Test Bed Environment Research Articles

Sampling-Based Path Planning Algorithm for a Plug & Produce Environment

The purpose of this article is to investigate a suitable path planning algorithm for a multi-agent-based Plug & Produce system that can run online during manufacturing. This is needed since in such systems, resources can move around frequently, making it hard to manually create robot paths. To find a suitable algorithm and verify that it can be used online in a Plug & Produce system, a comparative study between various existing sampling-based path planning algorithms was conducted. Much research exists on path planning carried out offline; however, not so much is performed in online path planning. The specific requirements for Plug & Produce are to generate a path fast enough to eliminate manufacturing delays, to make the path energy efficient, and that it run fast enough to complete the task. The paths are generated in a simulation environment and the generated paths are tested for robot configuration errors and errors due to the target being out of reach. The error-free generated paths are then tested on an industrial test bed environment, and the energy consumed by each path was measured and validated with an energy meter. The results show that all the implemented optimal sampling-based algorithms can be used for some scenarios, but that adaptive RRT and adaptive RRT* are more suitable for online applications in multi-agent systems (MAS) due to a faster generation of paths, even though the environment has more constraints. For each generated path the computational time of the algorithm, move-along time and energy consumed are measured, evaluated, compared, and presented in the article.

Medium-Earth-Orbit Spaceflight Radiation Effects in Triple Modular System on Programmable Device

This work presents experimental results of medium-Earth orbit (MEO) spaceflight radiation effects in a COTS SRAM-based FPGA (commercial off-the-shelf static random access memory-based field programmable gate array) device implementing a case-study digital system design protected with the classical triple modular redundancy (TMR) technique. This UGA/TIMA’s experiment is a part of the NASA/GSFC’s SET (space environment testbeds) payload launched in the AFRL’s DSX (demonstration and science experiments) spacecraft in June 2019. Results report 69 single-event upset (SEU)-induced failures in the TMR-protected case-study system, moreover total ionizing dose effects for nearly two years of space mission seem not to contribute to an increment of SEU-induced failures.

Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.

While dental disease is largely preventable, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients. Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors. In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors. One of the main challenges in developing such an algorithm is ensuring that the algorithm considers the impact of current actions on the effectiveness of future actions (i.e., delayed effects), especially when the algorithm has been designed to run stably and autonomously in a constrained, real-world setting characterized by highly noisy, sparse data. We address this challenge by designing a quality reward that maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden. We also highlight a procedure for optimizing the hyperparameters of the reward by building a simulation environment test bed and evaluating candidates using the test bed. The RL algorithm discussed in this paper will be deployed in Oralytics. To the best of our knowledge, Oralytics is the first mobile health study utilizing an RL algorithm designed to prevent dental disease by optimizing the delivery of motivational messages supporting oral self-care behaviors.

Developing Cybersecurity in an Industrial Environment by Using a Testbed Environment

Critical infrastructure protection requires a testing environment that allows the testing of different kinds of equipment, software, networks, and tools to develop vital functions of the critical industrial environment. Used electrical equipment must be reliable, capable and maintain a stable critical industrial ecosystem. An industrial business needs to develop cybersecurity capabilities that detect and prevent IT/ICT and OT/ICS threats in an industrial environment. The emerging trend has been to create security operations center (SOC) services to detect ICS-related threats in enterprise networks. The energy supply sector must consist of crucial elements for safe business continuity and supply chain management in the industrial sector. Threats have changed into a combination of threat types. Hybrid threats may prevent everyday industrial activities, processes, and procedures so that supply chain problems may become long-lasting and affects business continuity management. The project CSG belongs to the (Cybersecurity governance of operational technology in the sector connected smart energy) research project consortium of Business Finland’s Digital Trust Programme. The first research paper regarding the CSG (Cyber Security Governance) project concentrates on the applied theory background of this project. The research provides a research approach for investigating cyber security at the operational and technical levels. It answers the questions of where to concentrate on OT-related cyber security research and how we aim to deploy a testbed to develop a governance model in the CSG project. The study's primary purpose is to describe the operating OT-SOC environment and analyze system requirements for optimizing situational awareness in the testbed environment.

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi.

Recent advancements in target tracking using Wi-Fi signals and channel state information (CSI) have significantly improved the accuracy and efficiency of tracking mobile targets. However, there remains a gap in developing a comprehensive approach that combines CSI, an unscented Kalman filter (UKF), and a sole self-attention mechanism to accurately estimate the position, velocity, and acceleration of targets in real-time. Furthermore, optimizing the computational efficiency of such approaches is necessary for their applicability in resource-constrained environments. To bridge this gap, this research study proposes a novel approach that addresses these challenges. The approach leverages CSI data collected from commodity Wi-Fi devices and incorporates a combination of the UKF and a sole self-attention mechanism. By fusing these elements, the proposed model provides instantaneous and precise estimates of the target's position while considering factors such as acceleration and network information. The effectiveness of the proposed approach is demonstrated through extensive experiments conducted in a controlled test bed environment. The results exhibit a remarkable tracking accuracy level of 97%, affirming the model's ability to successfully track mobile targets. The achieved accuracy showcases the potential of the proposed approach for applications in human-computer interactions, surveillance, and security.

Experimental Investigation of the Influence of NO on a PEM Fuel Cell System and Voltage Recovery Strategies

Air contaminants can have detrimental effects on the performance and durability of proton exchange membrane (PEM) fuel cell vehicles. This research focuses on the experimental investigation of the effect of nitrogen monoxide (NO) in the cathode gas stream, which provokes a cell voltage decrease due to the partially reversible adsorption of NO on the platinum catalyst. The concentration and exposure time of NO in the cathode gas stream are varied at selected constant current densities and load ramps to assess the effects throughout the fuel cell system operating range. The results show the cell voltage loss in the presence of NO and reveal a near-catalyst saturation with increased injected NO mass. Additionally, several voltage recovery and mitigation strategies are introduced and discussed by presenting conclusions about the general effect of NO on a fuel cell system in operation. The most promising recovery strategy for fuel cell systems is identified, and the overall system degradation is discussed. All experiments are performed in a test bed environment on a 25 kW low-temperature fuel cell system via controlled injection of NO into the cathode gas stream.

The Living With a Star Space Environment Testbed Payload

The objectives, instrumentation, methods, and data leading up to launch of the NASA Living With a Star (LWS) Space Environment Testbed (SET) payload onboard the Air Force Research Laboratory Demonstration and Science Experiments (DSX) spacecraft are described. The experiments characterize the space radiation environment and how it affects the hardware performance. The payload consists of a compact space weather instrument and a carrier containing four board experiments.

AKN-FGD: Adaptive Kohonen Network Based Fine-Grained Detection of LDoS Attacks

Low-rate denial of service (LDoS) attacks exploit the security vulnerabilities of network protocols adaptive mechanisms to launch periodic bursts. These attacks result in the severe destruction of the quality of service of TCP applications. Therefore, detection of LDoS attacks is a concern among scientific communities. However, the existing coarse-scale detection methods yield poor detection performance and adaptability. To achieve the accurate detection of LDoS attacks, an adaptive Kohonen Network based fine-grained detection (AKN-FGD) model for LDoS attacks is proposed. Based on the burst and periodicity characteristics of attack traffic, the Smith-Waterman (SW) algorithm is used to estimate the pulse period, which is the length of the detection unit. Subsequently, cluster analysis is performed for each detection unit using the adaptive Kohonen network (AKN) algorithm because the discreteness of traffic suffering from LDoS attacks is more pronounced than that of legitimate traffic. Finally, the existence of LDoS attacks can be verified in view of a novel decision metric, denoted as the anomaly degree, based on the clustering results. We conducted experiments not solely in traditional networks using NS3 and in a test-bed environment but also in a software-defined network (SDN), with accuracies of 99.7%, 99.8%, and 95.6% for detecting LDoS bursts, respectively. The experimental results show that the AKN-FGD scheme not only enables accurate fine-grained detection, that is, it can detect every attack burst, but also estimates the start and end times of the attacks. Moreover, we have compared the AKN-FGD scheme with some other detection methods, and a comparison of the results show that our proposed approach displays better detection performance.

Access protocol aware controller design for eMBB traffic in SD-CDN

Under a tremendous increase in 5th Generation (5G) traffic, the conventional Domain Name Server(DNS)-based routing in Content Delivery Network (CDN) cannot meet enhanced Mobile BroadBand (eMBB) communication requirements. It routes the traffic flow to the local CDN server which is closest to the end-user geographically using Border Gateway Protocol (BGP). Therefore; the overloading in the local CDN server is inevitable. According to literature, the access types of eMBB content achieve a higher hit-ratio in CDN caching and the anycast-based routing can redirect some of the traffic flows to the original server despite having a geographical disadvantage. Therefore, we consider access protocol types of a user such as TCP-based Hyper-Text Transfer Protocol 2 (HTTP2) and Quick User Datagram Protocol Internet Connections (QUIC) which are commonly used today in iphone Operating System (ioS) and Android-based applications. However, they do not have a central view to select optimal server according to access type in CDN and they are unaware of network dynamics such as protocol-based load and heterogeneity; i.e. the number of total matches packets and the ratio of the packets according to access protocols (HTTP2/QUIC). Therefore, we propose access protocol aware Software Defined Content Delivery Networks (SD-CDN) for eMBB traffic which decouples Data and Control planes and enables easy implementation of anycast routing on this infrastructure. It dynamically takes statistics from OpenFlow switches in the Data plane to calculate a novel load and the heterogeneity metrics per device periodically. It embeds the OpenFlow rules by using a novel access protocol-aware routing algorithm in SD-CDN. According to performance evaluation in a real test-bed environment; the Quality of Server (QoS) of received eMBB content by end-user from either the local or the original CDN servers, the proposed SD-CDN outputs 60% better PSNR than the conventional DNS-based routing with an acceptable rate for 5G requirements.

The impact and mitigation of ICMP based economic denial of sustainability attack in cloud computing environment using software defined network

High availability in network services is a crucial requirement for quality of experience. Denial of Service (DoS) and Distribute Denial of Service (DDoS) attacks are under contemplation by many researchers across the globe because these attacks directly target services availability. For this reason, cloud providers use the auto-scaling feature in Cloud Computing Environments (CCE), in which cloud resources scale dynamically on demand. DoS/DDoS attacks on CCE, using auto-scaling, do not deny services but cause high resource usage and substantial financial damages that become an Economic Denial of Sustainability (EDOS) attack. One of the DoS/DDoS attacks, resulting EDOS attack is the Internet Control Messaging Protocol (ICMP) flooding attack. In this paper, a novel technique, ICMP detection and mitigation model (EDOS-IDM) is proposed that can detect and mitigate Volumetric and Normal Behavioral ICMP traffic attacks. The results from the proposed technique are compared with the Normal Behavioral ICMP traffic attack because it causes least resource usage among all the mitigation techniques. According to our study, there is no such technique that can handle normal behavioral ICMP traffic attack. The technique is practically tested and evaluated on OpenStack production Cloud Environment test bed. According to the results, the technique is proved to save extra resource consumption and customer's bills in a cloud computing environment.

Nanophotonic materials for space applications

Abstract

A Distributed Multimodel Cosimulation Platform to Assess General Purpose Services in Smart Grids

Future smart grids with more distributed generation and flexible demand require well-verified control and management services. This article presents a distributed multimodel cosimulation platform based on smart grid architecture model to foster general purpose services in smart grids. It aims at providing developers with support to easily set-up a test-bed environment, where they can simulate realistic scenarios to assess their algorithms and services. The proposed platform takes advantages of internet-of-things communication paradigms and protocols to enable the interoperability among different models and virtual or physical devices that compose a use case. Moreover, the integration of digital real-time simulators unlocks hardware-in-the-loop features. To test the functionality of our platform, a novel scheme of fault detection, isolation, and restoration is developed, in which communication and interoperability of different functions and devices are crucial. This service is applied on a realistic portion of a power grid in Turin, Italy, where devices communicate over the Internet. Finally, the laboratory experimental results achieved during a real-time cosimulation are discussed.

Conceptualisation of Cyberattack prediction with deep learning

The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users. New paradigms add more concerns with big data collected through device sensors divulging large amounts of information, which can be used for targeted attacks. Though a plethora of extant approaches, models and algorithms have provided the basis for cyberattack predictions, there is the need to consider new models and algorithms, which are based on data representations other than task-specific techniques. Deep learning, which is underpinned by representation learning, has found widespread relevance in computer vision, speech recognition, natural language processing, audio recognition, and drug design. However, its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify benign and malicious network packets. In this paper, we model cyberattack prediction as a classification problem. Furthermore, the deep learning architecture was co-opted into a new model using rectified linear units (ReLU) as the activation function in the hidden layers of a deep feed forward neural network. Our approach achieves a greedy layer-by-layer learning process that best represents the features useful for predicting cyberattacks in a dataset of benign and malign traffic. The underlying algorithm of the model also performs feature selection, dimensionality reduction, and clustering at the initial stage, to generate a set of input vectors called hyper-features. The model is evaluated using CICIDS2017 and UNSW_NB15 datasets on a Python environment test bed. Results obtained from experimentation show that our model demonstrates superior performance over similar models.

Field Test-Based UAS Operational Procedures and Considerations for Construction Safety Management: A Qualitative Exploratory Study

Current studies have focused on the exploration of the unmanned aircraft system (UAS) applications in the construction and infrastructure domains, such as progress monitoring, condition inspections, and particularly safety inspection. While their works have had significant contributions to highlighting the potential of the UAS technology, there are still gaps in the lack of operational considerations and workflows established to integrate the UAS into the current safety management program. To fill this gap, this study conducted field tests at commercial building construction worksite by adopting conceptually developed UAS flight protocol. A total of eight flights were performed to collect visual assets and provide for safety professionals at the jobsite with the insight of how this technology can be utilized in accordance with the developed workflow. A total of eight project personnel participated in the UAS flight test at their jobsite as well as post-flight interview to discuss how this technology can meet their safety management task goals. As the qualitative study, this paper narratively described the conceptual procedures consist of three different decision-making processes for ensuring the safe flight of the UAS: pre-flight, flight, and post-flight decision makings. Based on the potential users’ perceptions obtained during the field testing, this presented study focused on three different operational considerations: involving project-based, UAS’s capability and hardware specifications and UAS and project team-based aspects. The main goal of this study proposes a decision-driven workflow for UAS-integrated safety management that encourages sustainable and continuous improvement and documents the operational considerations related to this application. As the qualitative exploratory study, this research is limited to employ only one test-bed environment and the small number of interviewees. However, this study can contribute to a better understanding of the concerns, processes, and further recommendations to safely and effectively implement the UAS into the construction safety management program.

Building temperature regulation in a multi-zone HVAC system using distributed adaptive control

During recent years there have been considerable research efforts on improving energy efficiency of buildings. Since Heating, Ventilation and Air-Conditioning (HVAC) systems are responsible for a big part of energy consumption, developing efficient HVAC control systems is crucial. In most of the developed approaches, precise knowledge of system parameters and/or adequate historical data is required. However, these approaches may not perform as well in the presence of dynamic parameter changes due to human activity, material degradation, and wear and tear, or disturbances and other operational uncertainties due to occupancy, solar gains, electrical equipment, and weather conditions. In this paper, we consider buildings with several climate zones and propose a distributed adaptive control scheme for a multi-zone HVAC system which can effectively regulate zone temperature by applying on-line learning and assuming exchange of information between neighboring zones. The controller of each zone achieves the local objective of controlling zone temperature by compensating for the effects of neighboring zones as well as for possible changes in the parameters of the system. Despite the exchange of information, each local controller does not know how the control actions and temperature of a neighboring zone affect the temperature of its own zone. For this reason, each local controller is estimating the parameters of the interconnections in real time and uses them together with the exchanged information to provide a more accurate local zone temperature control. The proposed method is illustrated using an example of temperature control in a six-zone building as well as a large school building, which are implemented in a Building Controls Virtual Test Bed (BCVTB) environment using EnergyPlus and MATLAB/Simulink.

Reinforcement Learning Based Adaptive Duty Cycling in LR-WPANs

For conserving energy, duty cycle is defined by setting up the active and sleep periods of network nodes. In beacon enabled networks, to provide support for duty cycle, the IEEE 802.15.4 standard uses optional super-frame structure. This duty cycle is usually fixed and does not consider the topology changes that often occur in dynamic sensor networks. In this paper, existing energy conserving duty cycling approaches for 802.15.4 networks especially the adaptive duty cycling techniques for wireless sensor networks are summed up. Also, this paper highlights the shortcomings of the proposals in the literature, such as induced additional latency, so that they may not support the practical scenarios of Internet of Things (IoT). Further, this study highlights a gross shortcoming that relative performance comparison of RL-based proposals cannot be performed without using a benchmarking framework and real test-bed environment. In this paper, we have presented the future research directions that would lay the foundation for successful development of energy efficient RL-based duty-cycling techniques.

A methodology for digital mockup update based on the 3D scanned spatial information for the automated dismantling of nuclear facilities

An automated cutting procedure is required to dismantle high radioactive nuclear facilities. Also, the cutting scenario for the automated cutting procedure is built based on the digital mockup of the target facility before the physical cutting procedure begins. However, the digital mockup of the nuclear facility and the real site environmental data can be different in terms of the shape, location, and orientation of the nuclear facility’s components. Hence, without an as-built status verification, the worker needs to check the as-built status manually in the site. As a result, the automated cutting procedure is more costly than utilizing workers. Moreover, the procedures cannot be done successfully. We have also experienced the problem when we performed physical cutting tests based on the cutting scenario that was built using the digital mockup of the test-bed environment. In order to perform accurate physical cuttings in the site, the digital mockup needed to be updated based on the real site information before the physical cutting procedure was conducted. To do so, we obtained real site information using 3D scanning and applied spatial information processing technologies to update the digital mockup of a nuclear facility. Since creating a new model was time consuming, and because this process would have to be done manually, we did not create a whole as-built model based on the 3D scanning data. Instead, we updated the existing model, which is comprised of a complete and clean solid geometry. In this study, we proposed an overall digital mockup update framework based on the 3D scanned spatial information and implemented it based on the nuclear facility’s dismantling simulation software system.

Phishing e-mail is an increasing menace

The usage and importance of e-mails has been continuously growing despite the availability of alternative means, such as electronic messages, mobile applications, and social networks. Majority of peoples are using e-mail as communication mechanism. The e-mail users are receiving unwanted e-mails, these unwanted e-mails are called as spam e-mails. Some spam e-mail contains phishing attacks which are called phishing e-mails. The phishing e-mails are asking users to give out their secret information which includes passwords, PIN, debit or credit card number, CVV or other important information which enables phishers to reach to the bank accounts of these users. The main objective of this paper is to identify the research gap after carrying out literature survey of Anti-phishing techniques and to propose an architecture for classifying the phishing e-mails to block the phishing e-mails which can be tested in test bed environment.

Blockchain in internet‐of‐things: a necessity framework for security, reliability, transparency, immutability and liability

Blockchain is a distributed operation and information supervision technology programmed initially for Bitcoin cryptocurrency. The awareness in Blockchain technology is rapidly growing since the notion was invented in the year 2008. The motivation for the concentration in Blockchain is its significant characteristics that deliver security, privacy, and information reliability devoid of any additional system regulating the communications, and consequently it generates fascinating research domains, specifically from the viewpoint of methodological difficulties and restrictions. This study discovers the wide-ranging Blockchain technology and studies it's perspective with respect to ‘internet-of-things’ controlled nodes. A resilient prototype method has been programmed that reveals a basic system exhausting Blockchain. The outcome illustrates that the established method is functional in test-bed environment.

Participatory service design and community involvement in designing future-ready sustainable learning landscapes

The United Nations Sustainable Development Goals (SDGs) work as a new agenda for sustainable development globally. Many if not most of the SDGs can be combined with different levels of education. This paper leans on previous work in Sustainable Education Design (SED), which looked at sustainability from its multifaceted angles with a broad global scope. The context of the study is a campus at a research-intensive Finnish university. The methodology entailed participatory service design approaches. For piloting, one classroom was chosen as a test bed. The data consist of workshops, use walks and structured interviews. The analysis started from identifying KPIs of sustainable learning environment creation, after which these were tested against Sustainable Education Design Criteria described in a manual book earlier. The key findings include nine preliminary alternative KPIs that were merged with previous SED criteria and related SDGs. The alternative KPIs were trialled in the test bed environment. These proposed alternative KPIs can be used as indicators for sustainability, innovation and learning during participatory change processes and in evaluating the outcome.