Experimental Testbed Research Articles

A Soft Robotic Textile‐Actuated Anthropomorphic Artificial Shoulder Mechanism

Replicating the human shoulder in anthropomorphic systems is notoriously challenging due to its complex combination of mobility and strength. This study presents the design, fabrication, and control of a new soft artificial shoulder that achieves a broad range of motion, torque, and compliance. Powered by soft robotic textiles consisting of a network of hydraulic artificial muscles, the engineered shoulder effectively mimics intricate shoulder movements, including flexion/extension, abduction/adduction, and medial/lateral rotation. Experiments demonstrate that the artificial shoulder can generate a peak torque of 9.6 ± 0.1 Nm, covering 65.3% of the human shoulder workspace. The artificial shoulder capability is demonstrated through several experimental testbeds. First, it is employed to develop a gesture‐controlled telemanipulation robotic system, applicable to robot‐assisted surgery, hazardous environment operations, gaming, and rehabilitation. Second, it serves as a platform for simulating and studying neurological disorders, such as Parkinson's disease. This approach offers a reliable in vitro testing ground for wearable device validation, providing a crucial intermediary step before progressing to user studies. The artificial shoulder marks a significant advancement in next‐generation anthropomorphic systems, closely mimicking the human musculoskeletal system, with promising applications in wearable assistive devices, haptics, orthopedic testing, and medical technologies.

Automation of 5G network slicing security using intent-based networking

Network slicing is a fundamental technological advancement that facilitates the provision of novel services and solutions within the realm of 5G and the forthcoming 6G communications. Numerous challenges emerge when implementing network slicing on a large-scale commercial level since it necessitates comprehensive control and automation of the entire network. Cyberattacks, such as distributed denial of service (DDoS) and address resolution protocol (ARP) spoofing, can significantly disrupt the performance and accessibility of slices inside a multi-tenant virtualized networking infrastructure due to the shared utilization of physical resources. This article employs intent-based networking (IBN) to identify and address diverse threats through automated methods. A conceptual framework is presented in which the IBN manager is integrated into the network-slicing architecture to facilitate the implementation of automated security controls. The proposed work is assessed using an experimental test bed. The study's findings indicate that the network slice's performance exhibits improvement when successful detection and mitigation measures are implemented. This improvement is observed in various metrics: availability, packet loss, response time, central processing unit (CPU) and memory utilization.

When the Future Becomes no Longer a Threat but, More, an Opportunity

The City of London is entering new and relatively unexplored territory with its intention of making “Culture and Commerce stronger together”. An opportunity to demonstrate this ambition exists at London Wall West, a site in the cultural heart of the Square Mile, but plans drawn up so far have attracted considerable opposition as they involve the demolition of existing buildings. Even though the City stands firm in its conviction that new buildings are required, justifying this approach in a world where construction accounts for 39% of the planet’s global greenhouse emissions is challenging. The proposed answer, “Building Less is More”, will require the City to develop a degree of confidence in the idea that, in part, London Wall West can become virtual. We can anticipate that to design and construct a new virtual world, where people can spend time that is as good as in the outside physical world, London Wall West must become a test bed for experimentation on how to turn “conjecture” into reality. As in the physical world, the creation of a virtual realm depends on defining and understanding the determinants of people’s behavior. Architects are already involved in creating virtual flights of fancy but holding the reins of all those who are unleashing the power of generative artificial intelligence will be the “metaverse curator”–a completely new role to match the task of building “a new and better world”.

Quantitative Measurement of Cyber Resilience: Modeling and Experimentation

Abstract Cyber resilience is the ability of a system to resist and recover from a cyber attack, thereby restoring the system's functionality. Effective design and development of a cyber resilient system requires experimental methods and tools for quantitative measuring of cyber resilience. This paper describes an experimental method and test bed for obtaining resilience-relevant data as a system (in our case – a truck) traverses its route, in repeatable, systematic experiments. We model a truck equipped with an autonomous cyber-defense system and which also includes inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., “bonware”) strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious mathematical models to aid in quantifying systems’ resilience to cyber attacks. Using the models, we identify quantitative characteristics obtainable from experimental data, and show that these characteristics can serve as useful quantitative measures of cyber resilience.

Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption

Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of communication delays on CAV operations and their energy use remains sparse in the literature. To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. To our knowledge, this is one of the first attempts at evaluating the impacts of communication delays on CAV merging operational control with field data, making critical advancement in the field. The results suggest that communication delay has a more substantial effect on energy consumption under high-speed volatility compared to low-speed volatility. Among all factors examined, acceleration is the dominant characteristic that influences energy usage. It also revealed that even minor improvements in communication delay can yield tangible improvements in energy efficiency. The results provide guidance on CAV field experiments and the influence of communication delays on CAV operation and energy consumption.

FlowChronicle: Synthetic Network Flow Generation through Pattern Set Mining

Network traffic datasets are regularly criticized, notably for the lack of realism and diversity in their attack or benign traffic. Generating synthetic network traffic using generative machine learning techniques is a recent area of research that could complement experimental test beds and help assess the efficiency of network security tools such as network intrusion detection systems. Most methods generating synthetic network flows disregard the temporal dependencies between them, leading to unrealistic traffic. To address this issue, we introduce FlowChronicle , a novel synthetic network flow generation tool from mined patterns and Bayesian networks. As a core component, we propose a novel pattern miner in combination with statistical models to preserve temporal dependencies. We empirically compare our method against state-of-the-art techniques on several criteria, namely realism, diversity, compliance, and novelty. This evaluation demonstrates the capability of FlowChronicle to achieve high-quality generation while significantly outperforming the other methods in preserving temporal dependencies between flows. Besides, in contrast to deep learning methods, the patterns identified by FlowChronicle are explainable, and experts can verify their soundness. Our work substantially advances synthetic network traffic generation, offering a method that enhances both the utility and trustworthiness of the generated network flows.

Bit-exact simulation of 6TiSCH networks

This article presents an approach to building a bitexact simulation environment, capable of handling complex wireless communication stack such as 6TiSCH with enough performance, to be useful in large scale network topologies. The architecture of the simulator and the stack is presented along with the discussion of key features and design choices. The results of the simulation are compared to the results of extensive testbed experiments, which conclude that the designed tool can be used for practical design and verification of large distributed IoT applications.

A distributed and cooperative signature-based intrusion detection system framework for multi-channel man-in-the-middle attacks against protected Wi-Fi networks

A Multi-Channel Man-in-the-Middle (MC-MitM) attack is an advanced form of MitM attack, characterized by its ability to manipulate encrypted wireless communications between the Access Point (AP) and clients within a WiFi network. MC-MitM attacks can target any Wi-Fi client, regardless of the authentication method used with the AP. Notable examples of such attacks include Key Reinstallation Attacks and FragAttacks, which have impacted millions of WiFi systems worldwide, especially those involving Internet of Things devices. Current defense mechanisms are inadequate against these attacks due to interoperability challenges and the need for modifications to devices or protocols within the targeted Wi-Fi networks. This paper introduces a distributed and cooperative signature-based wireless intrusion detection mechanism designed for online passive monitoring to detect malicious traffic patterns during MC-MitM attacks in any environment, from apartments and houses to large areas like hotels, offices or industrial sites. We implemented the proposed framework on Raspberry Pis and evaluated it in real-world settings. Our evaluation demonstrates that this framework can effectively identify MC-MitM attacks with an average accuracy of 98% when deployed across different locations within our experimental testbed.

A piezoelectric-electromagnetic hybrid energy harvester for rotational motion driven by magnetic repulsion

Harvesting energy from rotational motion is an efficient and widely used technology that provides power support for various electronic devices and systems by converting mechanical kinetic energy into electrical energy. This energy harvesting method shows great potential and advantages in industrial automation, transportation, wind power generation, and smart homes. This study proposes a piezoelectric-electromagnetic hybrid energy harvester (HEH) for rotational motion driven by magnetic repulsion. HEH comprises of two parts: a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EMH). HEH utilizes the magnetic drive reciprocating motion to enhance the energy collection efficiency of PEH and EMH. Additionally, by adding the magnetic effect to the ends of bimorph piezoelectric sheets, the spectrum of energy harvesting is expanded. Its parameters are analyzed using theoretical analysis and simulation, and an experimental testbed is established to explore the influence of HEH output performance. The results indicate that the output power reaches its maximum when there are 2 circular magnets on the rotor, the gap distance of magnets is 15 mm, and 2 mass blocks at the end of the bimorph piezoelectric sheet. The PEH and EMH outputs are 173.36 V and 4.81 V, respectively. The maximum output power of HEH is 53.45 mW. The power density can reach 6.818 mW cm−3. Compared with PEH and EMH, the output performance is improved by 46.94% and 174.95%, respectively. When the rotation speed is 500 r min−1, HEH can effortlessly light up 80 LEDs. The experimental results all demonstrate the potential of HEH to power low-power sensors.

A Smart Farm Monitoring and Control System using Wireless Sensor Network

Agriculture is one of the most important sector that accounts for economic growth in the developing countries of the world. Many developing countries are now focusing on agricultural development, yet the sector is not without challenges including climate changes, drought, flooding to mention but a few. These result in poor yield of agricultural products. In this work, we l developed a robust smart system to enhance both irrigation and flooding monitoring and control, leveraging on Wireless Sensor Networks (WSNs) to boost agricultural production. In the system’s implementation, Arduino based instrumentation, integrated with temperature, soil moisture and water level sensor shall be adopted to monitor the agricultural environment, while reporting the status wirelessly through the Radio Frequency (RF) modules to the base station. The base station will evaluate the received data and either activates or deactivates the irrigation or drainage pump using specified threshold values. The remote reporting the state of farm shall be done by deploying a 900Mhz transmitter interfaced with the system’s controller. This work shall be validated using an experimental test bed, which shall be used for field experiments, data collection and evaluation. The result of the work shall highlight the potentials of WSN technology in monitoring and control technology of both irrigation and flooding within the farm and in turn boost productivity

Collaborative credentials for the Internet of Things

The Self-Sovereign Identity (SSI) paradigm has emerged as a decentralized Identity Management model with interesting capabilities for the Internet of Things (IoT). However, current standard SSI protocols and procedures only consider that individuals store their own identity, failing to provide an accurate solution for the identity management of groups where participants might use credentials from different identities and collaborate to meet a set of verifier´s requirements. The present work introduces the concept of Collaborative Credentials (CCs) to formalize identity management procedures that model the collaboration within a group of participants. CCs allow to leverage use cases requiring collaboration that cannot be solved with standard SSI verifiable credentials, increase the privacy of group participants and enable the development of a software framework that any verifier/holder could use to generate a generic application. In the paper, a generic model for CCs is presented, together with an implementation example that is subsequently evaluated in an experimental testbed.

Validation Framework of a Digital Twin: A System Identification Approach

AbstractThe constant improvement and developments in Artificial Intelligence/Machine learning models coupled with increased computing power have led to the incorporation of AI/ML for simulating learning and problem‐solving in simple and complex engineering systems. This latent uncertainty and unpredictable characteristics of AI‐enabled systems challenges engineers and industry stakeholders who care about ensuring the right systems are built (system validation). Digital Twins are an excellent example of such AI‐enabled systems due to their data‐dependent nature when tasked with replicating, monitoring, and updating physical assets for structural health monitoring and control. However, Digital Twins' system validation has not been well‐researched. This study delves into existing research and frameworks for validating Digital Twins and proposes a novel model‐centric validation framework based on system identification techniques. As a case study, we apply this model‐centric validation framework towards partially validating a Digital Twin for a single‐heat‐pipe test article for a Microre‐actor Agile Non‐nuclear Experimental Testbed.

Discovery of Cloud Applications from Logs

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data–network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate not only diverse kinds of applications, but also their specific deployments, thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in cloud management scenarios.

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

Autonomic end-to-end quality-of-service assurance over QKD-secured optical networks

Quantum key distribution (QKD) provides future-proof security for data communications over optical networks. Currently, sophisticated QKD systems are developed and the scale of QKD-secured optical networks (QKD-ONs) becomes larger. Given the complex network conditions and dynamic end-to-end security services in QKD-ONs, autonomic management and control becomes a promising paradigm to support end-to-end quality-of-service (QoS) assurance in an efficient and stable way without requiring human intervention. Hence, to enable and utilize the autonomic functionalities over QKD-ONs for realizing the end-to-end QoS assurance becomes a challenge. This work enhances the software defined networking (SDN) technique to tackle this challenge because SDN can add programmability and flexibility for QKD-ON’s management and control. A new architecture of SDN-based QKD-ONs supporting autonomic end-to-end QoS assurance is designed, where a knowledge engine with autonomic control loops is developed in the SDN controller. We present the autonomic end-to-end QoS assurance procedure, and the cross-layer collaborative QoS assurance (CLC-QA) strategy for implementing the autonomic functionalities in the network level over QKD-ONs. We also establish an experimental testbed of SDN-based QKD-ONs supporting autonomic end-to-end QoS assurance, and perform the numerical simulation to verify our proposed approaches. Experimental results demonstrate that our presented approaches can achieve the millisecond-level overall latency of 337 ms and 618 ms, during the first and second autonomic adjustment without human intervention in case of the autonomic QoS protection. Moreover, the CLC-QA strategy is evaluated under different traffic loads by being compared with the baseline strategy without cross-layer collaboration. It can improve 22.5% protection success ratio and save 5.7% average key consumption.

Aquilo: Temperature-aware scheduler for millimeter-wave devices and networks

Millimeter-wave is the core technology to enable multi-Gbps throughput and ultra-low latency connectivity. But the devices need to operate at very high frequency and ultra-wide bandwidth: They consume more energy, dissipate more power, and subsequently heat up faster. Device overheating is a common concern of many users, and millimeter-wave would exacerbate the problem. In this work, we first thermally characterize millimeter-wave devices. Our measurements reveal that after only 10 s of data transfer at 1.9 Gbps bit-rate, the millimeter-wave antenna temperature reaches 68°C; it reduces the link throughput by 21%, increases the standard deviation of throughput by 6×, and takes 130 s to dissipate the heat completely. Besides degrading the user experience, exposure to high device temperature also creates discomfort. Based on the measurement insights, we propose Aquilo, a temperature-aware, multi-antenna network scheduler. It maintains relatively high throughput performance but cools down the devices substantially. Our testbed experiments under both static and mobile conditions demonstrate that Aquilo achieves a median peak temperature only 0.5°C to 2°C above the optimal while sacrificing less than 10% of throughput.

A regularized fourth-order adaptive method for seamless integration of charging station to the grid for EV and critical loads

ABSTRACT This paper proposes a real-time implementation and regularised fourth-order adaptive (RFOA) method of three-phase grid connectedsolar photo voltaic (SPV) array and battery-based charging station. The proposed system mainly uses a solar photovoltaic array and battery system to reduce the burden on the grid. The system feeds the surplus energy to the grid as well as operates during the outage and support the EV and home loads/critical loads. To minimise the error at various operating scenarios, the RFOA method is utilised for voltage source converter (VSC). The battery is integrated with DC link with the help of bi-directional converter. It works in boost mode when it is discharging and works in buck mode when it is charging. The perturbation & observation (P&O) maximum power point tracking (MPPT) is used to generate the reference DC link voltage. In order to improve the power quality of the charging station, the proposed system is designed to act as an active filter, load leveller, power factor corrector and reduce the harmonics to 5% as per IEEE 519 standards. The proposed charging is examined for different balanced and unbalanced load conditions and at the different environmental situations in MATLAB/Simulink based platform as well as in experimental test bed based on OPAL-RT platform.

Failure recovery in the MANTRA architecture with an IPoWDM SONiC node and 400ZR/ZR+ pluggables

The utilization of IP over wavelength division multiplexing (IPoWDM) boxes in optical networks introduces coordination issues at the control plane level between the optical and packet domains. Indeed, IPoWDM boxes are technically packet devices, but the configuration of coherent optical pluggables requires knowledge of optical network state information (e.g., wavelength availability). MANTRA (metaverse ready architectures for open transport) aims to define a new SDN control architecture for managing IPoWDM, enhancing coordination between the packet and optical controllers. Within MANTRA, two distinct architectures were presented:DualandSingle. However, only the roles related to controller management are defined, while clear procedures are not specified for dealing with resource provisioning and failure recovery. This leads to the need for the definition and implementation of tailored procedures. Besides the characterization of the coherent optical pluggables performance, this paper presents and compares three procedures based on MANTRA to coordinate and control IPoWDM nodes in a multi-layer network performing failure recovery. The considered solutions have been designed and experimentally validated with a focus on the achievable traffic recovery performance considering IPoWDM configuration and communication within the SDN control architecture. An experimental testbed, comprising a sliced IPoWDM node running an extended version of the open-source SONiC network operating system, has been deployed to validate these solutions, with a comprehensive analysis of the time required to recover a real traffic flow that spans both the packet and optical domains.

Automated aerial assessment for seamless adaptive adhoc restoration in partially collapsed network

Disasters often result in widespread infrastructure failures, leading to network blackouts that impede critical information dissemination and hamper search-and-rescue operations. This paper presents a novel methodology for expedited assessment and restoration of network connectivity in disaster-affected regions. While existing approaches have made significant contributions, they still face either one or a combination of challenges such as time-consuming procedures, limited coverage, integration complexities, and inaccessibility in certain areas. To address these challenges, we propose two innovative contributions: (1) a novel automated aerial assessment technique for remote identification of network blackouts, offering enhanced accessibility, reduced response times, and heightened reliability; (2) a pioneering automated approach utilizing personal mobile devices (PMDs) to establish alternate multi-hop ad hoc routes from affected regions, enabling auxiliary network formation and seamless integration with surviving networks. The proposed route setup, activated upon detection of network blackouts, facilitates self-managed network maintenance, which adapts to gradual restoration processes, thereby optimizing efficiency, connectivity span, stability, and energy conservation. Both methodologies are rigorously evaluated through simulations and test-bed experiments, demonstrating substantial advancements over existing methodologies. This research contributes to the field by significantly enhancing disaster response capabilities through improved network resilience and rapid connectivity restoration strategies.

Fully Distributed Cell-Free MIMO Systems: Architecture, Algorithm, and Testbed Experiments

Fully Distributed Cell-Free MIMO Systems: Architecture, Algorithm, and Testbed Experiments