A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-Things

Abstract

Crowdsourcing using mobile devices, known as mobile crowdsourcing, is a powerful approach incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. The problems that can be tackled include the use of geographically distributed tasks, and mobile sensing using the collective wisdom of the crowd. However, the implementation of mobile crowdsourcing applications has been found to be challenging to users due to the nature of dynamic sensing, crowd engagement with data distribution, and a process of data verification. In this paper, we provide an extensive survey of the literature on mobile crowdsourcing research, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development. We present a taxonomy based on the key issues in mobile crowdsourcing and discuss the different approaches applied to these issues. We also provide a critical analysis of some challenges and suggest directions for future work. In particular, with the future Internet-of-Things in view, we generalize the notion of mobile crowdsourcing to thing crowdsourcing, where crowdsourcing can be issued from smart Internet-connected things that need to harness the human resources to solve problems.

Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

“…Finally, it is worth noting that, with the rise of IoT [45,46], crowdsourcing is gaining momentum in a wide range of sectors and tasks [47,48,49,50,51]. In healthcare, crowdsourcing has been mainly employed to accomplish problem solving, data processing, surveillance/monitoring, and surveying [52], but there are not many mobile crowdsourcing applications [11,53] and even less decentralized while focused on healthcare [48]. In health crowdsourcing, engagement is essential since it can transform users from mere passive recipients of information to active participants in a collaborative community, raising awareness for diseases like diabetes and helping to improve their own health as well as the health of those around them [54].…”

“…In health crowdsourcing, engagement is essential since it can transform users from mere passive recipients of information to active participants in a collaborative community, raising awareness for diseases like diabetes and helping to improve their own health as well as the health of those around them [54]. In addition, there are incentive mechanisms that enable community participation [48]. For example, the authors of [55] proposed an incentive mechanism to encourage hospitals to share high-quality data, which can then be aggregated to generate prediction models with higher accuracy rates.…”

Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

“…Indeed, people may now gather various observations about the physical world using the sensors connected to their smartphones, along their journeys. This creates the potential to provide finegrained observations that cover large areas over 24/7 time periods, provided the engagement of a sufficiently large and diverse crowd [1], [2]. Still, the expected engagement of the crowd to sensing tasks differs depending on how the smartphones' owners contribute observations [3], [4], which may be either: (i) pro-actively, aka participatory crowdsensing [5], [6]; or (ii) passively in the background, aka opportunistic crowdsensing [7], [8].…”

In-network Collaborative Mobile Crowdsensing

“…Mobile crowdsensing [1] is a sensing paradigm that empowers ordinary citizens to contribute with data sensed or generated from their sensor-enhanced mobile devices (e.g., mobile phones, wearable devices, tablets). Crowdsensing supports two sensing strategies [2]: (1) Participatory crowdsensing that requires the proactive involvement of individuals who consciously contribute with sensing data; (2) Opportunistic crowdsensing that collects data in the background autonomously and does not necessitate any explicit action from the user. In that framework, opportunistic mobile crowdsensing appears as a scalable and cost-effective alternative to the deployment of static wireless sensor networks for the dense coverage of large areas, and especially urban areas.…”