EAI Endorsed Transactions on Cloud Systems
The EAI Endorsed Transactions on Cloud System (ToCS) is an international venue for publishing innovative and cutting edge results on the convergence of next-generation technologies and methodologies reshaping our way of living. The emerging converged platform for growth and innovation is built on four technology pillars: mobile computing, cloud services, big data and analytics, and social networking. INDEXING: CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
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Implementing Machine Learning on AWS Standalone Cluster
INTRODUCTION: This paper presents the implementation of a machine learning classification pipeline on Amazon Web Services (AWS) EC2 in standalone cluster mode using Apache Hadoop and the scikit-learn library. OBJECTIVES: To configure a cloud-based standalone cluster, deploy a Logistic Regression classifier on the Iris dataset, and evaluate its performance using standard metrics. METHODS: An AWS EC2 t2.micro instance running Ubuntu 24.04 LTS was provisioned and configured as a Hadoop standalone cluster. Python libraries including scikit-learn, pandas, Matplotlib, and Seaborn were installed. The 150-sample Iris dataset was split 80/20 for training and testing. Logistic Regression was applied with a maximum of 200 iterations. RESULTS: The model achieved 100% classification accuracy on the test set with a precision, recall, and F1-score of 1.00 across all three Iris species. The confusion matrix confirmed zero misclassifications. Training completed in under two seconds on the free-tier instance. CONCLUSION: Cloud-based standalone clusters on AWS EC2 provide a cost-effective environment for deploying machine learning workloads. Logistic Regression with scikit-learn delivers 100% accuracy on the Iris benchmark, demonstrating the viability of cloud-hosted ML pipelines for educational and small-scale production use cases.
Copyright (c) 2026 Dia Aldeen Farabi https://creativecommons.org/licenses/by-nc-sa/4.0
Application Programming Interface (API) Security in Cloud Applications
Many cloud services utilize an API gateway, which enables them to be offered to users through API platforms such as Platform as a Service (PaaS), Software as a service (SaaS), Infrastructure as a Service (IaaS) and cross-platforms APIs. APIs are designed for functionality and speed by developers who write a small portion of code, which has visibility and is secure. The code that is created from third-party software or libraries has no visibility, which makes it insecure. APIs are the most vulnerable points of attack, and many users are not aware of their insecurity. This paper reviews API security in cloud applications and discusses details of API vulnerabilities, existing security tools for API security to mitigate API attacks. The author’s study showed that most users are unaware of API insecurity, organizations lack resources and training to educate users about APIs, and organizations depend on the overall security of the network instead of the security of standalone APIs.
Copyright (c) 2023 Farhan Qazi https://creativecommons.org/licenses/by-nc-sa/4.0
System Modeling and Artificial Neural Network (ANN) Design for Lateral and Longitudinal of F-16
Today, the classical control methods are still widely used because of their excellent performance in a working environment with conditions of geo-graphical distance. They are suitable for functions of the system: more flexible operating system, easy to perform, less unwanted ricks occur, the efficiency of controlling a system better. Besides the traditional control methods, the author has applied more modern and smarter algorithms such as artificial intelligence to control a system on the ground or a system moving in the air. In this paper, artificial neural network (ANN) is applied for a flight model to demonstrate its effectiveness in all cases. ANN in this article to show off its amazing application for flying devices. This is a useful method because it is highly secure. Simulation is done by Matlab.
Copyright (c) 2022 Nguyen Cong Danh https://creativecommons.org/licenses/by-nc-sa/4.0
Neural Networks Application for the Data of LQG Regulator for the Flexible Joint Robot
This paper describes the investigation of a flexible robotic arm with Degree of Freedom (DOF) through Linear Quadratic Gaussian (LQG) regulator under the influence of noise signals with using diagrams below. The derived model is based on the Euler-Lagrange approach while LQG mode is proposed as a modern control method. A flexible robotic arm that is test in the modern regulator is essential for applications. This is even more interesting while these arms have joints that work independently of each other, but they still ensure a tight connection between the joints. Testing the stability characteristics of the system through this regulator helps readers determine the output signals of a system exactly. From here, the author came up with strategies the match the requirements. Although the initial stage of the operating cycle is not stable with the use of LQG regulator, most of later stages of this process of closed systems of robotic systems gradually become stable. In particular, adjusting LQG for flexible joint robot gives satisfactory simulation results. This promises a positive result of other joint robot. Neural networks applied in this paper have proven its effectiveness in information security issues. It is also very useful for tight control of possible situations such as a large scale attack on network systems, automated control systems. The main contributions of this paper have been clearly demonstrated through two strategies that need to be addressed: LQG, neural networks for the data of LQG regulator.The methods described above have not been previously published for this model. Therefore, this article was born to outline the above objectives and the plans have been satisfactorily implemented. These studies are important because they serve as a necessary database in the selection of control strategies according to the requirements. Simulation results are done by Matlab. The purpose of the study of this paper: the author has searched for a stability of the system through the above methods. The applied method is a thorough study of the structure of the system as well as the construction of algorithms to form the regulator. The result ia a reliable simulation of events after the system is controlled by these regulators.
The application of a optimal controller for the flexible joint robot
This paper describes the operation of a single Degree of Freedom (DOF) flexible robotic arm by the optimal controller. The derived model is based on Euler- Lagrange approach, while the first and second-order (super twisting) Sliding Mode Control (SMC) is proposed as a nonlinear control strategy. Exploration of a flexible robotic arm with using state-of-the-art controllers is essential for practical applications. This is even more refreshing when these arms have joints that work independently of each other to create a smooth connection between the joints, but it still ensures the natural properties like a real human arm. This system has many similarities with the flexible link system of the MIMO model in its operational state analysis. Control laws must be followed by logical rules in a coherent whole. The next step is to design a controller to fit the structure of the system. The author also compared the use of the above controller with a system without using any controller to determine the effectiveness of using the above controller.
Investigating the IoT Security and Privacy Challenges: Summary and Recommendations
With the development of innovative technology transformation like cloud and Internet of Things (IoT), more technology companies are pursuing research in employing such innovations. Smart homes and cities are just two examples of the many systems and technologies that the IoT can endorse. IoT - based smart objects communicate with other parts, such as proxies, portable devices, as well as data collectors. Although these components help to tackle a number of societal issues and offer users new, cutting-edge services, their confined processing power makes them susceptible to well-known privacy and security attacks. This in turn highlights the demand for a strong technical as well as legislative foundation and asserts the significance of validity and reliability in IoT. This paper provides an insight of the IoT, security, as well as privacy challenges, and also discusses the recommendations for IoT solutions. Further we also highlighting some unresolved problems that require further study.
Copyright (c) 2022 Premlata Chauhan, Shafeeq Ahmad, Pervez Rauf Khan, Naseem Ahmad Khan https://creativecommons.org/licenses/by-nc-sa/4.0
Social Aspect of Vehicular Communications
The interconnection of devices is expected to grow and to incorporate systems that used to be isolated. As the vehicle evolves from a simple transport machine to an intelligent entity that collects information from the environment and uses it in order to take decisions in real time, it is becoming an active member of a smart city. The integration of vehicular communications with smartphones helps vehicles connect with each other and take decisions that improve driving in terms of safety, reduced fuel consumption and comfort. The social behavior of drivers is an asset that can be used in order to predict their mobility in a city and to produce novel algorithms that can cope with these aspects in an ecient way. In this article, we present a social perspective of ad hoc vehicular networks and propose novel ranking, clustering and routing methods. We also discuss security issues that arise from the interconnection of vehicles.
Copyright (c) 2022 EAI Endorsed Transactions on Cloud Systems
Service Co-evolution in the Internet of Things
The envisioned Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. This service-based integration of IoT will be smarter, easier to communicate with and more valuable for enriching our environment. However, the interfaces and services can be modified due to updates and amendments. Such modifications require adaptations in all participating parties. Therefore, the aim of this research is to present a vision of service co-evolution in IoT. Moreover, we propose a novel agent architecture which supports the evolution by controlling service versions, updating local service instances and enabling the collaboration of agents. In this way, the service co-evolution can make systems more adaptive, efficient and reduce costs to manage maintenance.
Achieving Assistive Technology Systems Based on IoT Devices in Cloud Computing
This paper discusses how programmable embedded IoT systems may be a challenge for the development of Assistive Technologies (ATs), with particular reference to the Human-Computer Interaction (HCI). Nowadays, users with disabilities access computer-based devices by using tailored AT solutions equipped with specialized software components closely tied to the physical device. We propose to decouple the AT tools from the computers and/or ICT (Information and Communication Technology) systems. The main advantage of such a solution is the high flexibility for users with disabilities in using their personal AT equipments to access/control different types of computers and ICT systems. To avoid manual driver installation and software configuration on each available computer, the IoT component makes use of Cloud technology and services to customize the AT environment according to the user’s needs and preferences.
Multipath Bandwidth Scavenging in the Internet of Things
To meet the infrastructure coverage and capacity needed by future IoT applications, service providers may engage in mutually-beneficial modes of collaboration such as cooperative packet forwarding and gatewaying through fixed backhauls and Internet uplinks. In an effort to enable these modes of resource pooling while minimizing negative impact on collaborating providers, we developed a transport-layer approach that would enable IoT nodes to opportunistically scavenge for idle bandwidth across multiple paths. Our approach combinesmultipath techniques with less-than-best effort (LBE) congestion control methods. Initial tests using the TCP-LP and LEDBAT LBE algorithms on scavenging secondary flows show that this desired functionality can be achieved. To ensure however that IoT nodes are guaranteed at least one flow that fairly competes for fair share of network capacity, one flow called the primary flow uses standard TCP congestion control.
BETaaS platform – a Things as a Service Environment for future M2M marketplaces
Building the Environment for Things as a Service (BETaaS) is a novel platform for the deployment and execution of contentcentric Machine-to-Machine (M2M) applications, which relies on a local cloud of gateways. BETaaS platform provides a uniform interface and services to map content with things in a context-aware manner. Deployment of services for the execution of applications is dynamic and takes into account the computational resources of the low-end physical devices used. To this aim, BETaaS platform is based on a suitable defined Internet of Things (IoT) model, allowing the integration of the BETaaS components within the future Internet environment. In this paper we present the BETaaS concept, the high level platform architecture and application scenarios that extend the state-of-the-art in M2M communications and open the horizon for future M2M marketplaces.
Welcome message for the Special issue on: IoT-as-a- Service from the Editors-in-Chief
Large Scale Cross-media Data Retrieval based on Hadoop
With the rapid development of the Internet and speedy increase of the data size, there are more and more data intensive applications which often involve hundreds of megabytes of data. It is important and necessary to obtain the retrieval results from cross-media data quickly and accurately. Large scale cross-media data retrieval based on Hadoop is proposed to speed up the retrieval in this paper. We divide cross-media feature extraction and cross-media retrieval into paralleled pipeline, and implement with the combination of the HDFS, HBase and MapReduce framework. To verify the performance of the proposed method, comparisons with stand-alone mode on different sizes of the image dataset are conducted, and the experimental results demonstrate the good performances of proposed method, which sharply decreases time-consuming, and meanwhile keeps the same query precision.
A New QoE-Driven Video Cache Allocation Scheme for Mobile Cloud Server
With the advent of mobile cloud computing, video cache technologies at local cellular networks have attracted extensive attention. Nevertheless, existing video cache allocation schemes mostly made decisions only according to the video coding requirements, without considering users’ individual perception for the video service. In this paper, we propose a new video cache allocation scheme with the consideration of quality of experience (QoE) of users under limited storage space. We make use of the linear regression algorithm to map the relationship between the requested video rate, the replied video rate, the channel condition and the QoE value, which then helps to obtain the different video rates to be stored in the server. Meanwhile, we define the parameter to represent the popularity of a video clip. We optimize the cache space allocation for each video clip based on these parameters in the mobile cloud server of local cellular networks. The experiments demonstrate that the proposed scheme has a better performance in terms of the overall QoE of users with the constraint of the total cache size.
A Dynamic Self-adaptive Resource-Load Evaluation Method in Cloud Computing
Cloud resource and its load have dynamic characteristics. To address this challenge, a dynamic self-adaptive evaluation method (termed SDWM) is proposed in this paper. SDWM uses some dynamic evaluation indicators to evaluate resource state more accurately. And it divides the resource load into three states -- $Overload$, $Normal$ and $Idle$ by the self-adaptive threshold. Then it migrates overload resources to balance load, and releases idle resources whose idle times exceed a threshold to save energy, which can effectively improve system utilization. Experimental results demonstrate SDWM has better adaptability than other similar methods when resources dynamically join or exit. This shows the positive effect of the dynamic self-adaptive threshold.
Cloud-based IoT Analytics for the Smart Grid: Experiences from a 3-year Pilot
The transformation of electrical grids into smart-grid is seen as one of the major technological challenges of our times and at the same time as one of the key domains for Internet of Things (IoT). Smart-home technologies and corresponding analytics are an integral part of many use cases in this field. In this paper we present a cloud-based test bed for capturing and analyzing smart-home data and report on experiences from a 3 year pilot with a cloud-based system. We discuss on real-world challenges that we encountered throughout the pilot - e.g. related to big data volumes and data quality - and describe corresponding technical solutions.
Accessible Wayfinding Testbed: Infrastructure and Components
Despite considerable recent interest in research related to wayfinding and navigation of pedestrians, the needs and preferences of people with disabilities (PWDs) are not yet fully addressed. Some of still unaddressed issues are related to understanding the different mobility challenges of PWDs, while others are related to the accessibility of routes and navigation systems/services. Emergence of advanced systems and services that can assist PWDs in wayfinding and navigation calls for the development of an accessible wayfinding platform facilitating the evaluation of accessibility of indoor and outdoor routes. In this paper, we propose and present an accessible wayfinding testbed which has three components: database, accessibility index, and visualization. The database component includes networks of sidewalks outdoors and building elements indoors with accessibility elements for PWDs. The accessibility index determines the level of accessibility of each element in a network as is perceived by PWDs. The visualization component visualizes the routes, accessible and others, in a simple form allowing PWDs, urban planners, and software developers, among others, to evaluate the accessibility of the travelling environment. The paper discusses the details of the testbed and a prototype accessible wayfinding testbed.
Solving Queueing Network Models in Cloud Provisioning Contexts
In recent years the research community and most of cloud users are trying to propose flexible and general mechanisms to determine how much virtual resources need to be allocated to each tier of the applications executed on cloud infrastructures. The objective of this virtual provisioning is twofold: minimizing resources consumption and meeting the service level agreement (SLA). Most of the current cloud provisioning and scaling solutions are based on analytical models of applications, trying to automate the provisioning decisions making "what-if" response time predictions. Queueing network (QN) models have demonstrated to be a good choice in this kind of contexts. In this work we compare, performing an exhaustive set of experiments on a real cloud architecture with a new provisioning mechanism, exact solutions with approximate solutions estimated from bounding techniques in order to obtain conclusions about the most efficient way of solving these models when making cloud provisioning decisions.
Modeling user and topic interactions in social networks using Hawkes processes
We present in this paper a framework to model information diffusion in social networks based on linear multivariate Hawkes processes. Our model exploits the effective broadcasting times of information by users, which guarantees a more realistic view of the information diffusion process. The proposed model takes into consideration not only interactions between users but also interactions between topics, which provides a deeper analysis of influences in social networks. We provide an estimation algorithm based on nonnegative matrix factorization techniques, which together with a dimensionality reduction argument is able to discover, in addition, the latent community structure of the social network. We also provide several numerical results of our method.
PerfCenterLite: Extrapolating Load Test Results for Performance Prediction of Multi-Tier Applications
Performance modeling is an important step in the lifecycle of a typical Web-based multi-tier application. However, while most practitioners are comfortable carrying out load tests on a Web application on a testbed, they ﬁnd sophisticated performance modeling tools diﬃcult to use because many inputs required by them are diﬃcult to obtain. Chief among these is the service times of various types of requests at various resources in the multi-tier system (e.g. CPU execution time required at the Web server by a “Login” request). In this paper, we present PerfCenterLite, a tool focused on ease of use for practitioners of performance analysis. The tool (a) provides a spread-sheet template for describing the application architecture and (b) accepts standard performance metrics obtained from load testing of the application. PerfCenterLite then uses mathematical estimation techniques and transforms this input into a full-ﬂedged performance model as required by a sophisticated performance modeling tool. Validation experiments show that performance metrics predicted using PerfCenterLite match well with measured values.
A Model Transformation Tool: PMIF+ to QNAP
An extension to the Performance Model Interchange Format (PMIF+) to move models among tools has been recently introduced. The original PMIF was limited to models solvable with efficient, exact solution methods such as mean value analysis and product form solutions. The extensions allow models with features that require simulation or analytical approximation solutions, such as passive resources, workloads that fork into multiple concurrent workloads, etc. This paper presents a tool for the automated transformation from PMIF+ to Qnap, and explains some of the challenging transformations required by the new PMIF+ features.
Hybrid Scheduling for Quality of Service Guarantee of Multimedia Data Flows in Software Defined Networks
Supporting diverse Quality of Service (QoS) performance for heterogeneous data flows generated by multimedia applications has been a challenging issue that is not fully addressed in the Internet. Software Defined Network, which decouples data forwarding and network control, offers a promising approach to QoS guarantee of multimedia flows in the future Internet. However, the currently available QoS mechanisms proposed for SDN have not fully considered the diversity in performance requirements of multimedia data flows. In order to tackle this challenge, we propose a hybrid scheduling scheme in this paper to combine priority queueing with Packet General Processor Sharing (PGPS) algorithm to provide QoS guarantee for multimedia applications in SDN. We apply network calculus to develop modeling and analysis techniques to evaluate the QoS performance of the proposed scheduling scheme. Both analytical and numerical results obtained in this paper show that the proposed scheme can provide QoS guarantee for meeting diverse requirements of multimedia applications.
PETFEN: A Performance Evaluation Tool for Flow-Level Network Modeling of Ethernet Networks
We present in this paper PETFEN, a Performance Evaluation Tool for Flow-level network modeling of Ethernet Networks. Flow-level network models are a useful tool to dimension and predict various performances of networks with TCP and UDP flows, providing information such as mean flow bandwidths, link utilizations or queue sizes. While the literature on flow-level network models is extensive, there is still a lack of tools for numerical evaluations on user provided topologies. In this paper, we describe the three components of PETFEN: (i) an effective domain specific language used for algorithmically describing topologies, (ii) a mathematical toolbox for the numerical evaluation of flow-level network models on the provided topologies, (iii) modules for the evaluation of the topologies with external tools. Via various numerical evaluations, we compare the results of PETFEN with results of SimGrid, another tool based on flow-level network models, as well as results of the discrete event simulator OMNeT++.
Specifying and Monitoring Properties of Stochastic Spatio-Temporal Systems in Signal Temporal Logic
We present an extension of the linear time, time-bounded, Signal Temporal Logic to describe spatio-temporal properties. We consider a discrete location/ patch-based representation of space, with a population of interacting agents evolving in each location and with agents migrating from one patch to another one. We provide both a boolean and a quantitative semantics to this logic. We then present monitoring algorithms to check the validity of a formula, or to compute its satisfaction (robustness) score, over a spatio-temporal trace, exploiting these routines to do statistical model checking of stochastic models. We illustrate the logic at work on an epidemic example, looking at the diffusion of a cholera infection among communities living along a river.
Design of novel screening environments for Mild Cognitive Impairment: giving priority to elicited speech and language abilities
Recent cognitive decline screening batteries have highlighted the importance of language deficits related to semantic knowledge breakdown to reveal the incipient dementia. This paper proposes the introduction of novel enriched linguistic tests and examines the hypothesis that language can be a sensitive cognitive measure for Mild Cognitive Impairment (MCI). A group of MCI and healthy elderly were administered a set of proposed linguistic tests. Performance measures were made on both groups to indicate that concrete verbal production deficits such as impaired verb fluency can distinguish the MCI from normal aging. In addition, it was found that even in cases where the MCI subjects preserved scores, language tests took significantly more time compared to healthy controls. These findings indicate that language could be a sensitive cognitive marker in preclinical stages of MCI.
On the Performance of General Cache Networks
The performance evaluation of cache networks has gain a huge attention due to content-oriented delivery technologies. If general network topologies are more realistic than hierarchical networks widely studied in the literature, their analysis is significantly challenging. Existing models mainly focus on trees where content custodians are located at the root and the one-way child-to-parent request forwarding schema is common. In this paper, we consider complex and irregular networks where requests may flow possibly in opposite directions from/to several sources/destinations. Moreover, we assume that caches may run one of Time-To-Live (TTL)-based policies recently introduced for content-centric networks and modern Domain Name System [5]. We then derive an analytical framework and a polynomial-time algorithm that approximate accurately performance metrics of arbitrary graph-based and heterogeneous TTL-based cache networks. Simulations show that our simplified methodology may accurately predict metrics of interest on networks of caches running popular replacement algorithms (e.g. LRU, FIFO, or Random) without restricting its scope of application to this interesting use case. Unlike existing approaches, ours scales as network and content catalog sizes increase.
Towards an Interactive Experiment Framework: DynamiQ
Interactive network experiments are useful for finding solutions to network problems, for teaching and for training purposes. In this demonstration we shall present an interactive experiment framework that allows users to directly control the experiment scenario by using a touch panel interface. This framework uses the network emulation module named dynamiQ for the dynamic emulation of networks. The demonstration uses a scenario with up to 55 emulated nodes, out of which 30 nodes form an emulated vehicular network and 5 are static buildings. The other nodes represent wireless towers and unmanned aerial vehicles that can be freely placed in the virtual experiment area. Participants are tasked with creating a multi-hop mesh network for sending video traffic between two predefined remote lo- cations. A similar demonstration has already been shown at Interop Tokyo 2014, where it received a special jury award.
Defining an Elasticity Metric for Cloud Computing Environments
Elasticity is a key property of cloud computing environments and one of the features which distinguishes this paradigm from other ones. An elasticity metric could be used to define and to monitor Service Level Agreements (SLAs), to compare and to benchmark different cloud providers or to improve provisioning and management decisions in real time if it could be measured with some simple procedure, to mention only some examples. Unfortunately, there are not still standard elasticity metrics capable of easily capturing its main components or aspects, these are, scalability, accuracy, time and cost. This work defines a new elasticity metric for cloud computing environments which not only captures these four essential components but also provides a simple procedure to analyse elasticity in cloud contexts. The obtained experimental results on a real cloud environment evidence that this new elasticity metric is able to quantify the degree of elasticity obtained in different cloud scenarios and to establish interesting comparisons and analysis.
Cloud based Software Defined Wireless Body Area Networks Architecture for Virtual Hospital
Recent developments in wireless communication inflate a new area of research named as Wireless Body Area Networks. WBANs enhance the concept of virtual hospital with the help of cloud computing (CC) technology. Still numerous technical challenges could be found to integrate Cloud Computing (CC) and WBANs. From networking perspective, Software Defined Networking (SDN) becomes popular for its centralized nature to manage complex network infrastructure. In this paper, the concept of cloud enabled virtual hospital architecture based on software defined networking has been proposed and studied for wireless body area networks. This paper highlights a new, simplified architecture for virtual hospital concepts harnessing the benefits of software defined networking to mitigate the complexity of cloud architecture and its integration with WBAN.
Using Cloud-assisted Body Area Networks to Track People Physical Activity in Mobility
This paper describes a novel BSN-based integrated system for detecting, monitoring, and securely recording human physical activities using wearable sensors, a personal mobile device, and a Cloud-computing infrastructure supported by the BodyCloud platform. An integration with a smart-wheelchair system is also presented. BSNs are a key enabling technology for the revolution of personal-health services and their integration with Cloud infrastructure can effectively supports the diffusion of such services in our daily life. Many of these personal-health systems - regardless of their final aim - are based, use or are supported by contextual information on user's physical activity (body posture, movement or action) being performed. This work, hence, aims at providing a basic physical activity service that is capable of supporting personal, mobile-Health applications with real-time activity recognition and labeling both on the personal mobile device and on the Cloud.