Smart Context-aware Rejuvenation of Engagement on Urban Ambient Augmented Things

TL;DR

This paper proposes a Bayesian model and systematic framework to enhance urban engagement through context-aware ambient assisted living, addressing personalization challenges and improving urban experience in smart cities.

Contribution

It introduces a novel Bayesian-based approach and framework for monitoring and rejuvenating citizen engagement in urban ambient environments, considering personalization and context complexities.

Findings

Bayesian model effectively captures context-engagement relationships.

Framework improves urban experience by personalized engagement strategies.

Addresses challenges of context observability and scalability.

Abstract

The concern over global urbanization trend imposes smart-city as enabling information and communication technology (ICT) to improve urban governance. However, the light trance on better living space is stimulated by socioeconomic impact of escalated senior generation. Hence, ambient assisted living (AAL) emerges for the autonomous provisioning of pervasive things or objects from relevant perturbation for advanced scientific instrumentation. Meanwhile, citizens are observed in being transfixed by lively stimuli of monotonous urban events with the advent of virtual reality or augmented things. Thus, due to the involvement of situation-awareness or contextualization, engagement/participation information as a utility promises to improve urban experience. However, it is complex to grapple meaningful concepts due to personalization obstacles, such as citizen psychology, information gap, service-visualization. Moreover, recommended practices deficit adaptation to monochromatic choice, disparate impairments, mobility and annotation-richness in urban space. Hence, rejuvenation of engagement relates to monitoring and quantification of 'service consumption and graceful degradation' of experience. However, paramount challenges are imposed on this stipulation, such as, unobservability, independence and composite relationship of contexts. Therefore, a parametric Bayesian based model is envisioned to address observability and scalability of contexts and its conjugal relationship with engagement. Last but not the least, systematic framework is demonstrated, which pinpoints key goals of context-aware engagement from participants' opinions, usages and feed-backs.