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Objective: Collaboration of most promising upcoming technologies like big data and Internet of Things (IoT) plays a significant role in the sustainable development of the smart world. One of the important applications is the amalgamation of such technology in the e-healthcare sector, where private information is being transferred from one end device to other equipment. Sensors and different other instruments engaged in the e-healthcare sector, for transferring important vital information of the patients, are categorized as Wireless Mesh Networks (WMN). An attacker can introduce many malicious activities via different types of attacks; ultimately, such activities can produce an outturn in Denial of Service (DoS) of important routines which need to be completed in stipulated time. Therefore, it is important to showcase the effect of various attacks affecting the routing methodology of the protocols of such networks. Methods: In this research study, the most suitable routing protocol to handle DDoS attacks is simulated and estimated for the Quality of Service (QoS) in smart network infrastructure in terms of energy consumption and jitter in changing nodes scenario, which aids in providing implications to enhance existing protocols and alleviate the consequence of DDoS instigated by such attacks. Results: The performance of AODV (Adhoc on Demand Vector), SAODV (Secured Adhoc On Demand) and HWMP (Hybrid Wireless Mesh Protocol) is compared and tabularized, which are the most popularly utilized protocols in the healthcare environment. Conclusion: The simulation results show that the HWMP outperformed well than the other two routing protocols in terms of evaluation metrics, namely energy consumption and jitter, which could be considered much less vulnerable against DDoS attacks prevailing in the sustainable healthcare sector.

The Covid-19, a pandemic situation, effects the economy of the whole world severely and is gaining much huge attention in the field of research currently across the globe. The Internet of things (IoT) technology is playing a great role for taking care of the patients by monitoring and controlling the symptoms and is very much essential for the developing countries, where monitoring of health of huge population has its own challenges. So the IoT and its amalgamation with mobile ad hoc network (MANET) acts as base of networks where devices send information among each other wirelessly thus also named as wireless mesh networks (WMN), in which various nodes are either stationary or allied with static position. Sensors and different other devices involved in e-healthcare sector used in WMN converse wirelessly and hence become the main gate to a numerous susceptibilities. The main aim of this research study is to evaluate the performance of reactive, secured and hybrid routing protocols for throughput as one of the important quality of service (QoS) parameters in absence as well as in presence of distributed denial of service (DDoS). The NS-2(network simulator) is used to simulate AODV (ad hoc on-demand vector, SAODV (secured AODV) and hybrid wireless mesh protocol) in scenario of changing nodes. The comparative analysis concludes the HWMP as most suitable protocol among the other two routing protocols with impact on throughput for handling DDoS attacks. This research study aids in providing implications to enhance existing protocols and alleviate the consequence of DDoS instigated by such attacks.

The most promising upcoming technologies these days are wireless mesh networks (WMN) as it being established with the amalgamation of various wires and wireless networks. This is also known to be one category of mobile adhoc network (MANET) in which various nodes are either stationary or allied with static position. There are many design issues in designing the protocols for MANET routing protocol. Since the applications of such routing protocols vary from remote monitoring of defense-related operations of military, monitoring during crisis situation. Similarly these days due to pandemic situation, remote monitoring is very crucial in e-healthcare sector. Thus, it is important to come up with the best routing protocol for transferring the data from one end to other. An attacker node or device can introduce many malicious activities via internal and external attacks at multiple layers, thus impacting the tasks of important routines which need to be completed in stipulated time. In this, initially, the distributed denial of service (DDoS) attacks are implemented on three most suitable routing protocol on NS-2. Than the most suitable routing protocol among the three were compared, and results were tabularized for two different scenario for the packet loss ratio (PLR) as quality of service (QoS), which aids in providing implications to enhance the most suitable protocols and improve the consequence of instigated DDoS. The performance of adhoc on demand vector (AODV), secured adhoc on demand (SAODV) and hybrid wireless mesh protocol (HWMP) are compared and tabularized. The simulation results show that the HWMP outperformed well than other two routing protocols as to evaluation metrics PLR under the varying number of nodes and is much less vulnerable against DDoS attack dominant in e-healthcare region.

Cognitive radio being a key factor to fulfill demand of high data transmission and has improved the utilization of available frequency spectrum for next generation technology. For dynamic allocation of frequency hole to unlicensed user/secondary user, cognitive radio (CR) network requires detection of licensed user/primary user. Spectrum sensing phenomenon is used to estimate the unutilized frequency band in the available spectrum area. This is one of the important functions and challenging task in cognitive radio. This paper lists the comprehensive literature review of the spectrum sensing technique and presents various research areas of spectrum sensing (SS) in cognitive radio networks (CRN). An attempt to summarize currently existing research issues with spectrum sensing technique is also made through this paper. This paper also includes the classification of different spectrum sensing techniques, their comparison and finally presents future scope of this research.

The Internet of things (IoT) technology brings a significant revolution traditional healthcare sector. However, the amalgamation of IoT with medical equipment, also widely known as internet of medical things (IoMT), gave numerous susceptibilities at different layers of modern healthcare sector. The field of IoMT is increasing because of the various advantages associated mainly for detection, prevention and diagnosis of numerous diseases in patients. The risks associated with the security of IoMT, still needs to be addressed as in the core of this new technology, Wireless Sensor Networks (WSN) along with Mobile Ad-hoc Network (MANET) are most widely employed for picking information physically from environment by the IoT for better user friendly experience. This interaction used in various systems is famously called as MANET-IOT systems and IT based networks. The comparison has been made between reactive, secured and hybrid protocols for the various quality of service (QoS) parameters under the scenario of changing number of connections. The result emphasized that Hybrid Wireless Mesh Networks (HWMP) performed more efficiently when compared with Ad-hoc on Demand Distance Vector (AODV) and Secured AODV (SAODV) routing protocol.

The progressive research in the field of internet of things provides a platform to develop high performance and robust automated systems to control external devices via internet data transfer and cloud computing. The present emerging IoT research including user-friendly and easily-wearable sensors and signal acquisition techniques have made it possible to expand the IoT application areas towards healthcare sector. This chapter aims at providing a rationale behind development of IoT applications in healthcare, architecture details of internet of healthcare things (IoHT), and highlights a step-by-step development of IoT-based heart rate measurement and monitoring system using Arduino. The developed module has been advanced to transmit data over the internet on the ThingSpeak channel to allow remote monitoring in real time. This may help to improve/restore useful life among cardiac patients via real-time monitoring through remote locations.

Contrast enhancement and brightness preservation are the crucial steps for image quality control for developing informative and visually pleasing images. Histogram equalization based image enhancement tool does not provide the brightness preservation and better contrast enhancement. This may cause loss in diagnostic information in case of medical images. An attempt has been made to integrate range limited and weighted histogram equalization with adaptive gamma correction followed by homomorphic filtering to study the improvement in contrast as well as to preserve the essential details of the image. An image segmentation based on an efficient Otsu’s method has been implemented. The experimental results obtained are found to be optimal for generating enhanced images according to both quantitative estimation and qualitative human visual inspection. An excellent performance in terms maximum entropy preservation, better contrast enhancement and the best visual appearance of low contrast medical images has been achieved.

Image contrast enhancement plays a vital role in various applications of digital image processing field like face recognition, satellite imaging and medical imaging. This paper proposes an efficient algorithm to cater the limitation of over enhancement with maximum entropy preservation. In the proposed algorithm, input image histogram is segmented first based on its valley positions and then weighted distribution is applied to all segmented sub histograms followed by the histogram equalization, gamma correction and homomorphic filtering. Results reveal that the proposed technique outer performs other conventional histogram equalization techniques both in terms of visual quality along with maximum entropy preservation and contrast enhancement.

The objective of this paper is to present a holistic approach to continuously evaluate the cognitive, affective and behavioral aspects of student’s learning in engineering programs. An attempt has been made to develop a concrete operating model to appraise the learning outcomes of the engineering graduates. The methodology has been implemented and accepted optimistically by all teachers and learners, as it provides a continuous evaluation approach in terms of timely assessment and feedback on their learning outcomes. It assists educators in devising methods and rules to support every student depending on their learning capabilities and to show that success is for all. Engagement is ensured at each Course level, Program level and also at Campus level for all teachers and learners. Data is pulled every semester for Courses up for assessment based on internal evaluation and end semester result. Survey feedbacks are collected from all prime stake holders viz. students, parents, alumni, employers periodically and actions are planned. Course Outcomes and Program Outcomes are evaluated for a particular batch of students. The methodology is explained, taking one pass out batch of the Electronics & Communication Engineering program at our institute as a sample. The analytics report gives an insight into effective teaching learning model and a comprehensive assessment tool to measure learning outcomes attainment level at our campus with the prime objective of enhancing employability of our graduates.

An exponential rise in amount of medical image data generation has been recorded since two decades. In telemedicine based applications, this enormous amount of data is initially stored and later, transmitted to medical experts for required diagnosis. This whole storage and transmission process may require extremely high memory space and large transmission bandwidth. An attempt has been made to compress MRI (magnetic resonance imaging) data by exploiting neighbouring pixel redundancy found in images along with the relevant diagnostic information. A detailed analysis of MRI image compression using asymmetric wavelet based frequency transformation techniques has been carried out in MATLAB. It implements discrete wavelet transform using asymmetric mother wavelet daubechies (db) in conjunction with a hierarchal embedded bit coding scheme, set partitioning in hierarchal trees. Distinct daubechies (db6, db12, db16 and db20) mother wavelets have been applied to a set of ten MRI image (512 × 512) dataset. The performance of applied compression scheme has been evaluated by analyzing Compression-Ratio achieved, Peak-Signal-to-Noise-Ratio and Bits-Per-Pixel. A high compression ratio (CR - 3.84) with considerably high peak signal to noise ratio (PSNR - 43.35) is achieved with db20 wavelet. This high value of PSNR may help in preserving the diagnostic details in input MRI image data to a great extent and high CR would lead to less storage space followed by small transmission bandwidth requirement as compared to earlier compression techniques. It could definitely lead to development of more efficient remote healthcare systems.

Automated analysis of electroencephalogram signals for emotion state analysis has been emerging progressively towards the development of affective brain computer interfaces. However, conventional EEG signal analysis techniques such as event related potential (ERP) and power spectrum estimation fail to provide high emotion state classification rates due to Fourier phase suppression. A four-dimensional emotional model in terms of arousal, valence, liking and dominance is proposed using emotion specific EEG signals from DEAP dataset. A compact set of temporal peak/interval related features and spectral features have been extracted to map the feature space. Through the feature map, a multiclass-support vector machine (SVM) based classifier using one-against-one algorithm is configured to yield a maximum classification accuracy of 81.6% while classifying four emotional states. A comparison of multi-class-SVM with other classifiers such as feed forward neural network and radial basis function network has been made. Significant improvement using a proposed compact hybrid EEG feature set and a multi-class-SVM has been achieved for automated emotion state classification.

Automated assessment of human emotions via physiological signals has gained remarkable significance in the development of affective human–machine interfaces for stress detection. However, manual emotion analysis procedure is solely dependent upon the expertise of the analyst. An attempt has been made in this research to characterize and classify emotions from associated human neural responses via electroencephalography (EEG). Emotion-specific multichannel EEG dataset is acquired using 14-channel emotiv EEG neuroheadset. An efficient EEG signal analysis technique for human emotion classification using temporal and morphological features of EEG is presented. It uses power spectral and maximum/minimum peak features extracted from each EEG segment as an outcome to characterize emotion-specific EEG dynamics. A feed-forward neural network classifier is configured using Levenberg–Marquardt training algorithm to classify human emotions in two categories, viz, normal and stress states with classification accuracy of 60%. The experimental results reveal that the methodology adopted can further be explored to develop an automated clinical application to assist patients suffering from stress disorders with more efficient classification rates.

Every year trillions of medical imaging data are generated through different imaging techniques. Demand for communication of this data through telecommunication networks using the internet is growing rapidly for various applications, hence requires a lot of memory space for storage, and high bandwidth for transmission. Efficient storage and transmission of this data is a problem to be resolved. Compression of this huge medical imaging data would be a suggested solution to this which primarily considers neighboring pixel redundancy. The proposed work includes calculation of compression on a set of ten different real time MRI (512 × 512) images acquired by the neural department of the hospital. The compression of the MRI image includes decomposing the image by a chosen discrete wavelet transform’s (DWT) mother wavelet function which is followed by its encoding through set portioning in the hierarchal tree (SPIHT). After applying each of the compression methods their performance is quantified in terms of Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR) and Bits per pixel (BPP). The calculated performance parameters have been compared in order to get a higher compression ratio with considerably high PSNR values from all of the applied mother wavelets. DWT (Discrete Wavelet Transform)-based symmetric mother wavelets like Haar, bior4.4, bior6.8, sym4 and sym20 have been tested and compared in virtue of comparative analysis of them. Among all of the used wavelets, sym4 and sym20 have not been explored yet for compression. MATLAB and wavelet toolbox have been used for implementation of algorithms. Haar wavelet gives a higher compression ratio and a good PSNR value after compression. The images compressed with these takes less time for uploading and downloading also can be reconstructed without a loss of diagnostic details for utilization of telemedicine services.

Tachycardia and bradycardia are the most common type of heart beat abnormalities. These are caused by disruption of normal heart impulses which makes flow of blood and oxygen to vital body organs too rapid in tachycardia and too slow in bradycardia. This may lead to heart, brain and other organs damage. An attempt has been made in this paper to distinguish the normal heart rhythm from that of irregular ones. Electrocardiogram (ECG) signal processing provides information regarding the functionality of heart and thus helps in detecting cardiac abnormalities. Heart rate analysis of ECG provides the associated heart rate, an indicator of heart beats per minute. However, it does not give any information regarding strength of variations and thus distribution of power. Power spectral analysis using the fast Fourier transform (FFT) is performed to detect and characterize strength of variations in ECG with respect to frequency. The algorithm was tested on normal, bradycardia and tachycardia ECG rhythms loaded from MIT-BIH arrhythmia database of Physiobank ATM. Distinct heart beat abnormalities are found to be correlated with distinct frequency components in respective power spectrum. The inferences drawn from results demonstrate the potential of proposed algorithm that could be developed as an effective computer-aided diagnostic tool to identify heart beat abnormalities in ECGs.

Telemonitoring of Electroencephalogram (EEG) through wireless and information technology is very crucial as EEG is a primary clinical diagnostic tool for many neurological disorders. EEG signals are continuously recorded through many channels over prolonged time periods at extremely high resolution and thus possess enormous data size. This may lead to large storage space and more transmission bandwidth requirement for remote EEG signal analysis. An attempt has been made to compress multichannel EEG by exploiting neighboring channel and sample redundancy, present along with the information content. This paper presents a high performance hybrid multichannel EEG compression algorithm based on frequency transformation and parameter extraction techniques. It compares discrete cosine transform (DCT) with Fast Fourier transform (FFT) to compress the multichannel EEG. This compressed EEG is embedded with corresponding EEG sub band power information to obtain a high quality signal reconstruction for accurate brain state analysis. The performance of proposed hybrid compression scheme is quantified in terms of compression level achieved and reconstruction error introduced. A high compression ratio and considerably low percent root-mean-square difference (PRD) error is found thereby preserving diagnostic information while transmission/ recovery of real time multichannel EEG for efficient telehealth monitoring applications with reduced computational complexity. This research has also resulted in repeatability and thus reliability testing of an efficient hybrid compression technique developed earlier for ECG signals.

Electrocardiogram (ECG) is a primary clinical diagnostic tool for detection of cardiac arrhythmias. As ECG signals are generally acquired over longer time periods at extremely high resolution and thus are highly data intensive. This leads to the requirement of large storage space for database construction and more transmission bandwidth for remote ECG signal analysis, respectively. Successive ECG beats and sample values however, show some redundancy along with the information content. By removing this redundancy, ECG signal compression can be achieved. This paper comprises implementation of hybrid ECG signal compression system based on frequency transformation and parameter extraction techniques. It uses discrete cosine transform (DCT) and Fast Fourier transform (FFT) to compress the ECG signal. This compressed ECG signal is embedded with corresponding heart rate information in order to obtain a high quality reconstructed signal required for accurate cardiac state diagnosis. The proposed algorithm is tested for compression of bradycardia and tachycardia ECG rhythms selected from MIT-BIH arrhythmia database and the performance is evaluated using compression ratio and percent root-mean-square difference (PRD). The high compression ratio, low reconstruction error and less computational complexity justify the efficiency of hybrid techniques in ECG signal compression and thus in telecardiology.

Electrocardiogram (ECG) signal analysis is an established clinical tool for the cardiac state diagnosis. Being a physiological signal, ECG is non-linear, non-stationary and non-Gaussian in nature. Most of the ECG signal analysis tools such as linear and power spectrum estimation ignore random variations and the Fourier phase relationship among signal components. This can lead to inaccurate classification of cardiac states. A highly accurate and efficient ECG signal analysis technique for cardiac state classification using higher order statistics is presented that caters to these limitations. It uses fourth order statistics i.e. trispectrum related features of ECG signal. The trispectrum dependent features are extracted from each QRS complex of normal and abnormal ECG signals selected from MITBIH arrhythmia database. These are used to train a feedforward neural network classifier using Levenberg Marquardt training algorithm. An overall classification accuracy of 98% is achieved in the classification of three cardiac states viz. normal, left bundle branch block and nodal (junctional) escape beat using proposed method. The experimental results reveal that higher order statistics is a promising cardiac signal analysis tool to capture significant features from ECG signals accurately and efficiently.