ABSTRACT In recent years, there has been a rise in the use of ChatGPT for education, healthcare, smart cities and emerging technologies. However, no available studies or reviews have provided a consistent and reliable depiction of the situation regarding its usage and evaluation. The reporting of datasets, evaluation indicators, factors influencing performance and conditions of deployment has also varied from study to study. This fragmented state of affairs seriously inhibits attempts to assess ChatGPT's capabilities and limitations and thus improve the design of future versions. Earlier reviews were often conducted in a way pertaining to a single area or were mainly descriptive, with less emphasis on methodological evaluation and issues in deployment and ethics. To fill this void, we undertook a systematic review, according to PRISMA guidelines, limiting our searches to English‐language journal articles published during 2021–2025 by reputable publishers. Such studies focused directly on GPT models, provided assessment conditions and were cited extensively as preprints, while the exclusion criteria encompassed poorly linked studies, those not in English and studies employing ChatGPT as an adjunct. An analysis of these studies revealed that the vast majority of research has taken place in the area of education (32%) and health (28%). The review revealed significant variation in assessment accuracy across domains, frequent challenges with doubtful sensitivity, unpredictable and rapid changes and risks associated with specific domains that impact reliability and safety. This study's primary contribution is an effort to develop an integrated analytical framework that puts together these interdisciplinary results in a streamlined manner for interpreting the capabilities and limitations of ChatGPT. Because of the methodological heterogeneity of existing studies, the results can be viewed as qualitative trends instead of standard quantitative evidence. The results thereby accentuate the need for consistent criteria, domain‐informed evaluation practices and stronger methodological reporting to underpin a more reliable deployment of ChatGPT‐based systems.

ABSTRACT This paper presents a soft and novel context‐aware prompt engineering framework to enable adaptive and safe integration of large language models (LLMs) into autonomous vehicle (AV) systems. Unlike prior works, which employed static prompts or offline decision logic, our method dynamically creates prompts based on multimodal context, including speech commands, environmental cues (e.g., traffic, weather), and urgency levels. A hierarchical prioritization model is introduced to classify instructions according to safety sensitivity, enabling fine‐grained control and real‐time response in high‐risk AV scenarios. The system integrates speech‐to‐text (STT) transcription, text inputs, environmental context, and LLMs. Assessment of the Talk2Car dataset and a complementary noisy‐speech testbed indicated consistent improvements in accuracy, precision, recall, and F1 across four backbone LLMs (BERT, GPT‐2, SALMON, and SALMONN). These results demonstrate the effectiveness of prompt‐level adaptation in ensuring robustness and scalability in real‐world AV deployments.

Extracting valuable information from vast sources of social networks while protecting confidentiality and preventing data disclosure is a significant challenge in big data environments. Traditional anonymization methods often fall short in handling the volume, variety, and velocity of big data, leading to high data loss and inefficiency. This article addresses these challenges by proposing a novel anonymization method based on K-means clustering within the Spark framework, leveraging its in-memory processing capabilities. Our model uses K-means clustering to determine optimal cluster heads, significantly reducing data loss and identity disclosure risks. By utilizing Spark's RDD abilities and the MLlib component, our method achieves faster processing times compared to traditional methods that rely on non-in-memory big data tools. Performance evaluation demonstrates that at k = 9, the cost factor is minimized to 0.20, indicating the efficiency and effectiveness of our approach. The proposed method not only enhances processing speed but also ensures minimal data loss, making it suitable for real-time anonymization of big data streams. This work provides a balanced solution that addresses the critical need for high-speed data anonymization while maintaining data privacy and utility.

Summary Examining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high‐quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data‐trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1‐score, and 5% run time complexity.

Summary The Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments.

Abstract The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

Summary Fog computing is a suitable platform for the Internet of Things (IoT). However, it faces threats that pose security and privacy challenges. This article proposes two new, faster, and more secure schemes based on attribute‐based encryption (ABE) address some security concerns. The new schemes are Cipher text‐policy‐ABE‐anonymous authentication (CP‐ABE‐AA) and key‐policy‐ABE‐anonymous authentication (KP‐ABE‐AA), providing more secure communications and anonymous recognition between servers and users employing registration center (RC). Users and servers register with RC and receive time parameter t and aliases to build an access tree. During the valid time, t a more secure transfer is established. After each t has expired, the servers and users obtain new aliases. RC provides anonymous authentication, and t determines the key's validity period. For simulation, a multi‐paradigm programming language, Rust, was installed on Ubuntu 20. Compared to five existing schemes, the simulation results indicated that, on average, CP‐ABE‐AA has a 5.48% reduction time for key generation but an increased time of 11.28% in encryption and a reduction time of 11.33% in decryption. Also, compared to two other schemes, KP‐ABE‐AA produced an average time increase of 17.31% for key generation, but encryption and decryption times were reduced by 17.31% and 9.57%.

Summary The Covid‐19 pandemic has affected many lives over the past year. In addition to the enormous health cost, the necessary lockdowns and government‐mandated suspension to prevent the spread of the virus had a huge economic impact. The new challenges in 2021 were combating new virus mutations and providing effective vaccines globally. Artificial intelligent (AI) and machine learning have made significant improvements in many different applications during the last decades. One of the advanced and robust technologies in machine learning is deep learning (DL), which can be employed to help prevent initial infections and detect and monitor their progress and side effects. Fast and accurate Covid‐19 infection detection and treatment of suspected patients is essential to make better decisions, ensure treatment, and even save patients' lives. Modern technologies are required to achieve these objectives and create a sustainable society. This article presents a taxonomy in DL algorithms to cover both the technical novelties and empirical results techniques for Covid‐19 in smart cities. In this regard, (i) we demonstrate possible DL algorithms capable of combating Covid‐19; (ii) we propose an up‐to‐date perspective of DL algorithms in social prevention and medical treatment; and (iii) we identify the challenges in combating Covid‐19 outbreaks.

Summary Internet of Things (IoT) is a new phenomenon that proposes novel business opportunities. IoT allows the world to be programmable and might provide several benefits for organizations. Based on the IoT survey, cyber‐security issues are among the most extensive and complicated challenges faced by IoT devices. Threat detection is considered a preventive measure against malware threats, ransomware, and attacks, which become more serious each year because of the dramatic rise in malware attacks. This article investigates threat detection techniques that fall into three categories: malware detection, attack detection, and ransomware detection, published from 2017 to August 2021. We examine solutions, techniques, features, classifiers, and tools proposed by IoT researchers. Some questions are proposed, and answering the questions may help the researchers suggest a more efficient solution in future works. Furthermore, the achievement and disadvantages of each study are discussed. Finally, based on the reviewed studies, some open challenges and practical measures to future directions are suggested, worth further studying and researching threat detection techniques in the IoT.

Summary Recent advances in technology have resulted in generating or collecting massive volumes of data from rich data resources such as sensors and mobile devices in Internet of Things (IoT). Using data mining techniques can help overcome the mining problem in Fog computing environments which include millions of IoT devices. In addition, it can optimize response times, recourse consumption, and scalability in IoT applications. Frequent pattern mining, as one of the fundamental data mining tasks, is used for finding hidden patterns in such large datasets. The traditional data mining algorithms have many challenges such as scalability and resource consumption. This systematic review aimed to investigate the data mining algorithms, which focus on handling massive datasets, and present a technical taxonomy including the transaction‐centric, item‐centric, distributed, and parallel topics. The transaction‐centric and MapReduce‐based approaches were mostly utilized by 37% and 38%, respectively. Additionally, item‐centric, distributed, and parallel algorithms were employed 12% and 13%, respectively. The response time as a Quality of Service (QoS) factor had the highest percentage in the estimations of data mining algorithms (55%), followed by scalability (25%), and cost (20%). To the best of our knowledge, no study has focused on fog‐computing frequent pattern mining algorithms as one of the most important data mining tasks. This article aims to present a systematic review of the frequent pattern mining algorithms in fog computing and discuss the issues, challenges, and research perspectives for helping academia and industry leverage the power of data mining algorithms in fog computing.

Abstract Plant competition is a fundamental process in plant communities. In one neighbourhood, different plants compete with each other to access shared resources. This paper presents a novel evolutionary algorithm, plant competition optimization (PCO) algorithm, inspired by plant competition processes. In this algorithm, each feasible solution to an optimization problem is assumed to be a plant, with the underlying assumption that each plant grows in competition with its neighbours. In contrast to other techniques inspired by natural phenomena, we attempted to fit the formulation with a known plant growth model, Richards' growth model, and simulate what happens in nature. As the number of plants in a specific area increases, the available resources are decreased, and competition occurs in smaller areas. So, to use this aspect of competition, we develop some mathematical formulation to simulate the decrease of neighbouring area for each plant related to its size to compete on the share resources with the other neighbouring plants. This competition will conduct a smart local search around the most fitted solutions in the optimization context. Furthermore, the reproduction is simulated by producing some seeds in their neighbouring area, which a few of them can migrate to far distances as well. Summing up together, the most powerful plants can grow more. Under competition pressure, their neighbouring area will decrease more than the others, producing more seeds in the next generation. Just as happened in nature, the losers of this competition will die. Our algorithm's efficiency is shown by performing numerical tests on well‐known optimization problems and comparing the results with the other evolutionary algorithms. The results confirm that PCO is effective and efficient in finding sub‐optimal solutions and gives better results than genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), grasshopper optimisation algorithm (GOA), dragonfly algorithm (DA), salp swarm Algorithm (SSA), and comparable results with whale optimization algorithm (WOA) and marine predators algorithm (MPA) on multimodal optimization functions because it efficiently explores the entire search space efficiently and intelligently.

Abstract Increasing resource efficiency and reducing the energy consumption of cloud data centers is critical, especially during the global CORONA virus pandemic. Virtual machines' consolidation using live migration maximizes the hosts' and the reduction of energy consumption. An increase in the host's virtual machines in the consolidation process and the dynamic workload of the virtual machines may cause the overloading in the hosts. One approach to overcome this problem is reducing the hosts' virtual machines. One crucial issue to improve the quality of the consolidation process's quality is determining the best virtual machine for the migration process. Although the selection process has lower computational complexity than other challenges (like placement and overload prediction) in the consolidation process, this issue has received less attention. This article aims to present an efficient algorithm for the selection process. We first considered five main criteria for the selection process: migration time, migration risk, virtual machine connectivity, releasable resources, and penalty for SLA violation. Then, we propose an algorithm based on analytic hierarchy process multi‐criteria decision‐making technique. Next, to determine the weight of the proposed criteria, we simulate thousands of virtual machines of the PlanetLab workloads. These weights are tunable based on the data center preferences. The results of the suggested approach results show 23% reduction in the hosts' energy consumption, 49% reduction in the number of migrations, and 18% reduction in the SLA violation compared with other techniques. So, using the proposed method may significantly reduce the overall cost of the data centers.

Abstract Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

Abstract Cloud systems have become an essential part of our daily lives owing to various Internet‐based services. Consequently, their energy utilization has also become a necessary concern in cloud computing systems increasingly. Live migration, including several virtual machines (VMs) packed on in minimal physical machines (PMs) as virtual machines consolidation (VMC) technique, is an approach to optimize power consumption. In this article, we have proposed an energy‐aware method for the VMC problem, which is called energy‐aware virtual machines consolidation (EVMC), to optimize the energy consumption regarding the quality of service guarantee, which comprises: (1) the support vector machine classification method based on the utilization rate of all resource of PMs that is used for PM detection in terms of the amount' load; (2) the modified minimization of migration approach which is used for VM selection; (3) the modified particle swarm optimization which is implemented for VM placement. Also, the evaluation of the functional requirements of the method is presented by the formal method and the non‐functional requirements by simulation. Finally, in contrast to the standard greedy algorithms such as modified best fit decreasing, the EVMC decreases the active PMs and migration of VMs, respectively, 30%, 50% on average. Also, it is more efficient for the energy 30% on average, resources and the balance degree 15% on average in the cloud.

Vehicle speed is known as one of the most important parameters in the various goals for driving. Although the most critical goal of driving is safety, each driver can consider another secondary goal, such as reducing travel time, economical driving, green driving, passenger comfort, etc. In this paper, we propose a smartphone-based application that utilizes the cloud/fog computing service infrastructure in order to recommend speed according to primary safety goal and another secondary objective. Due to the strategic nature of the driving speed selection issue, we have modeled the problem as a game that the drivers are the players, and the speed of the vehicle is their strategy. We solve the proposed game via evolutionary dynamics with appropriate convergence time, and the resulting equilibrium profile is announced to the drivers as the recommended speed at specified time intervals. Finally, the experimental results obtained from the simulation of the proposed scheme confirm that the deviation from the proposed equilibrium velocity is not profitable for the offending player. Indeed, deviation from equilibrium conditions leads to the negative impacts on the formal evaluation parameters presented in this paper.

The fitness evaluation (FE) management has been successfully applied to improve the performance of multi-population methods for dynamic optimisation problems (DOPs). In this work, we extend one of its variants to address DOPs which was recently proposed by the authors. The aim of our proposal is to increase the efficiency of the FE management. To this end, we propose a technique based on hierarchical learning automata that manages FEs at two level: at first level the algorithm decides which population should be executed, and at the second level it specifies the operation that should be performed by the selected population. A detailed experimental analysis shows the effectiveness of our proposal.

The exclusion operator is a key component in separating the search territory of each population in multi-population optimisation algorithms for unconstraint continues dynamic optimisation problems (DOPs) with the aim of maintaining the overall diversity of the population and avoiding redundant search. Although extensively used by the researchers, the role of exclusion has been barely studied in detail. Therefore, in this paper, we solely study the role of exclusion as a part of multi-population methods in DOPs. For this purpose, a comprehensive review of the various exclusion strategies reported in the literature is provided. Four strategies are also introduced to reduce the shortcomings of exclusion operator. Experimental results show that proposed strategies compared to other schemes such as reinitialized midpoint check, hill-valley detection with three checkpoints, and merging information of collided populations have the same or even higher ability to improve the performance of the multi-swarm PSO algorithms in moving peaks benchmark.

There are two different concepts [Internet of Things (IoT) and cloud computing] influencing our lives in many ways as they will further be used and highlighted in the future of the Internet. The present systematic study discusses a combination of these two concepts. Many studies have focused on IoT and cloud computing separately. These studies lack a deep investigation of their combination, which has new challenges and issues. Yet, the recent integration of them has been paid a primary focus. This systematic study attempts to analyse how the combination of IoT and cloud has been presented and detects the challenges and metrics of such integration. Further, this analysis aims to develop an understanding of the current affair of this integration by overviewing a collection of 38 recent papers. The contributions of this study, in brief, are: (i) overviewing the current challenges correlated with combination of cloud computing and IoT; (ii) presenting the anatomy of some proposed combination platforms, applications, and integrations; (iii) summarising major areas to boost the integration of cloud and IoT in the upcoming works.

Jahantigh, M. N.,Rahmani, A. M.,Navimirour, N. J., and Rezaee, A., 'Integration of Internet of Things and cloud computing: A systematic survey', IET Communications, 2020, 14, (2), pp. 165–176, doi: 10.1049/iet-com.2019.0537. The following corrections to this paper should be noted: The full name of the third author is Nima Jafari Navimipour.

In recent years, peer‐to‐peer (P2P) live streaming is popularised by the scalability and cost‐effectiveness of P2P networks. User satisfaction in P2P live streaming systems depends on several factors, including chunk scheduling techniques and overlay construction mechanisms in these systems. P2P live streaming systems are involved with the peer and chunk selection problems to improve quality parameters of streaming, such as playback continuity, startup delay, and playback latency. In this study, WidePLive as a P2P live video streaming system is proposed. In WidePLive, the authors proposed a low‐delay overlay construction mechanism and a mixed strategy based chunk scheduling scheme which are coupled together by a contribution‐aware peer selection strategy as a coupling feature to improve the quality parameter. The proposed overlay construction mechanism allows new peers to have the opportunity to connect with previous peers near the server and forms a low‐depth and low‐delay overlay. The proposed chunk scheduling scheme uses the benefits of Rarest First and Greedy strategies to trade‐off between quality parameters. The evaluation of WidePLive simulation results demonstrates an acceptable improvement in streaming performance and shows that WidePLive has lower startup delay and playback latency and higher playback continuity compared to previous works.

Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random-access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non-fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.

Summary In this article, a mutual multilevel trust framework is proposed, which involves managing trust from the perspective of cloud users (CUs) and cloud service providers (CSPs) in a multicloud environment based on a set of trusted third parties (TTPs). These independent agents are trusted by CUs and CSPs and distributed on different clouds. The TTPs evaluate the CUs' trustworthiness based on the accuracy of feedback ratings and assess the CSPs' trustworthiness based on the quality of service monitoring information. They are connected themselves through the trusted release network, which enables a TTP to obtain trust information about CSPs and CUs from other clouds. With the objective of developing an effective trust management framework, a new approach has been provided to improve trust‐based interactions, that is, able to rank the trusted cloud services (CSs) based on CU's priorities via fuzzy logic. Fuzzy logic is applied to manage the different priorities of CUs, all the CUs do not have the same priorities to use trusted CSs. Customizing service ranking allows CUs to apply trusted CSs based on their priorities. Experiments on real datasets well matched the analytical results, indicating that our proposed approach is effective and outperforms the existing approaches.

Summary The cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost‐efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority‐based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority‐based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area.

Summary Recent years have seen a great deal of attention in the aspects of cloud manufacturing. Generally, in cloud manufacturing, the capabilities and manufacturing resources that distributed in different geographical places are virtualized and encapsulated into manufacturing cloud services. The literature confirms that applying queuing theory to optimize service selection and scheduling load balancing (SOSL) while taking into account logistics is still scarce and an open issue for practical implementation of cloud manufacturing. This reason motivates our attempts to present a cloud manufacturing queuing system (CMfgQS) as well as a load balancing heuristic algorithm based on task process times (LBPT), simultaneously among the first studies in this research area. Hence, a novel optimization model as mixed‐integer linear programming is developed by implementing both CMfgQs and LBPT. Due to the natural complexity of the problem proposed, this study applies a genetic algorithm to solve the developed optimization model in large instances. Finally, the computational results ensure the effectiveness of the proposed model as well as the performance of the employed heuristic algorithm.

Vehicular ad‐hoc networks (VANETs) despite their potential benefits, especially for intelligent transportation and safety systems face some important challenges. The main challenges are caused by high mobility of vehicles and dynamic environment. In addition, VANET channel is wireless and is extremely error prone. To overcome these problems, several routing strategies have been proposed in order to find paths with high reliability and low delay. One of these strategies is the opportunistic routing (OR) paradigm where its performance can be significantly affected by the method applied for prioritised transmission to the nodes within the relays set. The OR protocol proposed in this study is based on the Dempster–Shafer evidence theory. In this method, the source vehicle employs the packet advancement, vehicle density, and packet delivery probability as parameters for determining the appropriate vehicle as to the next hop. The number of vehicles participating in forwarding the packets is optimised using trust‐based calculations. In addition, the source vehicle can schedule a set of relay nodes based on their degree of trust. Highway environment simulations suggest that compared to other published methods in the literature, the proposed method can improve routing performance in terms of all quality‐of‐service metrics.

Game theory as one of the most progressive areas in AI in last few years originates from the same root as AI. The unawareness of the other players and their decisions in such incomplete-information problems, make it necessary to use some learning techniques to enhance the decision-making process. Reinforcement learning techniques are studied in this research; regret minimisation (RM) and utility maximisation (UM) techniques as reinforcement learning approaches are widely applied to such scenarios to achieve optimum solutions. In spite of UM, RM techniques enable agents to overcome the shortage of information and enhance the performance of their choices based on regrets, instead of utilities. The idea of merging these two techniques are motivated by iteratively applying UM functions to RM techniques. The main contributions are as follows; first, proposing some novel updating methods based on UM of reinforcement learning approaches for RM; the proposed methods refine RM to accelerate the regret reduction, second, devising different procedures, all relying on RM techniques, in a multi-state predator-prey problem. Third, how the approach, called RMRL, enhances different RM techniques in this problem is studied. Estimated results support the validity of RMRL approach comparing with some UM and RM techniques.

Abstract Ant‐based clustering algorithms are inspired by the behaviour of real ants. In most ant‐based clustering algorithms, each ant lacks global visibility and only uses local search to find the best place on a grid to drop its data item. This paper presents a new clustering method called learning automata‐based clustering algorithm using learning automata (LA) and ant swarm intelligence. In this paper, the problem of finding the best location of grid for dropping a data item is solved using LA. We introduced a new drop operation using the global search capability of LA that can increase the quality of clustering. In the proposed method, each ant is equipped with an LA and a two‐dimensional grid is partitioned into a number of clusters. To drop a data item, the LA of each ant will be responsible for finding one of the best clusters on a grid unlike other methods that use the random search of a grid. To evaluate the results, some clustering evaluation criteria, such as the number of obtained clusters, f‐measure, Rand index, intracluster variance, Dunn index, error rate, and time cost were employed. Additionally, the proposed algorithms were compared with k‐means, Lumer and Faieta, ant clustering algorithm, modified version of the ant‐clustering algorithm, Chaotic Ant clustering (CAC), chaotic ant clustering algorithm, adaptive ant‐based clustering algorithm, adaptive artificial ant clustering algorithm, and ant clustering algorithm with information theoretic learning algorithms on both real and synthetic datasets. Experimental results show that the proposed method can increase the efficiency of the real dataset by 11.13% and that of the synthetic dataset by 16.11%. It also reduces the number of neighbouring function calls by 16% for the real dataset and by 28% for the synthetic dataset.

Social networks have become popular due to the ability to connect people around the world and share videos, photos, and communications. One of the security challenges in these networks, which have become a major concern for users, is creating fake accounts. In this paper, a new model which is based on similarity between the users’ friends’ networks was proposed in order to discover fake accounts in social networks. Similarity measures such as common friends, cosine, Jaccard, L1-measure, and weight similarity were calculated from the adjacency matrix of the corresponding graph of the social network. To evaluate the proposed model, all steps were implemented on the Twitter dataset. It was found that the Medium Gaussian SVM algorithm predicts fake accounts with high area under the curve=1 and low false positive rate=0.02.

Summary At the virtualized data centers, services are presented by active virtual machines (VMs) in physical machines (PMs). The manner in which VMs are mapped to PMs affects the performance of data centers and the energy efficiency. By employing the server consolidation technique, it is possible to configure the VMs on a smaller number of PMs, while the quality of service is guaranteed. In this way, the rate of active PM utilization increases and fewer active PMs would be required. Moreover, the server consolidation technique reacts to the management of underloaded and overloaded PMs by using the VM migration technology. Considering the capabilities of the server consolidation technique and its role in developing the cloud computing infrastructure, many researches have been conducted in this context. Still, a comprehensive and systematic study has not yet been performed on various consolidation techniques to check the capabilities, advantages, and disadvantages of current approaches. In this paper, a systematic study is conducted on a number of credible researches related to server consolidation techniques. In order to do so and by studying the selected works, proposed solutions are categorized based on the type of decision for running the consolidation algorithm in 4 groups of static method, dynamic method, prediction‐based dynamic method, and hybrid method. Thereafter, the advantages and disadvantages of suggested approaches are studied and compared in each research by specifying the technique and idea applied therein. In addition, by categorizing aims of researches and specifying assessment parameters, optimization approaches and type of architecture, a possibility has been provided to get familiarized with the views of the researchers.

Summary In recent years, users are becoming increasingly accustomed to using the Internet to gain software resources in the form of web services provided by information technology organizations. Cloud computing is a service delivery paradigm that shares services and resources to access the web services to the end users over the Internet. In the cloud environment, based on the user's needs, various types of services with similar functionalities but different quality‐of‐service (QoS) criteria can be delivered, which often must be combined to meet the users' requests. The optimal selection and composition of these services are realized as an interesting issue. In this paper, we propose a moth‐flame optimization (MFO) algorithm, which is a novel nature‐inspired metaheuristic paradigm for the web service composition (WSC) problem called “MFO‐WSC,” to improve the QoS criteria in the distributed cloud environment. Also, formal modeling is presented for the QoS‐aware MFO‐WSC algorithm with the model checking approach that receives the particular benefits to collaborate the correctness of the proposed algorithm. The correctness of the proposed behavior model is examined using some logical problems such as deadlock‐free, fairness, and reachability conditions in the new symbolic model verifier model checker. The experimental results indicate the effectiveness of the proposed algorithm in comparison with similar related works.

Summary This paper presents an iterative mathematical decision model for organizations to evaluate whether to invest in establishing information technology (IT) infrastructure on‐premises or outsourcing IT services on a multicloud environment. This is because a single cloud cannot cover all types of users’ functional/nonfunctional requirements, in addition to several drawbacks such as resource limitation, vendor lock‐in, and prone to failure. On the other hand, multicloud brings several merits such as vendor lock‐in avoidance, system fault tolerance, cost reduction, and better quality of service. The biggest challenge is in selecting an optimal web service composition in the ever increasing multicloud market in which each provider has its own pricing schemes and delivers variation in the service security level. In this regard, we embed a module in the cloud broker to log service downtime and different attacks to measure the security risk. If security tenets, namely, security service level agreement, such as availability, integrity, and confidentiality for mission‐critical applications, are targeted by cybersecurity attacks, it causes disruption in business continuity, leading to financial losses or even business failure. To address this issue, our decision model extends the cost model by using the cost present value concept and the risk model by using the advanced mean failure cost concept, which are derived from the embedded module to quantify cloud competencies. Then, the cloud economic problem is transformed into a bioptimization problem, which minimizes cost and security risks simultaneously. To deal with the combinatorial problem, we extended a genetic algorithm to find a Pareto set of optimal solutions. To reach a concrete result and to illustrate the effectiveness of the decision model, we conducted different scenarios and a small‐to‐medium business IT development for a 5‐year investment as a case study. The result of different implementation shows that multicloud is a promising and reliable solution against IT on‐premises deployment.

Summary Cloud computing has become an essential part of the computational world, offering a variety of server capabilities as scalable virtualized services. Big data centers that deliver cloud computing services contain thousands of computational nodes that consume a significant amount of energy. By introducing the virtual machine (VM), virtualization technology is trying to overcome this problem. One impressive technique for minimizing the total number of active physical servers that lead to improved energy consumption is VM consolidation. To optimize the consolidation process, effective VM placement can be used. In this paper, we first present a mathematical model aimed at reducing power consumption and costs by employing an effective VM consolidation in the cloud data center. Subsequently, we propose a genetic algorithm–based meta‐heuristic algorithm, namely, energy and cost‐aware VM consolidation for resolving the problem. Finally, we compare our proposed model with the well‐known first fit, first fit decreasing, and permutation pack algorithms. The experimental results show that our proposed model reduced power consumption and costs when compared with the three demonstrated algorithms.

Recently, cloud computing is growing fast and helps to realise other high technologies. In this paper, we propose a hieratical architecture to satisfy both providers' and consumers' requirements in these technologies. We design a new service in the PaaS layer for scheduling consumer tasks. In the providers' perspective, incompatibility between specification of physical machine and user requests in cloud leads to problems such as energy-performance trade-off and large power consumption so that profits are decreased. To guarantee Quality of service of users' tasks, and reduce energy efficiency, we proposed to modify Particle Swarm Optimisation to reallocate migrated virtual machines in the overloaded host. We also dynamically consolidate the under-loaded host which provides power saving. Simulation results in CloudSim demonstrated that whatever simulation condition is near to the real environment, our method is able to save as much as 14% more energy and the number of migrations and simulation time significantly reduces compared with the previous works.

Abstract Dynamic wireless sensor network (DWSN) is a group of mobile sensor nodes deployed in an intended area. Secure communication in DWSNs depends on the existence of an efficient key management scheme. Because of the movements of sensor nodes and unknown mobility pattern, dynamic key management is an important issue in such networks. In this paper, we propose a new key management scheme, which uses key pre‐distribution and post‐deployment key establishment mechanisms for DWSNs. The proposed approach ensures that the two communicating nodes share at least one common key. It also provides efficient ways for key generation and revocation as well as addition or deletion of mobile sensor nodes. Compared with the other key management schemes, which are designed for DWSNs, our simulation and analytical results demonstrate the efficiency of the proposed approach in terms of confidentiality, resilience, memory usage, energy consumption and overhead. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract One of the main features of the hypervisor is the isolation among virtual machine (VM) environments. By isolation between VMs, malicious activity in one VM could not affect all other VMs, so it is necessary to apply security mechanisms in order to improve isolation between VMs. Before applying security policies to a virtualization system, it is necessary to quantitatively measure the hypervisor from isolation point of view aiming at increasing security of isolation among VMs; then considering the circumstances of the VM execution environments and the results of measurements, we can find areas in the virtualization system with the most effective on enhancing isolation. This paper, proposed a semi‐Markov model for evaluation of isolation, by studying the Xen virtualization architecture. We considered certain type of vulnerability that successfully exploiting it can lead to the attacker's malicious codes execution in part of memory address space. We included all three layers in virtualization for the evaluation purpose, because we wanted to consider strength and weakness areas in virtualization system and not just specific layer such as hypervisor, so it can be figured out that improving security in which layer of virtualization is most effective in improving security of isolation, in respect to the increasing or decreasing attacker's (defender's) ability to be successful. The sensitivity analysis results show that MTTSF is more sensitive to the increasing ability of defensive mechanisms to be successful at the application layer and decreasing the attacker's ability to successfully exploit vulnerabilities at the guest operating system layer, model parameters. Copyright © 2015 John Wiley & Sons, Ltd.

Soft-errors are increasingly considered as a major cause for computer system failures. Software techniques are used as cost-effective and flexible techniques to tolerate soft-errors but the introduced overhead is not acceptable in some safety-critical real-time systems. The identification of the program blocks and protecting only vulnerable blocks against soft-errors reduces the performance overhead. In this paper, we present a genetic algorithm to identify the vulnerable program blocks as well as the derating program blocks against soft-errors. Then, only vulnerable blocks are protected by some software-based soft-error tolerance techniques to achieve a lower performance and space overhead. This genetic algorithm is implemented by the C++ programming languages as an automatic tool. To evaluate the algorithm, errors are injected using the Simple scalar toolset. The experimental results indicate that the effectiveness of this method is higher than the previous methods.

One of the most important challenges in a densely wireless sensor network (WSN) with potential congestions and packet loss is dissemination of distrusted packets. In this paper we present FCCTF: Fairness Congestion Control for a disTrustful WSN using Fuzzy logic. FCCTF increases each node capability for detecting and isolating malicious nodes in order to improve packet delivery while some important packets endanger dropping due to overflowing. Indeed, FCCTF attempts to improve our previous scheme Fuzzy based trust estimation for Congestion Control in WSNs (FCC). Simulation results show that FCCTF improves packet delivery up to 18.5% more and it reduces the related packet drop of legitimate nodes 20% less than FCC.

Web mining is used to automatically discover and extract information from Web-related data source such as documents, services and user profiles. Although standard data mining methods are applied for mining on the Web, specific algorithms need to be developed and applied for Web based information processing in Web resources. In our paper, we develop a method to filter the relevant information to users based on user profile. Our methods reduce the probability of filtering out the relevant boundary documents and increase the probability of filtering out the irrelevant boundary documents. In this paper, for filtering the documents, first, the user profile is presented as a set of weighted concepts; then, mapping the words onto concepts is done so that all the words representing user profile's concepts can be found. Later, the documents are weighted based on the presence of the user profile's concepts according to their weights and with respect to the location of the concepts; finally, after the filtering process, user profile is updated. The results clearly show further improvements and the accomplishment of the present goal.

In service oriented Grids; everything is represented by a service. Computational resources, storage resources, programs, and so forth are all services and thus managing the services in a proper manner is an important issue. in this article an intelligent and adaptive agent for managing Grid services is presented named FAGS. The FAGS manages service consumers advanced reservation requests, schedules service instances, monitors the instances, and manages service factory. The main objective of the proposed agent is balancing between service instances utilization and service consumers QoS delivery in respect of their QoS class. As usually these parameters are in conflict with each other, the agent continuously monitors system status and uses its novel fuzzy-based algorithms to make proper decisions with aim to adapt with latest conditions. The simulation results confirm the validity of the proposed agent.

The rapid growth of Web has made it a huge source of information which will make the availability of data easier and more efficient if its content is well organized. Automatic classification of Web pages is one of the major methods in the Web content mining (WCM) which can be of great value in the development and maintenance of Web directories. Based on the analysis done, CMAC neural network showed faster learning in high dimensional problems, but considering the heavy data on the Web, the main challenge in Web page classification is how to deal with high dimensional feature space which increase the memory required by CMAC neural network. In the present paper a classifier-CMAC neural network model is proposed for use in content based Web page classification which requires less memory. The results reveal that the proposed model is more useful than any other algorithms.

Swarm intelligence, as demonstrated by a natural biological swarm, such as an ant colony, has many powerful properties that are desirable for effective routing in communications networks. In this paper, we propose an intelligent routing algorithm that we are calling Fuzzy Logic Ant-based Routing (FLAR), which is inspired by ant colonies and enhanced by fuzzy logic techniques. Using a fuzzy system as an intelligent and expert mechanism allows multiple constraints to be considered in a simple and intuitive way. Simulation results and a comparison of the proposed method with two state-of-the-art routing algorithms show better performance and a higher fault tolerance for our approach, particularly in regard to link failures.