Showing papers in "Wireless Personal Communications in 2021"
TL;DR: This paper proposed two-way image transmission to the Corvus Coron module, which presents an energy-effective with the CS model, as an inbuilt interaction in the CS transmission through the security framework, which results in energy-efficient and conserved transmission in the form of low error rate with low computational time.
Abstract: Two-way image communication in a wireless channel needs to be viable with channel properties such as transfer speed, energy-effective, time usage, and security because image capability consumes a huge space in the gadget and is quite effective. Is required in a manner. The figure goes through attacks. In addition, the quiesical issue for additional time of pressure is that the auxiliary interaction of pressure occurs through the dewar receiving extra time. To address these issues, compressed sensing emerges, which packs the image into hours of sensing, is generated in an expedient manner that reduces time usage and saves the use of data transfer capability, however Bomb in transmission. A variety of examinations cleared a way for dealing with security issues in compressive sensing (CS) through giving security as an alternative negotiation. In addition, univariate factors opted for CS as the issue of rearranging image quality is because of the aggregation of clutter. Along these lines related to the above issues, this paper proposed two-way image transmission to the Corvus Coron module, which presents an energy-effective with the CS model, as an inbuilt interaction in the CS transmission through the security framework. Receives what was designated as the pack-protected plot. Impeccable entertainment with the famous arbitrary network conjecture in CS. The result of the test is that the practical module presents energy-efficient and conserved transmission in the form of low error rate with low computational time.
230 citations
TL;DR: Comparisons between indoor localization systems in terms of accuracy, cost, advantages, and disadvantages are summarized and different detection techniques are presented.
Abstract: This paper introduces a review article on indoor localization techniques and technologies. The paper starts with current localization systems and summarizes comparisons between these systems in terms of accuracy, cost, advantages, and disadvantages. Also, the paper presents different detection techniques and compare them in terms of accuracy and cost. Finally, localization methods and algorithms, including angle of arrival (AOA), time of arrival (TOA), and recived signal strength (RSS) are introduced. The study contains concepts, requirements, and specifications for each category of methods presents pros and cons for investigated methods, and conducts comparisons between them.
144 citations
TL;DR: A new authentication scheme for the WBAN environment based on the light-weight scheme proposed for wearable devices is proposed, which minimizes the computation cost and maintains the privacy and security along with anonymous authentication.
Abstract: Over the years, the performance of devices used to gather sensitive medical information about individuals has increased substantially. These include implanted devices in the body, placed on or around the body, creating a Wireless body area network. Security and privacy have been a greater concern over a period of time due to the sensitive nature of the data collected and transmitted by the network. It has been noticed that various techniques have been applied to secure the data and provide privacy in WBANs but with a tradeoff of execution overhead. Although the latest available anonymous authentication schemes provide privacy and security but due to the limited computation capacity of WBAN devices, these schemes show greater time cost for authentication and consume more processing time. We review two latest anonymous authentication schemes for the WBAN environment in terms of computation cost. These two schemes provide anonymous authentication and use encryption to secure the data and ensure privacy. Then we analyze a recent lightweight authentication scheme proposed for wearable devices which provides anonymity and privacy along with security with very low computation cost. This scheme uses hash functions in order to obtain authentication and anonymity and doesn’t use encryption in the authentication process. This scheme is not proposed for the WBAN environment, but it can be applied on the WBAN environment with necessary variations. The comparison of these available schemes shows clearly that the computation cost is considerably decreased by applying the latest authentication scheme in the WBAN environment. We propose a new authentication scheme for the WBAN environment based on the light-weight scheme proposed for wearable devices. The detailed analysis shows that our proposed scheme minimizes the computation cost and maintains the privacy and security along with anonymous authentication.
84 citations
TL;DR: The main aim of the work is to propose a Machine learning-based healthcare model to early and accurately predict the different diseases and help doctors to diagnose the disease early.
Abstract: Artificial Intelligence (AI) is widely implemented in healthcare 4.0 for producing early and accurate results. The early predictions of disease help doctors to make early decisions to save the life of patients. Internet of things (IoT) is working as a catalyst to enhance the power of AI applications in healthcare. The patients' data are captured by IoT_sensor and analysis of the patient data is performed by machine learning techniques. The main aim of the work is to propose a Machine learning-based healthcare model to early and accurately predict the different diseases. In this work, seven machine learning classification algorithms such as decision tree, support vector machine, Naive Bayes, adaptive boosting, Random Forest (RF), artificial neural network, and K-nearest neighbor are used to predict the nine fatal diseases such as heart disease, diabetics breast cancer, hepatitis, liver disorder, dermatology, surgery data, thyroid, and spect heart. To evaluate the performance of the proposed model, four performance metrics (such as accuracy, sensitivity, specificity, and area under the curve) are used. The RF classifier observes the maximum accuracy of 97.62%, the sensitivity of 99.67%, specificity of 97.81%, and AUC of 99.32% for different diseases. The developed healthcare model will help doctors to diagnose the disease early.
81 citations
TL;DR: A novel Nature-Inspired algorithm-based Cross-layer Clustering (NICC) protocol is designed to find a reasonably better solution for clustering and routing in SF applications and explores the idea of a nature-inspired optimization algorithm called Bacterial Foraging Optimization with optimal fitness function, which models the trade-off among the energy efficiency and optimal data transmission.
Abstract: The Internet of Things (IoT) is a subclass of the Industry 4.0 standard. The functionality of IoT depends on the Wireless Sensor Networks (WSNs) design. The IoT-empowered WSNs received the researcher's attention for the Smart Farming (SF) applications. SF nowadays is required to enhance farm productivity while minimizing the cost and resources. The agriculture sensors devices disposed over the farm collect the on-field farm data and transfer it wirelessly to the base station for decision-making and agriculture monitoring. As the nodes are resource restrained, the process of periodic farm data gathering and multi-hop delivery needs to be effective in terms of Quality of Service (QoS) and energy-efficiency of information transmission by reflecting the long-distance transmission difficulties of SF applications. To enhance the network lifetime substantially of densely deployed WSN for periodically monitoring of farm conditions, we propose a novel Nature-Inspired algorithm-based Cross-layer Clustering (NICC) protocol. We design NICC to find a reasonably better solution for clustering and routing in SF applications. NICC explores the idea of a nature-inspired optimization algorithm called Bacterial Foraging Optimization (BFO) with optimal fitness function, which models the trade-off among the energy efficiency and optimal data transmission. We design a BFO algorithm to select the optimal sensor node for clustering and routing problems based on cross-layer parameters-based fitness value computation. The cross-layer parameter includes the sensor parameters from layers like network layer, physical layer, and Medium Access Control (MAC). The numerical results show the superiority of the NICC protocol for various WSN-assisted SF scenarios against state-of-art clustering techniques.
70 citations
TL;DR: Numerical result shows the significant improvement in latency by the proposed Smart Ant Colony Optimization (SACO) algorithm in task offloading of IoT-sensor applications comparison to Round Robin (RR), throttled, and MPSO and BLA.
Abstract: In the current scenario, Cloud computing is providing services to IoT-sensor based applications in task offloading. In time-sensitive real-time applications, latency is a major problem in cloud computing. Due to exponential growth in IoT-sensor applications huge amount of multimedia data is produced and only the use of cloud computing decreases the efficiency of quality of service (QoS) in IoT-sensor applications. Fog computing uses to resolve the aforementioned issues in cloud computing. Fog computing accomplishes the low-latency requirement of QoS in time-sensitive real-time IoT-sensor applications. Thus the tasks of IoT-sensor applications are computed by various fog nodes. In this paper, a meta-heuristic scheduler Smart Ant Colony Optimization (SACO) task offloading algorithm inspired by nature is proposed to offload the IoT-sensor applications tasks in a fog environment. The proposed algorithm results are compared with Round Robin (RR), throttled scheduler algorithm and two bio-inspired algorithms such as modified particle swarm optimization (MPSO) and Bee life algorithm (BLA). Numerical result shows the significant improvement in latency by the proposed Smart Ant Colony Optimization (SACO) algorithm in task offloading of IoT-sensor applications comparison to Round Robin (RR), throttled, and MPSO and BLA. Proposed technique reduces the task offloading time by 12.88, 6.98, 5.91 and 3.53% in comparison to Round Robin (RR), throttled, MPSO, and BLA.
61 citations
TL;DR: This paper attempts to explain a solution to tackle the problem of counterfeit medicines in India by proposing a resilient electronic health networks using blockchain based on recording the medicine logistics requirements from medicine manufacturing to the patient on the blockchain network.
Abstract: This paper attempts to explain a solution to tackle the problem of counterfeit medicines in India by proposing a resilient electronic health networks using blockchain. The distribution and consumption of fake medicines take thousands of lives every year. There are no effective measures to combat the network of the fake medicine syndicate in the country, and the stakeholders in the healthcare ecosystem have to work under trust-deficit relationships amongst them. Blockchain is a decentralized system of computer nodes, where each node stores the same data, and coexist with other nodes without having to trust them. The proposed solution is based on recording the medicine logistics requirements from medicine manufacturing to the patient on the blockchain network. If, at any stage, counterfeit medicine is introduced into the system, it will be detected immediately, and its further penetration will be stopped. The system is simulated using a hyper ledger fabric platform, and its performance is also compared with other existing methods. Results show that the system thus formed is computationally intensive but offers a reliable solution to the menace of fake medicines.
60 citations
TL;DR: A secure and lightweight health authentication and key agreement protocol using low-cost operations that takes less execution cost, computation time, and power consumption and requires around 57% less communication overhead and 15% less storage cost.
Abstract: The concept of wireless body area network (WBAN) is conquered the medical field while considering hospitals, patients, doctors to exchange crucial health data for efficient medical services. Health data includes personal medical information of patients, quality of life, reproductive outcomes, and behavioral information. While transferring health data for medical treatment/review, it is essential to provide proper security during device-to-device communication among WBAN players. Researchers have suggested different health data transmission mechanisms to achieve security, but these schemes are weak against various security attacks, such as modification, session key disclosure, impersonation, replay, man-in-the-middle, and stolen smart card, making crucial information in a susceptible situation specifically for patients. Further, they require high computational resources during WBAN communications. In this paper, we propose a secure and lightweight health authentication and key agreement protocol using low-cost operations. To confirm the robustness of the proposed mechanism, we do security analysis against various security attacks. Based on the computational analysis, the proposed protocol comparatively takes less execution cost, computation time, and power consumption. Moreover, LAKA requires around 57% less communication overhead and 15% less storage cost.
58 citations
TL;DR: The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.
Abstract: Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.
57 citations
TL;DR: A dilated transaction access and retrieval method that identifies the transaction history based on the non-replicated identity and recursive organization of the block and performs relevance based retrieval to improve the correctness of transaction assessment.
Abstract: Blockchain technology is designed to improve the security features and information access of a transaction in a connected Internet of Things platform The private information retrieval from the transactions using blockchain improves the quality of experience through systematic assessments However, the information retrieval from the fore-gone transaction does not result in maximum profit due to time and sequence of transactions This article introduces a dilated transaction access and retrieval method The proposed method identifies the transaction history based on the non-replicated identity and recursive organization of the block A non-recurrent binary searching process assists information access and retrieval randomly The random process increases the time, and therefore, a transaction-time constraint is used to limit the number of random searches In this method, multi-random searches are initiated in a branched manner for identifying the block Pursued by this access, the relevance based retrieval is performed to improve the correctness of transaction assessment
55 citations
TL;DR: An automated system for identification and classification of fish species and their habitats is presented and the proposed and modified AlexNet model with less number of layers has achieved the testing accuracy of 90.48% while the original Alex net model achieved 86.65% over the untrained benchmark fish dataset.
Abstract: In this paper, we presented an automated system for identification and classification of fish species. It helps the marine biologists to have greater understanding of the fish species and their habitats. The proposed model is based on deep convolutional neural networks. It uses a reduced version of AlexNet model comprises of four convolutional layers and two fully connected layers. A comparison is presented against the other deep learning models such as AlexNet and VGGNet. The four parameters are considered that is number of convolutional layers and number of fully-connected layers, number of iterations to achieve 100% accuracy on training data, batch size and dropout layer. The results show that the proposed and modified AlexNet model with less number of layers has achieved the testing accuracy of 90.48% while the original AlexNet model achieved 86.65% over the untrained benchmark fish dataset. The inclusion of dropout layer has enhanced the overall performance of our proposed model. It contain less training images, less memory and it is also less computational complex.
TL;DR: A new Quasi-Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration and employs the Quasi, Oppositional Based Learning principle to increase the standard convergence rate of the Dragonfly algorithm.
Abstract: In cloud computing (CC), load balancing tasks remain a critical problem in distributing resources from a data center. Ensure that every virtual machine (VM) has a balanced load to maximize capacity utilization. In the CC world, load balancing is a Non-Polynomial (NP) problem resolved with metaheuristic algorithms. A new Quasi-Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration. The proposed QODA-LB algorithm uses three variables to calculate an objective function: execution time, execution cost, and charge. The QODA-LB algorithm assigns tasks to VM according to its potential and the resulting objective function. Also, the QODA-LB algorithm employs the Quasi-Oppositional Based Learning principle to increase the standard convergence rate of the Dragonfly (DA) algorithm. A complete series of experiments were conducted, and the results were analyzed in various ways to ensure the increased efficiency of the QODA-LB algorithm. Simulation results demonstrated an optimal load balancing efficiency and outperformed key approaches.
TL;DR: This work demonstrates that a new patient healthcare monitoring protocol presented recently is vulnerable to many attacks, including replay attacks and key compromise impersonation attacks, and also that it suffers from privacy issues.
Abstract: Burgeoning wireless technology developments have positively affected nearly every aspect of human life, and remote patient-healthcare monitoring through the internet is no exception. By employing smart gadgets, wireless body area networks, and cloud-based server platforms, patients can submit their sensor-captured readings in real-time to e-health cloud servers and ultimately to medical professionals so that the latter may treat patients appropriately at any time and in any place. To make the system reliable, an authenticated key agreement is required for the participating entities in this system. Many remote patient-healthcare monitoring protocols have been seen so far; however, reliance on wireless technology brings many security challenges for existing protocols. Recently, Xu et al. presented a new patient healthcare monitoring protocol; however, we demonstrate that it is vulnerable to many attacks, including replay attacks and key compromise impersonation attacks, and also that it suffers from privacy issues. Thereafter, we have proposed an improved scheme and formally analyzed its security features by implementing BAN logic and an automated simulation tool.
TL;DR: An IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR- ICWSN is proposed, which has outperformed the compared methods interms of network lifetime and energy efficiency.
Abstract: In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.
TL;DR: ADS-PAYG (Attack Defense Shell- Pay As You Go) approach using Trust Factor method against the EDoS attack is proposed to improve more number of authenticated users by fixing a threshold value and produced an effective result based on response time, accuracy and CPU utilization.
Abstract: Cloud computing is a global technology for data storage and retrieving. Many organizations are switching their companies to cloud technology, so that they can lease cloud services for use on a membership or pay as you go basis rather than creating their own systems. Cloud service provider and the Cloud service accessibility are the two major problems in cloud computing. The Economic Denial of Sustainability (EDoS) attack is an important attack towards the cloud service providers. The attackers may send continuous requests to the cloud in a particular second. Hence the legitimate user cannot access the data due to heavy cloud traffic. Hence the paid user cannot access the data. However, this problem makes an economical issue to the users. So this paper presented a new technique as, ADS-PAYG (Attack Defense Shell- Pay As You Go) approach using Trust Factor method against the EDoS attack is proposed to improve more number of authenticated users by fixing a threshold value. The algorithm produced an effective result based on response time, accuracy and CPU utilization. The ADS-PAYG solution is applied using MATLAB, which outperforms other Trust factor estimation methods and effectively distinguishes attackers from legitimate users. The detection accuracy is 83.43% for the given dataset and it is high when compared to the existing algorithms.
TL;DR: The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols.
Abstract: In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.
TL;DR: Chaos theory and ARTFA have been considered in this paper to estimate reliable and robust thresholds for R-peak detection and show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias.
Abstract: Timely detection of cardiac abnormalities from an Electrocardiogram (ECG) signal is very essential. This requires its appropriate and efficient processing. In the literature, most of the researchers focussed on linear techniques that were applied on filtered ECG datasets leaving an ample scope for exploring the use of non-linear techniques in the presence and absence of natural noise-processes. Therefore, there is a need of supplementing the existing research on ECG signal interpretation by using non-linear techniques on noisy ECG data. Non-linear techniques are expected to yield supplementary clues about the non-linearities in the underlying cardiovascular system. One such promising non-linear technique, known as chaos theory (analysis), has been considered here to estimate reliable and robust thresholds for R-peak detection using fractal dimension, Approximate Entropy, Sample Entropy, correlation dimension, and Lyapunov exponent based on time-delay dimension (embedding). Also, time–frequency analysis techniques have shown their effectiveness for analyzing such types of non-linear and non-stationary signals due to simultaneous interpretation of the signal in both time and frequency domain. Among existing TFA techniques such as wavelet transform, short time Fourier transform, Hilbert transform, Auto-regressive Time Frequency Analysis (ARTFA) offers good time–frequency resolution. Therefore, Chaos theory and ARTFA have been considered in this paper. First, raw ECG signal was filtered using Savitzky Golay Digital Filtering (SGDF) because it retains all important signal features after filtering. Second, a novel optimal trajectory detection step was proposed on the basis of phase space reconstruction (attractors) in chaos theory. Third, ARTFA has been used to find the spectral components of the extracted features using chaos theory. Here, ARTFA has been used for finding the autoregressive coefficients in the first step and time–frequency description in the second step. Burg method has been considered for Auto-regressive modeling to fit an ARTFA model for analyzing ECG signal by minimizing (least squares) the forward and backward prediction errors. MIT-BIH arrhythmia database has been considered for validating the present research effort. Some real time signals were also tested to explore direct usage of the proposed technique in practical applications. The obtained results show that the proposed technique enhances sensitivity, positive predictive value and accuracy thereby improving the detection of arrhythmias. Computational cost of the proposed technique is reduced to a great extent by using the chaos theory (analysis) yielding efficient detection performance. All results have been obtained in MATLAB environment R2011a. Improved values viz. 99.96% SE, 99.97% PPV, and 99.93% ACC are obtained.
TL;DR: Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%.
Abstract: Twitter sentiment analysis is an automated process of analyzing the text data which determining the opinion or feeling of public tweets from the various fields. For example, in marketing field, political field huge number of tweets is posting with hash tags every moment via internet from one user to another user. This sentiment analysis is a challenging task for the researchers mainly to correct interpretation of context in which certain tweet words are difficult to evaluate what truly is negative and positive statement from the huge corpus of tweet data. This problem violates the integrity of the system and the user reliability can be significantly reduced. In this paper, we identify the each tweet word and we are assigning a meaning into it. The feature work is combined with tweet words, word2vec, stop words and integrated into the deep learning techniques of Convolution neural network model and Long short Term Memory, these algorithms can identify the pattern of stop word counts with its own strategy. Those two models are well trained and applied for IMDB dataset which contains 50,000 movie reviews. With huge amount of twitter data is processed for predicting the sentimental tweets for classification. With the proposed methodology, the samples are experimentally collected from the real-time environment can be discriminated well and the efficacy of the system is improved. The result of Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%.
TL;DR: This work investigates the convergence of WSN and MANET in IoT and considers a fundamental problem, how a converged (WSN-MANET) network provides quality of service (QoS) guarantees to rich multimedia applications.
Abstract: Wireless Sensor Network (WSN) and Mobile Ad hoc Network (MANET) have attracted a special attention because they can serve as communication means in many areas such as healthcare, military, smart traffic and smart cities. Nowadays, as all devices can be connected to a network forming the Internet of Things (IoT), the integration of WSN, MANET and other networks into IoT is indispensable. We investigate the convergence of WSN and MANET in IoT and consider a fundamental problem, that is, how a converged (WSN-MANET) network provides quality of service (QoS) guarantees to rich multimedia applications. This is very important because the network performances of WSN and MANET are quite low, while multimedia applications always require quality of services at certain levels. In this work, we survey the QoS-guaranteed routing protocols for WSN-MANETs, that are proposed in IEEE Xplore Digital Library over the last decade. Then, basing on our findings, we suggest future open research directions.
TL;DR: The proposed RCSO method shows superior performance by providing maximal energy, throughput, and the number of alive nodes with values 0.0351 J, 74.715%, and 18 respectively.
Abstract: Wireless Sensor Networks plays an outstanding role in providing dynamic cluster head (CH) selection. However, the selection of CH is a major challenge due to erroneous CH selection and can lead to unbalanced energy consumption. This paper addresses this challenge by proposing a hybrid optimization algorithm for CH selection. The proposed CH selection comprises three phases, which includes the setup phase, transmission phase, and measurement phase. At first, the energy and the node’s mobility in the network are initialized. The setup phase is processed by choosing CH using the Optimized Sleep-awake Energy-Efficient Distributed clustering, which is designed by determining the optimal threshold and CH using proposed Rider-Cat Swarm Optimization (RCSO) algorithm. The proposed RCSO is designed by integrating Rider Optimization Algorithm into Cat Swarm Optimization. Here, the threshold and CH are chosen using multi-objective constraints, which involves distance, energy, and delay. After determining the CHs, the data transmission begins from CHs to the base station. At last, in the measurement phase, the residual energies produced from the nodes are being updated. The proposed RCSO method shows superior performance by providing maximal energy, throughput, and the number of alive nodes with values 0.0351 J, 74.715%, and 18 respectively.
TL;DR: Two new heuristics algorithms namely gravitational approach based clustering method and a clustered gravitational routing algorithm have been proposed in this paper for providing an optimal solution for efficient clustering and effective routing.
Abstract: Wireless sensor networks consist of many tiny sensor nodes which are deployed in various geographical locations for sensing the normal spectacles and also to transmit the collected information to the base station which is also named destination node through multiple nodes present in the network. Most of the existing heuristics algorithms used for finding the optimal routes have limitations in the provision of effective solutions for routing and clustering mechanisms in larger search spaces. Hence, when the search space increases exponentially, the chance of creating the optimal solution for clustering and routing is decreasing and ultimately an un-optimized process depletes the sensor node resources. In order to address the challenges and limitations present in the existing routing systems, two new heuristics algorithms namely gravitational approach based clustering method and a clustered gravitational routing algorithm have been proposed in this paper for providing an optimal solution for efficient clustering and effective routing. Moreover, a fuzzy logic based deductive inference system has been designed and used in this work for selecting the most appropriate nodes as cluster head nodes from the nodes present in each cluster. The simulation results obtained from this work show that the clustering accuracy and the network lifetime are increased and the energy consumption as well as delay are reduced with the application of these proposed algorithms.
TL;DR: It is proposed that multiple parasitic patches are potential for cognitive radio applications where circular patch covers bandwidth of 85% with radiation pattern for Spectrum Utilization and CP with meander lines feeding behaves as communication antenna operating at Wireless Local Area Network 802.11y (3.637 GHz).
Abstract: A micro strip patch antenna with multiple parasitic patches for Cognitive Radio Network applications is presented to enhance the bandwidth. Multiple resonances are used for the design of antenna, with a view to broaden bandwidth. A modified Koch Fractal antenna is imprinted from micro strip radiating patch. A Parasitic Strip line helps to grasp micro hertz communication through antenna. A slotted patch energized by a gap feed was established before with a large angular coverage over a bandwidth of 13.1%. In this paper, it is proposed that multiple parasitic patches are potential for cognitive radio applications where circular patch (CP) covers bandwidth of 85% with radiation pattern for Spectrum Utilization (SU) and CP with meander lines feeding behaves as communication antenna operating at Wireless Local Area Network 802.11y (3.637 GHz). The transceiver in a communication network is powered by Proposed Antenna, to acquire improved energy efficiency of 95.7%. Thus, throughput and SU have been improved, a model of antenna has been fabricated and its radiation patterns, return losses were achieved which shows fine consistency with simulated results.
TL;DR: In this article, a compact size wideband circularly polarized (CP) 2-port multiple-input-multiple-output (MIMO) antenna is designed for the fifth generation (5G) region under the sub-6 GHz band.
Abstract: A compact size wideband circularly polarized (CP) 2-port multiple-input-multiple-output (MIMO) antenna is designed for the fifth generation (5G) region under the sub-6 GHz band The presented antenna has (≤ − 10 dB) impedance bandwidth of 900 MHz (33–42 GHz) and has 100% 3-dB axial ratio bandwidth The antenna covers the potential 5G band ranging from 33 to 38 GHz having left-handed circular polarization characteristics To validate performance attributes of the MIMO antenna designed various diversity parameters such as envelope correlation coefficient (ECC), total active reflection coefficient, and diversity gain are calculated The low envelope correlation coefficient (ECC < 010) and the isolation between the antenna elements greater than 15 dB justify the acceptance of the proposed design as a MIMO antenna The proposed design exhibits good agreement between the simulated and the measured results
TL;DR: Spectrum underlay finite element line feeding technique (SUFELF) is proposed to design MPA's are potential for cognitive radio applications (CRA), which can discover a lot of applications in designs for S band, efficient spectrum utilization in cognitive radio networks (CRN).
Abstract: A micro strip patch antenna (MPA) is fabricated to increase the bandwidth. The communication systems want antennas with high directivity, high signal strength and gain. In this paper, spectrum underlay finite element line feeding technique (SUFELF) is proposed to design MPA's are potential for cognitive radio applications (CRA). The proposed SUFELF is designed and simulated by using HFSS-14, simulation and calculated results of SUFELF for S-band is compared. The proposed SUFELF construction can discover a lot of applications in designs for S band, efficient spectrum utilization in cognitive radio networks (CRN). To improve gain, The MPA with circular patch (CP) was fabricated through SUFELF. This design can carry out a gain of 4.21 dBi, and percentage of impedance bandwidth is 85.2% at 3.546 GHz. A SUFELF model has made-up and calculated, the results have revealed a excellent concurrence by means of the simulations. To obtain efficiency of 95.9% the Proposed Antenna (PA) is powered. We conclude this work with a discussion on the expansion to the coexistence with different patch antennas.
TL;DR: An effort has been made towards automated disease detection from the plant leaves using Fuzzy Based Function Networkenabled with IoT, having the computational power of fuzzy logic and learning adaptability of neural network achieves higher accuracy for identification and classification of galls when compared with the other approaches.
Abstract: In recent years, the applications of the computer vision concepts and information communication technology has been observed in number of applications including home automation, healthcare, smart cities, precision agriculture etc. Internet of Things (IoT) is the underlying technology that indulges in almost all part of world infrastructure with the indispensable concept of connecting every device for collecting, contributing, experiencing, and analyzing the information. Smart or precision farming is known for achieving intelligence in agriculture. Therefore, in this article, an effort has been made towards automated disease detection from the plant leaves. For this a novel framework, a method named as IoT_FBFN using Fuzzy Based Function Network (FBFN) enabled with IoT has been proposed. At first, the images of leaf are acquired. Then these images are preprocessed and features are extracted using the Scale-invariant feature transform method. Finally, FBFN is used for the detection of the galls caused by the insect named as Pauropsyllatuberculate. The training process of the network is by optimizing with the help of Firefly algorithm, this increases the efficiency of the network. The proposed IoT_FBFN network having the computational power of fuzzy logic and learning adaptability of neural network achieves higher accuracy for identification and classification of galls when compared with the other approaches. The article concludes with the challenges encountered and future works.
TL;DR: A systematic review of QoS based on controller's problems is investigated, the current research is analyzed and the findings of the different controller’s performance based on QoS parameters e.g. reliability, scalability, consistency and load balancing are summarized.
Abstract: In modern life the internet has become backbone of digital society which is the packet switched distributed network and connected with almost every digital devices, available everywhere in the world. The traditional network carries few challenges like, dependency on vendors, difficulty to manage large network, dynamically changing of forwarding policies and more. To overcome such challenges, today the idea of Software Defined Networking (SDN) came into existence. The basic idea behind SDN is to implement programmable network by separately managing the control plane and data plane to improve the efficiency of network performance. The main problem with SDN is the Controller Placement Problem (CPP), which gives the overview of whole network. Today the main focus of the researchers is to solve the CPP. CPP is a NP-hard problem because the network should consist of minimum controllers and controllers should be placed on appropriate locations. For the large size network, controller deployment is difficult to manage. But the challenge in this area is Quality of Services (QoS) in respect of controller management. This paper investigates the systematic review of QoS based on controller’s problems, analyzed the current research and summarized the findings of the different controller’s performance based on QoS parameters e.g. reliability, scalability, consistency and load balancing. Finally, this paper also highlights the research challenges to improve the QoS in SDN.
TL;DR: A state-of-the-art review of IoT security and its challenges is provided, which overviews technical and legal solutions that are useful to private, organizational, and governmental enterprises and offers potential solutions to address the security challenges discussed.
Abstract: The Internet of Things (IoT) has emerged as a modern wave of Internet technologies that promises great transformation of life in areas such as smart health, smart cities, smart homes, intelligent transport, amongst others. However, security often serves as a critical reason for the widespread adoption of any innovation. While the IoT has increased business productivity and enriched diverse areas of life over the years, the world is yet to see a methodical revolution of its humongous application and transformation given its ubiquity and highly interconnected global network structure. The main culprit for such lapses is principally attributed to security and privacy issues which have been widely discussed in research articles and reviews but remain largely unaddressed in the literature. Hence, this paper provides a state-of-the-art review of IoT security and its challenges. It overviews technical and legal solutions that are useful to private, organizational, and governmental enterprises. The study encompasses the review and security analysis of IoT’s evolution and revolution, IoT security assessments, requirements, current research challenges in security and much more. Consequently, it offers potential solutions to address the security challenges discussed and further present open research issues, research gaps, opportunities, future development, and recommendations. This overview is intended to serve as a knowledgebase that will proffer novel foresight to guide users and administrators in positioning themselves and their organizations in a manner that is consistent with their overall objectives, mission, and vision for remarkable outcomes. Likewise, interested scholars and researchers can explore topics and directions from the study in providing better solutions to the numerous problems in IoT security.
TL;DR: In this paper, an ontology based model for preventing and detecting SQLIA using ontology (SQLIO) is proposed which implements Ontology Creation and prediction rule based vulnerabilities model, the proposed methodology provides prevents and detects SQLIA web vulnerability to a greater extent in cloud environment.
Abstract: Many modern day web applications deal with huge amount of secured and high impact data. As a result security plays a major role in web application development. The security of any web application focuses on data the application handles. The web application framework should prevent and detect web application vulnerabilities. Data will be stored in a database, so the OWASP categorized vulnerability SQL Injection Attacks (SQLIA) is the most critical vulnerability for a web application. An Ontology based model for preventing and detecting SQLIA using ontology (SQLIO) is proposed which implements Ontology Creation and prediction rule based vulnerabilities model. The proposed methodology provides prevents and detects SQLIA web vulnerability to a greater extent in cloud environment.
TL;DR: In this paper, an energy-aware cluster-based multi-hop routing algorithm is presented, in which the clusters would, if required, re-formed during the routing procedure.
Abstract: Wireless sensor networks (WSNs) consist of a large number of sensor nodes, which are primarily employed for collecting data from an environment of interest. Energy resources of WSN nodes are generally restricted, irreplaceable and non-rechargeable. Hence, lowering the level of energy consumption in such networks to save more energy is the key issue in the literature. Clustering, selecting the best Cluster Head (CH) among candidates, and performing the routing only among cluster heads would be an effective approach to reduce the WSN nodes energy consumption. Therefore, cluster-based routing leads to extending the network’s lifetime through aggregating data in CHs, uniformly distributing the energy among nodes, and, consequently, reducing the number of contributing nodes in the routing procedure. In this paper, an energy-aware cluster-based multi-hop routing algorithm is presented, in which the clusters would, if required, re-formed during the routing procedure. Furthermore, like other multi-hop routing algorithms, it guarantees minimizing the energy consumption through balancing energy within the network. In this paper, we have presented a cluster-based multi-hop routing algorithm. In our proposed approach, a combination of two algorithms, namely K-means and Open Source Development Model Algorithm (ODMA), are employed for clustering, and Genetic Algorithm, is applied for multi-hop routing. The simulation results confirm superiority of our proposed method in comparison with MH-FCM, EEWC, and GAFOR algorithms in terms of several metrics such as average residual energy, residual energy variance, number of packets received, number of dead nodes, and network lifetime.
TL;DR: A novel a feature selection-whale optimization algorithm-deep neural network (FS-WOA–DNN) method is proposed in this research article to mitigate DDoS attack in effective manner.
Abstract: In recent years, distributed denial of service (DDoS) attacks pose a serious threat to network security. How to detect and defend against DDoS attacks is currently a hot topic in both industry and academia. There have been a lot of methodologies and tools devised to detect DDoS attacks and reduce the damage they cause. Still, most of the methods cannot simultaneously achieve efficient detection with a small number of false alarms. In this case, deep learning techniques are appropriate and effective algorithm to categorize both normal and attacked information. Hence, a novel a feature selection-whale optimization algorithm-deep neural network (FS-WOA–DNN) method is proposed in this research article to mitigate DDoS attack in effective manner. Initially, pre-processing step is carried out for the input dataset where min–max normalization technique is applied to replace all the input in a specified range. Later on, that normalized information is fed into the proposed FS-WOA to select the optimal set of features for ease the classification process. Those selected features are subjected to deep neural network classifier to categorize normal and attacked data. Further to enhance the security of proposed model, the normal data are secure with the help of homomorphic encryption and are securely stored in the cloud. The proposed algorithm will be simulated using the MATLAB tool and tested experimentally that shows 95.35% accuracy in detecting DDoS attack.