Classical cryptography is the process of hiding information and it manages the secret knowledge by encrypting the plain text message through the translation of it to an unintelligible message. ...Quantum cryptography is also hiding the plain text through encryption and it works based on the law of quantum physics for providing absolute security of data communication. It uses the idea of quantum mechanics to gadget a cryptographic system and the key problems are solved by intrusion detection including the Eavesdropping detection using quantum expertise. On the contrary, principles of secured communication protocol systems are demonstrated with classical quantum cryptography where the keys are distributed securely and are applied in quantum key distribution as well. In this paper, we provide a survey of works on classical cryptography and quantum cryptography and compare them with respect to time, security level and the classification of the data. Moreover, we perform a concise analysis of perspective classical cryptography and the conception of Quantum cryptography with various protocols and highlight the benefits of classical cryptography and Quantum cryptography in different applications. Finally, we provide a set of recommendations for selecting the suitable encryption model for securing the communication.
A paper-based device (PBD) for the detection of chlorpyrifos pesticide at field application was fabricated based on the principles of enzyme inhibition and image processing. Rhizopus niveus lipase, ...p-nitrophenol palmitate and Whatman No.1 paper were used as an enzyme, substrate and support matrix, respectively. The performance of functionalized PBD was tested for lateral flow assay reaction in pure water (negative control), artificial pesticide water (positive control) and selected fruits and vegetables wash water (test). The digital image of the PBD after the test was captured using an android smartphone and analyzed in MATLAB software. Different colour space models such as, grey, RGB, HSV and YCbCr were studied and the Cb coordinate was chosen for its higher linearity (R2 = 0.988) with pesticide concentration. Experimental variations such as paper length, relative concentration ratio of the substrate and enzyme were investigated to minimize the product cost and analysis time. The developed PBD showed a significant response over wide range of sample solution's pH and operational temperature. Further, a long-term storage stability was measured for developed PBD. The LOD and LOQ were found to be 0.065 mgL−1 and 0.198 mgL−1. The results obtained from newly developed image processing method showed 92.8% accuracy with microtiter plate assay. Higher MRL was determined in the wash water of cauliflower, grapes, coriander leaves, brinjal and bitter guard. Overall, the developed paper biosensor was precise, cost effective and most suitable for field applications.
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•Lipase embedded paper based device was fabricated to detect chlorpyrifos pesticide in water samples.•An image processing technique was implemented for the first time to monitor chlorpyrifos.•Presence of pesticide in selected fruits and vegetable wash water was tested as real time applications.•The proposed method showed least error percentage with the conventional microtiter plate assay method.•The functionalized paper based device showed very good storage stability and sensitivity.
Underwater Wireless Sensor Networks (UWSNs) are the type of WSNs that transmit the data through water medium and monitor the oceanic conditions, water contents, under-sea habitations, underwater ...beings and military objects. Unlike air medium, water channel creates stronger communication barriers. In addition, the malicious data injection and other network attacks create security problems during data communication. Protecting the vulnerable UWSN channel is not an easy task under critical water conditions. Many research works proposed in the literature used cryptography principles and intelligent intrusion detection systems to secure the network activities from malicious nodes. However, the need for Machine Learning (ML) and Deep Learning (DL) associated Medium Access Control (MAC) principles is expected for handling the barriers in uncertain UWSN. In this regard, this article proposes a new Intrusion detection system with Integrated Secure MAC principles and Long Short-Term Memory (LSTM) architectures for organizing real-time neighbor monitoring tasks. The proposed system implements Generative Adversarial Network (GAN) driven UWSN channel assessment models and Secure LSTM-MAC principles to protect the data communication. In this regard, the proposed model creates the Intrusion Detection System (IDS) using trained distributed agents. These agents run in each legitimate sensor node contain novel LSTM-MAC engine, intrusion dataset, rule-based monitoring techniques, Secure Hashing Algorithm-3 (SHA-3), Two Fish algorithm and packet filtering tools. The proposed LSTM and agent-based model drives adaptive MAC channel operations to avoid malicious traffics in to legitimate nodes. In addition, this work implements neighbor-based packet monitoring, signal jamming and alert messaging procedures to build reliable security services against different types of attacks. The experiments and the observations reveal the performance of proposed techniques is proved to be 5% to 10% higher than existing techniques in various aspects measured with different metrics.
In this paper, an accurate and efficient Chebyshev wavelet-based technique is successfully employed to solve the nonlinear oscillation problems. Numerical examples are also provided to illustrate the ...efficiency and performance of these methods. Homotopy perturbation methods may be viewed as an extension and generalization of the existing methods for solving nonlinear equations. In addition, the use of Chebyshev wavelet is found to be simple, flexible, accurate, efficient and less computational cost. Our analytical results are compared with simulation results and found to be satisfactory.
A smart city is a phenomenon that combines information technology with physical and social infrastructure to regulate a city’s cooperative intelligence. Wireless sensor networks (WSN) are the ...fundamental technology that smart cities use to administer and sustain their service offerings. To decrease the network’s energy consumption, clustering and multihop routing algorithms have been suggested, verified, and put into practice in the literature. This inspiration led to the development of the “energy-aware clustered route approach” in the current study, which is suggested for WSNs in smart cities. The presented method focuses on choosing the right cluster heads (CHs) and the best pathways in a WSN. The presented model includes a fitness value-based clustering scheme for efficient CH selection to achieve this. The Deep Neural Network (DNN) algorithm is then used to carry out the routing operation. The suggested approach technique calculates a fitness function (FF) that consists of three variables, including node degree, base station distance, and residual energy. This fitness function aids in the WSN’s best route selection. Simulations were run to verify the presented model’s superiority in terms of network lifespan and energy efficiency, and the results demonstrated the model’s outstanding performance.
The consistency and duration of the menstrual cycle exhibit significant associations with specific psychiatric conditions throughout an individual’s lifespan. The proposed methodology surveys the ...relationship between psychiatric disorders and the length or regularity of the menstrual cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network learning. A decentralized secure learning procedure is implemented to ensure data privacy and security. The transfer learning techniques helps to enhance the ability to learn from diverse data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the learning process. This approach presents a practical and effective solution for IoMT network learning, allowing each participant to contribute their individual features to collectively extract valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required information.
Samarium (Sm)-based perovskites exhibit considerable potential as electrode materials for supercapacitors. Our primary objective here was to develop and fabricate a robust energy storage electrode ...material by decorating SmCoO
3
onto a MWCNT nanostructure. Here, we devised an ultrasonic-assisted hydrothermal strategy to fabricate MWCNT–SmCoO
3
and detailed characterization was conducted on the crystal structure, powder morphology and electrochemical performance of the composite. The SmCoO
3
exhibited a cubic structure without any detectable impurities. Morphological analysis revealed an interconnected matrix nanostructure of SmCoO
3
/MWCNT, forming agglomerates of hollow nanoflakes with a rough surface, alongside microstructures with average sizes ranging from 30 to 40 nm. Cyclic voltammetry results demonstrated the excellent capacitive behavior of the SmCoO
3
/MWCNT hybrid, exhibiting a specific capacitance of 1542 F/g at a current density of 1 A/g. Moreover, the hybrid showed a remarkable 95% retention of specific capacitance after 10,000 continuous charge–discharge cycles. The asymmetric supercapacitor, utilizing activated carbon (AC) as the negative electrode, exhibits remarkable performance across a wide voltage range of 1.8 V. It achieves a maximum energy density of 49.1 Wh kg
−1
at 875 W kg
−1
and maintains a high energy density of 28.75 Wh kg
−1
even at an ultra-high-power density of 2105 W kg
−1
, highlighting the significant advancements demonstrated in this study. This research introduces a facile approach for developing high-performance electrode materials customized for supercapacitor applications, leveraging synergistic effects for enhanced energy storage capabilities. The synergistic interaction between SmCoO
3
and MWCNTs results in improved charge transfer kinetics and enhanced ion accessibility, leading to superior electrochemical performance and prolonged cycle life in energy storage applications.
Security is an important phenomena for energy conservation in wireless sensor networks (WSN). Moreover, the management of trust in the WSN is a challenging task since trust is used when collaboration ...is critical to achieve reliable communication. In a military application using WSN, it is often necessary to communicate secret information such as military operation urgently. However, the existing routing algorithms do not consider security in the routing process. Moreover, since security is an important aspect in WSN, it is necessary to consider the security aspects in routing algorithms. Different approaches for providing security are trust management, intrusion detection, firewalls and key management are considered in the literature. Among them, trust management can provide enhanced security when it is compared with other security methods. Therefore, a new secure routing algorithm called energy aware trust based secure routing algorithm is proposed in this paper where the trust score evaluation is used to detect the malicious users effectively in WSN and spatio-temporal constraints are used with decision tree algorithm for selecting the best route. From the experiments conducted, it is proved that the proposed trust based routing algorithm achieves significant performance improvement over the existing schemes in terms of security, energy efficiency and packet delivery ratio.
We present results from the direct search for dark matter with the XENON100 detector, installed underground at the Laboratori Nazionali del Gran Sasso of INFN, Italy. XENON100 is a two-phase ...time-projection chamber with a 62 kg liquid xenon target. Interaction vertex reconstruction in three dimensions with millimeter precision allows the selection of only the innermost 48 kg as the ultralow background fiducial target. In 100.9 live days of data, acquired between January and June 2010, no evidence for dark matter is found. Three candidate events were observed in the signal region with an expected background of (1.8 ± 0.6) events. This leads to the most stringent limit on dark matter interactions today, excluding spin-independent elastic weakly interacting massive particle (WIMP) nucleon scattering cross sections above 7.0 × 10(-45) cm(2) for a WIMP mass of 50 GeV/c(2) at 90% confidence level.
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