Purpose
Internet of Things (IoT) adoption is a differentiating factor in the hospitality industry which facilitates the integration of the digital and real world. This paper aims to explore academic research and practical applications of IoT in the hospitality domain to help identify opportunities and challenges with implementing the technology for creating competitive advantages and service operations process improvements.
Design/methodology/approach
This paper uses previous works and exemplars to demonstrate the use of IoT in hospitality. Academic indexing websites such as Google Scholar and ScienceDirect are used to search for related terms. Whitepapers, IoT project websites of service providers and media coverage are accessed to collect information. Related work is investigated by classifying into major categories of hospitality.
Findings
Hospitality is one of the leading industries that has adopted IoT to create innovative services, but this topic has not been investigated deeply. A comprehensive study is needed to give guidance to decision-makers and helps to design better services by presenting practical and potential benefits.
Practical implications
The IoT will usher in great opportunities in hospitality by enabling novel applications for customization and personalization of the services. Operational processes will be redefined for efficiency and speed. It will alter the expectations and servicescape; thus, its integration will be vital in terms of competitiveness and success.
Originality/value
This study provides a comprehensive overview of IoT research and applications in the hospitality domain. It contributes to better understanding of recent trends and potentials. A holistic approach was used instead of focusing on a single sector which enables the consideration of all aspects of the topic. Theoretical support in addition to technical aspects, challenges and concerns are offered to the reader.
In hyperspectral imaging applications, the background generally exhibits a clearly non-Gaussian impulsive behavior, where valuable information stays in the tail. In this paper, we propose a new technique, where the background is modeled using the stable distribution for robust detection of outliers. The outliers of the distribution can be considered as potential anomalies or regions of interests (ROIs). We effectively utilize the stable model for detecting targets in impulsive hyperspectral data. To decrease the false alarm rate, it is necessary to compare the ROI with the known reference using a suitable technique, such as the Euclidian distance. Modeling data with stable distribution compensates a drawback of the Gaussian model, which is not well suited for describing signals with impulsive behavior. In addition, thresholding is considered to avoid misclassification of targets. Test results using real life hyperspectral image datasets are presented to verify the effectiveness of the proposed technique.
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