Publications | MB-Presents

2025

A realistic trust model evaluation platform for the Social Internet of Things (REACT-SIoT)

Marius Becherer, Omar K. Hussain, Frank Hartog, and 2 more authors

Journal of Network and Computer Applications, 2025

Abs DOI

The Social Internet of Things (SIoT) enables cross-organizational collaboration for various industrial applications. However, evaluating trust models within such environments remains challenging due to context-dependent dynamics in SIoT environments. Existing evaluation platforms often rely on overly domain-specific or generic datasets, overlooking the inherent uncertainty and dynamicity of real-world SIoT settings. Additionally, there is a lack of practical platforms to assess the feasibility and effectiveness of trust models across diverse scenarios. In this study, we present the Realistic Trust Model Evaluation Platform for the Social Internet of Things (REACT-SIoT) to rigorously assess trust models in SIoT environments, thereby facilitating trustworthy collaboration for sustainable IoT transformations. REACT-SIoT addresses 21 identified requirements essential for simulating a realistic SIoT environment, including categories of heterogeneity, dynamicity, incompleteness, uncertainty, interdependency, and authentic real- world dynamics. We developed a configurable evaluation procedure that mitigates dataset bias and supports the assessment of both existing and newly developed trust models under various scenario-dependent settings. A real-world example demonstrates the platform’s capability to satisfy these requirements effectively. Our analysis reveals that REACT-SIoT meets all defined requirements and outperforms existing evaluation environments based on accuracy, trust convergence, and robustness criteria. The platform has been successfully applied to existing trust models, showcasing its applicability and enabling comparative assessments that were previously constrained by disparate evaluation settings and datasets. In conclusion, REACT-SIoT offers a highly- adaptable evaluation framework that ensures unbiased and comprehensive trust model assessments in SIoT environments. This platform bridges a critical gap in trust evaluation research, enabling the comparison and validation of trust models across diverse, realistic scenarios, thereby supporting the development of more resilient and trustworthy collaborative SIoT systems.

2024

Enabling Trustworthy Collaboration for Sustainable Transformation

Marius Becherer, Michael Zipperle, Omar K Hussain, and 4 more authors

Engineering Intelligent Systems, 2024

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As global resource consumption surges, the ’Club of Rome’ highlights the risks of unchecked quantitative growth in our constrained world, advocating for a shift to qualitative growth. The Internet of Things (IoT)emerges as a pivotal tool in this transition, exemplified by its transformative impacts to coordinate complex activities efficiently while reducing resource consumption in cities like Barcelona and Singapore already today. However, intricate network challenges such as network control, interoperability, and resource constraints complicate efficient communication in a large-scale IoT network. Concurrently, establishing trust among diverse devices often becomes a paramount concern, hindering expansive collaboration across domains and organisations. In response to these multifaceted challenges, the Social Internet of Things (SIoT) has been introduced to address intricate network challenges issues but also fosters a foundation of trust among devices. Despite, the potential benefits of developing trust in SIoT, one of the key challenges for the innovation adaption remains the determination of trust in continuously evolving and uncertain environments. Therefore, there is a need to understand specific situation and provide fine-grained trust mechanism to address multifaceted requirements and consider prevailing constraints of the environment. In this work, we study the existing literature on trust in the SIoT and present potential use cases of how trustworthy collaboration between devices can help to enable the sustainable transformation. We discuss prevailing trust challenges and introduce a novel research framework for trustworthy collaboration to bridge the gap between theoretical trust research and their real-world applicability in the SIoT landscape. Achieving trustworthy collaboration between devices of various organisations will help to improve the activity orchestration while reducing resource consumption.
On Trust Recommendations in the Social Internet of Things - A Survey

Marius Becherer, Omar Khadeer Hussain, Yu Zhang, and 2 more authors

ACM Computing Surveys, Jun 2024

Abs DOI

The novel paradigm Social Internet of Things (SIoT) improves the network navigability, identifies suitable service providers, and addresses scalability concerns. Ensuring trustworthy collaborations among devices is a key aspect in SIoT and can be realized through trust recommendations. However, the outcome of trust recommendations depends on multiple factors related to the context-dependent nature of SIoT and practical constraints brought by the devices and networks embedded in the SIoT. While the existing literature has proposed numerous trust recommendation models to assess the trustworthiness of devices in various scenarios, researchers have not sufficiently examined the required features for trust recommendations in the SIoT. Consequently, trust recommendation models may inaccurately assess the true risk of device interactions. In this literature survey, we investigate the context-dependent features and recommendation methods used for the SIoT using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We propose a novel taxonomy to categorize trust recommendation models according to their input features and design. Our findings reveal limited attention is given to the context-dependent features, constraints of the information environment, and limited inference capabilities that impede more precise trust recommendations. Finally, we present the research gaps and outline future directions to enable trustworthy inter-domain operations within the SIoT.

2023

Enabling Digital Transformation with Sustainability Criteria through Resilient Cybersecurity: Challenges and Opportunities

Michael Zipperle, Marius Becherer, Yu Zhang, and 3 more authors

Engineering Intelligent Systems, Jun 2023

2021

Agent-Based Simulation for Testing Vehicle-On-Demand Services in Rural Areas

Marius Becherer and Achim Karduck

In Intelligent Systems and Applications, Jun 2021

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Conventional road traffic is reaching its limits in many cities because the existing infrastructure is often not designed for the number of vehicles used in a city. This causes inefficient traffic, which is apparent as congestion, and wasting of spatial resources. Therefore, the need for new mobility concepts such as “vehicle-on-demand” can open up new possibilities to succeed in challenges in contemporary mobility. This concept has already been simulated in various large cities scenarios with promising results. However, rural areas have not yet been taken into account in such simulations. Therefore, the concept “vehicle-on-demand” is implemented as part of the open-source simulation framework Simulation of Urban Mobility (SUMO). The considerations in the implementation of the vehicle-on-demand service are presented, and finally, the implemented service is evaluated with the rural area scenario of Furtwangen. By this, the result of the simulation reveals contrasting results in comparison to large cities. Ultimately, the concept of “vehicle-on-demand” is applicable in rural areas with the implemented service.

2020

An AI-Based Automated Continuous Compliance Awareness Framework (CoCAF) for Procurement Auditing

Ke Wang, Michael Zipperle, Marius Becherer, and 2 more authors

Big Data and Cognitive Computing, Sep 2020

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Compliance management for procurement internal auditing has been a major challenge for public sectors due to its tedious period of manual audit history and large-scale paper-based repositories. Many practical issues and potential risks arise during the manual audit process, including a low level of efficiency, accuracy, accountability, high expense and its laborious and time consuming nature. To alleviate these problems, this paper proposes a continuous compliance awareness framework (CoCAF). It is defined as an AI-based automated approach to conduct procurement compliance auditing. CoCAF is used to automatically and timely audit an organisation’s purchases by intelligently understanding compliance policies and extracting the required information from purchasing evidence using text extraction technologies, automatic processing methods and a report rating system. Based on the auditing results, the CoCAF can provide a continuously updated report demonstrating the compliance level of the procurement with statistics and diagrams. The CoCAF is evaluated on a real-life procurement data set, and results show that it can process 500 purchasing pieces of evidence within five minutes and provide 95.6% auditing accuracy, demonstrating its high efficiency, quality and assurance level in procurement internal audit.
Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

Marius Becherer, Michael Zipperle, and Achim Karduck

Computer Systems Science and Engineering, Sep 2020

Abs DOI

Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are explained. Then factors for the forecast are explained. Requirements for the data are collected and algorithms for machine learning are presented. Based on the relationships found, a process model is presented that shows a fast implementation of the predictive maintenance for machines.
Speech Triggered Mobility Support and Privacy

Michael Zipperle, Marius Becherer, and Achim Karduck

In Intelligent Information Processing X, Sep 2020

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Current voice assistants are offered by large IT companies such as Google, Amazon, Microsoft, Apple or Baidu. The voice assistants include numerous functionalities, which are usually executed centrally in the cloud by the providers. Nevertheless, the providers offer imprecise information on what happens to the input data of the users. Users cannot be sure whether their privacy and data are protected. The central research question is what is currently happening with the voice-based interaction between users and services, and what concepts for configurable data protection by users are conceivable in the future. In this article, we present the survey results obtained by speech assistant users. The results show, in particular, the willingness to pay for individually configurable privacy. The concept for a voice assistant with privacy-awareness is proposed and prototypically implemented.
Engineering a Trustworthy Private Blockchain for Operational Risk Management

Marius Becherer, Michael Zipperle, Stuart Green, and 3 more authors

In A Framework of Human Systems Engineering, Dec 2020

DOI
A Compromise-Tolerant Key Management Framework for Private Blockchain

Marius Becherer, Thien Bui-Nguyen, Michael Zipperle, and 1 more author

International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, Dec 2020