The interaction network shows most of the data collection occurred between October to June, which is the mobile sensed data interval chosen for experiments.
In typical mobile sensing architectures, sensing data are collected from users and stored in centralized servers at third parties, making it difficult to effectively protect users' privacy.
Rather than throwing existing systems to the wayside, by layering on today's mobile, sensing and data technologies to existing infrastructure, we can keep our infrastructure current.
The ubiquitous deployment of personal mobile devices today has boosted numerous mobile sensing applications where sensing data should be timely collected and exchanged among participating sensors.
We consider three forms of interactions from the mobile sensor data, (1) the Bluetooth interaction data, (K_I), (2) the phone communication logs including both call and SMS activity, (K_C), and (3) the total combined mobile sensed interaction data, (K_I + K_C).
Leveraging volunteers and their mobiles as a (sensing) data collection outlet is known as Mobile Crowd Sensing (MCS) and poses interesting challenges, with particular regard to the management of sensing resource contributors, dealing with their subscription, random and unpredictable join and leave, and node churn.
In this paper we suggest a mobile sensing platform that enables data requesters to discover data producers within a specific geographic region and acquire their data.
Microclimate conditions of three urban fringe neighborhoods with varying amounts of natural vegetation and development density were studied, along with ASTER remote sensing data, mobile climate transects, and spot infrared thermographic images.
Firstly, based on the characteristics of geographical spatial information resources and service time series, a mobile geographic crowd service system was established for providing the sensing data with the mobile geographic crowd service model.
