mohammadali pourmina

Cloud computing is a computing model that uses three instance, on-demand, reserved, and spot, to provide resources to users. The price of spot instances is on average lower than other patterns and fluctuates based on supply and demand. When a user requests a spot instance, they must provide an offer. Only if the price offered by the user is higher than the spot price, the user can use this type of resources. Therefore, predicting the price of spot instances is very important and challenging. Forecasting such dynamic time series that follow the nonlinear model requires intelligent tools such as neural networks to be able to predict the future values with the least error by observing the values of a time series. Therefore, the reliability and as a result the quality of the service is improved. For this purpose, we considered Amazon EC2 as an experimental platform and used the spot price history to predict the future price by building a new model based on deep learning. The obtained results showed that the model presented in the article based on the proposed structure of MGRU(modified GRU) can well predict nonlinear values and perform better than other methods used in this field.

In this article, a smart network is proposed to monitor a patient with breast cancer. Increasing the speed of the patient monitoring network depends on our observation, thinking and understanding of the patient. Various factors of the network make us more aware of the disease. has it. The cycle of controlling and monitoring the patient includes observation, orientation, decision and action, this cycle is a set of consecutive actions, which changes with the change of network arrangement, orientation, decision and action. In this article, we design the structure of the smart network according to the weight of the nodes, the communication paths in such a way that the cycle of control and monitoring (analysis, decision and action) performs well. We model the control and monitoring cycle with the proposed method of fuzzy hierarchical analysis process (FAHP). This model adjusts the decision-making criteria by recognizing the identification priorities in such a way that the speed of network detection increases with the least time. It is also a suitable tool for modeling the monitoring cycle and the results of the smart network. The simulation results show that this network has the necessary intelligence to assess the patient's condition in adverse conditions, has the power to continuously analyze and evaluate the patient, and also has the power to make timely decisions in different situations. These results show that according to the network arrangement and fuzzy hierarchical analysis, the speed rate of the network has improved by 10% compared to other networks.

Sensor nodes are prone to failure due to the various applications of operating environments. This paper presents a new architecture with a Markov model to improve reliability. In the modification of the previous ideas, due to work fatigue, energy consumption and high maintenance costs, the idea of increasing the repair and replacement rate to avoid failure with the availability of replacement nodes with detailed planning of the support unit.The advantages of this method are reducing the failure rate, increasing reliability, fast implementation and deployment, energy efficiency and economic savings, improving the performance and useful life of the network, reducing delay and system rejuvenation and dynamics.The structure of the proposed method is by using the sleep and wake mode of the hot or cold standby node in such a way that the spare node is placed parallel to the main node and if one or both nodes are damaged, the system is reversible and. the damage can be repaired or replaced. The technique of idle time and the availability of the backup spare sensor play an important role in reducing energy consumption. The experts of the support unit perform the configuration settings so that the equipment goes to sleep or standby mode during idle time. And if one or two nodes are damaged, first the healthy spare node is awake and in service, and then the damaged node is repaired and replaced and placed in standby or sleep mode. The results of the innovation, we can mention the emphasis on node health monitoring, failure avoidance, improving repair and replacement rates and efficiency. The simulation results show a better improvement compared to the previous models.

In this paper, a new method for estimating the tumor's location is presented; the technique is based on the arrangement of sensors and received wave changes. A new method has been developed for locating the return signal from the tumor. The main idea is that the location of the tumor is randomly assumed. And then estimates the location of the tumor. It presents a phase event. Various articles have used a combination of these methods to shape different issues. However, these methods have not been used to estimate tumor location. In this article, two types of wavelengths are used; the transmitter node first sends a signal with a specific frequency, it is assumed that the reflection is almost complete. Therefore, according to the reviews of the tumor signal, we find that the tumor is in the region of intensification, i.e., the dimensions of the tumor are in the order of the transmitted wavelength. Then, by changing the frequency, the signal passes through the tumor. The signal passing through the tumor (according to "Snell's" law) fails; that is, the received signal is received with angular deviation and delay. An estimator is suggested by the maximum likelihood maximum (MLE). The estimators have a nonlinear form. We use the SD reduction slope algorithm to LS linear search algorithm for optimizations.

Industrial and physical site information is sent to the monitoring center by sensors in wireless sensor networks so that they can easily control the process of a company in order to improve the optimal performance of the system until the failure occurs to monitor and control in wireless sensor networks. Sensors are exposed to a wide range of failures, possible hardware and software problems in normal conditions, extreme weather conditions or other conditions caused by harsh physical environment in the field of sensors. Therefore, there is a possibility of unpredictable failure for all types of sensors and with Industrial process monitoring, preventive status monitoring, prevented error and fault and failures. The focus of this article is to present a new architecture in improving the correct performance of the system, the replacement rate of more damaged nodes and timely replacement, at the time of the starting point of the failure, the main sensor with spare ones or healthy sensors with faulty ones. The proposed network structure is such that the spare node is placed in parallel with the main node; this method makes it possible for the spare node to be replaced in case of failure of the main node, and the failed node can be quickly repaired and put in a standby mode. Our proposed model is analyzed in terms of the average time of correct system operation until failure known as mean time to failure. In this article is presented and studied and evaluated, a new architecture to improve network performance against failure using Markov model and state probability, and mean failure rate for node fault tolerance, before failure with timely replacement in wireless sensor network. In the proposed architecture, the results show a better improvement of the system's correct performance in order to reduce the adverse effects of errors and failures and improve fault tolerance. The simulation results show that the advantage of using this method reduces the adverse effects of errors and failures and improves the optimal performance of the system in the industrial site.

One of the main reasons of death in the world, mostly affecting seniors, is brain stroke. Almost 85% of all brain strokes are ischemic due to internal bleeding in a part of the brain. Due the high mortality rate, quick diagnosic and treatment of ischemic and hemorrhagic strokes are of utmost importance. In this paper, to realize microwave brain imaging system, a circular array-based of modified bowtie antennas located around the multilayer head phantom with a spherical target with radius of 1 cm as intracranial hemorrhage target aresimulated in CST simulator. To obtain satisfied radiation characteristics in the desired band (from 0.5-5 GHz) an appropriate matching medium is designed. First, in the processing section, a confocal image-reconstructing method based using delay and sum (DAS) and delay, multiply and sum (DMAS) beam-forming algorithms is used. The reconstructed images generated shows the usefulness of the proposed confocal method in detecting the spherical target in the range of 1 cm. The main purpose of this paper is stroke classification using deep learning approaches. For this, an image classification algorithm is developed to estimate the stroke type from reconstructed images. By using the proposed deep learning method, the reconstructed images are classified into different categories of cerebrovascular diseases using a multiclass linear support vector machine (SVM) trained with convol uti onal neural networks (CNN) features extracted from the images. The simulated results show the suitability of the proposed image reconstruction method for precisely localizing bleeding targets, with 89% accuracy in 9 seconds. In addition, the proposed deep-learning approach shows good performance in terms of classification, since the system does not confuse between different classes.

Immediate monitoring of the patient has always been very important. Achieving this knowledge, which can be integrated to monitor damaged tissue, is very important. In previous methods, the tissue was monitored using a sensor. In this article, not only is a tissue monitored using a sensor, but also the monitoring and evaluation of the effect of other tissues on tumor tissue is evaluated. The smart grid discussed in this article is designed to monitor the condition of a patient with a breast tumor. The structure of the smart grid, given the weight of the communication paths between the nodes and the ability of the nodes, shows us a strong network to assess the patient's condition. As the patient's condition changes, the nodes and weights of the communication pathways change, indicating that there is important information in the network and helping specialists to better assess the condition of the disease. Network monitoring is such that the evaluator node continuously evaluates the tumor node, by changing the status of the tumor node, the status of other nodes and communication paths between them changes, the result of changes in the network by the node The evaluator is evaluated. The simulation results show that this network has the necessary intelligence to assess the patient's condition in adverse conditions.

Given the importance of reducing data latency in discrete wireless networks in critical situations, we present the combined routing protocol with a storage and forwarding approach in Throw-Box-based network topology concerning aspects such as proper relay prediction and effective buffer management. To reduce the data transfer time in the relay node selection criteria, we consider the effect of different factors: node records, end-to-end latency, the nodes' available buffer space, and information such as average speed and node movement direction. We also use artificial intelligence to perform optimal routing using the Simulated Annealing algorithm. Important common performance criteria such as average latency, delivery ratio, number of lost messages, and network overhead were used to evaluate the performance of the proposed model. The results showed that our proposed routing method has less reception delay than other routing methods and maintains maximum transmission.

In this article, a new method is proposed to find tumor's location. This process is based on the arrangement of sensors; the phase and distance to the cancer are given as well. Extraction of tumor distance and phase by Snell's law is the number of received pulses and the delay of receiving Signal. The transmitter antenna is in a fixed position, and the receiver rotates at a certain angular velocity around the tissue. Considering this information, a package solution in polar coordinates is presented. Meanwhile, angle and range information is extracted. Then the maximum probability estimate of the target location is given. This paper applies experimentally to simulated random data.

Image rectification is an essential part in most multi-view systems. It simplifies the stereo matching process by limiting the search path to horizontal and parallel lines. The main problem in conventional image rectification is complexity and mass computation load for calculation of calibration matrix, projection matrix and fundamental matrix. In this paper an efficient and confident method is presented to rectify stereo images based on the correspondence field (CF) that avoids the mentioned matrices. In general this algorithm is able to generate an image for any new camera position. Especially one can choose the new position in such a way that the virtual camera generates a rectified image. Both real and simulated stereo images are used to evaluate the proposed algorithm. SIFT algorithm is used to estimate how close is the generated image to original rectified one. Simulation results show the mean absolute error and standard deviation are about 0.25 and 0.26 pixels respectively and rectified image is independent of focal ratio.

The conventional Tracking-Learning-Detection (TLD) algorithm is sensitive to illumination change and clutter and low frame rate and results in drift even missing. To overcome these shortcomings and increase robustness, by improving the TLD structure via integrating mean-shift and co-training learning can be achieved better results undergo low frame rate (LFR) condition and the robustness and accuracy tracking of the TLD structure increases. Because of, the Mean-Shift tracking algorithm is robust to rotation, partial occlusion and scale changing and it is simple to implement and takes less computational time. On the other, the co-training learning algorithm with two independent classifiers can learn changes of the target features in during the online tracking process. Therefore, the extended structure can solve the problem of lost object tracking in LFR videos and other challenges simultaneously. Finally, comparative evaluations of the proposed method to other top state-of-the-art tracking algorithms under the various scenarios from the TB-100 known dataset, demonstrate the superior performance of the proposed algorithm compared to other tracking algorithms in terms of tracking robustness and stability performance. Finally, the proposed structure based on the TLD architecture, in scenarios with the various challenges mentioned, will improve on average about 33% of the results, compared to the traditional TLD algorithm.

With the expansion of the Internet and the Web, communication and information gathering between individual has distracted from its traditional form and into web sites. The World Wide Web also offers a great opportunity for businesses to improve their relationship with the client and expand their marketplace in online world. Businesses use a criterion called traffic ranking to determine their site's popularity and visibility. Traffic ranking measures the amount of visitors to a site and based on these statistics, allocates a ranking to the site. One of the most important challenges in the ranking is the creation of fake traffic that generated by applications called robots. Robots are malicious software components that used to generate spam, set up distributed denial of services attacks, fishing, identity theft, removal of information and other illegal activities .there are already several ways to identify and discover the robot. According to Doran et al., The identification methods are divided into two categories: offline and real-time. The offline detection method is divided into three categories: Syntactical Log Analysis, Traffic Pattern Analysis, and Analytical Learning Techniques. The real-time method is performed by the Turing test system. In this research, the identification of robots is done through the offline method by analysis and processing of access logs to the web server and the use of data mining techniques. In this method, first, the features of each session are extracted, then generally these sessions are labeled with three conditions into two categories of human and robot. Finally, by using data mining tool, web robots are detected. In all previous studies, the features are extracted from each sessions, for example in first studies, Tan&Kumar extracted 25 features of sessions. After that Bomhardt et al. used 34 features to identify the robots. In 2009 Stassopoulou et al. used 6 features that was extracted from sessions and so on. But in this research, features are extracted from sessions of a unique user. Experimental results show that the proposed method in this research, by discovering new features and introducing a new condition in session labeling, improves the accuracy of identifying robots and moreover, improves the ranking of web traffic from previous work.

The use of new physical layer security approaches such as intelligent reflective surfaces as well as the use of existing capabilities in the mobile network such as the participation of network users as network partners are solutions that can be very effective in the next generation of mobile telecommunications to improve and increase the total Secrecy Rate in the physical layer.The proposed solution for improving the secrecy rate of the transmitted signal in the physical layer performed by closed-form solution to the described system. In this paper, we analyzed a network consisting of a gNB, two users, an IRS and an eavesdropper in an environment full of obstacles. Using Simulations, we evaluate our solution mathematically and investigate the effect of the eavesdropper location on the total secrecy rate. Also, the analysis and simulation of secrecy rate for the proposed network is performed by taking into account practical network considerations such as changing the location of the Eavesdropper in the network. In addition, the impact of changing the number of elements of smart surface arrays has been analyzed. The numerical results reveal that increasing IRS elements can enhance the ergodic secrecy rate, the reason is that increasing of IRS elements can focuses the main beam of data signal on the first user which increase secrecy rate. Finally, we compare the performance of the OMA and NOMA techniques in the proposed system model. We show that the NOMA technique provides 50% more ergodic secrecy rate compared to the OMA technique.

In this review article, we aim to describe recent advances in compressive sensing-based data aggregation techniques in wireless sensor networks, including current research efforts, challenges, and research trends. Sparse and compressible signals are present in many applications of sensor networks, such as environmental monitoring and vehicle surveillance. Compressive sensing has many properties such as simplicity of sensing and compression operations, universality, and an acceptable decrease in the quality of signal reconstruction, which makes it attractive for use in sensor networks. Packet drops do not damage the network as much as other protocols, and only for each measurement that does not reach the sink will cause a slight decrease in the quality of signal reconstruction. We begin the discussion with a brief introduction to compressive sensing theory and then describe the use of this technique in wireless sensor networks. Finally, the research issues and challenges ahead are discussed to provide a perspective for future research

In the last decade, to reduce the costs of environmental monitoring, the data aggregation based on the joint dictionary learning and compressive sensing technique in wireless sensor networks has been considered. In this article, a deterministic and non-random sampling design for use in this data aggregation method is presented. This method is based on estimating the amount of mutual information of sensor data and is obtained by sampling all of them in a short part of the data collection round named the training phase. In the next and main stage of the data collection period, only the nodes that provide the most information about the non-sampled nodes are scheduled to sample. Simulation results for real signals show that when the number of sampling sensors comprises still about 25% of the total network nodes, average energy savings of more than 12% can be achieved over a reference sampling method.

Hybrid Spread Spectrum (HSS) link is a suitable and robust physical layer structure for the tactical ad-hoc network. In this link, synchronization consists of two stages: Frequency hopping pattern synchronization and direct sequence code synchronization. In the presence of jamming, the use of the conventional fixed-threshold detection method increases the false alarm rate. Increasing the false alarm rate increases synchronization time in the HSS link. To solve this problem, the noise and jamming power estimator block is usually used at the receiver. Threshold value is adjusted instantaneously based on the estimated power in this method. This method has a high computational load and hardware complexity, and error in estimating noise and jamming power leads to an increase in the false alarm rate. In this paper, the proposed synchronization algorithm of the hybrid spread spectrum system is presented as an adaptive threshold, based on the statistical characteristics of the received signal. In the proposed algorithm, the threshold value is changed adaptively so that the false alarm rate remains constant at a minimum value. The theoretical analysis and simulation results show that the proposed algorithm can improve the detection probability and false alarm rate and reduce the synchronization time in the presence of a jamming compared to the conventional fixed threshold method.

In this paper, we present the new positioning algorithm of ad-hoc network nodes based on the exchange of radio signals among nodes. The proposed algorithm is independent of GPS data. In conventional positioning methods, the location of nodes in a network is estimated in two steps. In the first step, each node calculates its local location and neighbors based on the Multi-dimensional scaling (MDS) method. In the second step, the local location of the nodes transforms into a unique global location. In these methods, the location of the positioned nodes is used as a rotation reference for the next nodes though this calculated location is not accurate.  Also, the Dijkstra method is not exact either and produces errors which transmitted to the next nodes. Therefore, the positioning error is very high in the final nodes positioned. In the proposed algorithm, using the optimal algorithm, the estimated locations in the conventional method use as initial estimates, and these errors reduce so that the whole network reaches a stable state. The simulation results show that the proposed algorithm can solve the error propagation problem compared to the MDS algorithm and achieved the appropriate accuracy and stability with the least increase of overhead and low computational complexity load by mobility of the existing nodes.

In this paper a modified Gaussian pulse stimulus is employed to improve the accuracy of tumor detection inside breast phantom for 2D reconstructed image results in cylindrical setup of the microwave imaging system. This pulse shaping puts more energy at higher frequencies in contrast with conventional Gaussian in impulse radar. Hence a wider bandwidth is available to achieve higher accuracy for precise spatial localization. In the following a simulated cylindrical setup of the microwave imaging system with a modified stimulated pulse will be generated. The main purpose of this paper is employing a new stimulating pulse to detect a tumor from a biological phantom for 2D visualization of time-domain results. In order to achieve the goal the advantages of generating a novel confocal image-reconstructing algorithm based on back-projection method will be employed. The advantage conferred by “high resolution imaging” is that more energy is used at reflected signal than with conventional confocal imaging, subsequently a relatively lower spatial resolution in identifying the reflected signal is achieved. Simulated results are presented to validate the effectiveness of the proposed method for precisely calculating the time-dependent location of targets.

The multi-path transmission is an appropriate transmission method for high data rate packets like video streaming. To provide video streaming with high quality, the video packets are divided into different frames for transmitting through various paths. Nevertheless, regarding the results of numerous inherent features of vehicular ad-hoc networks (VANETs), designing an efficient and stable routing protocol is difficult for various applications of VANETs. In particular, the dynamic nature of topology and intermittent connectivity make maintaining the quality of service (QoS) task very difficult. To provide QoS to entertainment applications and traffic safety in VANET, we offer a routing protocol based on the adaptive intersection with QoS support regarding delay, packet delivery ratio (PDR), and connectivity probability. To establish the best QoS routes, we considered the equivalent routing problem as the optimization problem and then proposed an algorithm based on ant colony optimization (ACO) for solving it. Furthermore, a fuzzy logic-based algorithm was employed to select the best next-hop vehicle by incorporating multiple metrics associated with the vehicle’s position, link quality, and vehicle mobility. According to the simulation results, the proposed approach achieves the average PDR of more than 84.2%, the end-to-end delay of less than 3.58 s, the overhead of less than 15.65%, and the peak signal to noise ratio (PSNR) of more than 20.82 dB. It is understandable from the result analysis that the performance of the proposed approach is excellent.

Software-Defined Networking(SDN) is a growing network technology that has brought significant benefits to a wide range of disciplines,from science to technology to various fields. This structure can be used in network-based environments, data centers and various research sites. OpenFlowis one of the most widely used protocols for interaction between a controller and a switch in a Software-Defined Networking. Understanding the performance and limitations of the network defined by open source software, including bottlenecks and security vulnerabilities due to the centralized network structure, areimportant prerequisitesfor the efficientdeployment ofthesesystems. Thesepoints of view led researchers to examine various related mathematical models to addresstheseissues. Queueing theory provides the most important and accurate model for evaluating the performance of the SDN networks affected by these restrictions, which has attracted the attention of researchers recently. Regarding to extensive mathematical modeling, this theory has also been used to improve network efficiency. Researchers have used this theory to investigate operational power and improve time consumption and control of data planes due to the nature of classification and storage of packets in SDN buffers. These methods overcomecontroller performance bottlenecks and increase SDN control capacity, especially forlarge distributed networks. In this paper, we examine the queueing models for different applications in different layers of SDN, in which researchers use these methods to monitor network loads,evaluateand predict performance changes due to diversityin network traffic. We introduce and review a collection of articles that explore, different applications of Queueing theory in SDNnetworks. In addition, in order to increase the efficiency of this research, detailed comparisons are performed in terms of structure, mathematical models and the final simulation results.

VANETs are oneofthe important technologiesin recent decade. This importance is evident which can be used in road and mountains trails and also provides other information to vehicle from roads. There are many challenges and problems in this networks which can cited as routing, clustering, quality of services criteria, energy consumption, RSU placement and etc. This article triesto study and survey about VANET’s clustering methods in roads and mountain trails with high reliability.

Body area networks is one of the types of wireless area networks which has been created to optimize utilizing hospital resources and for earlier diagnosis of medical symptoms, and ultimately to reduce the cost of medical care. This network like most of the wireless networks is without infrastructure and the embedded sensor nodes in the body have limited energy. Hence, the early power completion of the wireless nodes based on the transmission of messages in the network can disrupt the entire network. In this study, a fuzzy clustering based routing is presented to overcome the mention challenge. In this method, the sensor nodes are allocated to the nearest cluster, based on their distance from the cluster head node, and exchange information with the cluster-head at the near distances, and the cluster-head node, due to its high initial energy, can transmit data to the remote server. In this study, due to the movement of the person and the position shift in the sensor nodes and the distances between the cluster-head nodes, sensor nodes belonging to the clusters are also updated and placed in their proper cluster and the transmission of sensory messages was done with its nearest cluster- head node. Simulation results show that the proposed method can be better than other existing methods in and equal condition.

Auctions have been widely studied as an efficient approach of allocating spectrum among secondary users in recent years. On the other side, a wide range of frequency bands could be available in a spectrum auction considering the current trend of deregulating wireless resources, therefore, channels provided by the primary users may reside in widely separated frequency bands, and due to the difference in propagation profile, would show significant heterogeneity in transmission range, channel error rate, path-loss, etc. Also, we can consider the channels with similar propagation and quality characteristics, for example, channels located in the same frequency band, are homogeneous and can be located in one spectrum type. Therefore, in this paper, we propose a novel double auction mechanism for both homogeneous and heterogeneous spectrums, called hybrid spectrums. The hybrid auction design has its own challenges, especially it also inherits the challenges related to heterogeneity. We prove that our auction design can not only solve the challenges caused by hybrid spectrums but also preserve three important economic aspects including truthfulness, budget balance and individual rationality.