From participatory sensing to Mobile Crowd Sensing

Abstract

Abstract-The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.

Pervasive Crowd Mapping for Dynamic Environments

“…The strategies of participation have migrated to a sensory participation using mobile devices [23], combining data obtained by sensing, crowdsourcing and mashing-up with main geo-referenced social systems to provide urban and architectural accessibility maps [30]. However, these strategies only provide solutions for long term obstacles, failing to dynamicly adapt to the rapid changes in the environment.…”

“…Recently the strategies of participation have migrated to a sensory participation using mobile devices [23], combining data obtained by sensing, crowdsourcing and mashing-up with main geo-referenced social systems to provide urban and architectural accessibility maps [24].…”

Mobile crowd sensing architectural frameworks: A comprehensive survey

“…Concerning data transmission, users may adopt the opportunistic transmission paradigm, adopting a store-carry-forward behavior, relaying information to other devices in case a new forward opportunity is identified or the infrastructure-based transmission, utilizing a communication system (i.e., cellular networks, WLAN, WiMAX) in a centralized or a distributed manner [11], [10]. Data are collected both from the physical world (sensed data from mobile devices) as well as from online communities (mobile social network services related data) [7]. Data from different communities present different characteristics, being often complementary.…”

“…Data from different communities present different characteristics, being often complementary. To this respect, in [7], the authors advance the exchange of data across online and offline communities in order to identify and fully integrate their complementary features and merits. Additionally, as noted in [7], human involvement and participation in the collection, processing and sharing of data will necessitate and ultimately lead to a combination of human and machine intelligence.…”

“…Our view is that a coherent and unified architectural framework, including all key elements related to MCS processes and taking into account their inter-dependencies could constitute a step forward to enabling the unimpeded advancement of MCS. Even though there is a number of related research works that endeavor to define an architectural framework (e.g., [1], [7], [8], [9]), shedding more light to the underlying issues, balancing their inter-play and accounting for and systematically addressing all challenges in a generic, concrete, reliable and robust manner could enable MCS systems to achieve their full potentials. To this respect, in the current work, having as a starting point the MCS unique characteristics and open challenges posed, we comprehensively and critically survey recent MCS systems, models and architectures proposed in related research literature, while analyzing their distinct features and discussing on their relative merits and weaknesses.…”