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: Indoor Fingerprint Positioning Based on Wi-Fi: An Overview 
Chapter Title:  Empirical Modeling 
1. Abstract of Cited Paper (BIB001):  Indoor localisation continues to be a topic of growing importance. Despite the advances made, several profound problems are still present. For example, estimating an accurate position from a multimodal distribution or recovering from the influence of faulty measurements. Within this work, we solve such problems with the help of Monte Carlo smoothing methods, namely forward-backward smoother and backward simulation. In contrast to normal filtering procedures like particle filtering, smoothing methods are able to incorporate future measurements instead of just using current and past data. This enables many possibilities for further improving the position estimation. Both smoothing techniques are deployed as fixed-lag and fixed-interval smoother and a novel approach for incorporating them easily within a conventional localisation system is presented. All this is evaluated on four floors within our faculty building. The results show that smoothing methods offer a great tool for improving the overall localisation. Especially fixed-lag smoothing provides a great runtime support by reducing temporal errors and improving the overall estimation with affordable costs. 
2. Abstract of Cited Paper (BIB002):  Abstract. Over the last decade, harnessing the commercial potential of smart mobile devices in indoor environments has spurred interest in indoor mapping and navigation. Users experience indoor environments differently. For this reason navigational models have to be designed to adapt to a user’s personality, and to reflect as many cognitive maps as possible. This paper presents an extension of a previously proposed framework. In this extension the notion of placement is accounted for, thereby enabling one aspect of the ‘personalised indoor experience’. In the paper, firstly referential expressions are used as a tool to discuss the different ways of thinking of placement within indoor spaces. Next, placement is expressed in terms of the concept of Position, Location, Place and Area. Finally, the previously proposed framework is extended to include these concepts of placement. An example is provided of the use of the extended framework. Notable characteristics of the framework are: (1) Sub-spaces, resources and agents can simultaneously possess different types of placement, e.g., a person in a room can have an xyz position and a location defined by the room number. While these entities can simultaneously have different forms of placement, only one is dominant. (2) Sub-spaces, resources and agents are capable of possessing modifiers that alter their access and usage. (3) Sub-spaces inherit the modifiers of the resources or agents contained in them. (4) Unlike conventional navigational models which treat resources and obstacles as different types of entities, in the proposed framework there are only resources and whether a resource is an obstacle is determined by a modifier that determines whether a user can access the resource. The power of the framework is that it blends the geometry and topology of space, the influence of human activity within sub-spaces together with the different notions of placement in a way that is simple and yet very flexible. 
3. Abstract of Cited Paper (BIB003):  This paper focuses on WiFi indoor positioning based on received signal strength, a common local positioning approach with a number of prominent advantages such as low cost and ease of deployment. Weighted k nearest neighbor (WKNN) approach and Naive Bayes Classifier (NBC) method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of them need to handle carefully the issue of access point (AP) selection and inappropriate selection of APs may degrade positioning performance considerably. To avoid the issue of AP selection and hence improve positioning accuracy, a new WiFi indoor position estimation strategy via domain clustering (DC) is proposed in this paper. Extensive experiments are carried out and performance comparison based on experimental results demonstrates that the proposed method has a better position estimation performance than the existing approaches. 
4. Abstract of Cited Paper (BIB004):  The accuracy of a positioning system is usually expressed as its average position error in an experiment. However, when the ground truth is no longer available, it would still be useful to know the reliability of a position estimate based on a single measurement. To obtain a reliability metric, we hypothesize that there is a relation between the uncertainty in a position's posterior probability distribution, expressed as its conditional entropy, and the position error of the position that is derived from this distribution. In this paper, we present the correlation between these two metrics as calculated for the UJIIndoorLoc Wi-Fi fingerprinting dataset, using a new probabilistic sensor model. We found that there is no significant correlation between the conditional entropy and the position error. However, we learned that our sensor model is usually very certain in the dataset, and saw that the suggestion of a correlation improves when we increase the uncertainty by selecting a fixed, larger variance. Interestingly, the position error results improve as well. 
5. Abstract of Cited Paper (BIB005):  This paper investigates the role of quantization in the Received Signal Strength Indicator (RSSI) information used for fingerprinting (FP) applications. One of the common drawbacks of FP is the large data size and consequently the large search space and computational load as a result of either vastness of the positioning area or the finer resolution in the FP grid map: this limits the application of FP to small environments or scenarios with largely spaced grid points leading to poor localization performance. We show that the computational complexity can be limited adapting the RSSI quantization w.r.t. the variance of the measured RSSI error at the target. This approach turns out to be advantageous for the deployment of FP systems based on beacons equipped with inexpensive technologies, in which the measures precision loss could be compensated by larger numbers of beacons. An appropriate quantization and design of RSSI signatures will make possible the deployment of FP in larger areas maintaining the same computational load and/or the desired localization performance. 
6. Abstract of Cited Paper (BIB006):  A radio map is a collection of signal fingerprints labeled with their collected locations. It is known that the performance of a fingerprint-based positioning systems is closely related to the precision and accuracy of the underlying radio maps. However, little has been studied on the performance of radio maps in relation to the fingerprint collection methods and the radio map models, which determine the accuracy and precision of radio maps, respectively. This paper evaluates the performance of various radio map construction methods in both indoor and outdoor environments. Four radio map construction methods, i.e., a point-by-point manual calibration, a walking survey, a semisupervised learning-based method, and an unsupervised learning-based method, have been compared. We also evaluate the performance of various types of radio map models that represent the characteristics of collected fingerprints. To demonstrate the importance of the radio map model, a new model named signal fluctuation matrix (SFM) was developed, and its performance was compared with that of the three conventional radio map models, respectively. The evaluation revealed that the performance of the radio maps was very sensitive to the design of radio map models and the number of fingerprints collected at each location. The performance achieved by SFM-based positioning was comparable with that of the other models despite using a small number of fingerprints. 
7. Abstract of Cited Paper (BIB007):  This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.

Literature Review:
Empirical modeling is determining the number of RPs at certain intervals in the area to be located, and measuring the signal strength received from different APs at the location of every RP. The signal strength is stored with information of the RP's location, as well as other information (like MAC address) in the fingerprint database or radio map as a piece of fingerprint data. Since empirical modeling is based on many data samples, the workload in the early stages can be large and this modeling method depends on the indoor environment. However, this modeling method has a high level of accuracy. According to Jung BIB007 , construction of the empirical models involves fingerprint data collection and the fingerprint data component. Fingerprint data collection is how fingerprint data samples like RSS-which are used to build the empirical models or radio maps-are obtained. The fingerprint data component is another important factor as different positioning strategies use different data based on RSS. There are different strategies to collect fingerprint data. To cover the entire area, researchers distribute different numbers of AP and select various numbers of RP based on indoor environments. The most primitive way is by point-by-point manual calibration, which can achieve the greatest accuracy. The target area is partitioned into numerous pieces, i.e., locations, and dedicated surveyors collect fingerprint samples point-by-point by considering the center of each location as a measurement point BIB007 . Liu et al. BIB001 proposed a strategy using the smallest enclosing circle algorithm (SEC) which is widely used in the field of information security. They directly input the RP's coordinates instead of the RSS, and the smallest enclosing circle is the smallest circle that consists of all points to be positioned. Associated with the WKNN (Weighted K-nearest Neighbors) algorithm and clustering, this method decreased the calibration cost and improved the positioning accuracy by 29.1%. Fetzer et al. BIB002 proposed a strategy that combined varieties of assistive technology aimed at multi-floor indoor environments. The fingerprint data they used is described in Equation BIB003 . In Equation (1), S wifi and S ib contain the RSS measured from the AP and i-Beacons, respectively. ∆θ and n steps describes the relative angular change and the number of steps detected for the pedestrian. P rel indicates the relative barometric pressure used to detect the floor where users were located. Finally, Ω contains the activity currently estimated for the pedestrian by accelerometer and are: unknown, standing, walking, walking up the stairs, or walking down the stairs. To obtain the fingerprint data, they arranged four different walking paths instead of point-by-point manual calibration. This method is called a walking survey BIB007 and reduces the effort required to recognize each location. Moreover, this method was also used to obtain sensor signals like barometric pressure and the activity of the pedestrian. However, walking surveys require additional time, which increases the cost of effort. The IPIN (Indoor Positioning and Indoor Navigation) competition discussed in BIB008 introduced several new indoor positioning systems. The team called HFTS used the walking survey method to acquire fingerprint data containing heading directions, RSSI and step information, while the UMinho team's approach was based on Wi-Fi combined with placement records and assistive positioning technologies. The team tried different strategies to build the radio map, which was finally built by associating fingerprints to linearly interpolated coordinates between known positions, combined with the movement information extracted from the accelerometer data. Although many other strategies were introduced in reference BIB008 , they will not be discussed in this paper. Another indoor positioning competition called the EvAAL-ETRI Indoor Localization Competition (part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015) was presented in reference  where many novel and effective approaches were introduced. Additionally, references BIB004 BIB005 BIB006 discussed different ways to build empirical models by using multi-points, crowdsourcing, and so on.