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:  2) STRUCTURE HEALTH MONITORING (SHM) 
1. Abstract of Cited Paper (BIB001):  Abstract This paper provides an overview of recent advances in wireless communication technologies applied to industrial automation. Newly introduced communication concepts such as ad-hoc networks and wireless sensor/actuator networks now enable the deployment of extremely decentralised control architectures. Both open-loop and closed-loop applications are part of the roadmap, although data gathering and monitoring applications are expected to spread first. In addition to increased flexibility and facilitated operations, these new communication technologies have significant cost saving potential. Standardisation efforts led by international bodies such as the IEEE should propel prototyping and deployment activities in various industries. 
2. Abstract of Cited Paper (BIB002):  In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation. This paper is intended to serve as a summary review of the collective experience the structural engineering community has gained from the use of wireless sensors and sensor networks for monitoring structural performance and health. 
3. Abstract of Cited Paper (BIB003):  Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures. 
4. Abstract of Cited Paper (BIB004):  In this paper, we review our researches on the topics of the structural health monitoring (SHM) with the fiber-optic distributed strain sensor. Highly-dense information on strains in a structure can be useful to identify some kind of existing damages or applied loads in implementation of SHM. The fiber-optic distributed sensors developed by the authors have been applied to the damage detection of a single-lap joint and load identification of a beam simply supported. We confirmed that the applicability of the distributed sensor to SHM could be improved as making the spatial resolution higher. In addition, we showed that the simulation technique considering both structural and optical effects seamlessly in strain measurement could be powerful tools to evaluate the performance of a sensing system and design it for SHM. Finally, the technique for simultaneous distributed strain and temperature measurement using the PANDA-fiber Bragg grating (FBG) is shown in this paper, because problems caused by the cross-sensitivity toward strain and temperature would be always inevitable in strain measurement for SHM. 
5. Abstract of Cited Paper (BIB005):  The content of this article was originally prepared as a briefing note for the joint Academy of Technological Sciences and Engineering (ATSE)-Infrastructure Australia International Workshop on Infrastructure Planning that was held in Melbourne on 9-10 April 2013. The Workshop Communique 'Infrastructure Planning: Towards Best Practice' is available on the ATSE web site www.atse.org.au - follow the link to Built Environment Events. 
6. Abstract of Cited Paper (BIB006):  Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this distribution, which is essential for separating the true signal from the statistical fluctuations, deviates noticeably from the normal distribution for a finite number of accumulations. In this paper, we develop a theory to properly account for the statistical fluctuations when fitting a model to a given accumulated spectrum. The method is implemented in software for the purpose of automatically fitting a large body of such FFT-derived spectra. We apply this tool to analyze a portion of a dense cluster of spikes recorded by our FASR Subsystem Testbed instrument during a record-breaking event that occurred on 2006 December 6. The outcome of this analysis is briefly discussed. 
7. Abstract of Cited Paper (BIB007):  Ubiquitous smartphones have created a significant opportunity to form a low-cost wireless Citizen Sensor network and produce big data for monitoring structural integrity and safety under operational and extreme loads. Such data are particularly useful for rapid assessment of structural damage in a large urban setting after a major event such as an earthquake. This study explores the utilization of smartphone accelerometers for measuring structural vibration, from which structural health and post-event damage can be diagnosed. Widely available smartphones are tested under sinusoidal wave excitations with frequencies in the range relevant to civil engineering structures. Large-scale seismic shaking table tests, observing input ground motion and response of a structural model, are carried out to evaluate the accuracy of smartphone accelerometers under operational, white-noise and earthquake excitations of different intensity. Finally, the smartphone accelerometers are tested on a dynamically loaded bridge. The extensive experiments show satisfactory agreements between the reference and smartphone sensor measurements in both time and frequency domains, demonstrating the capability of the smartphone sensors to measure structural responses ranging from low-amplitude ambient vibration to high-amplitude seismic response. Encouraged by the results of this study, the authors are developing a citizen-engaging and data-analytics crowdsourcing platform towards a smartphone-based Citizen Sensor network for structural health monitoring and post-event damage assessment applications. 
8. Abstract of Cited Paper (BIB008):  Wireless sensor networks (WSNs) are one of the emerging technologies in the 21st century. In the structural health monitoring (SHM). WSNs are used as one of the vitally capable technologies in the SHM. The accelerometer module in the existing sensor nodes enables several novel applications. In this paper, a prototype for monitoring and detecting the damage for the real bridge using these sensor nodes is built. The prototype consists of sensor nodes, shaking table including its amplifier, and real bridge. The sensors are placed on a scaled down concrete bridge model that is mounted on a shaking table. The results are demonstrated in terms of acceleration on different nodes at a particular excitation frequency in the case of normal, single-side damage, and double-side damage. 
9. Abstract of Cited Paper (BIB009):  Over the past decade, numerous studies have estimated the economic impacts of a variety of disruptions. Most of these studies are based on macroeconomic models that quantify the direct and indirect economic losses from a disruption. Direct economic losses occur due to damaged facilities or when consumers change their purchasing behavior because of the disruption. Indirect economic losses occur when directly impacted businesses consequently reduce their orders to their suppliers. Indirect economic losses are often larger than direct economic losses. This paper compiles the results from these economic models in order to compare the costs of different disruptions and help decision makers prioritize among disruptions. We compare the direct and indirect economic losses from a variety of disruptions, including earthquakes, hurricanes, terrorist attacks, pandemic diseases, and port closures. Some studies model hypothetical scenarios, but other studies quantify the economic losses from historical events such as the September 11 attacks and the 2011 Japanese tsunami. This paper provides a useful benchmark to understand the consequences from disruptions and highlight areas that public officials could address in planning for future disruptions.

Literature Review:
Structural health monitoring research is based on the use of sensors to detect and localize damage through structural responses as well as patterns of vibrations induced in the structure. SHM itself is not a new concept. A close look at civil infrastructures everywhere and their importance shows how essential it is to use new technologies to monitor these structures on social and economic life. That leads to a great need for advanced methods for monitoring such structures and detecting (or even predicting) the damage BIB009 . Unfortunately, the normal practice these days is to detect/predict the damage based on visual inspection using very traditional methods such as nondestructive testing, reinforcement detectors, and using hammers to check for delamination. Not only are these tasks labor-intensive, but they are also carried out infrequently. Moreover, the traditional methods use personal computers cables that need to be deployed at the inspection site. This will increase the cost as well as the complexity of installation and maintenance. Researchers tried to save the cost by using wireless sensor networks (WSNs) as their main technique, which improved and facilitated the deployment of these WSNs. Compared with conventional methods, the use of WSNs in SHM provides the same functionality at a much lower cost, which enables much more efficient monitoring. Hence, SHM based on WSNs has recently gained growing interest, due to its efficiency and accuracy. Several structural health-monitoring techniques have been studied and investigated in the last three decades BIB003 , BIB004 . These approaches include vibration-based and time-frequency wave propagation BIB003 , localization in wireless sensor networks BIB004 , fiber-optic sensors and networks BIB005 , optical inspection methods  and optoelectronic scanning BIB002 . However, there are still several significant research challenges in SHM using WSNs that need to be investigated. One of the infrastructures that need continuous monitoring are bridges as they are vulnerable to natural wear and their collapse leads to disasters. Monitoring structural health of bridges is one of the goals of infrastructure monitoring BIB006 , BIB010 . Elsersy et al. BIB009 proposed SHM model using WSNs to monitor and detect the damage condition of the real bridge. The model have several components such as sensor nodes, shaking table with its amplifier, and real concrete bridge. The sensors were fixed on the scaled down bridge that is fixed on the shaking table. The experiment was conducted in the case of normal bridge, single-side damage, and double-side damage. The mode has detected the damage in terms of acceleration on different nodes at a particular excitation frequency. One of the challenging problems, is the identification of the modal parameters of civil engineering structures, mode shapes, from ambient excitation It can be addressed by sophisticated techniques such as the basic frequency domain (BFD) and frequency domain decomposition (FFD) methods BIB001 or the signal to model ratio (SMR) statistical estimator BIB007 . MCS allows complementing the role of traditional WSNs in SHM by using the sensors in the drivers' smartphones to monitor the structural health of bridges. In fact, smartphones present an important opportunity to form a low-cost citizen wireless sensor network and introduce big data for monitoring structural reliability and safety under operational and extreme loads. The research work in  presents a SHM platform integrating smartphone sensors, the web, and crowdsourcing for a prospective crowdsourcing-based SHM platform. They developed an iPhone (iOS) application to allow citizens to use their smartphones sensors for measuring structural vibrations, and then upload the data to a central server. They also developed a web-based platform to automatically collect and process the data then store the processed data. However, some challenges were noticed related to citizens such as location, connection conditions, and sampling length. After a major event such as an earthquake for example, the collected data could be very useful for performing a quick assessment of structural damage in a large urban setting. For measuring structural vibration, the research work in BIB009 and BIB008 discovered that smartphone accelerometers could monitor structural vibrations under normal and extreme loads. They tested smartphones under sinusoidal wave excitation with varying frequencies in a range of civil engineering structures. The experiments for normal and heavy loads show that the reference and the smartphone sensor measurements are close to each other in time and frequency domains. They also demonstrated the smartphone sensor's ability to measure structural responses ranging from low-amplitude ambient vibrations to a high-amplitude seismic response. Another research in  proposed a cluster-based data aggregation architecture to facilitate application development for efficient SHM. The authors developed a modular middleware on sensor nodes and an SQL-like user interface to configure the SHM in runtime mode. A three-level structure was built to filter unwanted samples and extract features from raw measurement data. The multiple measurements are then combined from different locations.