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:  D. MCS DATA SOURCE AGGREGATION/FUSION 
1. Abstract of Cited Paper (BIB001):  An efficient DFA (data fusion algorithm) plays an important role in tracking for moving objects over WSS (wireless sensor system) deployments in order to track the objects accurately. Accuracy in object tracking is mainly dominated by the prediction for those moving targets by filtering and refining the results from wireless mobile sensors deployed in WSS environment. A DFA based on CHHN (competitive Hopfield neural network) technique for obtaining the relationship between measurements results from wireless mobile sensors and estimation of existing tracks over WSS (wireless sensor system) is proposed in this paper. Embedded within the CHNN is also a competitive learning mechanism which creatively removes the dilemma of occasional irrational solutions in traditional HNN (Hopfield neural networks). In this research, except the proposed approach is established with CHNN, the methodology of data fusion over WSS is guaranteed to converge into a stable state when performing a data association. In words, the CHNN-based DFA is combined with wireless mobile sensors in a WSS environment to demonstrate the target tracking capabilities. Computer simulation results illustrate that the new methodology of data fusion based on CHNN is not only successfully able to solve the data association problems addressed over WSS environments, but the specified simulated targets can also be tracked without large scale missing. 
2. Abstract of Cited Paper (BIB002):  The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed. 
3. Abstract of Cited Paper (BIB003):  The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECₐ) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with 6 operating frequencies. Mixed support kriging was used to estimate ECₐ taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The individual ECₐ maps were weighted by the dispersion variance to obtain a map which was used for field partition in management zones. 
4. Abstract of Cited Paper (BIB004):  In this work, we present a novel module to perform fusion of heterogeneous data using fully convolutional networks for semantic labeling. We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture. Especially, we perform fusion of DSM and IRRG optical data on the ISPRS Vaihingen dataset over a urban area and obtain new state-of-the-art results.

Literature Review:
MCS also explores the data fusion from different sensor nodes acting as spatially distributed data sources  . Therefore, using MCS, both online and offline-denoted data can be leveraged by participants exploring through-space data fusion to develop modern applications. Several distinctive research challenges grow from the mobile crowd-sensing paradigm such as proper incentive mechanisms, data collection, and through-space data fusion. Moreover, MCS represents a mixture of human and machine intelligence that is not explored so far. A data fusion node collects the data from numerous nodes, and based on a decision criterion, it fuses the data with its own and transfers it to another node. The advantages are that it reduces the traffic load and conserves the battery of the smartphone. The data comes from different sensors, databases, or more accurate data sets. The data fusion algorithm is very important for any mobile monitoring system BIB002 , BIB001 . Finally, data fusion takes place closer to the sensors, especially for raw sensor data, to reduce the network load resulting from many sensors collecting data from different locations. Data fusion significantly improves accuracy of the statistical model by combining specific information of several heterogeneous sensing systems, where the datasets are represented in different feature spaces. This makes it hard to investigate relationships between the heterogeneous data, even in case the datasets are related to each other. A statistical approach to data fusion combines sensor datasets in a robust way as they benefit from variances between devices and their complementary features BIB003 . Castrignanò et al. BIB004 combined multiple sources of information in a statistical framework. The main advantage of the statistical framework is that straightforward probability models are used to describe the different relationships between sensors, taking into account the uncertainty behind it and the change of support. However, there are several statistical methods that analyze heterogeneous data based on Bayesian and machine-learning methods. Other research work in BIB005 introduces a novel model using heterogeneous data fusion through a fully convolutional neural networks to acheive semantic labeling. The authors presented the residual correction as a way of learning how to fuse predictions from a dual stream structure. The result shows that the residual correction is capable to recognize accurately, which stream is trusted for different classes.