A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> IV. MOBILE CROWD-SENSING APPLICATIONS <s> Supervised classification needs large amounts of annotated training data that is expensive to create. Two approaches that reduce the cost of annotation are active learning and crowdsourcing. However, these two approaches have not been combined successfully to date. We evaluate the utility of active learning in crowdsourcing on two tasks, named entity recognition and sentiment detection, and show that active learning outperforms random selection of annotation examples in a noisy crowdsourcing scenario. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> IV. MOBILE CROWD-SENSING APPLICATIONS <s> This paper evaluates and characterizes the technical performance of medical wireless personal area networks (WPANs) that are based on smartphones. For this purpose, a prototype of a health telemonitoring system is presented. The prototype incorporates a commercial Android smartphone, which acts as a relay point, or “gateway”, between a set of wireless medical sensors and a data server. Additionally, the paper investigates if the conventional capabilities of current commercial smartphones can be affected by their useas gateways or “Holters” in health monitoring applications. Specifically, the profiling has focused on the CPU and power consumption of the mobile devices. These metrics have been measured under several test conditions modifying the smartphone model, the type of sensors connected to the WPAN, the employed Bluetooth profile (SPP (serial port profile) or HDP (health device profile)), the use of other peripherals, such as a GPS receiver, the impact of the use of the Wi-Fi interface or the employed method to encode and forward the data that are collected from the sensors. <s> BIB002
In this section, we will introduce a brief summary of available mobile crowd-sensing applications, describe their characteristics, demonstrate several research challenges, and finally discuss possible solutions. Generally, MCS applications can be classified into two groups based on the type of phenomenon being monitored: personal and community sensing applications. In personal sensing applications, the phenomena pertain to an individual, while community-sensing applications apply to monitoring large-scale phenomena that are not easily measured using an individual application. These applications link computing devices together, share data, and then extract information to map phenomena of common nature, which generally belongs to one of the following four types: healthcare, environmental, infrastructure, and social life BIB002 . In healthcare monitoring, smartphones are capable of connecting patients with medical services through mobile communications networks for sensing and diagnostic capabilities. In environmental applications, some of the main phenomena to be sensed are water levels in creeks, air pollution in a city, and wildlife habitats in order to monitor their behavior for further study. In infrastructure applications, the main phenomena to be sensed are traffic congestion, road conditions, parking availability, and outages of public works Finally, social life, where individuals share sensed information between themselves. For instance, individuals can share their exercise time in a day and compare their daily exercise routines. Another example is that individuals can share their exercise data and then compare their exercise levels with those of the rest of the community BIB001 . Moreover, the individuals can use this comparison to help enhance their daily exercise routines. Table 5 shows the summary for the four different crowd-sensing types of measured phenomena. Mobile sensing and smartphone devices deliver an appropriate platform for the four categories of MCS monitoring applications listed in the Table 5 .