A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> TABLE 5. <s> In this paper, we present a portable intelligent electrocardiogram (ECG) monitoring system using the Zigbee wireless network technology. The system consist of both hardware and software development. For the hardware development, a Field Programmable Gate Arrays (FPGA), ECG sensor, preprocessing circuit, Zigbee transmission module, and external memory are used. And the FPGA with a Nios II soft-core CPU is the heart of the system, which is used to control all other units. As for the software development, a real-time system called μC/OS-II is used and some related application programs is designed. The objective of this system is to continuously monitor cardiac patients. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> TABLE 5. <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> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> TABLE 5. <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> BIB003
Crowd-sensing types of measured phenomena BIB002 , BIB003 , BIB001 . prediction corresponds to an external judgment/observation. Consequently, these terms compare the results under the test of the classifier with trusted external judgments. The procedure of feature, detection and classification is shown in Figure 5 