A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> A. APPLICATION MODES <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> A. APPLICATION MODES <s> Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> A. APPLICATION MODES <s> The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS. <s> BIB003
The participatory (active sensing mode) depends on the participants in performing some actions. In this method, the participant willingly takes the responsibility of entering the information. The active mode requires the user's participation and even the user's involvement in the operation  . For example, incident reporting requires the user to move to the incident's location, to take videos and photos, and finally send them to the monitoring center. The opportunistic (passive sensing mode) depends on a set of applications installed on the users smartphones performing a set of predefined actions. In this way, the application gathers data without notifying the user. This method should meet the application requirements and automatic data collection, such as collecting a user's geolocation information BIB003 , BIB001 . In the passive participant mode, the MCS applications do not require any involvement from the user of the mobile device except downloading the mobile application and allowing his/her mobile device to cooperate and participate in the MCS operation  . For instance, an application that monitors the environment in an urban area can rely solely on smartphones for data collection without the user's involvement. Figure 2 shows a comparison between the steps involved in participatory and opportunistic sensing. Jayaraman et al. BIB002 presented the Mobile Sensor Data EngiNe (MOSDEN) as a collaborative mobile crowd-sensing framework to develop and deploy opportunistic sensing applications. The framework was used for an environmental monitoring scenario (e.g. noise pollution) in smart cities. The framework scenario has three steps. Initially the remoteserver registers for the required data to be collected from the concerned users. Then, the smartphones collect the data using their embedded sensors, preprocess the data and send it to the remote-server. Finally, the crowd-sensing applications gets the data from the remote-server for further processing and visualization. The authors implemented and evaluated the framework performance on an Android-based mobile to validate the possibility and efficiency of running collaboratively in mobile opportunistic sensing applications. They mentioned that MOSDEN performs well under load in collaborative environment.