the Gas value required (indicating block size and expenditure) lies within current Etherum network Gas limits The proposed system is active because block utilization lies above 80% Automated smart contract execution is below 20 seconds A good number (average 3 per simulation time) is generated in the network that indicates a health competition Although there is error observed in simulation and implementation that lies between 055% and 424%, these errors are not affecting overall system performance because simulated and actual (taken in state-of-the-art) data variations are negligible

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques.

Abstract: In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

Phytochemicals as Antiviral Agents: Recent Updates

Abstract: The epidemic of viral diseases is a global concern, mandating an urgent need of most promising antivirals. Some of the viral diseases can be cured by approved antiviral drugs, but for others still do not have any vaccines or drugs available. Most of the approved antiviral drugs are somehow directly or indirectly associated with side effects, which eventually raise the need for the development of antivirals based on natural phytochemicals. Globally, the development of antivirals is shifting towards the plant-derived products as they are less toxic and has less chance to develop resistance. Phytochemicals have been exploited traditionally for the cure of many diseases, and also have been reported to inhibit viral replication/transcription. Most of them inhibit the viruses either during the viral entry inside the host cell or during their replication. Moreover, 50% of the drugs derived from plants are being used in the Western nations. Plants have a variety of phytochemicals like flavonoids, terpenoids, lignins, alkaloids, and coumarins that are having antioxidant activity, and help to inhibit viral genome. Various plant-derived products have been well studied against viruses like herpes virus, human immunodeficiency virus (HIV), influenza, and hepatitis virus. More recently, Coronavirus disease (COVID-19) caused by a newly identified coronavirus has become pandemic, and affected world’s population severely. However, there are still less explored phytochemicals for the inhibition of viruses like dengue virus, chikungunya virus, and other alphaviruses. In this chapter, we will emphasize on the reported phytochemicals and their derivatives, having antiviral properties and their mechanism to treat viral diseases.

A Distributed Framework for Energy Trading Between UAVs and Charging Stations for Critical Applications

Abstract: Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions.

DAGIoV: A Framework for Vehicle to Vehicle Communication Using Directed Acyclic Graph and Game Theory

Abstract: Data sharing and content offloading among vehicles is an imperative part of the Internet of Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly promising solution for fast communication among vehicles. To ensure security and data tracking, existing studies use blockchain as a solution. The Blockchain-enabled Internet of Vehicles (BIoV) requires high computation power for the miners to mine the blocks and let the chain grow. Over and above, the blockchain consensus is probabilistic and the block generated today can be eventually declared as a fork and can be pruned from the chain. This reduces the overall efficiency of the protocol because the correct work done initially is eventually not used if it becomes a fork. To address these challenges, in this paper, we propose a Directed Acyclic Graph enabled IoV (DAGIoV) framework. We make use of a tangle data structure where each node acts as a miner and eventually the network achieves consensus among the nodes. A game-theoretic approach is used to model the interactions between the vehicles providing and consuming offloading services. The proposed model is proven to be highly scalable and well suited for microtransactions or frequent data transfer among the nodes in the vehicular network.

EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs

Abstract: Wireless sensor networks are widely used in monitoring and managing environmental factors like air quality, humidity, temperature, and pressure. The recent works show that clustering is an effective technique for increasing energy efficiency, traffic load balancing, prolonging the lifetime of the network and scalability of the sensor network. In this paper, a new energy-efficient clustering technique has been proposed based on a genetic algorithm with the newly defined objective function. The proposed clustering method modifies the steady-state phase of the LEACH protocol in a heterogeneous environment. The proposed objective function considers three main clustering parameters such as compactness, separation, and number of cluster heads for optimization. The simulation result shows that the proposed protocol is more effective in improving the performance of wireless sensor networks as compared to other state-of-the-art methods, namely SEP, IHCR, and ERP.

A Parking Slot Allocation Framework Based on Virtual Voting and Adaptive Pricing Algorithm

Abstract: Parking lot allocation problem has received much attention in recent years. There have been various works in the literature that target the parking slot allocation problem. However, most of these works use algorithms that run on centralized servers and are based on some predictions on historical data. Due to the dynamic nature of vehicular networks, the accuracy of such prediction models is not high which ends up in a chaotic situation for the parking lot owners as well as the vehicle owners. Therefore, a distributed Parking slot Allocation Framework based on Adaptive Pricing Algorithm and Virtual Voting is proposed in this paper. The proposed model is based on virtual voting and hashgraph consensus algorithm. Using the model, all users and parking lot owners can easily come to consensus finality about the allocation of a parking slot with the use of minimal bandwidth. The proposed model provides a fair, fast and cost-optimal parking slot allocation method. The perfect ordering of allocation requests is also maintained based on consensus timestamp. Further, an adaptive pricing model is proposed to enhance the overall revenue of the parking lot owners and comfort of the users. The proposed model is deterministic and can reduce the average parking cost and time. Performance evaluations reveal that the proposed model outperforms its counterparts in terms of accurate parking slot allocation, reduced cost and parking lot resource utilization.

Blockchain for 5G: A Prelude to Future Telecommunication

Abstract: 5G and Blockchain are potentially revolutionizing future technologies. 5G promises high rates and QoS to the users and blockchain guarantees a high level of trust and security among the peers. Applications that would be using 5G have varying needs in terms of speed, bandwidth, latency and various other factors. Augmented reality, self-driving vehicles and other ioT applications tend to use 5G for reliable and fast communication. To work seamlessly and securely in such scenarios a more specialized and efficient approach would be required. in this article, we have identified the specific areas where blockchain could be utilized to enhance the security and privacy of the 5G services offered to the users. The current challenges faced in deployment and upliftment of 5G and their related solutions based on blockchain are discussed. A model for Multi-Operator Network Slicing in 5G using blockchain is also presented along with 5G blockchain implementation.

Wearable Devices and IoT

Abstract: Wearable devices are the significant ubiquitous technology of the Internet of Things in day-to-day life. The efficient data processing in various devices such as smart clothes, smart wristwear and medical wearables along with consumer-oriented service of the IoT technology becomes inevitable in smart healthcare systems. The wearable market is currently dominated by health, safety, interaction, tracker, identity, fitness etc. Wearables increase the convergence of physical and digital world which automatically bring people into the IoT. The popularity of wearable devices is growing exponentially since it entirely changes the way how the consumers interact with the environment. 74% people believe that the wearable sensors assist them in interacting with the physical objects around them. Henceforth, one out of three smartphone users will wear minimum 5 wearables in 2020. Moreover, 60% believe that wearables in the next five years will be used not only to track health related information, although it can be used to control objects, unlock doors, authenticate identity and transactions. Wearables must be evolved to cope with the future to meet the expectations of consumers, where the users will wear many devices that is connected with the internet to interact with the physical surroundings and receive data in a seamless secure way. By 2021, smartwatches are estimated to be sold to nearly 81 million units which signifies 16% sales of total wearable device. According to the latest figure of Gartner report, the global shipment of wearable devices are anticipated to raise by 25.8% every year to $225 million (GBP 176.3 million) in 2019. Researchers also forecasted that the usage of wearable devices by the end users will increase to $42 billion (GBP 32.9 million) in 2019. In recent years, the IoT based Smart Healthcare system has influenced greatly on growing demand of wearable devices. In fact, the Wearable IoT (WIoT) devices are generating huge volume of personal health data. Enabling technologies such as cloud computing, Fog computing and Big Data play vital role in leveraging WIoT services. These enabling services over the voluminous health data enhance clinical process at health care system at remote or local servers. The traditional remote healthcare information system involves data transfer, signal processing mechanism and naive machine learning models deployed on remote server to process the medical data of patients. This technique has several demerits like they are not suitable for resource constrained wearable IoT devices. The resources such as processing, memory, energy, networking capability are limited in WIoT devices. Traditional mechanism lacks optimization of resource usage, prediction of medical condition, and dynamic assessment based on available information. Further, the naive machine learning techniques does not perform knowledge generation, decision making and discover hidden valuable patterns from the available medical data. The integrated platform in which cloud computing serves as backend computing systems, Fog computing as edge computing and Big data as platform for data analysis, knowledge generation promise to provide valid solution to several issues of Wearable IoT devices. Next, the health data generated through WIoT devices are personal and sensitive. Hence, the security and privacy of such delicate data at all level of WIoT ecosystem is essential. This part of chapter will contribute towards understanding the recent research work, issues, challenges, and opportunities in applying enabling technologies for WIoT. Also, how well the security and privacy can be incorporated is also discussed.

Sampling Approaches for Imbalanced Data Classification Problem in Machine Learning

Abstract: Real-world datasets in many domains like medical, intrusion detection, fraud transactions and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets negatively affect the accuracy of class predictions. This skewness can be handled either by oversampling minority class examples or by undersampling majority class. In this work, popular methods of both categories have been evaluated for their capability of improving the imbalanced ratio of five highly imbalanced datasets from different application domains. Effect of balancing on classification results has been also investigated. It has been observed that adaptive synthetic oversampling approach can best improve the imbalance ratio as well as classification results. However, undersampling approaches gave better overall performance on all datasets.

On the Effect of Interference and Misalignment Error in Mixed RF/FSO Systems Over Generalized Fading Channels

Abstract: In this paper, the end-to-end performance of a mixed radio frequency (RF)/free space optical (FSO) affected by co-channel interference (CCI), is studied. We consider that the RF link experiences $\eta -\mu $ fading and the FSO link is subjected to atmospheric turbulence, which is modeled by the $\alpha -\mu $ distribution. Also, the statistics of the FSO link is presented for the case of zero and non-zero boresight pointing errors. Furthermore, we assume intensity modulation with direct detection (IM/DD) and coherent demodulation. In particular, we present a closed-form expression for the probability density function of the FSO link, which is then used to obtain a closed-form and an asymptotic expression for the outage probability. In order to quantify the system performance, we utilize this asymptotic result to yield the system’s coding gain and diversity order. Moreover, we have presented analytical expressions for the average bit error rate (BER) and ergodic capacity for the system design. In order to gain more insights, high signal-to-noise ratio (SNR) approximations expressions for the BER and ergodic capacity, are also derived. Finally, the analytical results presented in the paper are validated through computer simulations.

Cholesterol-Lowering Effects of Lactobacillus Species.

Abstract: Probiotics are the living and non-pathogenic microbial supplements which, upon administration in adequate quantities, influence the host organism positively by improving gut health and enhancing intestinal mucosal integrity. They suppress potentially pathogenic microorganisms by competing with them for nutrients as well as space for gut adherence. Lactobacillus species are the most commonly used bacteria in the probiotic preparations and studies show that they have cholesterol-lowering effects on the hosts. Lipids are biological molecules that are insoluble in water and bile salts play a major role in their digestion as they are synthesized and conjugated to taurine or glycine in the liver. Bile salt hydrolase deconjugates taurine or glycine from bile salts. Cholesterol metabolism is influenced by the effect of Lactobacillus species on microbial populations as well as overall metabolic activity of human intestinal microflora. Deconjugation of bile salt, concentration of short-chain fatty acids and molar proportion of propionate constitute the major processes by which cholesterol lowering is brought about by Lactobacillus species. This review summarizes the cholesterol-lowering properties of this species. A significant number of Lactobacillus strains have been known to display substantial bile salt hydrolase activities and identifying those strains for use in therapeutic purposes can be a great advancement. Here, this identification is done using phylogenetic relationship for different identified potential probiotic Lactobacillus strains.

In Silico Investigation of Phytoconstituents from Indian Medicinal Herb 'Tinospora cordifolia (Giloy)' against SARS-CoV-2 (COVID-19) by Molecular Dynamics Approach

Abstract: The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different possibilities (including herbal medicines having known therapeutic significance) have been explored by the scientists. The systematic scientific study (beginning with in silico study) of herbal medicines in particular and any drug in general is now possible as the structural components (proteins) of COVID-19 are already characterized. The main protease of COVID-19 virus is $\rm{M^{pro}}$ or $\rm{3CL^{pro}}$ which is a key CoV enzyme and an attractive drug target as it plays a pivotal role in mediating viral replication and transcription. In the present study, $\rm{3CL^{pro}}$ is used to study drug:3CLpro interactions and thus to investigate whether all or any of the main chemical constituents of Tinospora cordifolia (e.g., berberine $\rm{(C_{20}H_{18}NO_{4})}$, $\beta$-sitosterol $\rm{(C_{29}H_{50}O)}$, choline $\rm{(C_{5}H_{14}NO)}$, tetrahydropalmatine $\rm{(C_{21}H_{25}NO_{4})}$ and octacosanol $\rm{(C_{28}H_{58}O))}$ can be used as an anti-viral drug against SARS-CoV-2. The in silico study performed using tools of network pharmacology, molecular docking including molecular dynamics have revealed that among all considered phytochemicals in Tinospora cordifolia, berberine can regulate $\rm{3CL^{pro}}$ protein's function due to its easy inhibition and thus can control viral replication. The selection of Tinospora cordifolia was motivated by the fact that the main constituents of it are known to be responsible for various antiviral activities and the treatment of jaundice, rheumatism, diabetes, etc.

Feature selection method based on hybrid data transformation and binary binomial cuckoo search

Abstract: Feature selection is one of the key components of data mining and machine learning domain that selects the best subset of features with respect to target data by removing irrelevant data. However, it is a complex task to select optimal set of features from a dataset using traditional feature selection methods, as for n number of features, $$2^n$$ feature subsets are possible. Therefore, this paper introduces a novel metaheuristics-based feature selection method based binomial cuckoo search. Generally, metaheuristics-based feature selection methods suffer with stability issue since they select different set of features in different runs. Hence, to deal with stability issue, a hybrid data transformation method based on principal component analysis and fast independent component analysis has also been introduced. The proposed hybrid data transformation method first transforms the original data thereafter proposed binary binomial cuckoo search method is used to elect the best subset of features. The proposed feature selection method maximizes the classification accuracy and minimizes the number of selected features. The performance of the proposed method has been tested on the fourteen feature selection benchmark datasets taken from UCI repository and compared with other latest state-of- the art approaches including binary cuckoo search, binary bat algorithm, binary gravitational search algorithm, binary whale optimization with simulated annealing, and binary grey wolf optimization. Further, statistical analysis has also been carried out to validate the efficacy of the proposed method.

Impact of Interface Trap Charges on Analog/RF and Linearity Performances of Dual-Material Gate-Oxide-Stack Double-Gate TFET

Abstract: This paper investigates the impact of different interface trap charges (ITCs) on dual-material gate-oxide-stack double-gate TFET (DMGOSDG-TFET) by introducing localized charges (donor/acceptor) at the interface of semiconductor/insulator. For this, we have observed the effects of different ITCs on both conventional dual material control gate tunnel field effect transistor (DMCG-TFET) and dual-material gate-oxide-stack double-gate TFET with identical dimensions in terms of DC, analog/RF and linearity performance parameters. Both the devices with positive (donor) and negative (acceptor) ITCs, have been simulated using technology computer-aided design (TCAD) tool. To understand the impact of different ITCs on the DC and analog/RF performances, the parameters such as electric field, transfer characteristics, transconductance, parasitic capacitance, $f_{T}$ , GBP and TFP for DMGOSDG-TFET have been analyzed and compared with that of DMCG-TFET. Further, to analyze the effect of different ITCs on the linearity performances, the parameters VIP2, VIP3, IIP3 and IMD3 have been investigated and compared with that of the conventional DMCG-TFET. Simulation results demonstrate that DMGOSDG-TFET is more immune towards different types of ITCs as compared to the conventional DMCG-TFET. Hence, DMGOSDG-TFET is more reliable over the conventional device for ultra low power applications.

Design analysis of Bloch surface wave based sensor for haemoglobin concentration measurement

Abstract: In this paper, a Bloch Surface Waves (BSW) based sensor is proposed to estimate the haemoglobin concentration in human blood. The behaviour of the sensor is analysed using a transfer matrix method. The proposed structure is designed considering one-dimensional photonic crystal, where a defective top layer is deliberately introduced to confine a surface plasmon-like mode called Bloch mode at the top interface. The effective refractive index of top interface changes along with haemoglobin concentration. Thereby, monitoring the angel of incidence to confine BSW mode can helps in determining the haemoglobin concentration. The sensing capability, FWHM and figure-of-merit of the proposed structure are improved by optimizing the defect layer thicknesses, incident angels and wavelengths. Proposed structure shows an average FWHM and average sensitivity of around 0.00508 and 0.0133°/(g/L) respectively.

Memantine nanoemulsion: a new approach to treat Alzheimer’s disease

Abstract: Aim: A nanoemulsion loaded with memantine for intranasal delivery to bypass the blood-brain barrier for the treatment of Alzheimer disease.Method: The nanoemulsion was prepared using homogenisation...

Identification and expression analysis of conserved microRNAs during short and prolonged chromium stress in rice ( Oryza sativa )

Abstract: MicroRNAs (miRNAs) are one of the most critical epigenetic regulators of gene expression which modulate a spectrum of development and defence response processes in plants. Chromium (Cr) contamination in rice imposes a serious concern to human health as rice is used as staple food throughout the world. Although several studies have established the differential response of miRNAs in rice during heavy metal (arsenic, cadmium) and heat or cold stress, no report is available about the response of miRNAs during Cr stress. In the present study, we identified 512 and 568 known miRNAs from Cr treated and untreated samples, respectively. Expression analysis revealed that 13 conserved miRNAs (miR156, miR159, miR160, miR166, miR169, miR171, miR396, miR397, miR408, miR444, miR1883, miR2877, miR5072) depicted preferential up- or down-regulation (> 4-fold change; P value < 0.05). Target gene prediction of differentially expressed miRNAs and their functional annotation suggested the important role of miRNAs in defence and detoxification of Cr though ATP-binding cassette transporters (ABC transporters), transcription factors, heat shock proteins, auxin response, and metal ion transport. Real-time PCR analysis validated the differential expression of selected miRNAs and their putative target genes. In conclusion, our study identifies and predicts miRNA-mediated regulation of signalling pathway in rice during Cr stress.

Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

Abstract: Assuring the coverage towards the predefined set of targets, power-constrained wireless sensor networks (WSNs) consist of sensing devices (i.e., sensor nodes) that are associated with limited battery life and fixed sensing range. All the sensors are collectively responsible for covering these sets of objects. The standard target coverage problem is the one where continuous coverage is provided over a predefined set of targets for the maximum possible duration so that the scarce resource (battery power) can be optimally utilized. Therefore, in order to incorporate quality of service (QoS) in the network and ensure smooth monitoring of the desired target set, the paper addresses target Q-Coverage, which is one of the variants of standard target coverage problem where a target is covered by at least Q-sensors (pre-defined number) in every cover set. A cover set is a subset of sensors which cover whole targets in a single iteration. In this paper, a greedy heuristic based technique, i.e., maximum coverage small lifetime (MCSL) has been proposed, which restricts the usages of those sensors that poorly cover targets and promotes the usage of those sensors that have maximum coverage and energy. Simulations are performed on static wireless sensor network with varying Q values to test the efficiency of the proposed method. The performance of the proposed heuristic is compared with optimal upper bound based on network lifetime, and results prove that performance is improvised by 94%. The obtained results are further compared with the existing approaches to prove the superiority of the proposed work via extensive experimentations.