A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. MCS COMMUNICATION MEDIA <s> Wireless Body Area Networks (WBANs) provide efficient communication solutions to the ubiquitous healthcare systems. Health monitoring, telemedicine, military, interactive entertainment, and portable audio/video systems are some of the applications where WBANs can be used. The miniaturized sensors together with advance micro-electro-mechanical systems (MEMS) technology create a WBAN that continuously monitors the health condition of a patient. This paper presents a comprehensive discussion on the applications of WBANs in smart healthcare systems. We highlight a number of projects that enable WBANs to provide unobtrusive long-term healthcare monitoring with real-time updates to the health center. In addition, we list many potential medical applications of a WBAN including epileptic seizure warning, glucose monitoring, and cancer detection. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. MCS COMMUNICATION MEDIA <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> C. MCS COMMUNICATION MEDIA <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 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> C. MCS COMMUNICATION MEDIA <s> Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area. <s> BIB004
MCS have grown from small-scale with specific applications to large-scale ubiquitous data collection for a wide range of applications. However, a large-scale network is sometimes not achievable due to many factors including economical factors due to high cost associated with large number of sensors, and difficulty of full coverage due to VOLUME 7, 2019 large area. It will need many relay nodes to maintain a complete field coverage and communication connectivity. This will be hard to implement due to both the expensive sensor cost and the distribution, as well as the maintenance cost BIB003 . Most of the communication technologies in wireless sensor nodes (WSNs) are radio-frequency wireless networking technology-based. WSNs have some limitations such as low processing power, low processing speed (approximately 8 MHz clock speed) BIB001 very limited storage (few hundred kilobytes), a short communication range and high power consumption. The sensor has a small form factor (mm 3 ). Minimal energy sources such as batteries have a finite lifetime, and passive devices provide limited energy due to protocol constraints. Recently, researchers are considering the benefits of mobile sensor networks as an operational and reasonable solution to large scale sensing networks. However, there are several differences between the two sensing techniques BIB002 . First, MCS relies on mobile devices and smartphones, while WSNs use tiny sensor nodes. This difference allows MCS to perform local processing, as mobile devices and smartphones have more processing power, memory, and energy. Additionally, mobile devices and smartphones use chargeable batteries, which makes local processing for MCS less power-limited (compared with local processing in WSNs). The second difference is the larger scale of MCS (hundreds of thousands or millions of devices and phones across a city or a country) compared with WSNs, which typically have hundreds, or at most thousands, of sensor nodes. Deploying sensors for traditional WSNs at the city-wide level requires at least tens of thousands of sensors. In BIB004 , it was shown that 90,000 sensors and 1,000,000 relays are required to perform citywide (about 900 km 2 ) environmental monitoring to maintain full area coverage and communication connectivity. The third difference is the human involvement in MCS, which brings some issues such as concerns about user privacy but also brings some opportunities such as the ones emerging from using human involvement in a way that makes the system smarter. Human involvement also brings the issue of incentive policies to ensure user participation in the MCS operation. The fourth difference is the dynamic nature of MCS due to the user mobility, variation of power levels, and changes in user behavior and participation. Finally, the fifth difference is that in traditional WSNs, the sensors are usually stationary whether deployed in deterministic or random locations, while in MCS, the sensors (embedded in mobile devices and smartphones) are mobile and move randomly and independently. This paper presents two of the widely used MCS communication media: wireless access communications network and IP-based core network For the communication used in the MCS framework, there are two levels of communications: 1) Access communications network 2) IP-Based Core network. The access communications network is part of a telecommunications network that connects customers to their service provider directly. It ranges in diameter from a few hundred meters to several miles and includes all devices between the core network and the user terminal. The core network uses a fiber-optical structure due to its high transmission rate. A core network is the essential component of the telecommunications network that provides several services to customers who are connected by the access communications network. Servers, computer accessories, and applications represent the core network in this study. The following points will present a brief description of the two media and will show in detail the inside and outside communication media that are used in the above framework BIB003 .