A predictive energy-aware scheduling strategy for scientific workflows in fog computing

The antlion optimizer (ALO) is a new swarm‐based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth‐flame optimization algorithm (MFO), multi‐verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO‐BPNN, which is against the models: BPNN, SCA‐BPNN, PSO‐BPNN, MFO‐BPNN, MVO‐BPNN, and ALO‐BPNN. It is shown that the predicted model IALO‐BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

This study presents Hyper_ALO_HybML, a hybrid machine learning framework enhanced through Improved Ant Lion Optimization (ALO) for accurate Osteosarcoma Classification from H&E-stained histopathological images. The radiomics-based texture, shape, and intensity features were processed with three ensemble classifiers, including Random Forest, XGBoost (eXtreme Gradient Boosting), and LightGBM, all tuned using the modified ALO to enhance feature selection ability, learning stability, and anti-overfitting. The dataset is composed of 1,144 images classified into Non-Tumor, Viable Tumor, and Necrotic Tumor. The experimental results demonstrate that the system with ALO-optimized XGBoost obtains the best performance, achieving an accuracy of 93.29%, a precision of 94.08%, a recall of 91.94%, and an MCC of 0.8947, respectively. LightGBM also holds similar generalization, with 92.71% accuracy, and Random Forest stops at 85.42%. Confusion matrix review reveals that gradient boosted models (optimized) deliver the most stable predictions among the classes. Results indicate that the integration of radiomics with ALO-guided hyperparameter optimization helps enhance Osteosarcoma detection and can aid in more reasonable histopathological decision-making. This hybrid approach offers a feasible route toward improved early diagnosis, minimizing errors in manual interpretation as well as enhancing computer-aided pathology.

Oil production estimation plays a critical role in economic plans for local governments and organizations. Therefore, many studies applied different Artificial Intelligence (AI) based methods to estimate oil production in different countries. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a well-known model that has been successfully employed in various applications, including time-series forecasting. However, the ANFIS model faces critical shortcomings in its parameters during the configuration process. From this point, this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer (AO) with the Opposition-Based Learning (OBL) technique. The main idea of the developed model, AOOBL-ANFIS, is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS. The proposed model is evaluated using real-world oil production datasets collected from different oilfields using several performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), Standard Deviation (Std), and computational time. Moreover, the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization (PSO)-ANFIS, Grey Wolf Optimizer (GWO)-ANFIS, Sine Cosine Algorithm (SCA)-ANFIS, Slime Mold Algorithm (SMA)-ANFIS, and Genetic Algorithm (GA)-ANFIS, respectively. Additionally, it is compared to well-known time series forecasting methods, namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Neural Network (NN). The outcomes verified the high performance of the AOOBL-ANFIS, which outperformed the classic ANFIS model and the compared models.

A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues

A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

With the rapid growth of Internet of Things (IoT) applications, the classic centralized cloud computing paradigm faces several challenges such as high latency, low capacity and network failure. To address these challenges, fog computing brings the cloud closer to IoT devices. The fog provides IoT data processing and storage locally at IoT devices instead of sending them to the cloud. In contrast to the cloud, the fog provides services with faster response and greater quality. Therefore, fog computing may be considered the best choice to enable the IoT to provide efficient and secure services for many IoT users. This paper presents the state-of-the-art of fog computing and its integration with the IoT by highlighting the benefits and implementation challenges. This review will also focus on the architecture of the fog and emerging IoT applications that will be improved by using the fog model. Finally, open issues and future research directions regarding fog computing and the IoT are discussed.

The Internet of Things (IoT) is rapidly gaining popularity as a result of the advancements in portable embedded devices and wireless protocols, enabling a new class of services. On the other hand, edge clouds provide IoT services as a new paradigm called fog computing. As the number of available IoT devices increases, more efficient methods are required to select the optimal combination of services out of several existing candidates in edge clouds while composing more complex IoT workflow tasks. So, cloud-assisted fog computing requires a platform for management, composition and provisioning of IoT services for IoT–cloud integration. Resent works have some weaknesses and did not consider some aspects of fog computing such as low latency, low energy and efficient resource allocation. We propose a cloud-based platform for management of IoT service selection and composition in fog computing to enhance QoS parameters such as bandwidth usage, latency and distributed resource utilization. In particular, we propose a multi-objective evolutionary game theory, enhanced by evaporation-based water cycle algorithm (EG-ERWCA) to optimize CPU usage, power consumption and latency of the IoT workflows in cloud-assisted fog computing environments. Many different real IoT workflows are used for evaluation of the proposed method in comparison with the state-of-art algorithms. Simulation results show that the overall quality of service is improved by 2.66 times compared to rival algorithms.

With the rapid growth of Internet of Things (IoT) applications, fog computing enables the IoT to provide efficient services by extending the cloud computing paradigm to the edge of the network. However, the existing attribute-based encryption schemes rarely focus on user and attribute revocation in fog computing and most of them still impose high computational and storage overhead on resource-limited IoT devices. In this article, we propose an efficient attribute-based encryption outsourcing scheme with user and attribute revocation for fog-enabled IoT. The proposed scheme improves the existing encryption scheme that uses the concept of attribute groups to achieve attribute revocation, making it suitable for fog computing and improving the efficiency of ciphertext update. In addition, it implements a novel method of user revocation in fog computing based on the characteristics of fog computing. In order to reduce the computing and storage burden of IoT devices, the heavy computation operations of encryption and decryption are outsourced to fog nodes and part of secret keys are stored in fog nodes. The security analysis shows that the proposed scheme is secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. The performance analysis shows that the proposed scheme has high revocation efficiency and is efficient enough to be deployed in practical fog computing.

The rapid development of technology has made the Internet of Things an integral element of modern society. Modern Internet of Things’ implementations often use Fog computing, an offshoot of the Cloud computing that offers localized processing power at the network’s periphery. The Internet of Things serves as the inspiration for the decentralized solution known as Fog computing. Features such as distributed computing, low latency, location awareness, on-premise installation, and support for heterogeneous hardware are all facilitated by Fog computing. End-to-end security in the Internet of Things is challenging due to the wide variety of use cases and the disparate resource availability of participating entities. Due to their limited resources, it is out of the question to use complex cryptographic algorithms for this class of devices. All Internet of Things devices, even those connected to servers online, have constrained resources such as power and processing speed, so they would rather not deal with strict security measures. This paper initially examines distributed Fog computing and creates a new authentication framework to support the Internet of Things environment. The following authentication architecture is recommended for various Internet of Things applications, such as healthcare systems, transportation systems, smart buildings, smart energy, etc. The total effectiveness of the method is measured by considering factors such as the cost of communication and the storage overhead incurred by the offered integrated authentication protocol. It has been proven that the proposed technique will reduce communication costs by at least 11%.

In 21st century, Internet of Things (IoT) is playing an important role in developing Smart cities. With the growth of IoT, data is growing with acceleration. As the data is growing the need to store data is also increasing. More the data, the latency will be high to store and retrieve data from the cloud. The concept of fog computing was originated to diminish the latency for accessing the data to and from the cloud. Fog computing imparts the storage, computing as well as networking services at the end point of the network. Fog nodes also have limited computational capabilities. Due to some weaknesses, fog computing and cloud computing cannot sustain alone, so both these technologies are integrated to build smart IoT infrastructure for Smart city. Fog computing have a major role and supreme responsibility in development of a Smart city. This paper discusses various applications of fog computing and their implementations in a Smart cities. It also proposes a prototype for Waste management system in a city. Fog computing can help to govern the waste collection of the city in a smart way. On the basis of our review, several open concerns and challenges of fog computing are discussed, and the directions for future researchers have also been discussed.

The use of Internet of Things (IoT) in sports leads to an exponential growth in data generation, It creates a need for efficient processing and analysis mechanisms. Cloud computing works efficiently in such situations but due to its central location there are some issues like latency, Increased workload on cloud and Network congestion due to huge data transfer to cloud. Fog computing is technology that extends the cloud computing services closer to the data source and offers promising solutions for real-time analytics. In sports there is a need to process data in a time-effective manner such as data related to finding strategies of the opponent team or Players health related data which needs to process time effectively. Fog Computing may work effectively. This paper reviews existing literature on the applications of cloud computing and fog computing in sports. The study explores the benefits, challenges, and future directions in use of fog computing in sports Domain. Keywords: Fog Computing, Sports, IoT, Real-time Analytics, Performance Monitoring

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

The Internet of Things (IoT) is a network of interconnected computing devices having the ability to transfer data over a network, without needing human interaction. Software and communication technologies have seen a tremendous amount of growth in the past couple of decades, and this has resulted in a significant increase in IoT devices. Today, in 2017, IoT devices are ubiquitous and have pervaded almost every sphere of our lives ushering an era of 'smart things.' The explosive growth has resulted in much security and privacy concerns. The other concern is the scalability issue with the primary model of analyzing IoT data in the cloud. A cloud-only model leads to network congestion, data bottleneck and slower reaction times to security issues. By selectively moving computation and storage closer to the network edge, fog computing provides an effective solution for these issues. This paper presents a holistic picture of IoT, its growth, fog computing, and Machine Learning (ML) techniques for securing IoT devices and fog computing systems. It surveys ML techniques for detecting abnormalities and attacks, illustrates solutions to the data growth in IoT, and dives into the security issues concerning fog computing. It also discusses future research directions in this important topic.

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.