A scalable mobile context-aware recommender system for a smart city administration

Abstract

With the innovation of new Information and Communication Technologies and the needs of information and knowledge sharing among the city, a smart city system aims to improve it's citizens life quality by offering a set of public services. Within this context, this research work proposes a smart city approach for using these technologies in public services delivery to allow real-time interactions with citizens; we propose a service providing framework based on context-aware recommendation approach to improve the city's digital services according to citizen’s context and the backend as a service (BAAS) approach on cloud to enhance the scalability of the system in large workloads. In this study, we evaluated the scalability and interactivity of the proposed approach by measuring response time and exchanged data metrics. To estimate the scalability, we performed series of load tests where response time values increased with the addition of requests in different cases but remained acceptable. In terms of interactivity, the exchanged data between the citizen application and the city’s backend was measured at maximum by 117 Kb/s. The experiments revealed that context-aware recommendation approach optimised interactions by reducing the amount of exchanged data, and the BAAS approach improved the scalability of the system and allowed handling concurrent requests.

GRAPHICAL ABSTRACT

KEYWORDS:

• Smart city
• ICT
• citizens
• public services
• context-aware recommendations
• cloud backend
• scalability

Disclosure statement

No potential conflict of interest was reported by the authors.