Sharif Digital Repository

Wireless Sensor and Internet of Things (WSIoT) networks are characterized by nodes scattered throughout the environment, posing a significant challenge when it comes to battery replacement. The task of developing an algorithm that effectively reduces energy consumption in IoT networks through the utilization of artificial intelligence and fuzzy logic is a formidable one. Heuristic algorithms emerge as valuable tools in this context, capable of swiftly addressing non-deterministic polynomial problems or approximating optimal solutions with remarkable accuracy. Conversely, mathematical optimization approaches often falter due to their sluggishness or inefficiency, rendering them less suited... 

Today, cloud platforms for Internet of Everything (IoE) are facilitating organizational and industrial growth, and have different requirements based on their different purposes. Usual task scheduling algorithms for distributed environments such as group of clusters, networks, and clouds, focus only on the shortest execution time, regardless of the power consumption. Network energy can be optimized if tasks are properly scheduled to be implemented in virtual machines, thus achieving green computing. In this research, Dynamic Voltage Frequency Dcaling (DVFS) is used in two different ways, to select a suitable candidate for scheduling the tasks with the help of an Artificial Intelligence (AI)... 

Polymer-based composites are used for wound healing applications. This work aims to prepare an inorganic-polymer nanocomposite based on bentonite, poly(vinyl alcohol), and bacterial cellulose by electrospinning for wound healing. The nanocomposite is synthesized using a solution intercalation technique, with 1–2 wt% nanobentonite concentration variation. The effects of commercial and laboratory-synthesized nanobentonite as well as the extract of the green walnut shell (EGWS) are examined and characterized by different techniques. The addition of nanobentonite increases the average size of fibers and tensile strength up to 200 nm and more than 15 MPa, respectively, due to the presence of... 

Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert. In addition; the advancement of computer networks, the number of attacks and infiltrations is also increasing. In fact, the knowledge coming from expert will lose its value over time and must be updated and made available to the system and this makes the need for expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the... 

Task scheduling is a significant challenge in the cloud environment as it affects the network's performance regarding the workload of the cloud machines. It also directly impacts the consumed energy, therefore the profit of the cloud provider. This paper proposed an algorithm that prioritizes the tasks regarding their execution deadline. We also categorize the physical machines considering their configuration status. Henceforth, the proposed method assigns the jobs to the physical machines with the same priority class close to the user. Furthermore, we reduce the consumed energy of the machines processing the low-priority tasks using the DVFS method. The proposed method migrates the jobs to... 

Cloud Internet of Things (CIoT) environments, as the essential basis for computing services, have been subject to abuses and cyber threats. The adversaries constantly search for vulnerable areas in such computing environments to impose their damages and create complex challenges. Hence, using intrusion detection and prevention systems (IDPSs) is almost mandatory for securing CIoT environments. However, the existing IDPSs in this area suffer from some limitations, such as incapability of detecting unknown attacks and being vulnerable to the single point of failure. In this paper, we propose a novel distributed multi-agent IDPS (DMAIDPS) that overcomes these limitations. The learning agents in... 

As a way of obtaining useful information about the adversaries behavior with a low rate of false detection, honeypots have made significant advancements in the field of cybersecurity. They are also powerful in wasting the adversaries resources and attracting their attention from other critical assets in the network. A deceptive network with multiple honeypots is called a honeynet. The honeypots in a honeynet aim to cooperate in order to increase their deception power. Professional adversaries utilize strong detection mechanisms to discover the existence of the honeypots in a network. When an adversary finds that a deception mechanism is deployed, it may change their behavior and cause... 

Network slicing in 5G networks can be modeled as a Virtual Network Embedding (VNE) problem, wherein the slice requests must be efficiently mapped on the core network. This process faces two major challenges: covering the maximum number of requests and providing slice isolation. Slice isolation is a mechanism for protecting the slices against Distributed Denial of Service (DDoS) attacks. To overcome these two challenges, we have proposed a novel actor-critic Reinforcement Learning (RL) model, called Slice Isolation-based Reinforcement Learning (SIRL), using five optimal graph features to create the problem environment, the form of which is changed based on a ranking scheme. The ranking... 

Cloud Internet of Things (CIoT) environments, as the essential basis for computing services, have been subject to abuses and cyber threats. The adversaries constantly search for vulnerable areas in such computing environments to impose their damages and create complex challenges. Hence, using intrusion detection and prevention systems (IDPSs) is almost mandatory for securing CIoT environments. However, the existing IDPSs in this area suffer from some limitations, such as incapability of detecting unknown attacks and being vulnerable to the single point of failure. In this paper, we propose a novel distributed multi-agent IDPS (DMAIDPS) that overcomes these limitations. The learning agents in... 

Software-Defined Networking (SDN) is one of the promising and effective approaches to establishing network virtualization by providing a central controller to monitor network bandwidth and transmission devices. This paper studies resource allocation in SDN by mapping virtual networks on the infrastructure network. Considering mapping as a way to distribute tasks through the network, proper mapping methodologies will directly influence the efficiency of infrastructure resource management. Our proposed method is called Effective Initial Mapping in SDN (EIMSDN), and it suggests writing a module in the controller to initialize mapping by arriving at a new request if a sufficient number of... 

Network slicing within 5G networks encounters two significant challenges: catering to a maximum number of requests while ensuring slice isolation. To address these challenges, we present an innovative actor-critic Reinforcement Learning (RL) model named 'Slice Isolation based on RL' (SIRL). This model employs five optimal graph features to construct the problem environment, the structure of which is adapted using a ranking scheme. This scheme effectively reduces feature dimensionality and enhances learning performance. SIRL was assessed through a comparative analysis with nine state-of-the-art RL models, utilizing four evaluation metrics. The average results demonstrate that SIRL outperforms... 

In this study, natural super-cavitating flow around three different conical cavitators with wedge angles of 30°, 45° and 60° is investigated. We apply the k−ϵ turbulence model and the volume of fluid (VOF) technique to numerically study the three-dimensional cavitating flow around the cavitators. The turbulence approach is coupled with a mass transfer model which is implemented into the finite-volume package. Simulations are performed for different cavitation numbers. Finally, the effects of some important parameters such as the cavitation index, inlet velocity, Froude number and wedge angle of cavitators on the geometrical characteristics of the super-cavities are discussed. Our numerical... 

Monitoring security and quality of service is essential, due to the rapid growth of the number of nodes in wireless networks. In healthcare/industrial environments, especially in wireless body area networks (WBANs), this is even more important. Because the delays and errors can directly affect patients'/scientists' health. To increase the Monitoring Quality of Services (MQoS) in WBANs, a secure medium access control (MAC) protocol needs to be developed to provide optimal services. This article provides a comprehensive review of MAC protocols in WBANs with a technical security analysis approach. Time-based, contention-based, and hybrid protocols are compared in this article, regarding MQoS... 

Protecting large-scale networks, especially Software-Defined Networks (SDNs), against distributed attacks in a cost-effective manner plays a prominent role in cybersecurity. One of the pervasive approaches to plug security holes and prevent vulnerabilities from being exploited is Moving Target Defense (MTD), which can be efficiently implemented in SDN as it needs comprehensive and proactive network monitoring. The critical key in MTD is to shuffle the least number of hosts with an acceptable security impact and keep the shuffling frequency low. In this paper, we have proposed an SDN-oriented Cost-effective Edge-based MTD Approach (SCEMA) to mitigate Distributed Denial of Service (DDoS)... 

Mafia, also known as Werewolf, is a game of uncertainty between two teams, which aims to eliminate the other team's players from the game. The similarities between detecting the Mafia members in this game and botnet detection in a computer network motivate us to solve the botnet detection problem using this game's winning strategies. None of the state-of-the-art researches have used the Mafia game strategies to detect the network's malicious nodes. In this paper, we first propose the Mafia detection strategies, which are applied using linear relation and reinforcement learning techniques. We then use the suggested strategies in a network infected by the Mirai botnet, using Mininet, to... 

One of the key parts of perovskite solar cells which has great influence on their performance and stability is hole transporting layer. Spiro-OMeTAD is extensively used as organic hole transporting material in perovskite solar cells. However, Spiro-OMeTAD is expensive and has low chemical stability. In this study, the solution processed Sb2S3 and Cu3SbS4 nanocrystals have been synthesized and then the n-i-p mesoscopic perovskite solar cells have been fabricated using Spiro-OMeTAD, Sb2S3 and Cu3SbS4 nanocrystals as hole transporting layer at ambient air condition. It is shown that the conduction and valence band levels of the synthesized Sb2S3 and Cu3SbS4 nanocrystals are in the proper... 

A novel intelligent semi-active control system for an eleven degrees of freedom passenger car's suspension system using magnetorheological (MR) damper with neuro-fuzzy (NF) control strategy to enhance desired suspension performance is proposed. In comparison with earlier studies, an improvement in problem modeling is made. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the force needed for a semi-active damper. The fuzzy logic rules are extracted based on Sugeno inference engine. The inverse mapping model is based on an artificial neural network... 

Using CNG as an additive for gasoline is a proper choice due to higher octane number of CNG enriched gasoline with respect to that of gasoline. As a result, it is possible to use gasoline with lower octane number in the engine. This would also mean the increase of compression ratio in SI engines resulting in higher performance and lower gasoline consumption. Over the years, the use of simulation codes to model the thermodynamic cycle of an internal combustion engine have developed tools for more efficient engine designs and fuel combustion. In this study, a thermodynamic cycle simulation of a conventional four-stroke spark-ignition engine has been developed. The model is used to study the... 

With the declining energy resources and increase of pollutant emissions, a great deal of efforts has been focused on the development of alternatives for fossil fuels. One of the promising alternative fuels to gasoline in the internal combustion engine is natural gas [1-5]. The application of natural gas in current internal combustion engines is realistic due to its many benefits. The higher thermal efficiency due to the higher octane value and lower exhaust emissions including CO2 as a result of the lower carbon to hydrogen ratio of the fuel are the two important feature of using CNG as an alternative fuel. It is well known that computer simulation codes are valuable economically as a cost... 

Gaining insight into possible vibratory responses of dynamical systems around their stable equilibria is an essential step, which must be taken before their design and application. The results of such a study can significantly help prevent instability in closed-loop stabilized systems by avoiding the excitation of the system in the neighborhood of its resonance. This paper investigates nonlinear oscillations of a rotary inverted pendulum (RIP) with a full-state feedback controller. Lagrange’s equations are employed to derive an accurate 2-DoF mathematical model, whose parameter values are extracted by both the measurement and 3D modeling of the real system components. Although the governing...