Abstract
Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.
Highlights
M OBILE crowdsensing (MCS) is a human-driven activity which leverages the pervasiveness of wireless connectivity and various mobile devices with built-in sensing capabilities as well as the inherent user mobility to create dense and dynamic data sets characterizing our environments
EVALUATION Hereafter we present the evaluation of the proposed Mobile edge computing (MEC) architecture for Mobile crowdsensing (MCS) to investigate the overhead incurred due to the need to orchestrate MCS services at network edge
The data set was originally used for autonomous place detection, and we needed to process it to match our need of simulating movements of a large user base which is adequate for MCS deployments
Summary
M OBILE crowdsensing (MCS) is a human-driven activity which leverages the pervasiveness of wireless connectivity and various mobile devices with built-in sensing capabilities as well as the inherent user mobility to create dense and dynamic data sets characterizing our environments. Cloud services perform device management functions: They coordinate the sensing tasks on many user devices and keep track of device context to choose the best data sources for defined sensing tasks Both device management and real-time data processing require significant computational resources in case of large-scale MCS deployments. In addition to preprocessing of raw sensor data on mobile devices, edge resources can be used for processing/aggregation of data streams contributed by a subset of users involved in MCS tasks and can even support real-time usage scenarios autonomously without the need to contact cloud services. The architecture identifies a minimal set of features to be placed at the edge computing resources in order to satisfy the requirements of future massive-scale MCS deployments Those features are put into the context of the MEC reference architecture proposed by ETSI [11].
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