Many receiver-based Preamble Sampling Medium Access Control (PS-MAC) protocols have been proposed to provide better performance for variable traffic in a wireless sensor network (WSN). However, most of these protocols cannot prevent the occurrence of incorrect traffic convergence that causes the receiver node to wake-up more frequently than the transmitter node. In this research, a new protocol is proposed to prevent the problem mentioned above. The proposed mechanism has four components, and they are Initial control frame message, traffic estimation function, control frame message, and adaptive function. The initial control frame message is used to initiate the message transmission by the receiver node. The traffic estimation function is proposed to reduce the wake-up frequency of the receiver node by using the proposed traffic status register (TSR), idle listening times (ILTn, ILTk), and "number of wake-up without receiving beacon message" (NWwbm). The control frame message aims to supply the essential information to the receiver node to get the next wake-up-interval (WUI) time for the transmitter node using the proposed adaptive function. The proposed adaptive function is used by the receiver node to calculate the next WUI time of each of the transmitter nodes. Several simulations are conducted based on the benchmark protocols. The outcome of the simulation indicates that the proposed mechanism can prevent the incorrect traffic convergence problem that causes frequent wake-up of the receiver node compared to the transmitter node. Moreover, the simulation results also indicate that the proposed mechanism could reduce energy consumption, produce minor latency, improve the throughput, and produce higher packet delivery ratio compared to other related works.
This paper presents the design and development of a planar Aligned-Gap and Centered-Gap Rectangular Multiple Split Ring Resonator (SRR) for microwave sensors that operates at a resonance frequency around 5 GHz. The sensor consists of a microstrip transmission line loaded with two elements of rectangular SRR on both sides. The proposed metamaterial sensors were designed and fabricated on Rogers RT5880 substrate having dielectric constant of 2.2 and thickness of 0.787 mm. The final dimension of the proposed sensor was measured at 35 × 14 mm2. Measured results show good agreement with simulated ones as well as exhibiting high Q-factor for use in sensing application. A remarkably shift of resonance frequency is observed upon introduction of several sample with different dielectric value.
A tri-enzyme system consisting of choline kinase/choline oxidase/horseradish peroxidase was used in the rapid and specific determination of the biomarker for bacterial sepsis infection, secretory phospholipase Group 2-IIA (sPLA2-IIA). These enzymes were individually immobilized onto the acrylic microspheres via succinimide groups for the preparation of an electrochemical biosensor. The reaction of sPLA2-IIA with its substrate initiated a cascading enzymatic reaction in the tri-enzyme system that led to the final production of hydrogen peroxide, which presence was indicated by the redox characteristics of potassium ferricyanide, K₃Fe(CN)₆. An amperometric biosensor based on enzyme conjugated acrylic microspheres and gold nanoparticles composite coated onto a carbon-paste screen printed electrode (SPE) was fabricated and the current measurement was performed at a low potential of 0.20 V. This enzymatic biosensor gave a linear range 0.01-100 ng/mL (R² = 0.98304) with a detection limit recorded at 5 × 10-3 ng/mL towards sPLA2-IIA. Moreover, the biosensor showed good reproducibility (relative standard deviation (RSD) of 3.04% (n = 5). The biosensor response was reliable up to 25 days of storage at 4 °C. Analysis of human serum samples for sPLA2-IIA indicated that the biosensor has potential for rapid bacterial sepsis diagnosis in hospital emergency department.
Timely detection and diagnosis are essentially needed to guide outbreak measures and infection control. It is vital to improve healthcare quality in public places, markets, schools and airports and provide useful insights into the technological environment and help researchers acknowledge the choices and gaps available in this field. In this narrative review, the detection of coronavirus disease 2019 (COVID-19) technologies is summarized and discussed with a comparison between them from several aspects to arrive at an accurate decision on the feasibility of applying the best of these techniques in the biosensors that operate using laser detection technology. The collection of data in this analysis was done by using six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar and PubMed. This review includes an analysis review of three highlights: evaluating the hazard of pandemic COVID-19 transmission styles and comparing them with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) to identify the main causes of the virus spreading, a critical analysis to diagnose coronavirus disease 2019 (COVID-19) based on artificial intelligence using CT scans and CXR images and types of biosensors. Finally, we select the best methods that can potentially stop the propagation of the coronavirus pandemic.
Recent advances in nanotechnology have revealed the superiority of nanocarbon species such as carbon nanotubes over other conventional materials for gas sensing applications. In this work, analytical modeling of the semiconducting zigzag carbon nanotube field-effect transistor (ZCNT-FET) based sensor for the detection of gas molecules is demonstrated. We propose new analytical models to strongly simulate and investigate the physical and electrical behavior of the ZCNT sensor in the presence of various gas molecules (CO2, H2O, and CH4). Therefore, we start with the modeling of the energy band structure by acquiring the new energy dispersion relation for the ZCNT and introducing the gas adsorption effects to the band structure model. Then, the electrical conductance of the ZCNT is modeled and formulated while the gas adsorption effect is considered in the conductance model. The band structure analysis indicates that, the semiconducting ZCNT experiences band gap variation after the adsorption of the gases. Furthermore, the bandgap variation influences the conductance of the ZCNT and the results exhibit increments of the ZCNT conductance in the presence of target gases while the minimum conductance shifted upward around the neutrality point. Besides, the I-V characteristics of the sensor are extracted from the conductance model and its variations after adsorption of different gas molecules are monitored and investigated. To verify the accuracy of the proposed models, the conductance model is compared with previous experimental and modeling data and a good consensus is observed. It can be concluded that the proposed analytical models can successfully be applied to predict sensor behavior against different gas molecules.
This paper proposes a novel hybrid arithmetic-trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.
Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
The use of the eddy current technique (ECT) for the non-destructive testing of conducting materials has become increasingly important in the past few years. The use of the non-destructive ECT plays a key role in the ensuring the safety and integrity of the large industrial structures such as oil and gas pipelines. This paper introduce a novel ECT probe design integrated with the distributed ECT inspection system (DSECT) use for crack inspection on inner ferromagnetic pipes. The system consists of an array of giant magneto-resistive (GMR) sensors, a pneumatic system, a rotating magnetic field excitation source and a host PC acting as the data analysis center. Probe design parameters, namely probe diameter, an excitation coil and the number of GMR sensors in the array sensor is optimized using numerical optimization based on the desirability approach. The main benefits of DSECT can be seen in terms of its modularity and flexibility for the use of different types of magnetic transducers/sensors, and signals of a different nature with either digital or analog outputs, making it suited for the ECT probe design using an array of GMR magnetic sensors. A real-time application of the DSECT distributed system for ECT inspection can be exploited for the inspection of 70 mm carbon steel pipe. In order to predict the axial and circumference defect detection, a mathematical model is developed based on the technique known as response surface methodology (RSM). The inspection results of a carbon steel pipe sample with artificial defects indicate that the system design is highly efficient.
Embedded encryption devices and smart sensors are vulnerable to physical attacks. Due to the continuous shrinking of chip size, laser injection, particle radiation and electromagnetic transient injection are possible methods that introduce transient multiple faults. In the fault analysis stage, the adversary is unclear about the actual number of faults injected. Typically, the single-nibble fault analysis encounters difficulties. Therefore, in this paper, we propose novel ciphertext-only impossible differentials that can analyze the number of random faults to six nibbles. We use the impossible differentials to exclude the secret key that definitely does not exist, and then gradually obtain the unique secret key through inverse difference equations. Using software simulation, we conducted 32,000 random multiple fault attacks on Midori. The experiments were carried out to verify the theoretical model of multiple fault attacks. We obtain the relationship between fault injection and information content. To reduce the number of fault attacks, we further optimized the fault attack method. The secret key can be obtained at least 11 times. The proposed ciphertext-only impossible differential analysis provides an effective method for random multiple faults analysis, which would be helpful for improving the security of block ciphers.
Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.
Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED).
A toxicity electrochemical DNA biosensor has been constructed for the detection of carcinogens using 24 base guanine DNA rich single stranded DNA, and methylene blue (MB) as the electroactive indicator. This amine terminated ssDNA was immobilized onto silica nanospheres and deposited on gold nanoparticle modified carbon-paste screen printed electrodes (SPEs). The modified SPE was initially exposed to a carcinogen, followed by immersion in methylene blue for an optimized duration. The biosensor response was measured using differential pulse voltammetry. The performance of the biosensor was identified on several anti-cancer compounds. The toxicity DNA biosensor demonstrated a linear response range to the cadmium chloride from 0.0005 ppm to 0.01 ppm (R2 = 0.928) with a limit of detection at 0.0004 ppm. The biosensor also exhibited its versatility to screen the carcinogenicity of potential anti-cancer compounds.
Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network's lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network's lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.
It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.
"Internet of Things (IoT)" has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g. human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.
This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage.
Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.
The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.
Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user's emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.
The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor's server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio.