Non-Parametric Bayesian Rejuvenation of Smart-City Participation through Context-aware Internet-of-Things (IoT) Management

TL;DR

This paper introduces a non-parametric Bayesian model to predict citizen participation in smart city services by effectively managing unobservable, independent, and composite contexts in a scalable, context-aware IoT environment.

Contribution

It develops a novel non-parametric Bayesian approach to model and predict participation considering complex, unobservable contexts in smart city IoT systems.

Findings

Effective modeling of unobservable contexts

Improved scalability of participation prediction

Enhanced context-awareness in IoT-based smart city services

Abstract

Tweaking citizen participation is vital in promoting Smart City services. However, conventional practices deficit sufficient realization of personal traits despite socio-economic promise. The recent trend of IoT-enabled smart-objects/things and personalized services pave the way for context-aware services. Eventually, the aim of this paper is to develop a context-aware model in predicting participation of smart city service. Hence, major requirements are identified for citizen participation, namely (a) unwrapping of contexts, which are relevant, (b) scaling up (over time) of participation. However, paramount challenges are imposed on this stipulation, such as, un-observability, independence and composite relationship of contexts. Therefore, a Non-parametric Bayesian model is proposed to address scalability of contexts and its relationship with participation.