Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning

Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is proactive device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources.

Identying Factors That Influences Fradulences in Non-Life Insurance Companies

Insurance fraud is the most common form of fraud in the world, aside from tax evasion. By its very existence, the insurance industry is prone to deception. Basic income levels in Vietnam have a tendency to steadily rise as a result of improving socioeconomic conditions. As a result, the need for citizen security has increased and become more diverse.The aim of this study is to study the predictor/s of anti-insurance fraud among non-insurer companies in Vietnam. This study was conducted using a questionnaire that was completed by 51 employees who are currently working in the 11 non-life insurance company in Vietnam. It can be concluded that there exists a significant relationship between all the four independent variables which are namely external regulations, public context, management functions as well as underwriting guidance.

SETUCOM: Secure and Trustworthy Context Management for Context-Aware Security and Privacy in the Internet of Things

IoT technologies facilitate the development and the improvement of pervasive computing by enabling effective context-awareness features. These features enable the IoT applications to detect the user’s situation and adapt their behavior. They also enable context-aware security and privacy, which consist in adapting security and privacy mechanisms’ deployment to the user’s situation. Research studies on context-aware security and privacy focus on security and privacy mechanisms’ implementation but do not consider the secure and trustworthy context management. In this paper, we introduce a new secure and trustworthy context management system for context-aware security and privacy in the smart city: “SETUCOM.” SETUCOM is the implementation of the DTM (Device Trust Management) module of the CASPaaS (Context-Aware Security and Privacy as a Service) architecture. It secures context information exchange by using a lightweight hybrid encryption system adapted to IoT devices and manages trust through artificial intelligence techniques such as Bayesian networks and fuzzy logic. A detailed description of the proposed system is provided, and its main performances are evaluated. The results prove SETUCOM feasibility in context-aware security and privacy for the smart city.

Device Classification-based Context Management for Ubiquitous Computing using Machine Learning

Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is automatic device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources. This paper fulfills an identified need of proactive device classification for device management.

REACT-ION: A Model-based Runtime Environment for Situation-aware Adaptations

Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication systems nor easily usable without knowledge in self-adaptive systems development. Accordingly, in previous work, we proposed REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. REACT addresses heterogeneity and distribution aspects of such systems and reduces development effort. In this article, we propose REACT-ION—an extension of REACT for situation awareness. REACT-ION offers a context management module that is able to acquire, store, disseminate, and reason on context data. The context management module is the basis for (i) proactive adaptation with REACT-ION and (ii) self-improvement of the underlying feedback loop. REACT-ION can be used to optimize adaptation decisions at runtime based on the current situation. Therefore, it can cope with uncertainty and situations that were not foreseeable at design time. We show and evaluate in two case studies how REACT-ION’s situation awareness enables proactive adaptation and self-improvement.