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Place Lab: Device Positioning Using Radio Beacons in the Wild
Abstract
Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning—a requirement for wide-scale adoption of location-aware computing—has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons' positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-30 meter median accuracy with nearly 100% coverage measured by availability in people's daily lives.
... FCC [3]. City-wide WiFi-based localization for cellular phones has been investigated in [4], [5] and commercial products are currently available [6]. However, WiFi chips, similar to GPS, are not available in many cell phones and not all cities in the world contain sufficient WiFi coverage to obtain ubiquitous localization. ...
... On the other hand, GSM-based localization, by definition, is available on all GSM-based cell phones, which presents 80-85% of today's cell phones [10], works all over the world, and consumes minimal energy in addition to the standard cell phone operation. Many research work have addressed the problem of GSM localization [3], [5], [11], [12], including time-based systems, angle-of-arrival based systems, and received signal strength indicator (RSSI) based systems. Only recently, with the advances in cell phones, GSM-based localization systems have been implemented [5], [11], [12]. ...
... Many research work have addressed the problem of GSM localization [3], [5], [11], [12], including time-based systems, angle-of-arrival based systems, and received signal strength indicator (RSSI) based systems. Only recently, with the advances in cell phones, GSM-based localization systems have been implemented [5], [11], [12]. These systems are mainly RSSI-based as RSSI information is easily available to the user's applications. ...
Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, a prob- abilistic RSSI-based fingerprinting location determi- nation system for GSM phones. We discuss the chal- lenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense systemand how it addresses these challenges. We then extend the proposed system using a hybrid technique that combines probabilistic and deterministic estimation to achieve both high ac- curacy and low computational overhead.Moreover, the accuracy of the hybrid technique is robust to changes in its parameter values. To evaluate our proposed system, we implemented CellSense on Android-based phones. Results from two different testbeds, represent- ing urban and rural environments, for three differ- ent cellular providers show that CellSense provides at least 108.57% enhancement in accuracy in rural areas and at least 89.03% in urban areas compared to the current state of the art RSSI-based GSM localization systems. In additional, the proposed hybrid technique provides more than 6 times and 5.4 times reduction in computational requirements compared to the state of the art RSSI-based GSM localization systems for the rural and urban testbeds respectively.We also evaluate the effect of changing the different system parameters on the accuracy-complexity tradeoff and how the cell towers density and fingerprint density affect the system performance.
... WiFi-based outdoor localization systems have been proposed, e.g. [8,28,43,48]. They leverage the Received Signal Strength (RSS) overheard from WiFi access points deployed in buildings along the roads as a metric for localization. ...
... The techniques in this category leverage WiFi APs [8,28,35,48,49] or smart-phones augmented sensors [1,2,5,10,32,40,46] to localize users. The basic idea behind these techniques is to use a WiFi fingerprint or landmarks detected by the different phone sensors to localize the phone. ...
Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.
... • Wi-Fi: This uses radio waves to transmit information between a computing device and a router. It has high indoor user coverage (94.5%) [48], with an accuracy of 15 to 20 m in indoor environments [48]. • Bluetooth Sensors: Have 75% accuracy for partial coverage and 98% accuracy if there is full coverage in a room. ...
... • Wi-Fi: This uses radio waves to transmit information between a computing device and a router. It has high indoor user coverage (94.5%) [48], with an accuracy of 15 to 20 m in indoor environments [48]. • Bluetooth Sensors: Have 75% accuracy for partial coverage and 98% accuracy if there is full coverage in a room. ...
Traditional methods of delivering construction information, relying on paper-based shop drawings, specifications , and verbal instructions, often lead to increased mental workload, error rates, and difficulties in accessing information. This raises the question of how the traditional approach to accessing design and construction information on construction sites can be improved to address these issues. This paper describes a hybrid approach known as AR-QR code, which aims to enhance the conventional paper-based method of accessing such information using the power of Augmented Reality (AR). The research methodology involved: process mapping, prototype development, and evaluation. The evaluation results revealed that professionals expressed confidence in the potential of the AR-QR code approach to alleviate the limitations associated with traditional methods at construction sites. The outcomes of this study provide valuable insights into the need for a hybrid approach that preserves the advantages of traditional methods while avoiding their drawbacks.
... To address the shortcomings of GPS-based techniques, WiFi-based outdoor localization systems have been proposed, e.g. [11,31,44,51]. These systems can estimate the user's position using the ambient WiFi signals overheard from access points deployed in buildings along the roads. ...
... Another set of techniques leverage WiFi APs [11,31,40,51,52] or augmented sensors on the smart-phones [1,2,5,8,13,37,47] to localize users, without using the GPS. The idea is to use a WiFi fingerprint or landmarks detected by the different phone sensors to localize the phone. ...
Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly. In this paper, we propose DeepLoc: a deep learning-based outdoor localization system that obtains GPS-like localization accuracy without its limitations. In particular, DeepLoc leverages the ubiquitous cellular signals received from the different cell towers heard by the mobile device as hints to localize it. To do that, crowd-sensed geo-tagged received signal strength information coming from different cell towers is used to train a deep model that is used to infer the user's position. As part of DeepLoc design, we introduce modules to address a number of practical challenges including scaling the data collection to large areas, handling the inherent noise in the cellular signal and geo-tagged data, as well as providing enough data that is required for deep learning models with low-overhead. We implemented DeepLoc on different Android devices. Evaluation results in realistic urban and rural environments show that DeepLoc can achieve a median localization accuracy within 18.8m in urban areas and within 15.7m in rural areas. This accuracy outperforms the state-of-the-art cellular-based systems by more than 470% and comes with 330% savings in power compared to the GPS. This highlights the promise of DeepLoc as a ubiquitous accurate and low-overhead localization system.
... The main sources for user's raw coordinates on mobile phones as Global Positioning System (GPS) and Wireless Positioning System (WPS) using cell tower and Wi-Fi access points (AP) [2]. ...
Many of modern location-based services are often based on an area or place as opposed to an accurate determination of the precise location. Geo-fencing approach is based on the observation that users move from one place to another and then stay at that place for a while. These places can be, for example, commercial properties, homes, office centers and so on. As per geo-fencing approach they could be described (defined) as some geographic areas bounded by polygons. It assumes users simply move from fence to fence and stay inside fences for a while. In this article we replace geo-based boundaries with network proximity rules. This new approach let us effectively deploy location based services indoor and provide a significant energy saving for mobile devices comparing with the traditional methods.
... Nevertheless, the best method to precisely locate a user's mobile device seems to be using BLE transmitters [22] mainly due to their low cost and long operation time. To compute the position of the user device in the building space, methods such as multilateration [33] or fingerprint [16] can be used. ...
While supporting navigation outside buildings is currentlywell solved through GPS-based systems, only a few solutions support indoor navigation and user positioning in large buildings. In this paper, we propose a solution to support such navigation by creating a dedicated hierarchical map of the building and associating it with the infrastructure of Bluetooth Low Energy (BLE) transmitters installed in the building. Then a user equipped with a smartphone can easily read the signals of these transmitters and, thanks to the map, can determine their position in space.We use our method in a system that supports navigation and user safety, emphasizing users with special needs, which we are implementing on our academic campus. We describe the idea, architecture, and implementation of this system.
... The received signal strength (RSS) fingerprinting is a positioning approach using RSS measurements from radio beacons, such as wireless fidelity (WiFi) access points [17], Bluetooth low energy (BLE) beacons [18], and cellular radio towers [19]. It first generates the fingerprint database (map) by collecting RSS values from radio beacons at positions where the localization is required and then estimates the current position of the user that best matches the measured RSS value from the fingerprint database. ...
Due to the unavailability of GPS indoors, various indoor pedestrian positioning approaches have been designed to estimate the position of the user leveraging sensory data measured from inertial measurement units (IMUs) and wireless signal receivers, such as pedestrian dead reckoning (PDR) and received signal strength (RSS) fingerprinting. This study is similar to the previous study in that it estimates the user position by fusing noisy positional information obtained from the PDR and RSS fingerprinting using the Bayes filter in the indoor pedestrian positioning system. However, this study differs from the previous study in that it uses an enhanced state estimation approach based on the ensemble transform Kalman filter (ETKF), called QETKF, as the Bayes filer for the indoor pedestrian positioning instead of the SKPF proposed in the previous study. The QETKF estimates the updated user position by fusing the predicted position by the PDR and the positional measurement estimated by the RSS fingerprinting scheme using the ensemble transformation, whereas the SKPF calculates the updated user position by fusing them using both the unscented transformation (UT) of UKF and the weighting method of PF. In the field of Earth science, the ETKF has been widely used to estimate the state of the atmospheric and ocean models. However, the ETKF algorithm does not consider the model error in the state prediction model; that is, it assumes a perfect model without any model errors. Hence, the error covariance estimated by the ETKF can be systematically underestimated, thereby yielding inaccurate state estimation results due to underweighted observations. The QETKF proposed in this paper is an efficient approach to implementing the ETKF applied to the indoor pedestrian localization system that should consider the model error. Unlike the ETKF, the QETKF can avoid the systematic underestimation of the error covariance by considering the model error in the state prediction model. The main goal of this study is to investigate the feasibility of the pedestrian position estimation for the QETKF in the indoor localization system that uses the PDR and RSS fingerprinting. Pedestrian positioning experiments performed using the indoor localization system implemented on the smartphone in a campus building show that the QETKF can offer more accurate positioning results than the ETKF and other ensemble-based Kalman filters (EBKFs). This indicates that the QETKF has great potential in performing better position estimation with more accurately estimated error covariances for the indoor pedestrian localization system.
... Recently, many projects have been developed that resulted in smart infrastructures for monitoring and learning from human behaviour at both individual and organizational scales. Mobile Sensing Platform (MSP) an embedded activity recognition system [42], MavHome and CASAS smart home projects [45,47] and PlaceLab a radio beacon-based localization system [93] are examples of collaborative efforts between academia and industry for real-life deployment of human activity recognition. ...
The growing popularity of mobile smart devices and advances in wireless technologies, coupled with state of the art machine learning techniques have created a
new era in context aware computing. Context and location aware systems provide
the opportunity of the collection of large amount of rich information about human
behavior and activities. Therefore, automatic activity recognition, whose goal is to
infer semantic patterns and routines from data gathered by such systems has become
a great interest among researchers in the field. This thesis applies machine learning
techniques to infer meaningful patterns about human activities and mobility from
sensory data. We present a sensor selection strategy for activity recognition on smart
mobile devices, which requires energy-efficient learning techniques due to the limited
computational and energy resources of such devices. We propose an online approach
that actively selects a smaller subset of sensors that are the most informative, yet
energy-effective, for each time frame. The empirical results confirm that the proposed
online method provides good power efficiency, while maintaining accuracy. Secondly,
we introduce a novel methodology for analyzing human location and mobility data.
Our approach aims to provide an understanding of human trajectories by modeling
Places of Interests for individuals, and constructing behavioral signatures for different groups of users based on their interests and specific mobility patterns. We
propose an unsupervised learning framework for clustering and labeling mobile data
trajectories based on Hierarchical Dirichlet Processes. This hierarchical clustering
model adapts the number of clusters identified in a dataset to the complexity of the
iv
data. We evaluate our method on three different real data sets including both finegrained indoor trajectories and coarse-grained outdoor mobility traces. The results
demonstrate that the proposed model is capable of learning the underlying structure of human mobility behavior, even in the presence of noisy and complex data.
Third, we present a device-free activity recognition system in the context of smart
spaces, consisting of three main building blocks: entrance detection, user identification and localization. These recognition modules leverage a very recent sensing
technology based one wifi network coverage from off-the-shelf wireless devices, in
order to monitor the behavior and movements of users within an indoor space. The
experimental results of the proposed wifi-based system demonstrate that device-free
activity recognition is a promising line of research both for academia and industry.
... B ECAUSE of the recent proliferation of smart devices such as smartphones, and smartwatches, context recognition technologies such as indoor positioning that use sensors mounted on these smart devices are actively studied. Indoor positioning, i.e., estimating the indoor coordinates of a smart device user or estimating the location class (location semantics) of a smart device user primarily relies on signaling technologies such as infrared [1], ultrasound [2], active sound probing [3], [4], bluetooth [5], and Wi-Fi [6]. Particularly, the estimated location classes are useful for investigating a user's daily life because the semantic information of a user's current location is strongly related to his/her activities. ...
This study presents a method for predicting location classes of a room such as a kitchen, and restroom, where a user is located by discovering location-specific sensor data motifs in sensor data observed by user’s sensor devices, such as smartwatch, without requiring labeled training data collected in a target environment. For example, we can observe similar waveforms corresponding to kitchen knife chopping actions using body-worn accelerometers in kitchens and can also observe similar sound features by active sound probing in bathrooms because of their water-resistant walls. This indicates that such location-specific sensor data motifs can be inherent information for location class prediction in almost every environment. This study proposes a novel method that automatically detects location-specific motifs from time series sensor data by calculating a score that represents the “location specificity” of each motif in a time series. Previous studies on location class prediction assume that location-specific sensor data are always observed in a room or use handcrafted rules and templates to detect location-specific sensor data resulting in difficulties in applying them to several realistic environments. In contrast, our method, named IndoLabel, can automatically discover short sensor data motifs, specific to a location class, and can automatically build an environment-independent location classifier without requiring handcrafted rules and templates. The proposed method was evaluated in real house environments using leave-one-environment-out cross-validation and achieved a state-of-the-art performance although labeled training data in the target environment was unavailable.
... Therefore, for when the client device receives a signal from each of the access points, the location is calculated as the average of retrieved longitude and latitude. In this, for each location-aware application, you would have to fetch a list of access points rather than one request for the whole system [4]. ...
... For this, Wi-Fi is widely used, because it comes standard with smartphones. Positioning algorithms for Wi-Fi include proximity [1], fingerprint [2], [3], and lateration [4], [5] methods. Proximity regards the device's location as the position of the closest access point (AP) that the strongest received signal strength indicator (RSSI) has been observed. ...
This paper indicates a design methodology of Wi-Fi round-trip time (RTT) ranging for lateration through the performance evaluation experiments. The Wi-Fi RTT-based lateration needs to operate plural access points (APs) at the same time. However, the relationship between the number of APs in operation and ranging performance has not been clarified in the conventional researches. Then, we evaluate the ranging performace of Wi-Fi RTT for lataration focusing on the number of APs and channel-usage conditions. As the results, we confirm that the ranging result acquisition rates decreases caused by increasing the number of APs simultaneously operated and/or increasing the channel-usage rates. In addition, based on positioning performace comparison between the Wi-Fi RTT-based lateration and the Wi-Fi fingerprint method, we clarify the points of notice that positioning by Wi-Fi RTT-based lateration differs from the conventional radio-intensity-based positioning. Consequently, we show a design methodology of Wi-Fi RTT ranging for lateration as the following three points: the important indicators for evaluation, the severeness of the channel selection, and the number of APs for using. The design methodology will help to realize the high-quality location-based services.
... Many activity recognition studies employ bodyworn sensors, including acceleration sensors, gyroscopes, cameras, and microphones to recognize such daily activities as walking, running, and house cleaning [1]- [5]. Indoor positioning studies rely on signaling technologies, for example, infrared [6], ultrasound [7], active sound probing [8], [9], Bluetooth [10], and Wi-Fi [11], [12]. The recognized context information can be used in real-world services, e.g., context-aware systems, lifelogging, and the surveillance of the elderly [13]- [17]. ...
As smart speakers continue to proliferate, question answering (QA) by smart devices is being woven into our daily lives. This study assumes question answering related to daily life events detected by context recognition systems, such as activity recognition and indoor positioning systems, e.g., answering questions like “Did my grandma eat dinner?” and “How many times did my grandpa go to the toilet?” These questions can effectively support human memory-aids, locate lost items, and monitor human activities. However, training a question-answering model requires large amounts of labeled training data (i.e., questions, answers, and the time-series of real-world event triplets) collected in a target environment. In this paper, we propose a novel simulation to real QA (Sim2RealQA) framework that completely trains a QA model with QA datasets produced in a life simulator and use it for solving real-word QA problems without answer labels. Our proposed QA model can learn a general reasoning process for QA that is independent of environments and deal with diverse types of questions specific to question answering in real-world environments, e.g., counting the number of occurrences of a real-world event and enumerating the names of those who are performing an activity together. Experiments show that using life simulations is a promising approach for solving real-world QA problems when no real-world answer labels are available.
... Client vs. Infrastructure-based solutions: There is a rich history of client-based indoor location solutions, to name a few, SignalSLAM [37], SurroundSense [30], UnLoc [15], and many others [2,3,6,35,39,44]. All of them share some commonalities in that they extract sensor signals (of various types) from client devices to localize. ...
Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, %provided by Aruba, Cisco, etc., that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. We show that server-side localization is not trivial and present two unique challenges associated with this approach, namely \emph{Cardinality Mismatch} and \emph{High Client Scan Latency}. The "Mismatch" challenge results in a significant mismatch between the set of access points (APs) reporting a client in the offline and online phases, while the "Latency" challenge results in a low number of APs reporting data for any particular client. We collect three weeks of detailed ground truth data ( landmarks), from a WiFi setup that has been deployed for more than four years, to provide evidences for the extent and understanding the impact of these problems. Our analysis of real-world client devices reveal that the current trend for the clients is to reduce scans, thereby adversely impacting their localization accuracy. We analyze how localization is impacted when scans are minimal. We propose heuristics to alleviate reduction in the accuracy despite lesser scans. Besides the number of scans, we summarize the other challenges and pitfalls of real deployments which hamper the localization accuracy.
... Localization and tracking methods usually depend on the use case. For instance, indoor localization can be performed by monitoring the radio signal strength of known beacons utilizing Bluetooth [6], while outdoors applications can use Wi-Fi beacons [7] or the LoRa network [8]. Mobile phones and other GSM-enabled devices can be tracked by triangulating the position of local cell towers [9]. ...
... • MIT Smart House, better known as PlaceLab [36], which is considered the first Living Lab conceptualized as such [37]. It includes a location-enhanced mobile computing system using radio beacon [38]. • Ubiquitous Home which integrates a robot and floor and ambient sensors [39]. ...
Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
... To reduce WRM construction efforts, many researchers have tried to utilize fingerprint collection without location information [1]. Implicit and explicit crowdsourcing approaches [2]- [4] have been introduced to make use of contributed received signal strength indicator (RSSI) measurements. Because the true location of the collected data is unknown, this type of data can be regarded as an unlabeled data sample. ...
Sufficient training data and high positioning accuracy are crucial components of indoor positioning. However, the collection of learning data consumes much time and manual effort, inhibiting the global spread of indoor positioning technology. The use of crowdsourcing-based data collection that does not require user intervention can reduce deployment effort, but results in loss of positioning accuracy. Pedestrian dead reckoning partly resolves the problem by using a variety of sensors to provide a relatively accurate position; however, the accumulation of errors is yet to be successfully addressed. In this study, we introduce a highly accurate positioning method that implements error correction based on a crowdsourced database. The proposed method constructs learning data without manual effort and the need of reference points in the target area, and improves positioning accuracy by continuously learning and correcting the error distribution of the inertial sensor. The obtained positioning accuracy was approximately 3.38 m for roughly 83% of the collected fingerprints. Furthermore, the error correction algorithm improved the moving distance and direction accuracy by up to 2.86% and 25.7%, respectively. The proposed method was verified through experiments in an office building where it successfully constructed a learning dataset and reflected a dynamic environment by deriving accurate tracking results.
... Place Lab uses radio beacons which physically send radio signals in a periodic manner by wireless LAN access points, Bluetooth beacons, and GSM towers. It reaches an accuracy of 10-13 meters [16]. The work in [17] is a prototype of tracking a physical direction and location. ...
... Mobile devices usage has increased substantially over the past years. The statistics shows that at the end of 2013, global smartphone penetration has reached 22% of world population where there are estimated 1.4 billion smartphones in use [1]. As this Mobile devices increases, then the chances for increase in mobile technology crawl in. ...
he usage of mobile devices such as smartphones and tablets has increased dramatically over the past years. Most of people carry at least one mobile device wherever they go. Mobile devices are becoming really important nowadays because they are usually the main tool for communications. The dispersion of mobile devices across the world brought about the applications of geo-locational system in these mobile devices. However, the consumption of energy increases, even as innovations increases in geo-location for mobile development system[5]. The problem occurs because, users will continue to change state and while doing so, location's request will also be continually made, and the current geo-locational systems applications in mobile devices makes request based on either passive, wifi or using cell tower techniques. To address the problem, this paper presents a novel concept of energy conservation in geo-locational system for mobile devices. This concept gives an optimized way to make location request using fuse provider API techniques. This technique is in such a way that, other request made by several geo-locational apps on a device, can be fetched by this fuse provider technique oriented system and by so doing, the frequently energy consumption as a result of location request can be reduced. Therefore, in this research, I will design a location-based personal schedule reminder for Android-based smartphones and tablets and apply the same fuse provider API technique.
... The most ubiquitous indoor localization techniques are either WiFi-based or deadreckoning based. WiFi-based techniques [4], [19], require calibration to create a prior "RF map" for the building to counter the wireless channel noise. This calibration process is time consuming, tedious, and requires periodic updates. ...
We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55%, highlighting DynamicSLAM promise for ubiquitous indoor localization.
... An assisted GPS is used for obtaining location information when the network device is in a location where the penetration of satellite signals is limited. Information for indoor positioning is obtained from Bluetooth beacons installed on the walls or ceilings of buildings and POIs [80]. Several Estimote iBeacons (available from: https://developer.estimote.com/ibeacon/) ...
Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge–desire–intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users’ beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.
The need for indoor localization systems that can provide reliable access to location information in areas that are not serviced sufficiently by GPS has continued to grow. There are a wide variety of use cases for this localization data and increasing interest from industry, academia, and government agencies that has fueled research in this area. On the individual front, smartphones have permeated into every aspect of our lives and are a unique category of embedded platform packed with a wide variety of sensors. In this chapter, we explore the capabilities of these sensors with respect to indoor localization techniques and frameworks that combine two or more of these techniques. Many localization frameworks utilize a combination of complimentary methods to enhance accuracy and reliability and to decrease energy consumption of the overall system. Several of these localization frameworks are also explored. We lay special emphasis on the challenges associated with fingerprinting-based indoor localization frameworks that employ machine learning to achieve state-of-the-art performance. Lastly, we describe the major challenges that are being faced in current research on indoor localization with smartphones, as they are critical for charting the path for future advances in indoor localization.KeywordsSmartphonesIndoor localizationSensorsIndoor positioningFingerprinting
Transport is one of the main branches of the economy and the costs associated with transport affect the prices of various goods and services to a large extent. Companies are constantly striving to optimize costs related to transport. Today they are largely dependent on fuel prices, but also on labor costs. Innovations in the field of transport solutions will be a response to emerging problems in the field of transport services. One of the directions of transport development is mobile infrastructure and autonomous vehicles for transport. In the article authors analyze the use of IT systems in transport and forwarding, and present the directions of innovations in this area. The article was based on the analysis of the literature and questionnaire research. The research methods used allowed to identify the problems faced by transport companies and those that use transport as their auxiliary activity. The main idea of this article is the need to implement standardized transport management systems. The aim of the article is to analyze the use of IT systems in transport and shipping and to present the possibilities and directions of innovation in this area. The article presents the level of use of IT systems dedicated to transport. It was based on the results of surveys conducted by the authors. The result of the conducted research is the idea of implementing a single IT system for transport, which will cover all aspects of transport management.
Location-based services and applications have grown rapidly over the past decade. These location-based services and applications usually use maps to display user locations. However, the availability of digital indoor maps and indoor route information is far from satisfactory. At present, most indoor applications rely on manually created indoor maps, which requires huge costs and effort for editing and maintenance. Because mobile phones have rich sensors, scientists hope to explore the use of smartphone crowdsourcing to generate indoor maps in recent years. This article provides a systematic review of these works. Unlike former surveys, we divide the indoor maps into indoor route maps and indoor floor plans. For these two kinds of indoor maps, we summarize their common steps and differences in the crowdsourcing-based map learning process. Basically, they share the trajectory collection, landmark detection, trajectory alignment, and graph optimization steps. The floor plan learning needs additional trajectory segmentation and boundary extraction steps. We introduce the state-of-the-art techniques of each of these key processing steps. Finally, we systematically compare existing map learning systems of these two categories in terms of sensor used, users’ participation types, and key technologies used in different steps.
Recently, indoor target localization and tracking with only one access point (AP) have attracted significant attention in wireless sensor networks (WSNs). Despite much research in this field, a universal and accurate solution is still rare in reality. To this end, we propose a system that achieves reliable decimeter-level indoor tracking with only one AP in this article. We first use the 3-D orthogonal matching pursuit (OMP) to recover the angle of arrival (AoA), angle of departure (AoD), and time of flight (ToF) of the multipath signals. We construct a fusion tracking model based on these channel parameters, which can adapt to different situations. Concretely, when there are available reflection paths, we combine the target motion features and the geometric features of multipath reflections to achieve improved multipath-assisted tracking. In addition, we provide an auxiliary tracking method with only the direct path in the absence of available reflective paths. After that, we use a Bayesian framework to analyze the fusion tracking model and formulate it as a hidden Markov model (HMM). Then, we construct a particle filter to address the HMM instead of the Kalman filter because of the nonlinear model. Finally, we validate the proposed system in various indoor environments on a
4 multiple input multiple output (MIMO) system implemented by software-defined radio (SDR) X310 devices. The experimental results show that the proposed system has good tracking performance and a median trajectory error of 0.44 m, comparable to the prior state-of-the-art systems.
A general non-asymptotic theoretical analysis is developed for fingerprinting localization system designs. Based on this analysis, hybrid fingerprinting and propagation-based methods are proposed using 5G-like received signal strength (RSS), time of arrival (TOA), and direction of arrival (DOA) measurements which have been a focus in recent 3GPP Rel 16 and Rel 17 positioning activities. The proposed hybrid methods have the flexibility and robustness of fingerprinting methods in dealing with none-line-of-sight (NLOS) problem while inheriting the efficiency and accuracy of propagation-based methods in 3-D localization. Specifically, a ray extension technique is developed as the propagation-based method. Then the ray extension is combined with two fingerprinting methods, the conventional weighted k-nearest neighbors (WKNN) and the proposed optimal WKNN (OWKNN), in order to remedy the geometrical deficiency in fingerprinting methods. Based on the non-asymptotic study, the proposed hybrid methods are guaranteed to outperform the fingerprinting methods without ray extension. Verification of the proposed methods is performed in a large none-line-of-sight (NLOS) urban San Jose region using simulation data provided by a previously developed super-efficient ray launcher.
Seamless positioning services are of a critical concern in building smart cities. In a multisource fusion indoor positioning system, providing the guidance information for the deployment of positioning sources is a key technology, which can optimize the infrastructure resources to provide higher positioning accuracy. The error models of single‐source positioning such as the received signal strength (RSS) fingerprint and the pedestrian dead reckoning (PDR) should be extended to meet the requirement of multisource indoor positioning for positioning error estimation. This paper proposes a model that combines the RSS fingerprint and PDR positioning error models for fusion positioning error simulation, which weights the PDR and RSS fingerprint positioning results and calculates the mean square error for the fusion positioning according to their positioning variances. This model is also used to establish an indoor positioning simulation system. To validate the proposed model, an experiment is performed which compared the actual positioning errors using the fusion positioning with the errors of the simulate model. The results show that the actual positioning error curves and the error curve predicted by the model are consistent. As a result, the proposed error model provides a solution for optimizing the deployment of positioning sources.
Indoor optical wireless communication (IOWC) is a key technology complementing radio communication in 6G. Typically, the downlink of IOWC is implemented using visible light and uplink is implemented using infrared. Though the downlink of IOWC has been studied well, the uplink of multiple-access IOWC requires further research. In this paper, power allocation strategies in the uplink of IOWC with multiple non-cooperative users is studied. Optimizing power allocation in IOWC that maximize sum-rate with power constraints becomes a non-convex problem. First, this paper proposes an alternating maximization algorithm to compute near optimal power allocation policies for small number of users. Next, a phenomenon of invariance is observed when the number of users in the system is large. Under invariance, the optimal power allocation policy of a user becomes independent of other users, thereby facilitating fast and decentralized computation of power allocation strategies. From simulations, it was observed that the invariance can occur even with five to ten users. For the first time, a rigorous analysis of the invariance phenomenon is provided. An algorithm to compute the optimal power allocation at invariance is proposed; this algorithm is proven to achieve the global maxima in sum-rate without the channel knowledge of other users.
This paper evaluate positioning performance considering the influence of communication state on wireless LAN positioning systems.
First, we evaluate positioning performance considering the influence of the presence/absence of connection to access points on the system.
As a pre-experiment, we check the influence to access point lookups.
With this in mind, the positioning peformance evaluation using gathered data in the indoor corridor is carried out.
As a result, we show that the positioning performance is high in the case of the absence of connection to access points.
Next, we evaluate positioning performance considering the influence of the communication state of peripheral devices.
As pre-experiments, we check the influence of date, time zone, and communication state of peripheral devices to access point lookups.
With this in mind, the positioning peformance evaluation using gathered data in the indoor corridor is carried out.
As a result, we show that the positioning performance is high in the case of using the learning data by non-communication.
Consequently, we show necessity of considering the influence of communication state on the wireless LAN positioning system.
Location estimation is one of the critical requirement for developing smart environment products. Due to huge utilization and accessibility of WiFi infrastructure facility in indoor environments, researchers widely studied this technology to locate users accurately to provide several services instantly. In this research work, a hybrid algorithm namely fuzzy decision tree (FDT) with evolutionary fuzzy clustering methods is adopted for optimal user localization in a closed environment. Here we consider the wireless signal strengths received from the smart phones as predictors and the location of the user as the classification label. The required data for the current research is collected from the physical facility available at an office location in USA. The classification results obtained are promising enough to show that the evolutionary clustering approaches provide good fuzzy clusters for FDT induction with better accuracy.
This chapter provides an overview of the state of the‐art in the area of indoor localization and navigation. It discusses performance metrics that are necessary to understand, in order to compare and contrast the landscape of indoor localization approaches. In general, the signals discussed can help improve the accuracy of indoor localization when used in tandem with other more robust and comprehensive localization signals, for example, dead reckoning or RF‐signal‐based localization. The chapter provides an easy reference to the key technical terms that are used throughout the rest of the chapter. It presents a review of the various signals that can be used to provide tracking in indoor locales, for the purpose of localization. The chapter also provides an overview of the vast landscape of solutions for indoor localization. Lastly, it discusses open research issues and challenges that still remain to be overcome for viable indoor localization.
Localisation is a standard feature in many mobile applications today, and there are numerous techniques for determining a user’s location both indoors and outdoors. The provided location information is often organised in a format tailored to a particular localisation system’s needs and restrictions, making the use of several systems in one application cumbersome. The presented approach models the details of localisation systems and uses this model to create a unified view on localisation in which special attention is paid to uncertainty coming from different localisation conditions and to its presentation to the user. The work discusses technical considerations, challenges and issues of the approach, and reports on a user study on the acceptance of a mobile application’s behaviour reflecting the approach. The results of the study show the suitability of the approach and reveal users’ preference toward automatic and informed changes they experienced while using the application.
This paper presents an indoor positioning system based on FM radio. The system is built on commercially available short-range FM transmitters. This is the first experimental study of FM performance for indoor localisation. FM radio possesses a number of features, which make it distinct from other localisation technologies. Despite the low cost and off-the-shelf components, this FM positioning system reaches a high performance, comparable to other positioning technologies such as Wi-Fi. The authors’ experiments have yielded a median accuracy of 1.0 m and in 95% of cases the error is below 5 m.
The 5th Generation (5G) of mobile communication networks is being developed to address the demands and business contexts of 2020 and beyond. Its vision is to enable a fully mobile and connected society and also to trigger socio-economic transformations in ways eventually unimagined today. This means that the physical world to be covered with planned 5G networks including communication networks, humans and objects is becoming a type of an information system. So as to improve the experience of individuals, communities, societies, etc. within such systems, a thorough comprehension of intelligence processes responsible of generating, handling and controlling those data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have crucial role in forthcoming communication technologies. Human mobility patterns models can be categorized into synthetic, trace-based and community-based models. Synthetic models are largely preferred and widely applied to simulate mobile communication networks. They try to capture the patterns of human movements by means of a set of equations. These models are traceable, however, not capable of generating realistic mobility models. The key idea of trace-based models is the exploitation of available measurements and traces achieved in deployed systems to reproduce synthetic traces characterized by the same statistical properties of real traces. A main drawback of trace-based modeling of human patterns is the tight coupling between the trace-based model and the traces collected, the network topology deployed and even the geographic location, where the traces were collected. This is why the results of various trace-based models deviate clearly from each other. Sure, this prohibits the generalization of trace-based models. When one also considers that the traces themselves are rarely available, one can understand why synthetic models are preferred over trace-based ones. Community-based modeling of human movements depends on the fact stating that mobile devices are usually carried by humans, which implies that movement patterns of such devices are necessarily related to human decisions and socialization behaviors. So, human movement routines heavily affect the overall movement patterns resulting. One of the major contributions in this context is the application of social networks theory to generate more realistic human movement patterns. The chapter highlights the state of art and provides a comprehensive investigation of current research efforts in the field of trace- and social-based modeling of human mobility patterns. It reviews well-known approaches going through their pros and cons. In addition, the chapter studies an aspect that heavily relates to human mobility patterns, namely the prediction of future locations of users.
Target localization using a single receiver is highly needed due to the mobility of the target. In indoor environment, multipath signals are rich and usually relate the target location and structure of indoor environment through geometry parameters such as Angle of Arrival (AOA) and Time of Flight (TOF). Based on this, in this article we propose a multipath-assisted indoor localization system using commodity WiFi signals which contain phase errors caused by imperfect hardware and non-synchronized clocks. The proposed system is different from conventional localization algorithms which treat multipath signals as enemies, and only uses a single receiver. To realize accurate localization without interferences of phase errors, we firstly construct a geometry model for jointly estimating the locations of the target and scatterers which can be regarded as objects such as furniture, by using TOF differences between reflection paths and direct path. Considering more constrains on target location, we select the direct path as the reference. Then, with the help of AOAs, we develop a location searching algorithm of the target and scatterers based on Particle Swarm Optimization (PSO). We have implemented the proposed system on the commodity WiFi devices, and the experiment results in actual indoor environment show that the median location error is around 1.5m by using only one receiver. What’s more, the intensive simulation results show that the proposed system is promising in the emerging networks such as 5G, where higher bandwidth of signal and more antennas can be used for providing accurate AOA and TOF.
We are committed to using various indoor and outdoor images, 3D objects and static scenes as recognition objects to build an augmented reality world. This paper focuses on the research and application of indoor augmented reality navigation. Indoor navigation has a variety of technical solutions, such as Wi-Fi Based and indoor sensor based. As one of them, augmented reality has the advantages of no need to deploy additional hardware devices in advance, six degrees of freedom and high precision. By analyzing the development of augmented reality indoor navigation and the underlying technology, we summarize and implement three solutions: map based (MB), point-cloud based (PCB), image based (IB). We first conducted a control experiment, and compared these schemes with the flow theory and the experimental data. At the same time, we collected the feedback and suggestions during the experiment, and carried out a second experiment on some components of augmented reality navigation (such as path, point of interest), and obtained the corresponding quantitative data.
Understanding which transportation modes people use is critical for smart cities and planners to better serve their citizens. We show that using information from pervasive Wi-Fi access points and Bluetooth devices can enhance GPS and geographic information to improve transportation detection on smartphones. Wi-Fi information also improves the identification of transportation mode and helps conserve battery since it is already collected by most mobile phones. Our approach uses a machine learning approach to determine the mode from pre-prepocessed data. This approach yields an overall accuracy of 89% and average F1 score of 83% for inferring the three grouped modes of self-powered, car-based, and public transportation. When broken out by individual modes, Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features without decreasing performance. Our results suggest that Wi-Fi and Bluetooth can be useful in urban transportation research, for example by improving mobile travel surveys and urban sensing applications.
To be widely adopted, location-aware computing must be as effortless, familiar and rewarding as web search tools like Google. We envisage the global scale Place Lab, consisting of an open software base and a community building activity as a way to bootstrap the broad adoption of location-aware computing. The initiative is a laboratory because it will also be a vehicle for research and instruction, especially in the formative stages. The authors draw on their experiences with campus and building-scale location systems to identify the technological and social barriers to a truly ubiquitous deployment. With a grasp of these "barriers to adoption," we present a usage scenario, the problems in realizing this scenario, and how these problems will be addressed. We conclude with a sketch of the multi-organization cooperative being formed to move this effort forward.
sent out by a central facility that addresses a particular receiver unit (beeper) and produces an audible signal. In addition, it may display a number to which the called-party should phone back (some systems allow a vocal message to be conveyed about the call-back number). It is then up to the recipient to use the conventional telephone system to call-back confirming the signal and determine the required action. Although useful in practice there are still circumstances where it is not ideal. For instance, if the called party does not reply the controller has no idea if they: 1) are in an area where the signal does not penetrate 2) have been completely out of the area for some time 3) have been too busy to reply or 4) have misheard or misread the call-back number. Moreover, in the case where there are a number of people who could respond to a crisis situation, it is not known which one is the nearest to the crisis and therefore the most suitable to contact. A `tagging system' does not
The Personal Server is a mobile device that enables you to readily store and access the data and applications you carry with you through interfaces found in the local environment. Unlike conventional mobile computers with relatively poor user interfaces, it does not have a display at all, instead wirelessly utilizing displays, keyboards and other IO devices found nearby. By co-opting large screens such as those found on desktop PCs, public display monitors, information kiosks, and other computers, a Personal Server is more effective than relying on a small mobile screen. This model goes beyond the mobile context and has wider implications for how we think about computing in general. A prototype system, including applications, system infrastructure, and a mobile platform, has been built to fully explore this model. This prototype sheds light on the suitability of standard components to support such a computing model, and from this illuminates directions for the design of future ubiquitous computing systems.
One of the main prerequisites for location-based services is knowl- edge of location. We present a simple algorithm for computing the location of a device based on signal strengths from FM radio stations. The motivation for this method comes from a new class of smart personal objects that will receive digital data encoded in regular FM radio broadcasts. Given their built-in ability to receive FM and to measure signal strengths, we show how to exploit this ability to measure the device's location. Our algorithm, called RightSPOT, is designed to be robust to manufacturing variations among devices that affect how they measure signal strength. Toward this end, we present a location clas- sification algorithm based not on absolute signal strengths, but on a ranking of signal strengths from multiple FM radio stations. In tests with three devices in six suburban areas, we show that we can correctly infer the device's location about 80% of the time.
The continuing proliferation of handheld computing devices offers a new platform for mobile computing applications that could enrich our experience of the world around us. Yet many questions about realizing this vision remain open. We have been investigating these questions at the University of California, San Diego, through the ActiveCampus Project. ActiveCampus explores technologies that can enrich the learning community in the midst of these changes. It explores wireless context-aware computing as a means to enhance the "learning community" experience of a large urban university. We have tested two applications: ActiveClass supports classroom activities, such as asking questions during a lecture, and ActiveCampus Explorer supports context-aware activities, such as instant messaging and location-aware maps annotated with dynamic hyperlinked information.
This paper describes the UbiquitousComputing philosophy, the PARCTAB system, user-interface issues for small devices, and our experience developing and testing a variety of mobile applications.
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.
The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF) based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
The ParcTab system integrates a palm-sized mobile computer into an office network. This project serves as a preliminary testbed for Ubiquitous
Computing, a philosophy originating at Xerox PARC that aims to enrich our computing environment by emphasizing context sensitivity,
casual interaction and the spatial arrangement of computers. This paper describes the Ubiquitous Computing philosophy, the
ParcTab system, user-interface issues for small devices, and our experience developing and testing a variety of mobile applications.
Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic ap- proximations which the robotics community has shown to be eective for tracking robots' positions. This paper presents a case study of applying particle filters to location estimation for ubiquitous computing. Using trace logs from a deployed multi-sensor location system, we show that particle filters can be as accurate as common deterministic algorithms. We also present performance results showing it is practical to run parti- cle filters on devices ranging from high-end servers to handhelds. Finally, we discuss the general advantages of using probabilistic methods in lo- cation systems for ubiquitous computing, including the ability to fuse data from dierent sensor types and to provide probability distributions to higher-level services and applications. Based on this case study, we conclude that particle filters are a good choice to implement location estimation for ubiquitous computing.
Location-enhanced applications use the location of people, places, and things to augment or streamline interaction. Location-enhanced applications are just starting to emerge in several different domains, and many people believe that this type of application will experience tremendous growth in the near future. However, it currently requires a high level of technical expertise to build location-enhanced applications, making it hard to iterate on designs. To address this problem we introduce Topiary, a tool for rapidly prototyping location-enhanced applications. Topiary lets designers create a map that models the location of people, places, and things; use this active map to demonstrate scenarios depicting location contexts; use these scenarios in creating storyboards that describe interaction sequences; and then run these storyboards on mobile devices, with a wizard updating the location of people and things on a separate device. We performed an informal evaluation with seven researchers and interface designers and found that they reacted positively to the concept.
The proliferation of mobile computing devices and local-area
wireless networks has fostered a growing interest in location-aware
systems and services. In this paper we present RADAR, a radio-frequency
(RF)-based system for locating and tracking users inside buildings.
RADAR operates by recording and processing signal strength information
at multiple base stations positioned to provide overlapping coverage in
the area of interest. It combines empirical measurements with signal
propagation modeling to determine user location and thereby enable
location-aware services and applications. We present experimental
results that demonstrate the ability of RADAR to estimate user location
with a high degree of accuracy
Configuration of the computing and communications systems found at
home and in the workplace is a complex task that currently requires the
attention of the user. Researchers have begun to examine computers that
would autonomously change their functionality based on observations of
who or what was around them. By determining their context, using input
from sensor systems distributed throughout the environment, computing
devices could personalize themselves to their current user, adapt their
behaviour according to their location, or react to their surroundings.
The authors present a novel sensor system, suitable for large-scale
deployment in indoor environments, which allows the locations of people
and equipment to be accurately determined. We also describe some of the
context-aware applications that might make use of this fine-grained
location information
Location-awareness is useful for mobile and pervasive computing. We present a novel adaptive framework for recognizing personally important locations in cellular networks, implementable on a mobile device and usable, e.g., in a presence service. In comparison, most previous work has used service infrastructure for location recognition and the few adaptive frameworks presented have used coordinate-based data. We construct a conceptual framework for the tasks of learning important locations and predicting the next location. We give algorithms for efficient approximation of the ideal concepts, and evaluate them experimentally with real data.
This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, locationdependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration. 1
Configuration of the computing and communications systems found at home and in the workplace is a complex task that currently requires the attention of the user. Recently, researchers have begun to examine computers that would autonomously change their functionality based on observations of who or what was around them. By determining their context, using input from sensor systems distributed throughout the environment, computing devices could personalize themselves to their current user, adapt their behavior according to their location, or react to their surroundings. The authors present a novel sensor system, suitable for large-scale deployment in indoor environments, which allows the locations of people and equipment to be accurately determined. We also describe some of the context-aware applications that might make use of this fine-grained location information.
The Personal Server: Changing the Way We Think about Ubiquitous Computing, Paper presented at the Ubicomp 2002 A New Location Technique for the Active Office
- R Want
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- A Hopper
(Ed.) Mobile Computing, pp. 45-101 (Kluwer Publishing).(1997) WANT, R., PERING, T., DANNEELS, G., KUMAR, M., SUNDAR, M. & LIGHT, J. The Personal Server: Changing the Way We Think about Ubiquitous Computing, Paper presented at the Ubicomp 2002.(2003) WARD, A., JONES, A. & HOPPER, A. A New Location Technique for the Active Office, IEEE Personal Communications, 4, 42-47. (1997) 11
Adaptive On-Device Location Recognition, Paper presented at the Pervasive Computing: Second International Conference
- K Laasonen
- M Raento
- H Toivonen
LAASONEN, K., RAENTO, M. & TOIVONEN, H. Adaptive On-Device Location Recognition, Paper presented at the Pervasive Computing: Second International Conference.(2004)
RADAR: An In-Building RF-Based User Location and Tracking System Proceedings of IEEE INFOCOM Place Lab: Device Positioning Using Radio Beacons in the Wild 133
- P Bahl
- V Padmanabhan
BAHL, P. & PADMANABHAN, V. RADAR: An In-Building RF-Based User Location and Tracking System Proceedings of IEEE INFOCOM, pp. 775-784 (Tel-Aviv, Israel).(2000) Place Lab: Device Positioning Using Radio Beacons in the Wild 133
The Cricket Location-Support System Proceedings of MOBICOM
- N B Priyantha
- A Chakraborty
- H Balakrishnan
- B Schilit
- A Lamarca
- G Borriello
- W Griswold
- D Mcdonald
- E Lazowska
- A Balachandran
- J Hong
- V Iverson
PRIYANTHA, N. B., CHAKRABORTY, A. & BALAKRISHNAN, H. The Cricket Location-Support System Proceedings of MOBICOM 2000, pp. 32-43 (Boston, MA, ACM Press).(2000) 11. SCHILIT, B., LAMARCA, A., BORRIELLO, G., GRISWOLD, W., MCDONALD, D., LAZOWSKA, E., BALACHANDRAN, A., HONG, J. & IVERSON, V. Challenge: Ubiquitous Location-Aware Computing and the Place Lab Initiative Proceedings of the First ACM International Workshop on Wireless Mobile Applications and Services on WLAN (WMASH).(2003)
JSR 179 Location API for
- K Loytana
Adaptive On-Device Location Recognition, Paper presented at the Pervasive Computing
- K Raento
- M Toivonen
LAASONEN, K., RAENTO, M. & TOIVONEN, H. Adaptive On-Device Location
Recognition, Paper presented at the Pervasive Computing: Second International
Conference.(2004)