Publication Date: 2024
Multimedia Tools and Applications (13807501)83(35)pp. 83275-83309
The atmosphere is one of the game elements that can significantly influence player's emotions. However, creating an immersive atmosphere that effectively influences player emotions poses several challenges, necessitating the utilization of various elements, such as audio-visual coordination and gameplay design. This paper introduces a general framework for procedurally generating dungeons with joyful and horror atmospheres in games, providing an abstract perspective to address these challenges. The proposed framework introduces a categorization system for game elements based on their role within the game. Leveraging this categorization, the Comprehensive Arrangement of Game Elements (CAGE) pattern is introduced, which facilitates the appropriate placement of elements within the dungeon environment. Subsequently, the General Framework for Generating Dungeons with Atmosphere (GFGDA) is employed to procedurally create the dungeon using the Feasible–Infeasible Two-Population (FI-2Pop) algorithm. To enhance gameplay experience, similar elements in the dungeon environment that impact gameplay are grouped and their coordination is evaluated by creating a graph based on the CAGE pattern. The transition and coordination of audio-visual elements along the path between these impactful elements are assessed in order to generate an immersive atmosphere within the dungeon. To ensure diversity, examining the variety of dungeons generated over 100 runs demonstrates that our method consistently produces distinct results in each iteration. Moreover, two comparative studies were conducted, one with 51 volunteers and another with 10 volunteers. In the first study, the Game Experience Questionnaire (GEQ) was utilized to assess the emotional impact of dungeons generated by our method. These were compared to dungeons created using a uniform random approach, alongside relevant research. The results suggest that our method significantly influences player emotions across the four components of the GEQ—sensory and imaginary immersion, flow, negative effects, and challenge—when compared to dungeons generated by the uniform random approach and another researched method. In another study, the emotional impact of two dungeons, one generated with joyful elements and the other with eerie elements, was evaluated using the GEQ. The findings indicate significant differences between the two components of the GEQ—tension and positive effects—when players interacted with the level containing joyful elements compared to the one with eerie elements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
The tourism industry has undergone a significant shift towards data-driven strategies in recent years. As a means of improving the quality of their service and performance, service providers are analyzing feedback from their customers to increase the number of tourists they attract. Negative feedback also provides valuable insights into the factors that detract from a location's appeal. Datasets that gather information on people's experiences and opinions of tourist destinations can be analyzed to extract valuable information. However, there are currently few existing datasets that specifically capture user reviews about historical and tourist attractions in Iran. To fill this gap, users have shared their travel experiences on various websites, and sentiment analysis can be employed to extract insights from this data. Effective sentiment analysis requires a suitable approach for data extraction, pre-processing, and storage. This study provides a framework for the user review dataset preparation, including data collection, ETL, data storage, and evaluation phases. A rich dataset containing user reviews about 178 Iran's historical and tourist attractions was prepared through the proposed framework in which automated crawlers were developed to collect data from Tripadvisor platforms. Data labelling was achieved using the DistilBERT-base-uncased language model for sentiment analysis and human evaluators for final annotations. A total of approximately 25 thousand samples were included in the dataset, and positive user comments outnumbered negative user comments by a wide margin. This high percentage of positive comments suggests that the locations were of a satisfactory standard, making it likely that users would return in the future. The findings of this study can help providers to improve the overall quality of their services by analyzing user reviews. The proposed framework and achieved dataset can also guide future efforts to leverage data for improved performance and customer satisfaction in the tourism industry by identifying areas that need improvement. © 2023 IEEE.
Publication Date: 2022
Applied Soft Computing (1568-4946)122
Despite the empirical success of Genetic programming (GP) in various symbolic regression applications, GP is not still known as a reliable problem-solving technique in this domain. Non-locality of GP representation and operators causes ineffectiveness of its search procedure. This study employs semantic schema theory to control and guide the GP search and proposes a local GP called semantic schema-based genetic programming (SBGP). SBGP partitions the semantic search space into semantic schemas and biases the search to the significant schema of the population, which is gradually progressing towards the optimal solution. Several semantic local operators are proposed for performing a local search around the significant schema. In combination with schema evolution as a global search, the local in-schema search provides an efficient exploration–exploitation control mechanism in SBGP. For evaluating the proposed method, we use six benchmarks, including synthesized and real-world problems. The obtained errors are compared to the best semantic genetic programming algorithms, on the one hand, and data-driven layered learning approaches, on the other hand. Results demonstrate that SBGP outperforms all mentioned methods in four out of six benchmarks up to 87% in the first set and up to 76% in the second set of experiments in terms of generalization measured by root mean squared error. © 2022 Elsevier B.V.
Publication Date: 2020
Road Materials and Pavement Design (14680629)21(3)pp. 850-866
Fatigue cracking is the most important structural failure in flexible pavements. The results of a laboratory study evaluating the fatigue properties of mixtures containing precipitated calcium carbonate (PCC) using indirect tensile fatigue (ITF) test were investigated in this paper. The hot mix asphalt (HMA) samples were made with four PCC contents (0%, 5%, 10%, and 15%), and tested at three different testing temperatures (2°C, 10°C and 20°C) and stress levels (100, 300, and 500 kPa). Due to the complex behaviour of asphalt pavement materials under various loading conditions, pavement structure, and environmental conditions, accurately predicting the fatigue life of asphalt pavement is difficult. In this study, genetic programming (GP) is utilised to predict the fatigue life of HMA. Based on the results of the ITF test, PCC improved the fatigue behaviour of studied mixes at different temperatures. But, the considerable negative effect of the increase of the temperature on the fatigue life of HMA is evident. On the other hand, the results indicate The GP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
Publication Date: 2018
Applied Intelligence (0924669X)48(6)pp. 1442-1460
Semantic schema theory is a theoretical model used to describe the behavior of evolutionary algorithms. It partitions the search space to schemata, defined in semantic level, and studies their distribution during the evolution. Semantic schema theory has definite advantages over popular syntactic schema theories, for which the reliability and usefulness are criticized. Integrating semantic awareness in genetic programming (GP) in recent years sheds new light also on schema theory investigations. This paper extends the recent work in semantic schema theory of GP by utilizing information based clustering. To this end, we first define the notion of semantics for a tree based on the mutual information between its output vector and the target and introduce semantic building blocks to facilitate the modeling of semantic schema. Then, we propose information based clustering to cluster the building blocks. Trees are then represented in terms of the active occurrence of building block clusters and schema instances are characterized by an instantiation function over this representation. Finally, the expected number of schema samples is predicted by the suggested theory. In order to evaluate the suggested schema, several experiments were conducted and the generalization, diversity preserving capability and efficiency of the schema were investigated. The results are encouraging and remarkably promising compared with the existing semantic schema. © 2017, Springer Science+Business Media, LLC.
Publication Date: 2018
Soft Computing (14327643)22(10)pp. 3237-3260
A considerable research effort has been performed recently to improve the power of genetic programming (GP) by accommodating semantic awareness. The semantics of a tree implies its behavior during the execution. A reliable theoretical modeling of GP should be aware of the behavior of individuals. Schema theory is a theoretical tool used to model the distribution of the population over a set of similar points in the search space, referred by schema. There are several major issues with relying on prior schema theories, which define schemata in syntactic level. Incorporating semantic awareness in schema theory has been scarcely studied in the literature. In this paper, we present an improved approach for developing the semantic schema in GP. The semantics of a tree is interpreted as the normalized mutual information between its output vector and the target. A new model of the semantic search space is introduced according to semantics definition, and the semantic building block space is presented as an intermediate space between semantic and genotype ones. An improved approach is provided for representing trees in building block space. The presented schema is characterized by Poisson distribution of trees in this space. The corresponding schema theory is developed for predicting the expected number of individuals belonging to proposed schema, in the next generation. The suggested schema theory provides new insight on the relation between syntactic and semantic spaces. It has been shown to be efficient in comparison with the existing semantic schema, in both generalization and diversity-preserving aspects. Experimental results also indicate that the proposed schema is much less computationally expensive than the similar work. © 2017, Springer-Verlag GmbH Germany.
Publication Date: 2016
Soft Computing (14327643)20(5)pp. 2031-2045
Determining suitable mesh density for complicated finite element analysis, e.g., laser forming process, has always been the main concern of analytical engineers because of its high computation time and costs. Few works addressed the application of optimization methods for finite element analysis of linear path laser scan; however, no study has yet considered optimum finite element analysis of circular path laser forming. The main objective of this article is to develop a method for determining optimum mesh density to estimate the deflection caused by laser beam circular path scan considering analysis time and forming accuracy. Optimum ranges of mesh densities are investigated first and then a deflection estimating process based on adaptive-network-based fuzzy inference system has been introduced. The proposed model was finally optimized using genetic algorithm considering accuracy and time. The numerical analysis results were finally confirmed by the conducted experimental results. © 2015, Springer-Verlag Berlin Heidelberg.
Publication Date: 2016
Applied Intelligence (0924669X)44(1)pp. 67-87
Schema theory is the most well-known model of evolutionary algorithms. Imitating from genetic algorithms (GA), nearly all schemata defined for genetic programming (GP) refer to a set of points in the search space that share some syntactic characteristics. In GP, syntactically similar individuals do not necessarily have similar semantics. The instances of a syntactic schema do not behave similarly, hence the corresponding schema theory becomes unreliable. Therefore, these theories have been rarely used to improve the performance of GP. The main objective of this study is to propose a schema theory which could be a more realistic model for GP and could be potentially employed for improving GP in practice. To achieve this aim, the concept of semantic schema is introduced. This schema partitions the search space according to semantics of trees, regardless of their syntactic variety. We interpret the semantics of a tree in terms of the mutual information between its output and the target. The semantic schema is characterized by a set of semantic building blocks and their joint probability distribution. After introducing the semantic building blocks, an algorithm for finding them in a given population is presented. An extraction method that looks for the most significant schema of the population is provided. Moreover, an exact microscopic schema theorem is suggested that predicts the expected number of schema samples in the next generation. Experimental results demonstrate the capability of the proposed schema definition in representing the semantics of the schema instances. It is also revealed that the semantic schema theorem estimation is more realistic than previously defined schemata. © 2015, Springer Science+Business Media New York.
Publication Date: 2023
Journal of Supercomputing (15730484)79(2)pp. 1426-1450
The need for computation speed is ever increasing. A promising solution for this requirement is parallel computing but the degree of parallelism in electronic computers is limited due to the physical and technological barriers. DNA computing proposes a fascinating level of parallelism that can be utilized to overcome this problem. This paper presents a new computational model and the corresponding design methodology using the massive parallelism of DNA computing. We proposed an automatic design algorithm to synthesis the logic functions on the DNA strands with the maximum degree of parallelism. In the proposed model, billions of DNA strands are utilized to compute the elements of the Boolean function concurrently to reach an extraordinary level of parallelism. Experimental and analytic results prove the feasibility and efficiency of the proposed method. Moreover, analyses and results show that a delay of a circuit in this method is independent of the complexity of the function and each Boolean function can be computed with O(1) time complexity. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2023
Scientia Iranica (23453605)30(4 D)pp. 1279-1295
DNA computing is a new kind of computation for solving complex problems with signi cant parallelism. Research ndings indicate that DNA-based logic systems can be useful in many biomedical applications such as early cancer detection. DNA logic systems have been applied successfully to detect the risky patterns of nucleotide-based cancer biomarkers (microRNAs). Detection of real diseases requires large-scale DNA-based logical systems. Therefore, the issue of large-scale DNA-based logic circuits is a crucial research topic. In this paper, an automatic design ow is proposed to facilitate the design, veri cation, and physical implementation of multi-stage and large-scale DNA logic circuits. Digital Micro uidic Biochips (DMFB) have been used recently as a promising platform for efficient implementation of DNA-based computing systems and circuits. We used this technology as the physical platform for implementation of DNA-based circuits. Our experiments and implementations show the feasibility, accuracy, efficiency, and simplicity of the proposed design ow. Final DNA reactions that are synthesized by the proposed design ow are veri ed and simulated using stochastic DNA-reaction simulators to prove the correctness of the proposed design ow. This design ow can open a new horizon for researchers and scientists to design, implement, and evaluate the DNA-based logic systems. © 2023 Sharif University of Technology. All rights reserved.
Publication Date: 2018
Microprocessors and Microsystems (01419331)61pp. 217-226
DNA is known as the building block of live organisms for storing the life codes and transferring the genetic features through the generations. However, it is found that DNA strands can be used for a new kind of computation. DNA computation proposes a new level of impressive degree of parallelism that is not feasible with conventional electronic computers. However, available computational models cannot be used for massive parallelism in DNA computing and new computation models and techniques should be developed. In this paper, a new computational model and methodology is proposed to use the massive parallelism of DNA-based circuits. In the proposed model, billions of DNA strands are utilized to compute the elements of the Boolean function concurrently to reach a high level of parallelism. Simulation and analytical results prove the feasibility and efficiency of the proposed method. Moreover, analyses and results show that delay of a circuit in this method is independent from the complexity of the function and each Boolean function can be computed with O(1) time complexity. © 2018
Publication Date: 2017
IEEE Transactions on Biomedical Circuits and Systems (19324545)11(5)pp. 1077-1086
DNA is known as the building block for storing the life codes and transferring the genetic features through the generations. However, it is found that DNA strands can be used for a new type of computation that opens fascinating horizons in computational medicine. Significant contributions are addressed on design of DNA-based logic gates for medical and computational applications but there are serious challenges for designing the medium and large-scale DNA circuits. In this paper, a new microarchitecture and corresponding design flow is proposed to facilitate the design of multistage large-scale DNA logic systems. Feasibility and efficiency of the proposed microarchitecture are evaluated by implementing a full adder and, then, its cascadability is determined by implementing a multistage 8-bit adder. Simulation results show the highlight features of the proposed design style and microarchitecture in terms of the scalability, implementation cost, and signal integrity of the DNA-based logic system compared to the traditional approaches. © 2007-2012 IEEE.
DNA is known as the basic element for storing the life codes and transferring the genetic features through the generations. However, it is found that DNA molecules can be utilized for a new kind of computation that opens fascinating horizons in computation and medical sciences. Significant contributions are addressed on design of DNA-based logic gates for medical and computational applications. Microfluidic biochips are known as efficient platforms to implement the DNA circuits but current biochips architectures allow sequential implementation of DNA modules that leads to increase the run time. In this paper, a new Microfluidic biochip architecture and corresponding CAD flow is presented for parallel implementation of DNA circuits. In this flow, Verilog description of the circuit files are synthesized and converted into a bioassay file format. Then assay files are implemented on a microfluidic biochip based on parallel architecture that mane is PBCM architecture. Experimental results show that the experimental time of assays and pin number of biochips are reduced by 17% and 23% respectively. © 2017 IEEE.
Beiki, Z.,
Mirzakuchaki, S.,
Soryani, M.,
Mozayani, N. Publication Date: 2012
Journal of Computational and Theoretical Nanoscience (15461955)9(5)pp. 627-630
Majority and inverter gates together make a universal set of Boolean primitives in Quantum-dot Cellular Automata (QCA) circuits. However, an experimental evaluation has shown that MV is not efficiently used during technology mapping by existing logic-synthesis tools. In this paper, we propose an approach, based on Genetic Algorithm, which reduces the area size of QCA circuits. Simulation results show that the proposed method is able to reduce area in QCA circuits design. Copyright © 2012 American Scientific Publishers All rights reserved.
Quantum cellular automata (QCA) is a new nanotechnology that has attracted attentions due to its lower power consumption, smaller size and higher speed compared to CMOS technology. Majority and inverter gates together make a universal set in QCA circuits. An important step in designing QCA circuits is reducing the number of required cells. This paper introduces the structure of QCA and its basic circuits and then proposes a method to reduce the number of cells used in designing these circuits based on genetic algorithm. The results of this method compared with previous methods indicate a significant improvement in terms of number of cells used in the synthesis of QCA circuits. © 2011 IEEE.
Publication Date: 2025
Information and Software Technology (09505849)187
Context: Nowadays, developing data analysis software for the IoT domain faces challenges such as complexity, repetitive tasks, and developers’ lack of domain knowledge. To address these issues, methodologies like CRISP-DM have been introduced, providing structured guidance for data analysis. Objectives: Despite the availability of structured methodologies, building data analysis pipelines still involves managing complexity and redundancy. Model-driven approaches have been proposed to tackle these challenges but often fail to address all stages of the data analysis workflow and the interdependencies between stages and datasets comprehensively. This research introduces RAIDAD, a model-driven framework that addresses these gaps by covering all phases of the CRISP-DM methodology. Methods: RAIDAD includes a domain-specific modeling language for IoT data analysis, a graphical modeling editor, a code generation transformation engine, and a data model assistant for seamless model-data integration. These components are delivered as an Eclipse plugin. Results: The evaluation of RAIDAD is two-fold. First, a comparative operational evaluation with RapidMiner and ML-Quadrat shows RAIDAD achieves a 9.6% improvement in usability and productivity over RapidMiner and a 23% improvement over ML-Quadrat. Second, RAIDAD is compared to a general-purpose programming language, demonstrating its superiority in reducing effort and production time for IoT data analysis software. Conclusion: This comprehensive framework ensures an efficient and organized approach to data analysis, addressing key challenges in the IoT domain. Future research will focus on expanding RAIDAD's support for a wider range of data analysis and machine learning algorithms, enhancing automation capabilities, and incorporating continuous user feedback to ensure the framework evolves in line with emerging needs. © 2025 Elsevier B.V.
Publication Date: 2025
Computer Communications (1873703X)241
The emergence of 6th Generation (6G) cellular networks presents an opportunity to redefine Key Performance Indicators (KPIs) necessary for high-quality communications in the 2030s. 6G aims to innovate through novel architectural designs and the utilization of higher frequency bands, alongside incorporating aerial coverage to establish a three-dimensional network framework in contrast to its predecessor, 5G. Central to this innovation are Unmanned Aerial Vehicles (UAVs), which can be used as Drone Base Stations (DBSs). Despite the energy required for UAVs to hover, they can significantly decrease energy consumption and environmental impact by replacing terrestrial cellular infrastructure and switching off underutilized or inefficient Small Base Stations (SBSs) in Ultra-Dense Networks (UDNs). This work presents an energy-efficient UAV-assisted On-Off switching methodology that considers energy usage of DBSs’ backhaul links, in contrast to previous studies. By optimizing DBS placement, user association, and power control, the approach aims to improve energy efficiency. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) optimization, which is then decomposed into three manageable sub-problems that are solved using proposed algorithms. This methodological framework not only alleviates the complexity associated with the original problem but also enables practical implementations in energy-constrained UAV systems, ultimately leading to improved energy efficiency compared to existing approaches. Simulation results demonstrate about 90 % improvement of energy efficiency compared to prior studies even when fewer SBSs are switched off. Furthermore, the proposed approach exhibits 95 % better energy efficiency rather than previous methods when the serving time of UAVs increases. © 2025 Elsevier B.V.
Publication Date: 2025
Journal of Supercomputing (15730484)81(9)
With the increasing number of internet of vehicles devices and the exceptional growth in data traffic, the licensed spectrum is faced with limitations in meeting the growing demand for cellular vehicle-to-everything (C-V2X) applications. Opportunistic utilization of unlicensed bands is regarded as a solution to this issue. However, using unlicensed bands by cellular technologies poses challenges for coexisting with other unlicensed systems. This research examines how 5G new radio operating in the unlicensed band (5G NR-U) can coexist with Wi-Fi systems. It assumes that 5G NR-U exploits the duty cycle method for managing coexistence. An optimization problem is established that exploits the estimated load of Wi-Fi systems to enhance the total throughput of the cellular network while considering the rate constraint of Wi-Fi users. In most coexistence schemes, the cellular system exploits knowledge of the Wi-Fi traffic through a given signaling channel. However, this signaling channel is not always applicable in practice. As a solution, this paper proposes an approach that exploits a federated convolutional neural network (CNN) to gauge the intensity of Wi-Fi traffic by analyzing unlicensed channel activity. Based on CNN’s prediction, a Q-learning based algorithm is then developed to solve the resource allocation problem and adjust the parameters of the duty cycle based on the estimated Wi-Fi load and C-V2X network status. Simulation results demonstrate that even without signaling exchanges, the suggested approach enhances the throughput of the cellular network by about 35% on average in scenarios with medium traffic load compared to the previous method while the required rates of Wi-Fi users are not considerably violated. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Publication Date: 2025
IEEE Access (21693536)13pp. 6584-6593
Using a multi-hop routing protocol for unicast vehicle-to-vehicle (V2V) communications is crucial to facilitate data relaying from one vehicle to a distant one. The dynamic behavior of vehicular ad-hoc networks (VANETs) and unstable relays often cause frequent disconnections or switchovers, leading to higher latency, diminished reliability, and increased resource use. The stability of these routes depends not just on link connectivity, but also on the availability of adequate resources. Although numerous studies have explored traditional VANET routing protocols to tackle these issues, they often neglect the critical aspect of resource availability. In this study, we focus on a V2V routing method that considers resource availability, assuming the use of a geo-based resource allocation framework. In the proposed resource-aware approach, we utilize deep learning to predict vehicle trajectories and traffic load in various areas. The proposed resource-aware routing protocol aims at improving both spectrum efficiency and route stability by employing distance between vehicles, traffic load, and resource availability of areas to manage emergency messages (EMs) more efficiently and reduce unnecessary rebroadcasting and congestion. Our simulation results reveal that our proposed method surpasses traditional protocols in terms of packet reception ratio (PRR), latency, and average hop count. © 2013 IEEE.
Publication Date: 2024
Ad Hoc Networks (15708705)154
Restricted Access Window (RAW) and group-based media access are new features that are exploited by IEEE 802.11ah standard to resolve massive access problem in Internet of Things (IoT) applications. Nevertheless, inefficient device grouping and inappropriate contention resolution in a RAW may still result in collisions and consequently lead to degradation of QoS, energy efficiency, and channel utilization. In existing works, contention resolution schemes schedule all failed devices to retransmit in the next slot of the RAW, which increases the probability of collisions. In this paper, we propose a new retransmission scheme that allows the collided devices of each RAW slot to have another transmission chance in one of the next µ upcoming slots, randomly. We represent the proposed retransmission method via a probabilistic model and formulate a problem based on it, to adjust the number of slots of each RAW with the aim of increasing overall energy efficiency and overall channel utilization regarding delay constraint of devices. Solving the formulated problem, a meta-heuristic algorithm is used to adjust the number of RAW slots and so the size of each RAW regarding the results of the device grouping algorithm. To better exploit the capabilities of the proposed idea, we suggest a load-aware and distance-based device grouping algorithm that not only considers the hidden node problem, but also attends to the load balance of the groups. Simulation results show that the proposed retransmission and RAW adjustment scheme alongside the proposed device grouping algorithm, improve energy efficiency and channel utilization by 25 % and 17 % respectively, and reduce the access delay by 31 % in average, compared to the previous retransmission method. © 2023 Elsevier B.V.
Publication Date: 2024
Wireless Personal Communications (1572834X)135(1)pp. 593-617
The ever-increasing demand for the wireless communications specially in sub-6 GHz frequency ranges has led to radio resource scarcity where opportunistic spectrum access is its main solution. An online spectrum decision and prediction system can assist cognitive radio users in seeking idle frequency bands for opportunistic use. However, previous studies have not considered the use of crowd-sensing technique to collect spectrum and contextual information to present a hybrid spectrum decision/prediction service. In this paper, we propose a novel cloud-based service for spectrum availability decision and prediction, which brings more contextual parameters into the decision with the aim of improving the quality of decision. Location, time, and velocity of sensing nodes, the density of buildings around sensing nodes, and weather status have been considered as context information. In the proposed method, spectrum availability data and some of the mentioned context parameters are collected through crowd-sensing. Artificial neural network (ANN) classifiers are suggested to decide about the status of spectrum bands in the proposed architecture. We also propose a spectrum prediction service in our architecture to predict the future of spectrum bands and recommend ANN and k-nearest neighbor algorithms for prediction. The proposed architecture has been implemented and evaluated. Experimental results show that using the addressed contextual information, the quality of spectrum availability decision is improved. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Publication Date: 2024
Computer Communications (1873703X)214pp. 270-283
Full-duplex ultra-dense network (FD-UDN) is a promising technology in 5G mobile networks for handling the increase in network capacity. However, interference management and energy efficiency are two of the most important challenges that must be addressed. With the small base stations (SBSs) densification, employing sleeping strategy coupled with resource management becomes an effective approach to manage interference and power consumption in FD-UDN. To the best of our knowledge, this aspect has not been addressed in previous work. To this end, we develop a framework to optimize the BS sleeping and resource management with the aim of maximizing energy efficiency and maintaining the quality of service (QoS) requirements of users. The problem is formulated as a non-convex mixed-integer non-linear programming problem, which is difficult to handle. Employing the Dinkelbach method, the objective function of the problem is converted to an equivalent parametric subtractive form. Then, the problem is decoupled into two sub-problems: user association and resource allocation, as well as BSs on/off switching. The former is solved using the iterative reweighted lq-norm minimization (IRM) method, and the latter is solved using the Lagrangian dual method and the constrained concave-convex procedure (CCCP). The simulation results demonstrate that the proposed method is more effective than traditional ones in simultaneously improving the EE, reducing power consumption, and keeping fewer SBSs active, especially in high network loads. © 2023 Elsevier B.V.
Publication Date: 2024
Journal of Supercomputing (15730484)80(2)pp. 2067-2127
In Internet-of-Things (IoT)-based healthcare systems, real-time healthcare data are gathered from patients’ sensors with limited resources and transferred to end-users through gateways and healthcare service providers. Privacy of patients is a main challenge of these systems. Although privacy has already been considered in IoT-based healthcare systems, best centralized approaches yet suffer from collusion attack. Therefore, some researchers have come up with blockchain-based solutions to protect patients’ privacy in IoT-based healthcare systems. However, those methods assume that parts of the entities along the end-to-end communication path from patients’ sensors to the end-users are trusted or even assuming no privacy threats from internal attackers. Therefore, there is a lack of a blockchain-based approach in IoT-based healthcare systems to provide privacy for patients, assuming that all system entities are untrusted. To overcome these challenges, in this paper, we leverage a three-layered hierarchical blockchain, the zero-knowledge proof (ZKP), and the ring signature method to achieve data and location privacy of patients against both internal and external attackers. In addition, the proposed method provides anonymous authentication, authorization, and scalability, which are essential features in healthcare systems. Intuitive and formal security analyses demonstrate the resilience of our scheme against various attacks such as denial of service (DoS), modification, mining, storage, and replay attacks. The proposed method is compared to a recent blockchain-based method and also a centralized privacy-preserving scheme. Compared to the similar blockchain-based method, the computational overhead and delay of the authentication and data transfer phase are about 35% and 37% higher, respectively. Instead, the proposed method reduces memory usage of gateways by about 55% and diminishes the computational overhead and delay of information access phase by about 30% and 33% compared to the previous blockchain-based method. Therefore, the proposed method does not increase overhead and end-to-end delay considerably compared to the previous blockchain-based scheme, while some other performance metrics and security features are improved. Moreover, compared to a previous centralized method, the proposed approach shows more than 25% decrease in communication overhead and 22% improvement in memory usage of gateways, in average. Although the use of the blockchain imposes more computational overhead on service providers and may increase the latency compared to the centralized approach (depending on the type of the blockchain technology that is used), these weaknesses are negligible at the expense of increased security. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
In the future wireless networks, terrestrial, aerial, space, and maritime wireless networks are integrated into a unified network to meet the needs of a fully connected global network. Nowadays, vehicular communication has become one of the challenging applications of wireless networks. In this article, we aim to address the radio resource management in Cellular V2X (C-V2X) networks using Unmanned Aerial Vehicles (UAV) and Non-orthogonal multiple access (NOMA). The goal of this problem is to maximize the spectral efficiency of vehicular users in Cellular Vehicle-to-Everything (C-V2X) networks under a fronthaul constraint. To solve this problem, a two-stage approach is utilized. In the first stage, vehicles in dense area are clustered based on their geographical locations, predicted location of vehicles, and speeds. Then UAVs are deployed to serve the clusters. In the second stage, NOMA groups are formed within each cluster and radio resources are allocated to vehicles based on NOMA groups. An optimization problem is formulated and a suboptimal method is used to solve it. The performance of the proposed method is evaluated through simulations where results demonstrate superiority of proposed method in spectral efficiency, min point, and distance. © 2024 IEEE.
Publication Date: 2023
Computer Networks (13891286)234
The high density of small cells in the Ultra-Dense Network (UDN) has increased the capacity and the coverage of Fifth Generation (5G) cellular networks. However, with increasing the number of Small Base Stations (SBSs), energy consumption rises sharply. One suggested method to reduce energy consumption is to manage the SBS On/Off switching. Moreover, due to spectrum constraints, the Power Control and Resource Allocation (PCRA) are other significant issues in UDN, which affect the Energy Efficiency (EE) and the Spectrum Efficiency (SE). Recent works in UDN have not presented the optimal SBSs On/Off switching and PCRA technique simultaneously to maximize the EE and the SE while ensuring Quality of Service (QoS) requirements of User Equipments (UE). In this paper, a distributed method based on a multi-agent Deep Q-Network (DQN) is proposed to deal with the mentioned challenges simultaneously. Therefore, each SBS can learn a policy for managing On/Off switching and downlink PCRA using two DQNs. The proposed method seeks to optimize the EE and the SE as well as guarantee the minimum required data rate of UEs. Simulation results show that the proposed method improves the EE and the SE compared to previous solutions. Furthermore, unlike previous distributed approaches that use the UEs as learning agents, the proposed method uses the SBSs as agents. Thus, the signaling overhead and computational complexity of the UEs decrease. © 2023 Elsevier B.V.
Publication Date: 2023
PLoS ONE (19326203)18(10 October)
5G wireless networks are paying increasing attention to Vehicle to Everything (V2X) communications as the number of autonomous vehicles rises. In V2X applications, a number of demanding criteria such as latency, stability, and resource availability have emerged. Due to limited licensed radio resources in 5G cellular networks, Cellular V2X (C-V2X) faces challenges in serving a large number of cars and managing their network access. A reason is the unbalanced load of serving Base Stations (BSs) that makes it difficult to manage the resources of the BSs optimally regarding the frequency reuse in cells and its subsequent co-channel interference. It is while the routing protocols could help redirect the load of loaded BSs to neighboring ones. In this article, we propose a resource-aware routing protocol to mitigate this challenge. In this regard, a hybrid C-V2X/ Dedicated Short Range Communication (DSRC) vehicular network is considered. We employ cluster-based routing that enables many cars to interface with the network via some Cluster Heads (CH) using DSRC resources while the CHs send their traffic across C-V2X links to the BSs. Traditional cluster-based routings do not attend the resource availability in BSs that are supporting the clusters. Thus, our study describes an enhanced clustering method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that re-clusters the vehicles based on the resource availability of BSs. Simulation results show that the proposed re-clustering method improves the spectrum efficiency by at least 79%, packet delivery ratio by at least 5%, and load balance of BSs by at least 90% compared to the baseline. © 2023 Alrubaye. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
