You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era 
Chapter Title:  III. MOBILE CROWD-SENSING FRAMEWORKS 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks. 
3. Abstract of Cited Paper (BIB003):  Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different physiological brain states. In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring. The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression. To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie's wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification. Extensive experimental work is conducted, utilizing four classification models. The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value. The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application's accuracy. 
4. Abstract of Cited Paper (BIB004):  Smartphones have become the most pervasive devices in people’s lives and are clearly transforming the way we live and perceive technology. Today’s smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work.

Literature Review:
In a traditional sensing framework, the sensing level relies on a network of dedicated and fixed sensing nodes. The framework introduces many drawbacks such as higher cost, inefficient sensing coverage, maintenance issues and lack of scalability. The new paradigm shift towards MCS addresses the aforementioned drawbacks by replacing the dedicated sensing nodes with the MCS level. The remaining MCS framework levels resemble the corresponding levels in a typical wireless sensor network. Figure 3 shows an overview of the MCS framework, which is divided into several levels: crowd-sensing, data transmission, data collection, and applications on the server side. 1. The crowd-sensing level consists of the crowd-sensing elements, which represent the devices that are selected to be connected to large networks. These devices collect two types of data, mobile sensing data and mobile social network data. The raw data is transferred to the server for information extraction, and the users can decide on which category the data should be pooled. A quick discussion on mobile sensing elements is provided in Table 3 . In a sensor network, each node has three branches; first, the sensor will sense the environment; second, it performs some local computation on sensed data; and third, it is responsible for message exchange, which is communication BIB001 . Several classes of sensor groups are used in the MCS applications. Table 2 shows a comparison between these sensor groups, their types, communication environments, and applications used BIB002 , BIB004 . In the following, we discuss some of the existing MCS enabling platforms that can be deployed on the MCS framework. Medusa is one of the most popular MCS applications, which is a mobile sensing framework that has been classified in BIB003 as a multi-purpose mobile sensing system that introduces a significant performance improvement compared to a standalone system. It allows the use of the opportunistic approach within the crowd-sensing paradigm and enables data collection on the cloud component of the Medusa framework. As soon as the data collected is transferred to the server side, there is no room for monitoring or even amending the timeframe of the data collection process. However, synchronization between multiple sensors is not necessary and not recommended due to data privacy and communication security on smartphones. Another popular MCS application is Hive, which is a general software application framework that enables thirdparty developers to integrate their products in one application. These products include several operations such as data handling, user interactions, mobile or server analytics, and managing user participation  . Hive specifies new MCS tasks with minimized development effort that can simply spread its functionality. Thus, it allows developers to focus only on the novel characteristics of the mobile crowdsourcing application. A practical yet a challenging feature of Hive is to recognize the framework's future generality and extensibility. However, Hive framework is a little less flexible than Medusa in terms of the type of tasks that can be identified and this is due to the consecutive workflow that is only supported by this framework. A research group from IBM  , University of Illinois, and University of Minnesota has developed a middleware MCS platform that is called Citizen Sense. This platform allows individuals to propose, design, and manage distributed crowd-sensing campaigns. Another MCS application was developed by Google, which is called Science Journal (SJ) [54] . The Science Journal application acquires data from natural sources and apply real-time analytics by utilizing different built-in sensors in smartphones to produce useful information about natural phenomena of interest to the user. SJ allows users to investigate and participate with the world through several onboard sensors in Android phone and Chromebook along with well-matched peripheral sensors. Restuccia et al. BIB005 developed a new framework to define, survey and analyze the current state-of-the-art of the quality of information (QoI) in mobile crowd-sensing. Information quality is the amount of information received in a period of time and it is specified in reports received during one hour. The collected data from mobile devices suffer from inaccuracies, noise, and errors. Hence, data aggregation and filtering is needed to improve the accuracy. Indeed, one of the main challenges of dealing with MCS data is related to the problem of separating the characteristic signal from the background noise contaminating the data sets generated by spatially separated sensors. The solution to this problem is to apply the essential redundancy of such simultaneous parallel MCS data sources, which is suitable for the crosscorrelation analysis expected to reduce the local background noise, while revealing the true signal simultaneously present in all parallel data sets. However, even if such data preprocessing approaches successfully filter out all unwanted interfering signals, to perform an accurate modal analysis of the frequency response of the system, one still has to address the essential statistical fluctuations affecting the power spectral density (PSD) estimates obtained by Fast Fourier transform (FFT) analytically.