Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information.It cannot be easy to distinguish themodified region fromthe original image in somecircumstances.The demand for authenticity and the integrity of the image drive the detection of a fabricated image.There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files,including re-sampling or copy-moving.This work presents a high-level view of the forensics of digital images and their possible detection approaches.This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness.These methods have identified forgery and its type and compared it with state of the art.This work will help us to find the best forgery detection technique based on the different environments.It also shows the current issues in other methods,which can help researchers find future scope for further research in this field.

Energy Efficient Load Balancing and Routing Using Multi-Objective Based Algorithm in WSN

In wireless sensor network(WSN),the gateways which are placed far away from the base station(BS)forward the collected data to the BS through the gateways which are nearer to the BS.This leads to more energy consumption because the gateways nearer to the BS manages heavy traffic load.So,to over-come this issue,loads around the gateways are to be balanced by presenting energy efficient clustering approach.Besides,to enhance the lifetime of the net-work,optimal routing path is to be established between the source node and BS.For energy efficient load balancing and routing,multi objective based beetle swarm optimization(BSO)algorithm is presented in this paper.Using this algo-rithm,optimal clustering and routing are performed depend on the objective func-tions routingfitness and clusteringfitness.This approach leads to decrease the power consumption.Simulation results show that the performance of the pro-posed BSO based clustering and routing scheme attains better results than that of the existing algorithms in terms of energy consumption,delivery ratio,through-put and network lifetime.Namely,the proposed scheme increases throughput to 72%and network lifetime to 37%as well as it reduces delay to 37%than the existing optimization algorithms based clustering and routing schemes.

Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen saturation.Then,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery.The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics system.It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis.We use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely.Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission mechanisms.To validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot Check.Our pro-posed system provides improved care solutions,especially for those whose access to care services is limited.

An Improved Lifetime and Energy Consumption with Enhanced Clustering in WSNs

Awireless sensor network(WSN)is made up of sensor nodes that communicate via radio waves in order to conduct sensing functions.In WSN,the location of the base station is critical.Although base stations are fixed,they may move in response to data received from sensor nodes under specific conditions.Clustering is a highly efficient approach of minimising energy use.The issues of extending the life of WSNs and optimising their energy consumption have been addressed in this paper.It has been established that integrating mobile sinks into wireless sensor networks extends their longevity.Thus,this research proposes an optimal clustering and routing technique for optimising the energy usage and lifetime of WSNs.To minimise energy consumption,this research employs movable and stationary sink nodes.The K-Medoid clustering model is used to generate the initial number of nodes in the various clusters.After that,the cluster head is chosen using a hybrid Interval Type-2 Fuzzy technique that takes three aspects into account:residual energy,node centrality,and neighbourhood.A highly efficient backup cluster head(CH)collecting system can provide in significant energy savings while also prolonging the system’s life.Finally,better Reinforcement learning combined with a Genetic algorithm routing protocol is used to ensure effective data delivery.The suggested approach’s efficacy is evaluated in comparison to earlier approaches utilising residual node energy,delay or average delay,packet delivery ratio,throughput,network longevity,average energy consumption,and multiple alive nodes.In experiments,the proposed strategy outperforms existing strategies.

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medicalfield.In that case,the image proces-sing is performed to improve the image data,wherein it inhibits unintended dis-tortion of image features or it enhances further processing in various applications andfields.This helps to show better results especially for diagnosing diseases.Of late the early prediction of cancer is necessary to prevent disease-causing pro-blems.This work is proposed to identify lung cancer using lung computed tomo-graphy(CT)scan images.It helps to identify cancer cells’affected areas.In the present work,the original input image from Lung Image Database Consortium(LIDC)typically suffers from noise problems.To overcome this,the Gaborfilter used for image processing is highly enhanced.In the next stage,the Spherical Iterative Refinement Clustering(SIRC)algorithm identifies cancer-suspected areas on the CT scan image.This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages.These new methods help to remove unwanted por-tions of the CT image and better utilization the image.The subspace extraction of features approach is beneficial for evaluating lung cancer.This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures(CCPNN).By using the feature values from the fourth step of the procedure,the proposed CCPNN tends to categorize the lesion in the lung nodular site.The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall(%),precision(%),F-measure(percent),and accuracy(%).Finally,the incorrect classification ratios are determined to compare the trained networks’effectiveness,through these parameters of CCPNN,it obtains the outstanding per-formance of 98.06%and it has provided the lowest false ratio of 1.8%.

Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world tasks.Given the scope of this area,various techniques are found in the literature.One such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each other.The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed.As more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance issues.Taking a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL tasks.It uses multiple binary action DQNs having shared state and reward.The benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based approaches.The approach can potentially be extended to any type of DQN by forming its ensemble.Conducting extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent techniques.The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial ones.Since nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes.The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate.With this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for WSN.The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes.In addition,the IAOAB-NLS model is stimulated by the behaviour of Aquila.The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network.For guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS model.The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

High-Bandwidth,Low-Power CMOS Transistor Based CAB for Field Programmable Analog Array

This article presents an integrated current mode configurable analog block(CAB)system for field-programmable analog array(FPAA).The proposed architecture is based on the complementary metal-oxide semiconductor(CMOS)transistor level design where MOSFET transistors operating in the saturation region are adopted.The proposed CAB architecture is designed to implement six of thewidely used current mode operations in analog processing systems:addition,subtraction,integration,multiplication,division,and pass operation.The functionality of the proposed CAB is demonstrated through these six operations,where each operation is chosen based on the user’s selection in the CAB interface system.The architecture of the CAB system proposes an optimized way of designing and integrating only three functional cells with the interface circuitry to achieve the six operations.Furthermore,optimized programming and digital tuning circuitry are implemented in the architecture to control and interface with the functional cells.Moreover,these designed programming and tuning circuitries play an essential role in optimizing the performance of the proposed design.Simulation of the proposed CMOS Transistor Based CAB system is carried out using Tanner EDA Tools in 0.35μm standard CMOS technology.The design uses a±1.5 V power supply and results in maximum 3 dB bandwidth of 34.9 MHz and an approximate size of 0.0537 mm2.This demonstrates the advantages of the design over the current state-of-the-art designs presented for comparison in this article.Consequently,the proposed design has a clear aspect of simplicity,low power consumption,and high bandwidth operation,which makes it a suitable candidate for mobile telecommunications applications.

Dual-tree complex wavelet transform and super-resolution based video inpainting application to object removal and error concealment

Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels.The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches.To overcome these drawbacks,the authors propose a novel two-stage framework.In the first step,sub-bands of wavelets of a low-resolution image are obtained using the dualtree complex wavelet transform.Criminisi algorithm and auto-regression technique are then applied to these subbands to inpaint the missing regions.The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast.In the second step,the image is enhanced using super-resolution technique.The process of down-sampling,inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time.The framework is tested on video sequences by comparing and analysing the structural similarity index matrix,peak-signal-to-noise ratio,visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms.The experimental analysis gives visually pleasing results for object removal and error concealment.

Proactive Connection Recovery Strategy with Recovery Time Constraint for Survivable Elastic Optical Networks

This paper presents a halfway signaling exchange shared path protection(HSE-SPP)on the backup route for a fast connection recovery strategy.In the proposed HSE-SPP,a pre-assigned intermediate node on the backup route is chosen for signaling exchange.When connection fails,source and destination nodes simultaneously generate backup connection setup messages to the pre-assigned intermediate node on the reserved backup route.At the intermediate node,signaling process occurs,and acknowledgment is generated for data transmission to the respective end nodes.Consequently,connection recovery time by applying HSE-SPP becomes very low.Simulations are performed for network parameters and results are verified with existing strategies.The average recovery time(RT),bandwidth blocking probability(BBP),bandwidth provisioning ratio(BPR),and resource overbuild(RO)ratio of HSE-SPP for ARPANET is 13.54 ms,0.18,3.02,0.55,and for dedicated path protection(DPP)are 13.20 ms,0.56,6.30,3.75 and for shared path protection(SPP)22.19 ms,0.22,3.23,0.70 respectively.Similarly,average RT,BBP,BPR and RO of HSE-SPP for COST239 are8.33 ms,0.04,1.64,0.26,and for DPP 4.23,0.47,3.50,2.04,and for SPP 11.81,0.08,1.66,0.27 respectively.Hence,results of the proposed strategy are better in terms of RT,BBP,BPR,and RO ratio.

Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop.Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops.Recognition of diseases from the plant images is an active research topic which makes use of machine learning(ML)approaches.A novel deep neural network(DNN)classification model is proposed for the identification of paddy leaf disease using plant image data.Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search algorithm(CSA)during both the standard pre-training and fine-tuning processes.This DNN-CSA architecture enables the use of simplistic statistical learning techniques with a decreased computational workload,ensuring high classification accuracy.Paddy leaf images were first preprocessed,and the areas indicative of disease were initially extracted using a k-means clustering method.Thresholding was then applied to eliminate regions not indicative of disease.Next,a set of features were extracted from the previously isolated diseased regions.Finally,the classification accuracy and efficiency of the proposed DNN-CSA model were verified experimentally and shown to be superior to a support vector machine with multiple cross-fold validations.

RF performance evaluation of p-type NiO-pocket based β-Ga_(2)O_(3)/ black phosphorous heterostructure MOSFET

The radio-frequency (RF) performance of the p-type NiO-pocket based β-Ga_(2)O_(3)/black phosphorous heterostructureMOSFET has been evaluated. The key figure of merits (FOMs) for device performance evaluation include the transconductance(gm) gate dependent intrinsic-capacitances (Cgd and Cgs), cutoff frequency (fT), gain bandwidth (GBW) product and output-conductance(gd). Similarly, power-gain (Gp), power added efficiency (PAE), and output power (POUT) are also investigated for largesignalcontinuous-wave (CW) RF performance evaluation. The motive behind the study is to improve the β-Ga_(2)O_(3) MOS deviceperformance along with a reduction in power losses and device associated leakages. To show the applicability of the designeddevice in RF applications, its RF FOMs are analyzed. With the outline characteristics of the ultrathin black phosphorous layer belowthe β-Ga_(2)O_(3) channel region, the proposed device results in 1.09 times improvement in fT, with 0.7 times lower Cgs, and 3.27dB improved GP in comparison to the NiO-GO MOSFET. The results indicate that the designed NiO-GO/BP MOSFET has betterRF performance with improved power gain and low leakages.

Image Encryption Using Multi-Scroll Attractor and Chaotic Logistic Map

In the current scenario,data transmission over the network is a challenging task as there is a need for protecting sensitive data.Traditional encryption schemes are less sensitive and less complex thus prone to attacks during transmission.It has been observed that an encryption scheme using chaotic theory is more promising due to its non-linear and unpredictable behavior.Hence,proposed a novel hybrid image encryption scheme with multi-scroll attractors and quantum chaos logistic maps(MSA-QCLM).The image data is classified as inter-bits and intra-bits which are permutated separately using multi scroll attractor&quantum logistic maps to generate random keys.To increase the encryption efficiency,a hybrid chaotic technique was performed.Experimentation is performed in a Qiskit simulation tool for various image sets.The simulation results and theoretical analysis show that the proposed method is more efficient than its classical counterpart,and its security is verified by the statistical analysis,keys sensitivity,and keyspace analysis.The Number of changing pixel rate(NPCR)&the Unified averaged changed intensity(UACI)values were observed to be 99.6%&33.4%respectively.Also,entropy oscillates from 7.9 to 7.901 for the different tested encrypted images.The proposed algorithm can resist brute force attacks well,owing to the values of information entropy near the theoretical value of 8.The proposed algorithm has also passed the NIST test(Frequency Monobit test,Run test and DFT test).

Investigation of current collapse and recovery time due to deep level defect traps inβ-Ga2O3 HEMT

In this paper,drain current transient characteristics ofβ-Ga2O3 high electron mobility transistor(HEMT)are studied to access current collapse and recovery time due to dynamic population and de-population of deep level traps and interface traps.An approximately 10 min,and 1 h of recovery time to steady-state drain current value is measured under 1 ms of stress on the gate and drain electrodes due to iron(Fe)–dopedβ-Ga2O3 substrate and germanium(Ge)–dopedβ-Ga2O3 epitaxial layer respectively.On-state current lag is more severe due to widely reported defect trap EC–0.82 e V over EC–0.78 e V,-0.75 e V present in Iron(Fe)-dopedβ-Ga2O3 bulk crystals.A negligible amount of current degradation is observed in the latter case due to the trap level at EC–0.98 e V.It is found that occupancy of ionized trap density varied mostly under the gate and gate–source area.This investigation of reversible current collapse phenomenon and assessment of recovery time inβ-Ga2O3 HEMT is carried out through 2 D device simulations using appropriate velocity and charge transport models.This work can further help in the proper characterization ofβ-Ga2O3 devices to understand temporary and permanent device degradation.