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The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing (FSR) protocol. The proposed QFS-RPL is as lightweight and agile as the standardized RPL and partially outperforms the mRPL protocol on mobile networks. This method supports multi-path routing, and at any given time in the network lifetime, all possible paths for sending data from any node to the sink are available. Therefore, QFS-RPL provides high resilience against errors, failures, and sudden network changes. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The performance evaluation criteria considered for simulation are load balancing, energy consumption, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, and control packet overhead.

As the Internet of Things networks expand globally, it is becoming increasingly important to protect against threats. one of the main reasons for the high number of false positives and low detection rates is the presence of redundant and irrelevant features. To address this problem, we propose a binary chimpanzee optimization algorithm for the feature selection process. This paper presents accurate network-based intrusion detection network, named parallel convolutional neural network long and short-term memory network branch, which has two branches. The input vector of the network is permuted in a 3-dimention space. This allows the model to extract highly discriminative features using a small number of layers. On the second branch, we used long and short-term memory network in parallel. The efficacy of the proposed deep model has been evaluated using three benchmark internet of things intrusion detection datasets, namely ToN-IoT, UNSW-NB15, and IoTID20 datasets. The experimental results demonstrated that the proposed binary chimpanzee optimization approach reduces about 60% of features, and the effectiveness of the proposed model was demonstrated by experimental results showing a high detection rate, high accuracy, and a relatively low false positive rate, which are measured as 99.54%, 99.56%, and 0.024 in the ToN-IoT and 99.79%, 99.78%, and 0.0032 in UNSW-NB15 and 100%, 100%, and zero in IoTID20 datasets, respectively.

This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated PSO (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm con- verges to desired results faster than CM-PSO and modified GA without trapping in local minima. Validation is conducted by designing a single-element and simple-structure dipole antenna so that its optimized S 11 is better than -30 dB at the resonance frequency and covers the 3.3 to 3.8 GHz fre- quency band with S 11 < − 1 0 dB. Certainly, the -30 dB and covering frequency band criteria can be modified in the pro- posed algorithm. In the algorithm, the isolation between el- ements of a quad-Multiple-Input/Multiple-Output antenna, constructed using optimized dipole antennas, is set to be less than -20 dB (changeable criteria) so that the smallest size can be achieved. CST carries out electromagnetic and high- frequency simulations, and the novel developed optimization algorithm in MATLAB determines what and how much pa- rameter values need to be changed by CM-PSO or an innova- tive modified GA in order to enhance the antenna’s S 11 result and its Impedance Bandwidth (IBW). The input parameters of the algorithm are the dimensions of the proposed antenna’s elements, which significantly influence its performance.

With the increase in the number of processing cores on a chip, a highly efficient and scalable structure such as a network on a chip was proposed. Network-on-chip is a technology presented to solve the limitations of system-on-chip that includes a set of multiprocessors. In network-on-chip systems, delay in communication and reaching the ideal speed is one of the concerns of academic and industrial researchers today. Although network-on-chip offers a favorable solution to reduce the problem of the long delay of wires compared to traditional structures, communication delay is still a challenge. In this article, the various types of network router input ports architecture on the chip are reviewed and the operational comparison and evaluation of the technologies proposed in this field are mentioned.

Network on chip (NoC) is one of the communicative structures for multiple cores that has scalability. In designing the NoC micro-router architecture, the arbitration unit is very important due to its significant impact on performance, chip occupation level and NoC power consumption. In this paper, a router arbitration architecture is proposed with a combination of variable priority arbitration and Round Robin. In this architecture, arbitration examines the requests of other channels based on the Round Robin index after requesting the flit to exit the virtual channel in addition to checking the availability of the relevant virtual channel.The simulation results show that the architecture of the RR-SFVP arbitration unit compared to the standard RR method, is 13.7% smaller in area and has 5.7% less power consumption and 53.7% less critical path delay.

The Internet of Things (IoT) has rapidly grown recently, and mobile devices (MDs) have encountered widespread usage. All of these cause an increase in the demand for more powerful computing resources. Meanwhile, a new concept called mobile edge computing (MEC) was introduced as a promising technology to access powerful computing resources closer to the user side for a quick and effective response, especially for time-intensive applications. Task offloading has emerged as a solution to allocate resources among computing resources of smart devices or computational resources available in MEC. This study presents a new binary quantum approach based on an arithmetic optimization algorithm (BQAOA) for computational tasks offloading decisions on MDs with low complexity and guaranteed convergence. However, since task offloading is an NP-hard problem, there is a need to use methods that provide the optimal possible solution for various quality criteria, including response time and energy consumption. Indeed, this is where the advantages of arithmetic optimization algorithms (AOA) and quantum computing have been used to improve the performance of MDs. This paper introduces a 2-tier architecture from the user to the cloud computing server-side. Also, a Markov model is proposed to compute the average network bandwidth in the offloading problem. The proposed BQAOA is compared with the best state-of-the-art algorithms in heuristic and meta-heuristic fields in different scenarios. The simulation results showed 12.5%, 12%, and 26% improvement in energy consumption, makespan, and Energy SLA Violations (ESV) optimization parameters, respectively.

In modern societies, handwritten identification is a great practical need and challenge for forensic sciences. Moreover, few research studies have focused on automatic offline handwritten document analysis. Also, there are very few standard datasets on handwritten document identification. In addition, handwritten documents lose their nature over time due to the spread and drying of ink. From this standpoint, the present study presents an offline writer identification system (in presence of the uncertainties such as different experimental conditions and environmental noises for more realistic assumptions) that is a critical need in forensic studies. For this purpose, a comprehensive right-to-left dataset is developed and devised by gathering data from 62 participants at different time intervals under different experimental conditions. This dataset is designed based on American Society for Testing and Materials (ASTM) standards. A Deep Convolutional Neural Network (DCNN) model based on modified pre-trained networks is designed and developed to extract features from raw data hierarchically. The proposed DCNN model is tested and investigated not only on the designed dataset but also on different datasets. One notable advantage of the present study is that it has made use of heterogeneous data. Another remarkable aspect of the proposed study is that the proposed DCNN model is independent of any specific languages which can be applied on various languages. The results of the study indicate that the proposed DCNN model can learn features hierarchically from the handwriting raw data and achieve higher accuracy than other comparative methods.

Lung cancer is one of the deadliest cancers all over the world. One of the indications of lung cancers is the presence of the lung nodules which can appear individually or attached to the lung walls. The early detection of these nodules is crucial for saving the patient’s lives. Machine learning and image processing techniques, generally embedded in computer-aided diagnosis (CAD) systems, might help radiologists locate and assess the risk of these nodules. Accordingly, in this paper, we present a framework for identifying pulmonary nodules in lung CT images and a convolutional neural network (CNN) approach to automatically extract the features from lung images, followed by classifying the suspicious regions as either nodule or non-nodule objects. The proposed model is based on Le-Net architectural stylization and the light model is obtained after going through the innovative steps. A subset of LIDC public dataset including N = 7072 CT slices of varying nodule sizes (1 mm to 5 mm) is used to train and validate this approach. The proposed framework carries out all stages of lung segmentation as well as diagnosis and categorization of the existing nodules automatically. Training and validation steps of this network with configurations 2.4GHz Core i5 processor, 8GB memory, and Intel Graphics 520 are performed approximately in six hours and this system yields the performance with accuracy = 90.1%, sensitivity = 84.1%, specificity =91.7%, for identifying the nodules. Compared to other famous CNN architectures, the proposed model is agile (light and fast) and has appropriate performance, thereby is suitable for real-time medical image analysis.

Software defined network (SDN) has shown significant advantages in numerous real-life aspects with separating the control plane from the data plane that provides programmable management for networks. However, with the increase in the network size, a single controller of SDN imposes considerable limitations on various features. Therefore, in networks with immense scalability, multiple controllers are essential. Specifying the optimal number of controllers and their deployment place is known as the controller placement problem (CPP), which affects the network's performance. In the present paper, a novel controller placement algorithm has been introduced using the advantages of nature-inspired optimization algorithms and network portioning. Firstly, the Manta Ray Foraging Optimization (MRFO) and Salp Swarm Algorithm (SSA) have been discretized to solve CPP. Three new operators comprising a two-point swap, random insert, and half points crossover operators were introduced to discretized the algorithms. Afterward, the resulting discrete MRFO and SSA algorithms were hybridized in a promoting manner. Next, the proposed discrete algorithm has been evaluated on six well-known software-defined networks with a different number of controllers. In addition, the networks have been chosen from various sizes to evaluate the scalability of the proposed algorithm. The proposed algorithm has been compared with several other state-of-the-art algorithms regarding network propagation delay and convergence rate in experiments. The findings indicated the effectiveness of the contributions and the superiority of the proposed algorithm over the competitor algorithms.

The inherent features of software-defined networking (SDN) architecture revolutionize traditional network infrastructure and provide the opportunity for integrated and centralized network monitoring. One of the shortcomings of SDNs is related to its high vulnerability to distributed denial of service attacks and other similar ones. In this paper, a novel multi-stage modular approach is proposed for detecting and mitigating security anomalies in SDN environment (SADM-SDNC). The proposed approach uses NetFlow protocol for gathering information and generating dataset and information gain ratio in order to select the effective features. Also, the C-support vector classification algorithm with radial basis function kernel, and features of Floodlight controller for developing a structure with desirable performance were used in the proposed scheme. The experimental results demonstrate that the proposed approach performs better than other methods in terms of enhancing accuracy and detection rate, and reducing classification error and false alarm rate, which were measured as 99.67%, 99.26%, 0.33%, and 0.08% respectively. Finally, thanks to utilizing REST API and Static Entry Pusher technologies in the Floodlight controller, it makes it possible to disconnect any communications with the attacking factors and remove destructive users.

Software defined network (SDN) decouples the network control and data planes. Despite various advantages of SDNs, they are vulnerable to various security attacks such anomalies, intrusions, and Denial-of-Service (DoS) attacks and so on. On the other hand, any anomaly and intrusion in SDNs can affect many important domains such as banking system and national security. Therefore, the anomaly detection topic is a broad research domain, and to mitigate these security problems, a great deal of research has been conducted in the literature. In this paper, the state-of-the-art schemes applied in detecting and mitigating anomalies in SDNs are explained, categorized, and compared. This paper categorizes the SDN anomaly detection mechanisms into five categories: (1) flow counting scheme, (2) information-based scheme, (3) entropy-based scheme, (4) deep learning, and (5) hybrid scheme. The research gaps and major existing research issues regarding SDN anomaly detection are highlighted. We hope that the analyses, comparisons, and classifications might provide directions for further research.

Today, the relational database is not suitable for data management due to the large variety and volume of data which are mostly untrusted. Therefore, NoSQL has attracted the attention of companies. Despite it being a proper choice for managing a variety of large volume data, there is a big challenge and difficulty in performing online analytical processing (OLAP) on NoSQL since it is schema-less. This article aims to introduce a model to overcome null value in converting document-oriented NoSQL databases into relational databases using parallel similarity techniques. The proposed model includes four phases, shingling, chunck, minhashing, and locality-sensitive hashing MapReduce (LSHMR). Each phase performs a proper process on input NoSQL databases. The main idea of LSHMR is based on the nature of both locality-sensitive hashing (LSH) and MapReduce (MR). In this article, the LSH similarity search technique is used on the MR framework to extract OLAP cubes. LSH is used to decrease the number of comparisons. Furthermore, MR enables efficient distributed and parallel computing. The proposed model is an efficient and suitable approach for extracting OLAP cubes from an NoSQL database.

Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new databases known as NOSQL have been introduced. The issue is that such a database is difficult to analyze. Online analytical processing (OLAP) is the foundational technology for data analysis in business intelligence. Because these technologies were designed primarily for relational database systems, performing OLAP in NOSQL is difficult. We present a model for extracting OLAP cubes from a document-oriented NOSQL database in this article. A scalable Naïve Bayes classifier method was used for this purpose. The proposed solution is divided into three stages of preparation, Naïve Bayes, and NBMR. Our proposed algorithm, NBMR, is based on the Naïve Bayes classifier (NBC) and the MapReduce (MR) programming model. Each NOSQL database document with nearly the same attribute will belong to the same class, and as a result, OLAP cubes can be used to perform data analysis. Because the proposed model allows for distributed and parallel Naïve Bayes Classifier computing, it is appropriate and suitable for large-scale data sets. Our proposed model is a proper and efficient approach when considering the speed and reduced the number of required comparisons.

Introduction: Telemedicine in the pandemic of coronavirus disease 2019 (COVID-19) has responded to societal distancing in medical treatments by protecting health workers while also managing available resources. To attain best practices in telemedicine, a platform must be functional, and both patients and clinicians must be satisfied with the technology. To ensure the benefits of telehealth systems, usability refers to how easy the user interfaces of telehealth systems are to use. In this study, the usability of telemedicine systems has been investigated.Material and Methods: The authors of this study review the study from 2015 to 2021 using a combination of the keywords "health", "telemedicine", "telemedicine", "mobile health", "usability" "Software", "System" and "Program", which led to the extraction of 119 articles in this field.Results: Articles in the field of remote health software and evaluation of the usability of remote health applications in the form of applications based on mobile health technologies, web-based applications or a combination of both types with sample devices Primary are wearable electronics, sensors or robots.Conclusion: In this study, most of the remote health software are mobile based and their usability has been evaluated by a questionnaire. Satisfaction is the most important usability attributes to consider when designing Health mobile apps.

We consider the inverse problem with respect to domain. We suggested a new approach for reducing the inverse problem for a domain to an equivalent problem in a variational setting and gave an effective solution algorithm for solving such problems. In order to solve boundary problem, the artificial neural network is used in each step of the iteration.