CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Energy-efficient resource allocation scheme based on enhanced flower pollination algorithm for cloud computing data center

Cloud Computing (CC) has rapidly emerged as a successful paradigm for providing ICT infrastructure. Efficient and environmental-friendly resource allocation mechanisms, responsible for allocatinpg Cloud data center resources to execute user applications in the form of requests are undoubtedly required. One of the promising Nature-Inspired techniques for addressing virtualization, consolidation and energyaware problems is the Flower Pollination Algorithm (FPA). However, FPA suffers from entrapment and its static control parameters cannot maintain a balance between local and global search which could also lead to high energy consumption and inadequate resource utilization. This research developed an enhanced FPA-based energy efficient resource allocation scheme for Cloud data center which provides efficient resource utilization and energy efficiency with less probable Service Level Agreement (SLA) violations. Firstly, an Enhanced Flower Pollination Algorithm for Energy-Efficient Virtual Machine Placement (EFPA-EEVMP) was developed. In this algorithm, a Dynamic Switching Probability (DSP) strategy was adopted to balance the local and global search space in FPA used to minimize the energy consumption and maximize resource utilization. Secondly, Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) algorithm was developed. In this algorithm, Local Neighborhood Search (LNS) and Pareto optimisation strategies were combined with Clustering algorithm to avoid local trapping and address Cloud service providers conflicting objectives such as energy consumption and SLA violation. Lastly, Energy-Aware Multi-Cloud Flower Pollination Optimization (EAM-FPO) scheme was developed for distributed Multi-Cloud data center environment. In this scheme, Power Usage Effectiveness (PUE) and migration controller were utilised to obtain the optimal solution in a larger search space of the CC environment. The scheme was tested on MultiRecCloudSim simulator. Results of the simulation were compared with OEMACS, ACS-VMC, and EA-DP. The scheme produced outstanding performance improvement rate on the data center energy consumption by 20.5%, resource utilization by 23.9%, and SLA violation by 13.5%. The combined algorithms have reduced entrapment and maintaned balance between local and global search. Therefore, based on the findings the developed scheme has proven to be efficient in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violation

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Reliable Machine Learning Model for IIoT Botnet Detection

Due to the growing number of Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network’s hyper parameters to detect botnets efficiently. The proposed Harris Hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model’s performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques