Evaluation of Trust in the Internet Of Things: Models, Mechanisms And Applications

In the blooming era of the Internet of Things (IoT), trust has become a vital factor for provisioning reliable smart services without human intervention by reducing risk in autonomous decision making. However, the merging of physical objects, cyber components and humans in the IoT infrastructure has introduced new concerns for the evaluation of trust. Consequently, a large number of trust-related challenges have been unsolved yet due to the ambiguity of the concept of trust and the variety of divergent trust models and management mechanisms in different IoT scenarios. In this PhD thesis, my ultimate goal is to propose an efficient and practical trust evaluation mechanisms for any two entities in the IoT. To achieve this goal, the first important objective is to augment the generic trust concept and provide a conceptual model of trust in order to come up with a comprehensive understanding of trust, influencing factors and possible Trust Indicators (TI) in the context of IoT. Following the catalyst, as the second objective, a trust model called REK comprised of the triad Reputation, Experience and Knowledge TIs is proposed which covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation, personal experiences to global opinions. The mathematical models and evaluation mechanisms for the three TIs in the REK trust model are proposed. Knowledge TI is as “direct trust” rendering a trustor’s understanding of a trustee in respective scenarios that can be obtained based on limited available information about characteristics of the trustee, environment and the trustor’s perspective using a variety of techniques. Experience and Reputation TIs are originated from social features and extracted based on previous interactions among entities in IoT. The mathematical models and calculation mechanisms for the Experience and Reputation TIs also proposed leveraging sociological behaviours of humans in the real-world; and being inspired by the Google PageRank in the web-ranking area, respectively. The REK Trust Model is also applied in variety of IoT scenarios such as Mobile Crowd-Sensing (MCS), Car Sharing service, Data Sharing and Exchange platform in Smart Cities and in Vehicular Networks; and for empowering Blockchain-based systems. The feasibility and effectiveness of the REK model and associated evaluation mechanisms are proved not only by the theoretical analysis but also by real-world applications deployed in our ongoing TII and Wise-IoT projects

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Effective Strategies for Small Business Sustainability

AbstractSmall businesses in the United States experience a higher failure rate than medium-sized and large corporations. Small business owners without successful strategies to sustain their businesses may fail. Grounded in the McKinsey 7S model, the purpose of this multiple case study was to explore strategies small general construction contractor business owners use to sustain their businesses beyond 5 years. The participants were three small general construction contractor business owners within Miami, Florida, who sustained businesses for 5 years. Data were collected using semistructured and audio-recorded face-to-face interviews. Through thematic analysis, four themes emerged: (a) creating strategies for a solid customer base, (b) employee skills training, (c) creating leadership strategies, and (d) creating strategies for financial stability. A key recommendation for small general construction contractors is to develop emergency fund strategies to increase their cash flow in case of a business emergency, pandemic, or economic crisis. Small business leaders should also maintain ongoing training and development to develop employees with the skills necessary to produce quality work and seek entrepreneurial prospects. The implications for positive social change include the potential to create job opportunities and establish training programs for unskilled workers, which may enhance the quality of life within the local community

Effective Strategies for Small Business Sustainability

AbstractSmall businesses in the United States experience a higher failure rate than medium-sized and large corporations. Small business owners without successful strategies to sustain their businesses may fail. Grounded in the McKinsey 7S model, the purpose of this multiple case study was to explore strategies small general construction contractor business owners use to sustain their businesses beyond 5 years. The participants were three small general construction contractor business owners within Miami, Florida, who sustained businesses for 5 years. Data were collected using semistructured and audio-recorded face-to-face interviews. Through thematic analysis, four themes emerged: (a) creating strategies for a solid customer base, (b) employee skills training, (c) creating leadership strategies, and (d) creating strategies for financial stability. A key recommendation for small general construction contractors is to develop emergency fund strategies to increase their cash flow in case of a business emergency, pandemic, or economic crisis. Small business leaders should also maintain ongoing training and development to develop employees with the skills necessary to produce quality work and seek entrepreneurial prospects. The implications for positive social change include the potential to create job opportunities and establish training programs for unskilled workers, which may enhance the quality of life within the local community

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users’ trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang’s method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings

UMSL Bulletin 2020-2021

The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp