Ahmadreza Montazerolghaem - Profile on Academia.edu (original) (raw)

Papers by Ahmadreza Montazerolghaem

International Journal of Web Research (IJWR), 2025

IoT is a dynamic network of interconnected things that communicate and exchange data, where secur... more IoT is a dynamic network of interconnected things that communicate and exchange data, where security is a significant issue. Previous studies have mainly focused on attack classifications and open issues rather than presenting a comprehensive overview on the existing threats and vulnerabilities. This knowledge helps analyzing the network in the early stages even before any attack takes place. In this paper, the researchers have proposed different security aspects and a novel Bayesian Security Aspects Dependency Graph for IoT (BSAGIoT) to illustrate their relations. The proposed BSAGIoT is a generic model applicable to any IoT network and contains aspects from five categories named data, access control, standard, network, and loss. This proposed Bayesian Security Aspect Graph (BSAG) presents an overview of the security aspects in any given IoT network. The purpose of BSAGIoT is to assist security experts in analyzing how a successful compromise and/or a failed breach could impact the overall security and privacy of the respective IoT network. In addition, root cause identification of security challenges, how they affect one another, their impact on IoT networks via topological sorting, and risk assessment could be achieved. Hence, to demonstrate the feasibility of the proposed method, experimental results with various scenarios has been presented, in which the security aspects have been quantified based on the network configurations. The results indicate the impact of the aspects on each other and how they could be utilized to mitigate and/or eliminate the security and privacy deficiencies in IoT networks.

Contributions of Science and Technology for Engineering, 2025

Software-defined networking (SDN) represents a revolutionary shift in network technology by decou... more Software-defined networking (SDN) represents a revolutionary shift in network technology by decoupling the data plane from the control plane. In this architecture, all network decision-making processes are centralized in a controller, meaning each switch receives routing information from the controller and forwards network packets accordingly. This clearly highlights the crucial role that controllers play in the overall performance of SDN. Ryu is one of the most widely used SDN controllers, known for its ease of use in research due to its support for Python programming. This makes Ryu a suitable option for experimental and academic studies. In this research, we evaluate the performance of the Ryu controller based on various network metrics and across different network topologies. For experimental analysis, we use Mininet, a powerful network emulation tool that enables the creation of diverse network structures and the connection of switches to controllers. To facilitate the experiments, we developed a Python-based script that executes various network scenarios, connects to different controllers, and captures and stores the results. This study not only provides a comprehensive performance evaluation of the Ryu controller but also paves the way for evaluating other SDN controllers in future research.

Computer Communications, 2025

Deploying multiple controllers in the control panel of software-defined networks increases scalab... more Deploying multiple controllers in the control panel of software-defined networks increases scalability, availability, and performance, but it also brings challenges, such as controller overload. To address this, load-balancing techniques are employed in software-defined networks. Controller load balancing can be categorized into two main approaches: (1) single-level thresholds and (2) multi-level thresholds. However, previous studies have predominantly relied on single-level thresholds, which result in an imprecise classification of controllers or have assumed uniform controller capacities in multi-level threshold methods. This study explores controller load balancing with a focus on utilizing multi-level thresholds to accurately assess controller status. Switch migration operations are utilized to achieve load balancing, considering factors such as the degree of load imbalance of the target controller and migration efficiency. This includes evaluating the post-migration status of the target controller and the distance between the migrating switch and the target controller to select the appropriate target controller and migrating switch. The proposed scheme reduces controller response time, migration costs, communication overhead, and throughput rate. Results demonstrate that our scheme outperforms others regarding response time and overall performance.

Background: Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing m... more Background: Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison. Methods: Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms. Results: A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results. Conclusions: The competition is organized to evaluate the current AIbased classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets.

Background: The pharmaceutical industry has seen increased drug production by different manufactu... more Background: The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time. Methods: Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions-2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results. Results: A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics. Conclusions: In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models.

Journal of Engineering Research, 2024

This article presents a comprehensive exploration of the architecture and various approaches in t... more This article presents a comprehensive exploration of the architecture and various approaches in the domain of cloud computing and software-defined networks. The salient points addressed in this article encompass: Foundational Concepts: An overview of the foundational concepts and technologies of cloud computing, including software-defined cloud computing. Algorithm Evaluation: An introduction and evaluation of various algorithms aimed at enhancing network performance. These algorithms include Intelligent Rule-Based Metaheuristic Task Scheduling (IRMTS), reinforcement learning algorithms, task scheduling algorithms, and Priority-aware Semi-Greedy (PSG). Each of these algorithms contributes uniquely to optimizing Quality of Service (QoS) and data center efficiency. Resource Optimization: An introduction and examination of cloud network resource optimization based on presented results and practical experiments, including a comparison of the performance of different algorithms and approaches. Future Challenges: An investigation and presentation of challenges and future scenarios in the realm of cloud computing and software-defined networks. In conclusion, by introducing and analyzing simulators like Mininet and CloudSim, the article guides the reader in choosing the most suitable simulation tool for their project. Through its comprehensive analysis of the architecture, methodologies, and prevalent algorithms in cloud computing and software-defined networking, this article aids the reader in achieving a deeper understanding of the domain. Additionally, by presenting the findings and results of conducted research, it facilitates the discovery of the most effective and practical solutions for optimizing cloud network resources.

The Internet of Things (IoT) is a collection of different devices that contain different software... more The Internet of Things (IoT) is a collection of different devices that contain different software and hardware technologies to communicate with other devices using unique addressing methods [1,2]. The IoT devices collect data from their surroundings through various sensors and exchange them [2]. As a result, IoT system applied in various fields such as smart homes, smart cities, transportation, e-health care, agriculture, and industries. Cloud Computing (CC) [3] is an emerging computing technology that, due to its capabilities, can provide all the resources needed for the quality of IoT services for IoT. The CC system consists of a large number of Data Centers (DCs), each DC also consists of a large number of Virtual Machines (VMs). But due to the long geographical distance with IoT devices on the network Edge Computing (EC), the CC system is not suitable for delay-sensitive IoT devices such as emergency monitoring, and energy usage measurements from a smart grid, cause long delays that may not be acceptable for some applications in today's world [3,4]. Therefore, to solve this problem, the computing resources should be closer to the network EC devices, and the CC system is very suitable for this and can provide the resources needed to reduce the workload in cloud DC, facilitate task processing, facilitate networking, and facilitate the storage of data generated by IoT sensors, with the lowest amount of communication cost and delay [4-7]. Each server or Fog Computing (FC) node is a virtualized system equipped with a wireless communication unit, simpler processing and computing devices for data, and data storage cards. When FC nodes receive more task requests from IoT devices that exceed their capacity, they can offload some of their load to cloud layer DCs [8-10]. In other words, CC and FC are models of hosting services over the Internet for IoT devices. Fig. 8.1 shows the architecture of IoT-Fog-Cloud system, with CC in the top layer, FC in the middle layer, and IoT devices in the bottom layer. Task Scheduling (TSch) is an effective method for efficient management of virtual resources of the FC and EC environment [11] based on specific constraints and deadlines by different users, which can be used to assign the set of requested tasks by users or existing IoT devices to FC and CC resources in order to execute them [12-16]. According to Fig. 8.1, in the proposed TSch model that is considered for scheduling the task requests of IoT devices in the FC system, Fog Broker (FB) is the main part and is located in the FC layer, which includes three main parts: Task Administrator (TA), Resource Monitoring Service (RMS), and Task Scheduler (TSR). The TA receives all task requests from various IoT devices, and then forwards them to the TSR, maintaining their required resources and attributes. Also, RMS is responsible for collecting information on FC resources and monitoring the status of FC resources. TSR unit is the main core of FB unit, and TSch algorithms are executed in it. According to the characteristics of the sent task requests as well as the capabilities of the available FC resources, the TSR schedules the tasks for execution and processing by assigning the appropriate FC nodes to the task requests. Finally, the processed task requests are sent back to the FB and from there to the respective users or IoT devices [12-16]. In order to allocate FC resources based on the demand of users or IoT devices, fully flexible infrastructure virtualization that uses IoT task Handbook of Whale Optimization Algorithm.

Segmentation is considered as an essential step in image processing. This process divides differe... more Segmentation is considered as an essential step in image processing. This process divides different parts of the image into several categories. Multi-level Thresholding is a method that facilitates this process. The problem is to correctly segment each image to find the best set of thresholds [1]. Thresholding usually uses image processing methods due to its consistency and low Computational Complexity (CC). Two main methods are Otsu's method [2-4] and Kapur's method [5,6]. However, such approaches have high CC for Multi-level Thresholding [7]. Thresholds help each other to separate interesting objects from their background. The higher splitting quality depends on the selected thresholds [8]. Recently, Meta-Heuristic (MH) algorithms like Particle Swarm Optimization (PSO) [9], Whale Optimization Algorithm (WOA) [10], Moth-Flame Optimization (MFO) [11] have been successfully applied for Thresholding problems [3,8,12], and ABC [13,14] and, Harris Hawks Optimizer (HHO) [15] are used in other problems. MH algorithms have attracted the attention of researchers due to their excellent performance in finding threshold vectors in Multi-level Thresholding Image Segmentation (MTIS) systems. MH algorithms are either used separately in these problems, or been used in a combined version to solve the MTIS. Most MH algorithms are population-based and initially find a plausible answer by randomly moving through the search space. Such algorithms also include two phases of exploration and exploitation to search for the desired solution on the search space, through which the two phases search globally and locally, respectively. Therefore, several attempts have been made in the literature to achieve a better balance between exploration and exploitation phases to ensure maximum performance on a given optimization problem. In this chapter, our contribution is the design and implementation of an MTIS system using a combination of WOA, MFO, and the Inverse Otsu (IO) Function. This modification is developed using the operators of the MFO algorithm in an attempt to enhance the exploitation phase of WOA during the process of finding the optimal solution for a given optimization problem. It is used to increase the system's performance so that the combined MFWOA algorithm performs better than WOA and MFO and provides better solutions. Therefore, the optimal exploration and exploitation properties of MFO and WOA are used in the search space to find the best thresholds. The rest of our chapter is organized as follows: Section 45.2 presents an overview of related work. In Section 45.3, we describe the prerequisites used in the proposed method. Section 45.4 offers the proposed method. Section 45.5 describes the performance analysis and test results. Finally, Section 45.6 presents the conclusions. 45.2 Related work The works that have been done so far in the field of MTIS using MH algorithms are single MH and Hybrid MH, which are briefly described in the following. Handbook of Whale Optimization Algorithm.

In this work, we are going to find a way to estimate content popularity in clustered networks. Si... more In this work, we are going to find a way to estimate content popularity in clustered networks. Since software-defined methods make planning more automatic and flexible, in this paper we take help of software-defined methods for content estimation. For this purpose, the content popularity estimation algorithm is implemented by the base station located in the control plane. At the data plane level, there are users and among the users there are several helper users who use the cache content in their device to help other users and offload traffic from the base station. First, we cluster the helpers using the clustering method. Then we estimate the content popularity in each cluster and finally an optimal cache placement method is used in the D2D cellular network to maximize the offload traffic. Evaluations show that cache placement based on the proposed content popularity estimation algorithm increases the hit rate and offload.

IEEE Transactions on Intelligent Transportation Systems, Nov 30, 2023

Due to the rapid growth of the Internet of Vehicles (IoV) and the rise of multimedia services, Io... more Due to the rapid growth of the Internet of Vehicles (IoV) and the rise of multimedia services, IoV networks' servers and switches are facing resource crises. Multimedia vehicles connected to the Internet of Things are increasing; there are millions of vehicles and heavy multimedia traffic in the IoV network. The network's scarcity of resources results in overload, which, in turn, leads to a degradation of both Quality of Service (QoS) and Quality of Experience (QoE). Conversely, when resources are abundant, it leads to unnecessary energy wastage. Managing IoV network resources optimally while considering constraints such as Energy, Load, QoS, and QoE is a complex challenge. To address this, the study proposes a solution by decomposing the problem and designing a modular architecture named ELQ 2. This architecture enables simultaneous control of the mentioned constraints, effectively reducing overall complexity. To achieve this objective, Network Softwarization and Virtualization concepts are employed. This modern architecture allows dynamically adjusting of the scale of the resources on demand, effectively reducing energy usage. Additionally, this architecture provides some other potentials, such as "the distribution of multimedia traffic among servers", "determining the route with high QoS for traffic", and "selecting a media with high QoE". A real test field is provided by Floodlight Controller, Open vSwitch, and Kamailio Server tools to evaluate the performance of ELQ 2. The findings suggest that the utilization of ELQ 2 holds promise in reducing the count of active servers and switches via effective resource management. Additionally, it demonstrates enhancements in various QoS and QoE parameters, encompassing throughput, multimedia delay, R Factor, and MOS, accomplished through load balancing strategies. As an illustration, the deployment of flows has achieved a commendable success rate of 95% owing to the utilization of SDN-based and comprehensive management practices encompassing all network resources.

arXiv (Cornell University), Jul 12, 2013

Having facilities such as being in text form, end-to-end connection establishment, and being inde... more Having facilities such as being in text form, end-to-end connection establishment, and being independence from the type of transmitted data, SIP protocol is a good choice for signaling protocol in order to set up a connection between two users of an IP network. Although utilization of SIP protocol in a wide range of applications has made various vulnerabilities in this protocol, amongst which overload could make serious problems in SIP servers. A SIP is overloaded when it does not have sufficient resources (majorly CPU processing power and memory) to process all messages. In this paper the window-based overload control mechanism which does not require explicit feedback is developed and implemented on Asterisk open source proxy and evaluated. The results of implementation show that this method could practically maintain throughput in case of overload. As we know this is the only overload control method which is implemented on a real platform without using explicit feedback. The results show that the under load server maintains its throughput at the maximum capacity.

Optimal Location of Thyristor-controlled Series Compensators in Power Systems for Increasing Loadability by Genetic Algorithm

Electric Power Components and Systems, 2011

ABSTRACT

IMSクラウドコンピューティングのための負荷分散呼受付制御器【Powered by NICT】

IEEE Transactions on Network and Service Management, 2016

FMap: A fuzzy map for scheduling elephant flows through jumping traveling salesman problem variant toward software‐defined networking‐based data center networks

Concurrency and Computation: Practice and Experience, Jun 20, 2023

arXiv (Cornell University), Jul 25, 2018

Due to the limited energy of sensor nodes in wireless sensor networks, extending the networks lif... more Due to the limited energy of sensor nodes in wireless sensor networks, extending the networks lifetime is a major challenge that can be formulated as an optimization problem. In this paper, we propose a distributed iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) with the aim of maximizing sensor network lifetime. The features of this algorithm are use of local information, low overhead of message passing, low computational complexity, fast convergence, and consequently reduced energy consumption. In this study, we present the convergence results and the number of iterations required to achieve the stopping criterion. Furthermore, the impact of problem size (number of sensor nodes) on the solution and constraints violation is studied and finally, the proposed algorithm is compared to one of the well-known subgradient-based algorithms.

International journal of ambient systems and applications, Jun 30, 2013

An optimal workflow scheduling method in cloud-fog computing using three-objective Harris-Hawks algorithm

Internet of Things

In the future, it is anticipated that software-defined networking (SDN) will become the preferred... more In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network resource usage and overall performance due to the fast growth of the AI field. This survey focuses on the following: Firstly, analyzing the SDN architecture and investigating the problem of load balancing in SDN. Secondly, categorizing AI-based load balancing methods and thoroughly assessing these mechanisms from various perspectives, such as the algorithm/technique employed, the tackled problem, and their strengths and weaknesses. Thirdly, summarizing the metrics utilized to measure the effectiveness of these techniques. Finally, identifying the trends and challenges of AI-based load balancing for future research.

2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)

Today, the Internet of Things (IoT) use to collect data by sensors, and store and process them. A... more Today, the Internet of Things (IoT) use to collect data by sensors, and store and process them. As the IoT has limited processing and computing power, we are turning to integration of cloud and IoT. Cloud computing processes large data at high speed, but sending this large data requires a lot of bandwidth. Therefore, we use fog computing, which is close to IoT devices. In this case, the delay is reduced. Both cloud and fog computing are used to increasing performance of IoT. Job scheduling of IoT workflow requests based on cloud-fog computing plays a key role in responding to these requests. Job scheduling in order to reduce makespan time, is very important in realtime system. Also, one way to improve system performance is to reduce energy consumption. In this article, three-objective Harris Hawks Optimizer (HHO) scheduling algorithm is proposed in order to reduce makespan time, energy consumption and increase reliability. Also, dynamic voltage frequency scaling (DVFS) has been used to reduce energy consumption, which reduces frequency of the processor. Then HHO is compared with other algorithms such as Whale Optimization Algorithm (WOA), Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) and proposed algorithm shows better performance on experimental data. The proposed method has achieved an average reliability of 83%, energy consumption of 14.95 KJ, and makespan of 272.5 seconds.

Journal of Ambient Intelligence and Humanized Computing

Today, the multimedia over IP (MoIP) network has become a cost-effective and efficient alternativ... more Today, the multimedia over IP (MoIP) network has become a cost-effective and efficient alternative to the public switched telephone network (PSTN). Free applications for multimedia transmission over the Internet have become increasingly popular, gaining considerable popularity around the world. This communication consists of two phases, i.e., signaling phase and media exchange phase. The SIP protocol is responsible for the MoIP network signaling to provide services such as VoIP, voice and video conferencing, video over demand (VoD), and instant messaging. This application layer protocol has been standardized by the IETF for initiating, managing, and tearing down multimedia sessions and has been widely used as the main signaling protocol on the Internet. The signaling and media are handled by SIP proxies and network switches, respectively. One of the most critical challenges in MoIP is the overloading of SIP proxies and network switches. Because of these challenges, a wide range of network users experiences a sharp drop in service quality. Overload occurs when there are not enough processing resources and memory to process all the messages due to the lack of proper routing. This study aims to model the routing problem in MoIP by providing a framework based on software-defined networking (SDN) technology and a convex mathematical programming model to prevent overload. The proposed framework is simulated and implemented using various scenarios and network topologies. The results show that throughput, latency, message retransmission rate, and resource consumption have improved using the proposed approach.