Abstract 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.

Summary A natural disaster is an inevitable situation that can occur at any time and anywhere along with varied forms such as earthquakes, floods, hurricane, wildfire, and so forth, and different level of occurrences has been recorded from mild to an intense level. Timely disaster response plays an important role in reducing its deteriorate after‐effects and can save countless lives. Over the years, people have been developing guidelines and processes to cope up with such kinds of hazardous situations. In recent years, with the advent of the pervasive computing paradigm, situation‐awareness has been considered to be the most fascinating approach for situation assessment and provides decision support accordingly. Situation‐aware systems observe/perceive dynamic changes in the environment, understand/comprehend the situation, and perform actions according to the environment. Although state‐of‐the‐art formalisms have been developed to handle such kinds of hazardous situations intelligently and rescue the victims. However, belief‐desire‐intention (BDI) reasoning mechanism with the incorporation of situation‐awareness is still the thirsty area of research to manage hazardous situations. In this article, we propose a temporal epistemic situation‐aware formalism for BDI reasoning agents to model the context‐aware decision support system. To demonstrate the work of the proposed formalism, we develop a case study based on a disaster situation, in which BDI agents are modeled and simulated to present the results in terms of agents' reasoning processes. We demonstrate the scalability in temporal aspects of the system using different levels of disasters to monitor the hazardous situations and evaluate the overall behavior of the system.

Summary Parking vehicles in densely populated areas are often challenging, stressful, and sometimes it becomes a monotonous job for the drivers in jam‐packed areas. There are several reasons for the delay in finding parking spaces such as scarcity of parking slots, disordered or unmanaged parking of vehicles, lacking or unaware of parking information at the destination, which further leads to the wastage of time, fuel, energy and increase in environmental pollution. Literature has revealed a significant number of smart parking solutions based on the Internet of Things (IoT) and context‐awareness with the incorporation of routing strategies and vehicle detection techniques in a pervasive computing environment. With the rapid escalation of the smart and intelligent devices along with their applicability in a highly decentralized environment, real‐time traffic monitoring, and finding parking spaces have become quite trivial. Smart parking sensors and technologies assist drivers in finding vacant parking slots while they are on the way to their destination. Considering the needs, wants, and demands of metropolitan cities, in this article, we have reviewed the recently published articles, mostly from the last 5 years, on smart parking systems augmented with sensors, embedded systems, context‐awareness capability, and IoT which yields in saving time, fuel, energy, and reduces the stress of the drivers. To accomplish this, we have reviewed different models on smart parking solutions based on algorithmic formalisms, theoretical frameworks, formal models, smart device‐based prototypes as well as real‐time applications, and verifying the correctness properties of the system. The results shown may provide a base for the state of the art future research directions.

Abstract Parking spaces have been considered as vital resources in urban areas. Finding parking spaces in jam‐packed areas is often challenging, stressful, and uncertain for the drivers that causes traffic congestion with a consequent of wastage of time, fuel, and increase of pollution. In recent years, context‐aware computing paradigm has been considered to be the most effective approach to address these kinds of issues. Context‐aware systems acquire and understand contextual information according to the current situation, perform reasoning, and then act intelligently on behalf of the user. These applications often run on tiny resource‐bounded smart devices with the incorporation of embedded or attached sensors on these devices and they often exhibit complex and adaptive behaviour. In this paper, we propose a context‐aware parking application framework to assist drivers in finding parking slots dynamically while moving and/or arriving at the destination. We optimize the context‐aware parking framework with bounds on computational resources for the decision support dynamically in a highly decentralized environment. To illustrate the use of the proposed system, we model the context‐aware parking system using Uppaal model checker for formal analysis and verify the correctness properties of the system.

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. A Protégé plug-in for rules extraction from distributed ontologies has been developed, which allows us to model context-aware agents using the notion of multi-context systems. Extracted rules can be annotated to match the users’ needs and to develop a preference model to support their preferences so as to provide a user with a more personalized services. The use of the proposed framework is illustrated using a simple fact-based preference model developed from ontologies considering two different smart environment domains.

This paper complements our previous work on formal modeling of resource-bounded context-aware systems, which handle inconsistent context information using defeasible reasoning, by focusing on automated analysis and verification. A case study demonstrates how model checking techniques can be used to formally analyze quantitative and qualitative properties of a context-aware system based on message passing among agents. The behavior (semantics) of the system is modeled by a term rewriting system and the desired properties are expressed as LTL formulas. The Maude LTL model checker is used to perform automated analysis of the system and verify non-conflicting context information guarantees it provides.