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:  B. ENVIRONMENTAL 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme. 
3. Abstract of Cited Paper (BIB003):  The sensing range of a sensor is spatially limited. Thus, achieving a good coverage of a large area of interest requires installation of a huge number of sensors which is cost and labor intensive. For example, monitoring air pollution in a city needs a high density of measurement stations installed throughout the area of interest. As alternative, we install a smaller number of mobile sensing nodes on top of public transport vehicles that regularly traverse the city. In this paper, we consider the problem of selecting a subnetwork of a city’s public transport network to achieve a good coverage of the area of interest. In general case, public transport vehicles are not assigned to fix lines but rather to depots where they are parked overnight. We introduce an algorithm that selects the installation locations, i.e., number of vehicles within each host depot, such that sensing coverage is maximized. Since we are working with low-cost sensors, which exhibit failures and drift over time, vehicles selected for sensor installation have to be in each other’s vicinity from time to time to allow comparing sensor readings. We refer to such meeting points as checkpoints. Our algorithm optimizes sensing coverage while providing a sufficient number of checkpoint locations. We evaluate our algorithm based on the tram network of Zurich and show how an accurate selection of vehicles for installing measurement stations affects the overall system quality. We show that our algorithm outperforms random search, simulated annealing, and the greedy approach.

Literature Review:
A few years ago, the trend towards clean world technologies led to a large flood of environmental senor technologies. The environment needs to be sensed and monitored to deliver information about the variation of environmental conditions such as temperature, humidity, carbon-dioxide levels as well as all pollution sources. Increased industrialization and extensive agricultural activities due to the growth of population, have led to deterioration in the air quality due to emission of undesired materials into the atmosphere. The effect was not limited to air but it also affected the water quality which reached unprecedented levels of pollution in decades. Hence, natural life and environment is negatively affected. A mixture of fixed localized sensors and mobile smartphone sensors can provide a monitoring context from both personal and local perspectives. This includes many motivating technologies that have become more and more important BIB003 . The demand on wide range of applications in environmental sensors has increased in recent years. The environmental sensing category could be divided into three different groups: physical sensors, chemical sensors, and biological sensors. Physical sensors measure physical quantities such as dynamic forces, light, temperature, sound, magnetism, and electromagnetism. Chemical sensors measure humidity, gas, ions, CO, as well as chemical process and oil refining products. Finally, Biological sensors measure immunities, microorganism, tissues, bacteria, viruses, proteins, and enzymes BIB002 . Motion sensors and air pollution sensors are integrated into the air quality monitoring for both indoor and outdoor environments. The exposure to air pollution while being on the move in cities relies heavily on the choice of the means of transportation as well as the route chosen. High levels of air pollution are easily identifiable by the human sensory system (e.g. eye, nose). Similarly, human ears can sense high noise pollution levels  . Because people are able to easily sense the air pollution with smartphone devices while being on the move within the city, this may have an impact on mobility behavior as well as their awareness of air pollution. An increased awareness of air pollution will lead to people examining their own mobility behavior with the resulting impact on air quality BIB001 . Sensing air quality in real-time and providing it as an online service will lead to a crowdsensing city-wide air pollution map. However, to obtain a robust and extensive database of the air-quality in the city, the crowd-sensed data stream must be accompanied with measurements from official air quality monitoring devices as well as measurements from small sensing devices fixed on public transportation vehicles that move across a city regularly BIB004 . The research team in  developed an iPhone application called CreekWatch in order to monitor the local crisis with the support of crowdsourced data about the quantity of water, rate flow, amount of trash, and depiction of the channel. Each single user plays a key role in enhancing the quality of water resources by sharing captured data with water control panels. Consequently, to reach an integrated air quality monitoring network targeting improved urban life quality, data needs to be gathered from multiple sources of different types: a. Reference analyzer based, standard compliant, large monitoring stations, that can be deployed in limited numbers due to cost and size constraints; b. Medium-sized sensor nodes with acceptable accuracy and reasonable cost, that can provide street level pollution information in densely populated areas or localized details at certain road intersections for example; c. MCS/participatory sensing through the involvement of people sending ''personalized'' pollution information. This can be achieved through smartphone sensors or small portable sensors that can be as small as the size of a wrist watch. Although currently most of these sensors lack the accuracy level of reference analyzers or medium sized nodes, their performance is rapidly improving. They can be used to monitor personalized pollution exposure and track the impact of air quality on specific individuals. The third category of sensors can be used in order to give to the individual the possibility of becoming an air quality observation platform, thus building a citizen observatory through MCS. Collecting the data from the various sources (analyzers, WSNs, and MCS/participatory network) under a single platform allows the implementation of advanced quality assurance (QA) and quality control (QC) methods: data from standardized analyzer stations can be used to validate the measurements of the medium scale sensor nodes, and the measurements of these latter nodes can be used to check/correct/validate the measurements from the small portable sensors, which are the least accurate. Hence, data validation techniques can be automated, with the system receiving and integrating information from three layers, as shown in Figure 6 (the layers correspond to the same geographical area and overlap in reality. They are separated in the Figure for clarity) . Consequently, MCS can pave the way towards more spatially and temporally accurate environmental information that will enhance the ability to prevent and mitigate air pollution in urban areas. It will allow reaching very local environmental scales based on small sensors, wireless networks and phone applications. Furthermore, new information about temporal and spatial variability of pollution may allow to more accurately estimate both contribution of individual sources and the effects on human exposure: this knowledge can then be used to prevent pollution or mitigate the effects.