A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios

Abstract

This work presents an architecture to help designing and deploying smart mobility applications. The proposed solution builds on the experience already matured by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first “monolithic” prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The proposed platform exposes standardized interfaces to access data, implements common services to manage metadata associated with them, such as trustworthiness and provenance, and provides an orchestration language to create complex services, naturally mapping their internal workflow to code. The manuscript demonstrates the effectiveness of the approach by means of some case studies.

Edge-Based Microservices Architecture for Internet of Things: Mobility Analysis Case Study

“…In edge computing, mobility prediction aims for optimizing both the communication and edge application performance in the opportunistic IoT environment in large scale [13]. For example, edge-based location-and context-aware crowdsensing solutions have been developed through microservices [16], mobility enhanced Web-integrated smart objects [10] and with autonomous software agents [17].…”

Smart mobility for all: A global federated market for mobility-as-a-service operators

“…These are focussed on the provisioning of proprietary vehicles to move people and goods. Once deployed in SMAll, these applications could both i) federate, selling to each other the access to their vehicles/customers, and ii) automatise the inclusion of local providers and communities [39], generating a liquid market around the same core business. Analogously, applications for multi-modal journey planning like Rome2rio, Google Transit, Hyperdia, and NaviTime, once deployed in SMAll, could automatically enrich their results with real-time (possibly crowd sourced [40]) data, for dynamic trip planning [41], also considering traffic monitoring and accident detection [42], and the collection of crowd-sourced data on cognitive distraction [43], drowsiness [44], and behaviour of drivers [45] to avoid incidents.…”

Insider Threats in Emerging Mobility-as-a-Service Scenarios

“…The high flexibility of honeypotsable to play a huge variety of SMAll-compliant servicesis essential to make insiders expose themselves. Another useful method that can be easily built within SMAll is a reporting system for crowdsensing and crowdsourced data, implemented in [26]. The reporting system is based on the mapping of what the authors called Point of Interest (POI).…”