In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.

The recent developments in the Internet of Things (IoT) paradigms have significantly influenced human life, which made their lives much more comfortable, secure and relaxed. With the remarkable upsurge of the smart systems and applications, people are becoming addicted to using these devices and having many dependencies on them. With the advent of modern smart healthcare systems, and significant advancements in IoT enabled technologies have facilitated patients and physicians to be connected in real-time for providing healthcare services whenever and wherever needed. These systems often consist of tiny sensors and usually run on smart devices using mobile applications. However, these systems become even more challenging when there is a need to take intelligent decision making dynamically in a highly decentralized environment. In this paper, we propose a Belief-Desire-Intention (BDI) based multi-agent formalism for ontology-driven healthcare systems that perform BDI based reasoning to take intelligent decision making dynamically in order to achieve the desired goals. We illustrate the use of the proposed approach using a simple case study with the prototypal implementation of heart monitoring applications.

Recent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi-context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi-agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context-aware non-monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system.

Investigating prior methodologies, it has come to our knowledge that in smart cities, a disaster management system needs an autonomous reasoning mechanism to efficiently enhance the situation awareness of disaster sites and reduce its after-effects. Disasters are unavoidable events that occur at anytime and anywhere. Timely response to hazardous situations can save countless lives. Therefore, this paper introduces a multi-agent system (MAS) with a situation-awareness method utilizing NB-IoT, cyan industrial Internet of things (IIOT), and edge intelligence to have efficient energy, optimistic planning, range flexibility, and handle the situation promptly. We introduce the belief-desire-intention (BDI) reasoning mechanism in a MAS to enhance the ability to have disaster information when an event occurs and perform an intelligent reasoning mechanism to act efficiently in a dynamic environment. Moreover, we illustrate the framework using a case study to determine the working of the proposed system. We develop ontology and a prototype model to demonstrate the scalability of our proposed system.

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