A firefly-inspired scheme for energy-efficient transmission scheduling using a self-organizing method in a wireless sensor network

Various types of natural phenomena are regarded as primary sources of information for artificial occurrences that involve spontaneous synchronization. Among the artificial occurrences that mimic natural phenomena are Wireless Sensor Networks (WSNs) and the Pulse Coupled Oscillator (PCO), which utilizes firefly synchronization for attracting mating partners. However, the PCO model was not appropriate for wireless sensor networks because sensor nodes are typically not capable to collect sensor data packets during transmission (because of packet collision and deafness). To avert these limitations, this study proposed a self-organizing time synchronization algorithm that was adapted from the traditional PCO model of fireflies flashing synchronization. Energy consumption and transmission delay will be reduced by using this method. Using the proposed model, a simulation exercise was performed and a significant improvement in energy efficiency was observed, as reflected by an improved transmission scheduling and a coordinated duty cycling and data gathering ratio. Therefore, the energy-efficient data gathering is enhanced in the proposed model than in the original PCO-based wave-traveling model. The battery lifetime of the Sensor Nodes (SNs) was also extended by using the proposed model

Random traveling wave pulse coupled oscillator (RTWPCO) algorithm of energy-efficient wireless sensor networks

Energy-efficient pulse-coupled oscillators have recently gained significant research attention in wireless sensor networks, where the wireless sensor network applications mimic the firefly synchronization for attracting mating partners. As a result, it is more suitable and harder to identify demands in all applications. The pulse-coupled oscillator mechanism causing delay and uncharitable applications needs to reduce energy consumption to the smallest level. To avert this problem, this study proposes a new mechanism called random traveling wave pulse-coupled oscillator algorithm, which is a self-organizing technique for energy-efficient wireless sensor networks using the phase-locking traveling wave pulse-coupled oscillator and random method on anti-phase of the pulse-coupled oscillator model. This technique proposed in order to minimize the high power utilization in the network to get better data gathering of the sensor nodes during data transmission. The simulation results shown that the proposed random traveling wave pulse-coupled oscillator mechanism achieved up to 48% and 55% reduction in energy usage when increase the number of sensor nodes as well as the packet size of the transmitted data compared to traveling wave pulse-coupled oscillator and pulse-coupled oscillator methods. In addition, the mechanism improves the data gathering ratio by up to 70% and 68%, respectively. This is due to the developed technique helps to reduce the high consumed energy in the sensor network and increases the data collection throughout the transmission states in wireless sensor networks

Impact of the deafness problem on clock synchronization in a wireless sensor network

Observations of natural phenomena are considered to be the best information source of spontaneous synchronization. Natural phenomena tend to match wireless sensor network (WSN) responses closely. Such synchronization is vital for the proper coordination of power cycles for energy conservation. A large number of fireflies employ the principle of pulse-coupled oscillators for light flash emission to attract mating partners. With respect to WSNs, the nodes are generally unable to afford packet transmission and reception simultaneously, thus preventing complete network synchronization. This paper presents a literature overview concerning the impact of the deafness problem on clock synchronization in a WSN. Data transmission based on synchronization can also be ensured through the optimization of energy usage periodic data capturing in a WSN. This study serves as a useful information source of clock synchronization to assist WSN researchers and novices in obtaining a better understanding of the impact of the deafness problem on clock synchronization and to enable them to promote effective designs and systems that address this problem

Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning.

Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients' chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Firefly-inspired time synchronization mechanism for self-organizing energy efficient wireless sensor networks

One major issue faced by Wireless Sensor Network (WSN), which is based on pulsecoupled oscillators (PCOs) is the energy consumption and loss of data due to the deafness, high packet collision and high power in the application. Therefore, to overcome this problem this research proposes a technique for the efficient minimization of energy usage among WSNs, particularly during transmission scheduling (sender state) for time synchronization in WSNs. Specifically, the current work focuses on three decentralized methods of energy efficiency with scalability and robustness. Among the mechanisms used is the traveling wave pulse coupled oscillator (TWPCO), which is a self-organizing technique for energy efficient WSNs by adopting a traveling wave phenomenon based on phase locking of the PCO model regarding sensor nodes as observed in the flashing synchronization behaviors of fireflies and secretion of radio signals as firing to counteract deafness. The second mechanism is a self-organizing energy efficiency pulse coupled oscillator (EEPCO) mechanism for WSNs, which combines both the biologically inspired and non-biologically inspired network systems to counteract packet collision. The third proposed mechanism is the random traveling wave pulse coupled oscillator (RTWPCO), which reduces high-power to the smallest level by using phase-locking travelling wave in biologically inspired of the PCO model and random method based on anti-phase in non-biologically inspired of the PCO model. The performances of the proposed algorithms were studied using a simulation analysis. The results showed significant improvement in terms of reaching the steady state after a certain number of cycles, obtaining superior data gathering ratio, and reducing the energy consumption ratio of sensor nodes. Specifically, the TWPCO mechanism showed superior performance compared to other mechanisms with a deduction on the total energy consumption by 25 %, while improving the performance by 13 % in terms of data gathering. On the other hand, the EEPCO mechanism improved data collection by up to 100% when the number of sensor nodes is below 40. In such a scenario, the energy efficiency also improved by up to 15%. Finally, the proposed RTWPCO mechanism achieved up to 53% and 60% reduction in the energy usage mainly due to the increase in the number of sensor nodes as well as the increase in the data packet size of the transmitted data. In addition, the mechanism improved the data gathering ratio by up to 75% and 73% respectively. These mechanisms help to avoid deafness that occurs in the transmit state in WSNs, to counteract packet collision during transmission in WSNs and minimize the high-power utilization in the network and as well increase the data collection throughout the transmission states in WSNs

Travelling Wave Pulse Coupled Oscillator (TWPCO) using a self-organizing scheme for energy-efficient wireless sensor networks

Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, this paper proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25%. The results showed that the performance improved by 13% in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs

Self-organizing method for energy-efficient pulse coupled oscillator (EEPCO) in wireless networks

Energy-efficient pulse coupled oscillators (PCOs) have recently gained significant research attention in wireless sensor network (WSN) synchronization and PCO, which uses firefly synchronization for attracting mating partners. However, the PCO model is unsuitable for sensor networks because WSNs are unable to afford simultaneous transmission and data reception. For most scenarios, battery replacement is impossible upon the exhaustion of a node’s battery energy method (because of packet collision). To avert these limitations, this study proposes an energy-efficient pulse coupled oscillator (EEPCO), a new mechanism that uses the self-organizing method in WSN by combining biologically inspired network systems and non-biologically inspired network systems. The former systems employ phase-locking of the PCO model regarding sensor nodes as observed in the flashing synchronization behavior of fireflies. The latter systems utilize the anti-phase of the PCO model to counteract packet collision, obtain improved data gathering, and minimize the energy needs of the sensor nodes during transmission. From the simulation, it was found that the proposed EEPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms with a deduction on the total energy consumption by 15% . The results showed that the performance improved data collection by up to 100% when the number of sensor nodes is below 40. Based on the results, the proposed scheme avoids packet collision that occurs in the transmit state in WSNs and it increases the data collection throughout the transmission states in WSNs

TWPCO

Data of my result

Lattice-Based Lightweight Quantum Resistant Scheme in 5G-Enabled Vehicular Networks

Both security and privacy are central issues and need to be properly handled because communications are shared among vehicles in open channel environments of 5G-enabled vehicular networks. Several researchers have proposed authentication schemes to address these issues. Nevertheless, these schemes are not only vulnerable to quantum attacks but also use heavy operations to generate and verify signatures of messages. Additionally, these schemes need an expensive component RoadSide Unit (RSU)-aided scheme during the joining phase. To address these issues, we propose a lightweight quantum-resistant scheme according to the lattice method in 5G-enabled vehicular networks. Our proposal uses matrix multiplication instead of operations-based bilinear pair cryptography or operations-based elliptic curve cryptography to generate and verify signatures of messages shared among vehicles. Our proposal satisfies a significant reduction in performance, which makes it lightweight enough to handle quantum attacks. Our proposal is based on 5G technology without using any RSU-aided scheme. Security analysis showed that our proposal satisfies privacy and security properties as well as resists quantum attacks. Finally, our proposal also shows favorable performance compared to other related work