For service users to get the best service that meet their requirements, they prefer to personalize their nonfunctional attributes, such as reliability and price. However, the personalization makes it challenging for service providers to completely meet users’ preferences, because they have to deal with conflicting nonfunctional attributes when selecting services for users. With this in mind, users may sometimes want to explicitly specify their trade-offs among nonfunctional attributes to make their preferences known to service providers. In this article, we present a novel service selection method based on fuzzy logic that considers users’ personalized preferences and their trade-offs on nonfunctional attributes during service selection. The method allows users to represent their elastic nonfunctional requirements and associated importance using linguistic terms to specify their personalized trade-off strategies. We present examples showing how the service selection framework is used and a prototype with real-world airline services to evaluate the proposed framework's application.
In recent past, the security of cyber-physical systems (CPSs) has been the subject of major concern. One of the reasons is that, CPSs are often applied to mission-critical processes. Also, the automation CPSs bring in managing physical processes, and the detail of information available to them for carrying out their tasks, make securing them a prime importance. Securing CPSs is a difficult task as systems are interconnected. In order to achieve a continuous secured CPS environment, there is the need for an integrated methodology to analyze, specify and prioritize security requirements and also to develop policies to meet them. First, CPS assets are represented using high-order object models. Second, swimlane diagrams are extended to include malactivities and prevention or mitigation options to decompose use cases. We analyze security threats pertaining to the hardware components, software components and the hardware-software interaction. Security requirements are then specified, and an analytical prioritization approach, based on relative priority analysis is employed to prioritize them. Finally, security policies are then developed to meet the requirements. To demonstrate its effectiveness and evaluate its application, the proposed methodology is applied in a structured approach to a testbedAyushman, a Pervasive Health Monitoring System (PHMS).
Existing service recommendation methods, that employ memory-based collaborative filtering (CF) techniques, compute the similarity between users or items using nonfunctional attribute values obtained at service invocation. However, using these nonfunctional attribute values from invoked services alone in similarity computation for personalized service recommendation is not sufficient. This is because two users may invoke the same service, but their personalized preferences on nonfunctional attributes that describe the service may be different. Thus, to accurately personalize service recommendation, it is necessary for CFbased recommendation systems to incorporate users personalized preferences on nonfunctional attributes when recommending services to an active user. This paper proposes a CF-based service recommendation method that considers users' personalized preference on nonfunctional attributes. We first compute the satisfaction of an active user's preference on nonfunctional attribute(s) and then use these satisfaction values to obtain their similarity measures. We then employ the top-k algorithm to identify neighbors of the active user and subsequently, use the weighted average with mean offset method to predict his/her nonfunctional attribute. We evaluate our method using real-world services and also conduct experiments to show that the proposed method improves recommendation accuracy significantly.
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