You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In this article, we study the Cyber-Human Interaction (CHI) based approach that the "Human" part sets a list of location-based objectives and makes the pathway decision whereas the "Cyber" part provides the pathway suggestion, infer heuristics from the environment along the pathway and incrementally resolve the location-based objectives with new heuristics for indoor localization. For this study, we implement a CHI-based system on mobile phone. The CHI-based system offers the pathway suggestion and the solution of the location-based objectives based on its trajectory management. Without any priori knowledge on the area of interest and any aid from other equipments, a laborer can achieve his location-based objectives by walking through the area of interest and simultaneously online interacting with the CHI-based system installed in his phone. In evaluation, we conduct the experiments and show the advantage CHI in reducing the time cost and the expense cost for the laborer.

Cited Abstract(s):
[1]: 

[2]: We consider the problem of localizing a wireless client in an indoor environment based on the signal strength of its transmitted packets as received on stationary sniffers or access points. Several state-of-the-art indoor localization techniques have the drawback that they rely extensively on a labor-intensive 'training' phase that does not scale well. Use of unmodeled hardware with heterogeneous power levels further reduces the accuracy of these techniques. We propose a 'learning-based' approach, WiGEM, where the received signal strength is modeled as a Gaussian Mixture Model (GMM). Expectation Maximization (EM) is used to learn the maximum likelihood estimates of the model parameters. This approach enables us to localize a transmitting device based on the maximum a posteriori estimate. The key insight is to use the physics of wireless propagation, and exploit the signal strength constraints that exist for different transmit power levels. The learning approach not only avoids the labor-intensive training, but also makes the location estimates considerably robust in the face of heterogeneity and various time varying phenomena. We present evaluations on two different indoor testbeds with multiple WiFi devices. We demonstrate that WiGEM's accuracy is at par with or better than state-of-the-art techniques but without requiring any training.

[3]: A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87 when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.

[4]: With the technical advances in ubiquitous comput- ing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoreti- cal base and develop a localization algorithm for building a zero- configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters).

[5]: We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.

[6]: From the Publisher: The indispensable guide to wireless communicationsnow fully revised and updated! Wireless Communications: Principles and Practice, Second Edition is the definitive modern text for wireless communications technology and system design. Building on his classic first edition, Theodore S. Rappaport covers the fundamental issues impacting all wireless networks and reviews virtually every important new wireless standard and technological development, offering especially comprehensive coverage of the 3G systems and wireless local area networks (WLANs) that will transform communications in the coming years. Rappaport illustrates each key concept with practical examples, thoroughly explained and solved step by step. Coverage includes: An overview of key wireless technologies: voice, data, cordless, paging, fixed and mobile broadband wireless systems, and beyond Wireless system design fundamentals: channel assignment, handoffs, trunking efficiency, interference, frequency reuse, capacity planning, large-scale fading, and more Path loss, small-scale fading, multipath, reflection, diffraction, scattering, shadowing, spatial-temporal channel modeling, and microcell indoor propagation Modulation, equalization, diversity, channel coding, and speech coding New wireless LAN technologies: IEEE 802.11a b, HIPERLAN, BRAN, and other alternatives New 3G air interface standards, including W-CDMA, cdma2000, GPRS, UMTS, and EDGE Bluetooth wearable computers, fixed wireless and Local Multipoint Distribution Service (LMDS), and other advanced technologies Updated glossary of abbreviations and acronyms, and a thorolist of references Dozens of new examples and end-of-chapter problems Whether you're a communications network professional, manager, researcher, or student, Wireless Communications: Principles and Practice, Second Edition gives you an in-depth understanding of the state of the art in wireless technologytoday's and tomorrow's.

[7]: We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building's unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95 of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures.
Localization on utilizing the RSS of signal has been intensively studied for many years. A popular approach is the Fingerprinting-based approach that establishes the mapping of RSS-to-location for localization. Radar [1] is the first proposed method on Fingerprinting-based localization that estimates the location of the target by the knowledge of APs' location, transmit power and the floor map. Horus [5] built a probability distribution in RSS-to-location mapping and located the target by matching the mapping with maximum likelihood. A variety of improvements or extensions have also been made on Fingerprinting-based localization, such as considering the mobility constraints [7] and incorporating ambient signatures from environment [3] . Another approach on utilizing the RSS is the Model-based approach that directly build the relationship between RSS and distance e.g. the log-distance path loss model [6] . Efforts like locating by APs [4] and learning the ability of measurement [2] have been made based on the model of RSS-to-distance.