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 of hybridisation methods of GNSS and wireless LAN based positioning system 
Chapter Title:  Future research and outlook 
1. Abstract of Cited Paper (BIB001):  Estimating the location of a mobile device or a robot from wireless signal strength has become an area of highly active research. The key problem in this context stems from the complexity of how signals propagate through space, especially in the presence of obstacles such as buildings, walls or people. In this paper we show how Gaussian processes can be used to generate a likelihood model for signal strength measurements. We also show how parameters of the model, such as signal noise and spatial correlation between measurements, can be learned from data via hyperparameter estimation. Experiments using WiFi indoor data and GSM cellphone connectivity demonstrate the superior performance of our approach. 
2. Abstract of Cited Paper (BIB002):  Gainesville, Florida, 10 March 2 012. Today, the Mobile Location- Based Services Summit hosted a panel entitled "What Was Wrong with First-Generation Location-Based Services?" The panel chair, Sumi Helal of the University of Florida, invited two world-class experts in LBS history and technology to discuss the topic: Paolo Bellavista of the University of Bologna and Axel Kupper of the University of Munich. The panel discussed the popularity of today's LBSs and analyzed their distinguishing aspects in comparison with first-generation LBSs. The panel was anything but controversial, with all panelists in total agreement on what initially went wrong and why today's LBSs work. They analyzed how the failure unfolded to set the stage for a major paradigm shift in LBS business and technology and noted the milestones that shaped today's LBSs. 
3. Abstract of Cited Paper (BIB003):  The increased popularity of wireless networks has enabled the development of localization techniques that rely on WiFi signal strength. These systems are cheap, effective, and require no modifications to the environment. In this paper, we present a WiFi localization algorithm that generates WiFi maps using Gaussian process regression, and then estimates the global position of an autonomous vehicle in an industrial environment using a particle filter. This estimate can be used for bootstrapping a higher-resolution localizer, or for cross-checking and localization redundancy. The system has been designed to operate both indoors and outdoors, using only the existing wireless infrastructure. It has been integrated with an existing laser-beacon localizer to aid during initialization and for recovery after a failure. Experiments conducted at an industrial site using a large forklift-type autonomous vehicle are presented. 
4. Abstract of Cited Paper (BIB004):  The term “location fingerprinting” covers a wide variety of methods for determining receiver position using databases of radio signal strength measurements from different sources. In this work we present a survey of location fingerprinting methods, including deterministic and probabilistic methods for static estimation, as well as filtering methods based on Bayesian filter and Kalman filter. We present a unified mathematical formulation of radio map database and location estimation, point out the equivalence of some methods from the literature, and present some new variants. A set of tests in an indoor positioning scenario using WLAN signal strengths is performed to determine the influence of different calibration and location method parameters. In the tests, the probabilistic method with the kernel function approximation of signal strength histograms was the best static positioning method. Moreover, all filters improved the results significantly over the static methods. 
5. Abstract of Cited Paper (BIB005):  The development of an efficient and accurate location sensing systems for indoor environments, based on the received signal strength (RSS) data, is usually a challenging task. In this paper, we discuss the feasibility of using Gaussian Processes (GPs) regression for learning based indoor localization algorithm. The GP is one of the machine learning algorithms that can be used to model a complete RSS map from few training data. We investigate the use of three different covariance functions, i.e. Squared Exponential (SE), Matern, and Rational Quadratic (RQ), to find the suitable one for the indoor localization, and then compare their performance to the traditional weighted k-Nearest Neighbors (k-NN) algorithm. We show that GP regression can significantly outperform the k-NN, while keeping the training cost at a reasonable level. Furthermore, although, the smoothness property of SE covariance function, we demonstrate that GP-SE covariance provides better accuracy compared to GP-Matern and GP-RQ, particularly, when a few training data are available. 
6. Abstract of Cited Paper (BIB006):  This paper describes the design and implementation of a multi sensor, collaborative localization system for diverse environments. Many different approaches and systems are utilized depending on the requirements and settings of a particular environment, such as indoor, outdoor or urban environments. Location-based services could benefit from a localization system that is suitable for changing environments. We propose an architecture that facilitates multiple localization systems, such as GPS for outdoor as well as RFID-based and optical systems for indoor purposes. Due to the fact that access to respectively integration of localization systems in mobile handheld devices is limited, we introduce the Location Exchange protocol to share ascertained location information among nearby devices using Bluetooth and Wi-Fi communication. This information can be used to improve the accuracy of obtained positions and enables devices to benefit from localization systems and sensors incorporated by nearby devices. 
7. Abstract of Cited Paper (BIB007):  Wi-Fi positioning has found favour in environments which are traditionally challenging for GPS. The currently used method of Wi-Fi fingerprinting assumes that the devices used for training and locating perform identically. We have undertaken an experiment to determine how different devices behave in an empirical controlled test to identify the challenges and limitations which Wi-Fi fingerprinting positioning systems will face when deployed across many devices. We found that they performed significantly differently in respect to the mean reported signal strength — even those which came from the same vendor. We also found that multiple samples of the same device do not perform identically. Furthermore, it was found that certain devices were entirely unsuitable for positioning as they reported signal strength values uncorrelated with distance from the transmitter. Some other devices behaved in a way that made them poor candidates for use in fingerprinting. Temporal patterns were found in some wireless cards which suggest that filtering should be used. The tests also found that the use of 5GHz band signals had the potential to improve the accuracy of Wi-Fi location due to its higher stability compared to 2.4GHz. Ultimately however, the accuracy of Wi-Fi fingerprinting is limited due to many factors in the hardware and software design of Wi-Fi devices which affect the reported signal strength.

Literature Review:
The next crucial step towards an ubiquitous, more accurate positioning is a world wide unified WPS. Since, there exists nothing like a standard for WLAN positioning systems and GNSSs are explicitly designed to provide world wide location information, the most open issues are found on the aspect of WPS. Considering the advantages of WLAN location fingerprinting over WLAN ranging (higher accuracy, easy to implement, complement error to GNSS), and that it is widely spread and adopted by the majority of WPSs (particularly for indoor use), the future research based on location fingerprinting is illuminated in the following. WLAN positioning systems which have been presented so far, were either quite accurate, aligned for localisation in a single or a few buildings, or aimed for large-scale localisation with an accuracy about 10 m at the best. This trade-off is caused by the off-line phase calibration overhead. Due to the progress in all adjacent fields (data storage, processing power, transmission bandwidth and so on) it is believed that WLAN location fingerprinting system can be large-scale and accurate. For a unified, ubiqui-tous WPS, one would need to agree on some principle points: a) terminal-based or network-based, b) a minimum set of data base entries, c) a strategy to keep the radio map up to date and d) a strategy to manage the vast data base. This is only seen as a minimum accordance for such a world wide WPS. A lot of details are missing, of which some are discussed a little more in the following. The system should not be owned and managed by a few participants BIB003 , and therefore should be terminalbased. This decision additionally avoids a central control instance and privacy issues BIB001 . But on the other hand, the appropriate data base entries must be available on the mobile device or downloaded prior to estimate a position. One must agree on a minimum set of entry types for the data base BIB005 , on how to represent the location readings, units and the coordinate systems. A scheme to manage such a large data base must be developed. For example a division in different hierarchies from country over state, city, district, building, floor to room could help to overcome potential stability issues. In addition, a strategy how to create, extend and update the data base must be developed, see for example  . It must be elaborated in a way not losing, better improving the accuracy over time. With a free access and an open interface, users and organisations might be encouraged to contribute and so to overcome the extensive calibration effort. The acceptance and the world wide availability, including (public) indoor areas and other not vehicle accessible areas, would grow faster. But as well technical tools might assist to improve the radio map creation. Studies using Gaussian process regression models showed that with just a few reference locations, used as training points, an accurate position estimation is possible, see BIB006 BIB004 BIB002 . An agreement on how to compensate the different RSSI BIB007 readings (e.g. BIB008   ) from different IEEE 802.11 transceivers is necessary to achieve a consistent performance. Newer trends like the use of the 5 GHz industrial, scientific and medical (ISM) band and multiple-input and multiple-output (MIMO) systems need be considered. Leaving future research fields of stand-alone WPS, robustness of hybrid systems is further increased by data fusion on at least pseudo range level. Because the position algorithm could operate with less then 4 satellites in view, beneficial especially in urban areas, where signal blocking due to high-rise buildings occurs often. Consequently, GNSS receiver with pseudo range output must find their way into the future mobile devices. What is more likely are chips for localisation purpose with integrated GNSS receiver, WLAN transceiver and a processing unit, as already presented in [10] . Additional performance improvements are expected by further deployment of multi-constellation and multi-frequency receivers. Many open fields of activity can be found in the information processing/data fusion area. In addition to a relatively high availability, which already the presented developments achieve, a tighter data fusion improves the accuracy and precision of the hybrid positioning systems. It facilitates further mutual advantages due to the complementary errors of GNSS and WLAN location fingerprinting. To decrease the common error further, advanced multipath mitigation schemes for GNSS and GNSS integrity monitoring, based on a WPS, should be incorporated into such a data fusion algorithm. Conversely, data from GNSS could improve the over all accuracy by testing the integrity of the WLAN measurements. Approaches to extract more information from pseudo ranges, WLAN ranges and WLAN RSS, useful to assess the quality of measurements, should be investigated more deeply. The more significant information is integrated into the modelling of uncertainty, the more the performance of the data fusion algorithms will improve. All this additional information give reason to employ more flexible Bayesian filters, for example histogram and particle filter. When dealing with heavy multipath propagation and cluttered measurements, the probability hypothesis density (PHD) filter might be applied. What is more, such an accurate and reliable hybrid positioning technique, should be extended and employed to constantly update the WLAN location fingerprinting data base.