With the increasing complexity and interconnectivity of IoV, protecting in-vehicle networks from cyberattacks and controlling mobile dynamics have become increasingly difficult. Currently, most techniques rely on large amounts of labeled data, which are difficult to obtain and expensive to generate. Furthermore, they are not focused on real-time Intrusion Detection Systems (IDSs) and adaptive decision making. To address these issues, this paper proposes an Adaptive Decision Framework for IoV-IDS (ADFII). To ease a policy update in the ADFII, we use Deep Reinforcement Learning (DRL) as it can generalize the policy to different kinds of vehicles by updating them according to specific variables, and we use Variational Autoencoders (VAEs), which ease the detection of hidden features and generalizes anomalies and new attack patterns. This combination of cores ensures that ADFII is both stable and flexible, helping in the efficient detection of intrusions in IoV environments that are subject to property changes, making the decision-making process more secure and reliable. To learn to detect incursions, navigate, and locate, ADFII only has to learn the right rules for the environment. In tests, ADFII has 7.1%, 3.7%, 4.1%, and 5% improvement, respectively, for better decision-making, reward, faster time, and finding an attack than baseline algorithms.

Timely detection of cancer is essential for enhancing patient outcomes. Artificial Intelligence (AI), especially Deep Learning (DL), demonstrates significant potential in cancer diagnostics; however, its opaque nature presents notable concerns. Explainable AI (XAI) mitigates these issues by improving transparency and interpretability. This study provides a systematic review of recent applications of XAI in cancer detection, categorizing the techniques according to cancer type, including breast, skin, lung, colorectal, brain, and others. It emphasizes interpretability methods, dataset utilization, simulation environments, and security considerations. The results indicate that Convolutional Neural Networks (CNNs) account for 31 % of model usage, SHAP is the predominant interpretability framework at 44.4 %, and Python is the leading programming language at 32.1 %. Only 7.4 % of studies address security issues. This study identifies significant challenges and gaps, guiding future research in trustworthy and interpretable AI within oncology.

ABSTRACT Routing protocols are pivotal in Vehicular Ad hoc Networks (VANETs), serving as the backbone for efficient routing discovery, particularly within the realm of Intelligent Transportation Systems (ITS). However, ensuring their seamless functionality within VANET environments necessitates rigorous verification and formal modeling. Colored Timed Petri Nets (CTPNs) stand out as a valuable mathematical and formal method for this purpose. This study shows a new way to describe the Ad hoc On‐Demand Distance Vector (AODV) routing system in VANETs using CTPNs. There are nine pages of detailed analysis using this new modeling method, which allows you to examine success across many levels of a hierarchy. This study provides a strong foundation for building and testing the AODV routing system in VANETs, showing how well it functions in real‐life situations. It is interesting to see how the results of the CTPN–based model and simulations compare. Notably, the model finds routes in an average of 32 s, while tests show that it takes 56 s. Additionally, the model's overall number of sent and received packets closely matches the results from the exercise. Furthermore, the suggested plan shows a yield of 41%. Strict T‐tests indicate that the modeling results are highly reliable.

Neuro Degenerative Disorders (NDDs) involve progressive nerve cell loss, impacting functions like sensation, movement, memory, and cognition, posing life-threatening risks. Despite extensive research, viable therapies remain elusive due to complex pathophysiology. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), shows promise in NDD diagnosis and treatment by leveraging vast datasets for accurate predictions. However, because AI models are “black boxes,” explainable AI (XAI) had to be created to make sure that physicians and patients would trust and accept it. Early detection is critical to stop degeneration and make things better for patients. Many in-depth studies on XAI are designed explicitly for NDDs. Existing research does not constantly look at how to interpret NDDs, how to evaluate them, or how to keep them safe. This paper fills in these gaps by looking at and grouping XAI methods for different NDDs, to make them easier to understand and use in medical settings. In this paper, we look at the interpretability methods used in various NDD studies. The methods are split into five groups based on the conditions they are used to treat: Frontotemporal Dementia (FTD), Multiple Sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), and Alzheimer's Disease (AD). It organizes XAI methods into groups and talks about their pros, cons, and clinical importance. The study also finds some important research gaps. For example, it says that there are no good security frameworks and that XAI is hard to use in real-life healthcare settings. By giving helpful information and a plan for future research, this paper shows how XAI could change how NDDs are found, treated, and predicted. AI technologies will be used more in healthcare, and this will help us learn more about these challenging conditions.

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area.

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways.

Abstract Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.

Summary Wireless Sensor Networks (WSNs) are critical for communication within a mile radius and industrial applications. These networks are very prone to failure due to their enormous number of nodes and their unique hardware and software restrictions. To make sure network performance, a lot of study needs to be done to improve failure tolerance and stability. This study looks at how to judge the availability and dependability of WSNs that have long‐term issues. The suggested method checks how well a network works in various failure cases by using fault trees and Markov chain analysis. Such methods help us find and study possible failure scenarios and how they might impact the network's dependability in a planned way. The results show that WSNs have major flaws and give useful suggestions for making the systems work better. The findings show that using these evaluation methods may greatly enhance the ability to handle faults, lower the risk of damage, and allow developers of WSNs to make smart choices.

Summary Cloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP‐hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy‐saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost.

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network , recurrent neural network , generative adversarial network , multilayer perception , and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F -score, latency, adaptability, and scalability.

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.

Because of recent technological developments, such as Internet of Things (IoT) devices, power consumption has become a major issue. Atomic silicon quantum dot (ASiQD) is one of the most impressive technologies for developing low-power processing circuits, which are critical for efficient transmission and power management in micro IoT devices. On the other hand, multipliers are essential computational circuits used in a wide range of digital circuits. Therefore, the multiplier design with a low occupied area and low energy consumption is the most critical expected goal in designing any micro IoT circuits. This article introduces a low-power atomic silicon-based multiplier circuit for effective power management in the micro IoT. Based on this design, a$4\times 4$-bit multiplier array with low power consumption and size is presented. The suggested circuit is also designed and validated using the SiQAD simulation tool. The proposed ASiQD-based circuit significantly reduces energy consumption and area consumed in the micro IoT compared to most recent designs.

The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with “Drones” while retaining incomparable features. Because of its vital applications, IoD technologies have attracted much attention in recent years. Nevertheless, gaining the necessary degree of public acceptability of IoD without demonstrating safety and security for human life is exceedingly difficult. In addition, intrusion detection systems (IDSs) in IoD confront several obstacles because of the dynamic network architecture, particularly in balancing detection accuracy and efficiency. To increase the performance of the IoD network, we proposed a blockchain-based radial basis function neural networks (RBFNNs) model in this article. The proposed method can improve data integrity and storage for smart decision-making across different IoDs. We discussed the usage of blockchain to create decentralized predictive analytics and a model for effectively applying and sharing deep learning (DL) methods in a decentralized fashion. We also assessed the model using a variety of data sets to demonstrate the viability and efficacy of implementing the blockchain-based DL technique in IoD contexts. The findings showed that the suggested model is an excellent option for developing classifiers while adhering to the constraints placed by network intrusion detection. Furthermore, the proposed model can outperform the cutting-edge methods in terms of specificity, F1, recall, precision, and accuracy.

There are many issues and problems in cloud computing that researchers try to solve by using different techniques. Most of the cloud challenges are NP-hard problems; therefore, many meta-heuristic techniques have been used for solving these challenges. As a famous and powerful meta-heuristic algorithm, the Ant Colony Optimisation (ACO) algorithm has been recently used for solving many challenges in the cloud. However, in spite of the ACO potency for solving optimisation problems, its application in solving cloud issues in the form of a review article has not been studied so far. Therefore, this paper provides a complete and detailed study of the different types of ACO algorithms for solving the important problems and issues in cloud computing. Also, the number of published papers for various publishers and different years is shown. In this paper, available challenges are classified into different groups, including scheduling, resource allocation, load balancing, consolidation, virtual machine placement, service composition, energy consumption, and replication. Then, some of the selected important techniques from each category by applying the selection process are presented. Besides, this study shows the comparison of the reviewed approaches and also it highlights their principal elements. Finally, it highlights the relevant open issues and some clues to explain the difficulties. The results revealed that there are still some challenges in the cloud environments that the ACO is not applied to solve.

Reversible logic has recently gained significant interest due to its inherent ability to reduce energy dissipation, which is the primary need for low-power digital circuits. One of the newest areas of relevant study is reversible logic, which has applications in many areas, including nanotechnology, DNA computing, quantum computing, fault tolerance, and low-power complementary metal-oxide-semiconductor (CMOS). An electrical circuit is classified as reversible if it has an equal number of inputs and outputs, and a one-to-one relationship. A reversible circuit is conservative if the EXOR of the inputs and the EXOR of the outputs are equivalent. In addition, quantum-dot cellular automata (QCA) is one of the state-of-the-art approaches that can be used as an alternative to traditional technologies. Hence, we propose an efficient conservative gate with low power demand and high speed in this paper. First, we present a reversible gate called ANG (Ahmadpour Navimipour Gate). Then, two non-resistant QCA ANG and reversible fault-tolerant ANG structures are implemented in QCA technology. The suggested reversible gate is realized through the Miller algorithm. Subsequently, reversible fault-tolerant ANG is implemented by the 2DW clocking scheme. Furthermore, the power consumption of the suggested ANG is assessed under different energy ranges (0.5Ek, 1.0Ek, and 1.5Ek). Simulations of the structures and analysis of their power consumption are performed using QCADesigner 2.0.03 and QCAPro software. The proposed gate shows great improvements compared to recent designs.

Abstract Recently, quantum dot‐cellular automata (QCA) has fascinated much attention because of its potential less area consumption, low power usage, less intricacy, and low delay. The full‐adder circuit is used in this technology for several procedures, like multiplication, subtraction, and division in the arithmetic logic unit. For this reason, the full‐adder is generally investigated as a central unit in the development of QCA technology. The present investigation demonstrates a new efficient QCA‐based full‐adder layout utilizing the TIEO gate and new 3‐input majority gate. In this design, the inputs get inside one side, and the outputs are derived from another circuit side. Other cells do not embrace the output and input signals, and they may simply be available, helping produce a more impressive circuit layout. Concretely speaking, in this design, the 0.02 μm2 region and the latency of the 0.5 clock cycle have been implemented using only 18 cells. Utilizing the QCADesigner tool, the suggested design in the current investigation has been functionally approved. The simulation outcomes show that this layout conducts properly and works faster than the oldest designs.

In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) are an impressive technology. These networks are extremely resource constrained and require the design of energy-efficient routing techniques. The clustering and location prediction routing method based on multiple mobile sinks (CLRP-MMSs) for the Mobile Internet of Things (MIoT) is presented in this article. Recently, mobile sinks are used in routing more durability and energy saving in WSN. In this work, first, the entire nodes are divided into clusters, and then each cluster selects a cluster head (CH) by calculating the CH choosing function (CHCF). When clustering runs on networks with moving nodes, the possibility of disconnecting the nodes from CH nodes will cause a lot of data loss. It will change the amount of energy and rate of data received, but the amount of wasted energy is reduced by predicting the location and reducing the sink and CH nodes' distance. The simulation results using NS-2 clearly showed that the proposed method improves energy consumption at least 28.12% and increases throughput at least 26.74% compared to energy efficient routing algorithm with mobile sink support and high-available and location-predictive data gathering scheme using mobile sink methods.

Recently, a new technology topic has been known as the Internet of Things (IoT), where all devices like smartphones, smart TVs, medical and healthcare ones, and home appliances have been applied for data generating. Due to the variety of services, the numerous service composition problems, mostly related to the Quality-of-Service (QoS) parameters, are recognized in the IoT domain. Since this issue is an NP-hard obstacle, different metaheuristic approaches have been utilized up until now to solve it. Many varieties of services can be brought into the IoT, depending on users’ demands. In this research, we have proposed an effective way based on a hidden Markov model (HMM) and an ant colony optimization (ACO) to answer the service composition issue by enhancing the QoS. The HMM has been trained to predict QoS. The emission and transition matrices have been improved using the Viterbi algorithm. We have executed the QoS estimation using the ACO algorithm and found a suitable path. The outcomes have illustrated the efficacy of the introduced method regarding availability, response time, cost, reliability, and energy consumption compared to the previous methods.

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.

Information and communication technology (ICT) is one of the sectors that have the highest energy consumption worldwide. It implies that the use of energy in the ICT must be controlled. A software‐defined network (SDN) is a new technology in computer networking. It separates the control and data planes to make networks more programmable and flexible. To obtain maximum scalability and robustness, load balancing is essential. The SDN controller has full knowledge of the network. It can perform load balancing efficiently. Link congestion causes some problems such as long transmission delay and increased queueing time. To overcome this obstacle, the link load balancing strategy is useful. The link load‐balancing problem has the nature of NP‐complete; therefore, it can be solved using a meta‐heuristic approach. In this study, a novel energy‐aware dynamic routing method is proposed to solve the link load‐balancing problem while reducing power consumption using the multi‐objective artificial bee colony algorithm and genetic operators. The simulation results have shown that the proposed scheme has improved packet loss rate, round trip time and jitter metrics compared with the basic ant colony, genetic‐ant colony optimisation, and round‐robin methods. Moreover, it has reduced energy consumption.

Summary Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP‐hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non‐deterministic methods are two main categories to study these techniques. The deterministic and non‐deterministic query optimization methods can be further divided into three subcategories, cost‐based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future.

Summary Cloud computing is a type of parallel, configurable, and flexible system, which refers to the provision of applications on virtual data centers. However, reducing the energy consumption and also maintaining high computation capacity have become timely and important challenges. The concept of replication is used to face these challenges. By increasing the number of data replicas, the energy consumption, the performance, and also the cost of creating and maintaining new replicas also are increased. Deciding on the number of required replicas and their location on the cloud system is an NP‐hard problem. In this paper, the problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it. The algorithm uses the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the Tabu Search (TS) to get high‐quality solutions. The efficiency of the method is shown by comparing it with simple PSO, TS, and Ant Colony Optimization (ACO) algorithm on different test cases. The obtained results indicate that the method outperforms all of them in terms of consumed energy and cost.

Summary As the main factor in the distributed database systems, query optimization is aimed at finding an optimal execution plan to reduce the runtime. In such systems, because of the repeated relations on various sites, the query optimization is very challenging. Moreover, the query optimization issue with large‐scale distributed databases is an NP‐hard problem. Therefore, in this paper, an Artificial Bee Colony Algorithm based on Genetic Operators (ABC‐GO) is proposed to find a solution to join the query optimization problems in the distributed database systems. The ABC algorithm has the global–local search capabilities and genetic operators to create new candidate solutions for improving the performance of the ABC algorithm. The obtained results have shown that the cost of the query evaluation is minimized and the quality of Top‐K query plans is improved for a given distributed query. Moreover, this method decreases the overhead. However, it needs a longer execution time.

Summary Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta‐heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non‐dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta‐heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms.

Internet of Things (IoT) has been developed as one of the most significant technology in the future of the Internet, in which the physical objects are transformed into smart objects that can be handled and monitored via the Internet. The trust management takes a significant role in the IoT for enabling trustworthy data collection, context-awareness, and enhanced user privacy. Despite the critical significance of trust management techniques in the IoT, there is not any organized and comprehensive study in this field. Therefore, the aim of this article is to review the available methods in this field in a systematic way. In this regard, the selected techniques are categorized into four main classes, including recommendation-based, prediction-based, policy-based, and reputation-based. Then they are discussed and also compared based on some trust metrics, such as accuracy, adaptability, availability, heterogeneity, integrity, privacy, reliability, and scalability. Furthermore, some hints and challenges for further studies are outlined.

Summary Cloud computing is an Internet‐based computing and networking model, with elasticity and scalability capabilities where the services are delivered to its users in a non‐demand style. In this computing paradigm, the request and response between users and providers must be managed using the resource allocation strategies. Therefore, allocating the provided resources to the users based on their needs is the important challenge in this environment. Also, an auction in the cloud is a process of buying and vending the cloud services by offering them up for bid and then selling the service to the highest bidder. However, to the best of our knowledge, there has not been any comprehensive and detailed paper about reviewing the state‐of‐the‐art mechanisms on this important topic and providing open issues as well. Hence, this paper provides a comprehensive survey and review of the auction‐based resource allocation mechanisms, which have been employed in the cloud environments up to now. Also, we classified the important cloud resource allocation mechanisms into four categories: one‐sided, double‐sided, combinatorial, and other types of auction‐based mechanisms. Moreover, we reviewed the main progress in these four categories and defined the new issues. Finally, the paper offers the differences among reviewed mechanisms as well as guidelines for future investigation.

Internet of Things (IoT) is anticipated to bridge various technologies to allow new applications through linking physical things together in the future. Increasing facilities through the composition of services are considered as an essential module in this technology. Several types of services can be supplied in the IoT using the user's requirements to consider the user needs. However, the service composition in the IoT does not have any systematic and complete study about examining its significant techniques. So, this paper aims to investigate the available methods in this field using a systematic manner. To achieve a comprehensive view of the topic, all of the selected approaches are divided into four distinct categories, including framework, service oriented architecture and RESTful, heuristic, and model-based. The detailed classifications have been presented using various parameters based on the examination of the existing techniques. Also, the benefits and drawbacks of the state-of-the-art service composition techniques and the important challenges of them are discussed. Finally, we have explained future work in the field of service composition in the IoT with detail in order to make an effective and efficient way for researchers in this area. We also have identified four important parameters to examine the selected service composition mechanisms in the IoT. Scalability is improved in 45.4%, execution time in 36.3%, cost in 27.2%, reliability in 22.7%, availability in 18.1%, and response time in 13.6% of the reviewed articles.