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Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

As energy efficiency is becoming a subject of utter importance in today’s societies, the European Union and a vast number of organizations have put a lot of focus on it. As a result, huge amounts of data are generated at an unprecedented rate. After thorough analysis and exploration, these data could provide a variety of solutions and optimizations regarding the energy efficiency subject. However, all the potential solutions that could derive from the aforementioned procedures still remain untapped due to the fact that these data are yet fragmented and highly sophisticated. In this paper, we propose an architecture for a Reasoning Engine, a mechanism that provides intelligent querying, insights and search capabilities, by leveraging technologies that will be described below. The proposed architecture has been developed in the context of the H2020 project called MATRYCS. In this paper, the reasons that resulted from the need of efficient ways of querying and analyzing the large amounts of data are firstly explained. Subsequently, several use cases, where related technologies were used to address real-world challenges, are presented. The main focus, however, is put in the detailed presentation of our Reasoning Engine’s implementation steps. Lastly, the outcome of our work is demonstrated, showcasing the derived results and the optimizations that have been implemented.

Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in the landscape of practical tools in the field of DMS and advances the state of the art. This system represents a complex process of integrated AI-powered technologies that creates an ontology, extracts information from documents, defines profiles, maps the extracted data in RDF format, and applies inference through a reasoning engine. The DMS was designed to help all those companies that manage their clients’ tax and administrative documents daily. Automation speeds up the management process so that companies can focus more on value-added services. The system was tested in a case study that focused on the preparation of tax returns. The results demonstrated the efficacy of the system in providing document management service.

The future search engine should understand the content of Web pages and implement logical reasoning, in order to achieve complex search and correct results. This paper introduced relative theory of component and RDF, created a conceptual architecture for semantic search engine, and discussed its components and their relationships. Finally, advantages of this architecture are proved.

Our research was motivated by the growing aging population worldwide and the need to concentrate research efforts on a specific target group; it focuses on elderly persons with physical and cognitive deficiencies. The primary goal is to enable persons with mild dementia to maximize their physical and mental functions through assistive technologies in order to be able to continue to participate in social networks and lead independent and purposeful lives. Persons with mild dementia usually have problems in performing activities of daily living due to episodic memory decline. These can include simple activities, such as bathing, changing clothes and preparing meals. Through extended field test trials involving end users, we have demonstrated that assistive technology that provides timely prompts, alarms and reminders can enable them to preserve their abilities and improve their quality of life. Understanding the user context, especially when targeting demented individuals, and providing the required personalized assistive services is the objective of our research work. Finding the appropriate user interface to interact with the provided services is often a barrier. Thus, we have adopted the approach of a multimodal interactive system with the living environment including a TV set, iPad-like tablets, sensors/actuators, and wireless speakers connected to a reasoning engine that is able to consider the complexity of the users’ profile defined by his/her cognitive abilities. In this paper we will mainly focus on the interaction level with the system as well as on the validation stages performed to meet the users’ requirements. This is the result of several years’ work since 2006 in the frame of two projects (IST-FP6 COGKNOW European completed project and AMUPADH ongoing project in Singapore).

A considerable amount of information is quickly disseminated worldwide and users struggled to survive on such data tsunami. Context-recommender-aware systems (CAR) are then developed which enabling users to locate valuable and useful information from a large amount of disordered data. However, human decision-making contains multiple steps and a recursive loop, most users tend to adjust their decision many times instead of achieving the final decision-making immediately. Therefore, to replicate such a recursive process among multiple steps, the traditional CAR system should be altered as an interactive CAR (iCAR) system for improving the recommendation accuracy. In view of the deficiency in the present CAR, this study leads the concept of human-computer interaction in tradition CAR and establishes an interactive context-aware recommender System (iCAR). To validate the feasibility and applicability of the proposed iCAR system, a car rental website which is designed based on iCAR is shown as a demonstration. According to the car rental case shown, after couples of iterations, the decision criteria can be gradually clarified by the proposed algorithm of inferring engine. Also, iCAR can find users a car that most satisfies their requirements by using the contexts information. iCAR can improve the accuracy of traditional CAR system and provide user more precise recommendation results according to 3-dimensions information, including: user, item and context information. The iCAR system can be further expected to apply to various fields, such as online shopping or travel packages recommendations, to optimize recommendations results.

As digital sports video becomes increasingly pervasive, semantic video summary becomes one of the important components for the next generation of multimedia applications. Ontology is a feasible way to mine the semantic information from the video stream. However, current ontology-based methods did not concentrate on the effectiveness and soundness of semantic reasoning. Here, the authors propose a content-directed ontology reasoning approach to produce meaningful sports video summarisation. The proposed ontology can facilitate the metadata acquisition of video and the improvement of query performance. It also provides a flexible way to query the sports video database, which cannot be achieved by simple keyword search. For annotating, describing and managing the sports video content, we propose a sports video descriptive language (SVDL) based on the proposed ontology. Moreover, the semantically meaningful sports video abstraction is produced by reasoning engine which is based on the extension of the Tableau algorithm. Meanwhile, the soundness and completeness of the reasoning algorithm can be solidly proved. Subjective assessment experimental results reveal the reliability and efficiency of the propose scheme.

Physical world facts must be measured and translated into first-order-logic expression before participate the reasoning process in reasoning engine. However, mapping facts into first-order-logic form in real-time is not an easy task. This paper discusses the difficulties of such mapping in three aspects. Then, this paper proposes a multi-agent based scheme to solve these three problems. First, this paper introduces multi-agent frame to reduce system design complexity. Then, this paper proposes a table mapping algorithm to translate numeric facts into first-order-logic form. After that, a resampling mechanism considering overall system delay is introduced to selectively remove excessive facts. Finally, this paper gives a case study, which verifies that the scheme proposed in this paper is practical, efficient and easy to put into application.

The capability of adapting to environmental changes and fulfilling specific needs of nomadic users empowers mobile devices with new value-added features. Users on the move are expecting real time and personalized services that are adjusted to their needs and that fit within their current time and space settings. Context-aware systems are distinguished by: i) their ability to profile users; ii) their awareness about device capabilities; and iii) their environmental knowledge. The availability of wireless networks supports context-aware systems through ubiquitous sensors and web services used to gather contextual information in order to offer users exceptional interactive experiences. In order to cope with information overload, collected data on the changing environmental context needs efficient management. In this research, we present a constraint-based context management system which handles efficiently complex situations in adopting a desired behaviour whenever a specific change occurs in the environment. This is accomplished through a set of knowledge-based rules which validate the consistency of the context by monitoring system constraints to trigger automatic context updates. We evaluate our dynamic context-consideration approach through real-life scenarios while comparing three consistency-validation strategies.