Orchestration support for participatory sensing campaigns

Abstract

In this paper we argue the need for orchestration support for participatory campaigns to achieve campaign quality, and automatisation of said support to achieve scalability, both issues contributing to stakeholder usability. This goes further than providing support for defining campaigns, an issue tackled in prior work. We provide a formal definition for a campaign by extracting commonalities from the state of the art and expertise in organising noise mapping campaigns. Next, we formalise how to ensure campaigns end successfully, and translate this formal notion into an operational recipe for dynamic orchestration. We then present a framework for automatising campaign definition, monitoring and orchestration which relies on workflow technology. The framework is validated by re-enacting several campaigns previously run through manual orchestration and quantifying the increased efficiency.

IoT Community Technologies: Leaving Users to Their Own Devices or Orchestration of Engagement?

“…While researchers have studied many aspects of community sensing systems, including the influence of different design features on user engagement [16,26,18,48], data quality and reliability [40,43], novel forms of data visualisation [25,48], new perspectives on materiality [27], and the need to support orchestration for data gathering campaigns [12], there is little work exploring long term user participation with crowdfunded participatory sensing initiatives. As crowdfunding becomes a more common mechanism to fund and deploy tools for collaborative social action, there is a need to better understand: 1) how communities emerge around and appropriate the technologies they crowdfund in order to attempt to effect positive change; and 2) how these bottom-up approaches compare to more conventional ones such as those adopted in citizen science.…”

“…Researchers have argued that leveraging citizen engagement in crowdsensing requires tools for data collection and mechanisms to enable collaboration between experts and users with local knowledge [7]. Additionally, they have highlighted the need to provide features for campaign orchestration, understood as a predefined set of operations that are enacted in a specified order by a workflow engine [12]. Other technology-enabled collaborative systems have fostered less centralised forms of orchestration [14,8].…”

Managing Pervasive Sensing Campaigns via an Experimentation-as-a-Service Platform for Smart Cities

“…The area of crowdsensing in urban areas is discussed extensively in Reference [8], where authors provide both the theoretical background and a review of a number of approaches currently utilized. Our work has similarities with the approaches and ideas described in [9,10,11]. However, apart from aiming at providing a pragmatic solution to the crowdsensing problem, it is also integrated with a smart city platform, allowing end-users to benefit from this interoperability in various ways (e.g., data storage, visualization, interfacing to other systems, community management, knowledge extraction, and urban service creation), and not just basic management of crowd-sensing activities.…”

A flow-based programming framework for mobile App development

“…If a user wants slighty different functionality (e.g., collect different types of data), an app needs to be build from scratch. An interesting observation from our earlier work [6,13] is that PS solutions all share a similar aspect from a technological point of view: they typically consist of a combination of a mobile app that collects a type of data, a web app that visualises the collected data and underlying database to persistently store all the collected data. The only thing that differs between solutions is the actual type of data being gathered.…”

“…Managing such data gathering campaigns for a citizen observatory is a tedious task, which is primarily caused by the fact that a considerable amount of tasks still have to be performed manually due to the lack of technological support [6]. Examples include providing participant coordination on an individual level, ensuring the data quality of the result, etc.…”