Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> Understanding user mobility is critical for simula- tions of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17%. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> We describe a method called Predestination that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips. <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person's activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person's activities and places. In contrast to existing techniques, this approach takes the high-level context into account in order to detect the significant places of a person. Experiments show significant improvements over existing techniques. Furthermore, they indicate that the proposed system is able to robustly estimate a person's activities using a model that is trained from data collected by other persons. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> We describe our experiences deploying BikeNet, an extensible mobile sensing system for cyclist experience mapping leveraging opportunistic sensor networking principles and techniques. BikeNet represents a multifaceted sensing system and explores personal, bicycle, and environmental sensing using dynamically role-assigned bike area networking based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones. We investigate real-time and delay-tolerant uploading of data via a number of sensor access points (SAPs) to a networked repository. Among bicycles that rendezvous en route we explore inter-bicycle networking via data muling. The repository provides a cyclist with data archival, retrieval, and visualization services. BikeNet promotes the social networking of the cycling community through the provision of a web portal that facilitates back end sharing of real-time and archived cycling-related data from the repository. We present: a description and prototype implementation of the system architecture, an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a description of BikeNet system user interfaces. Visit [4] to see how the BikeNet system visualizes a user's rides. <s> BIB004 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results. <s> BIB005 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> We propose to use mobile phones carried by people in their everyday lives as mobile sensors to track mobile events. We argue that sensor-enabled mobile phones are best suited to deliver sensing services (e.g., tracking in urban areas) than more traditional solutions, such as static sensor networks, which are limited in scale, performance, and cost. There are a number of challenges in developing a mobile event tracking system using mobile phones. First, mobile sensors need to be tasked before sensing can begin, and only those mobile sensors near the target event should be tasked for the system to scale effectively. Second, there is no guarantee of a sufficient density of mobile sensors around any given event of interest because the mobility of people is uncontrolled. This results in time-varying sensor coverage and disruptive tracking of events, i.e., targets will be lost and must be efficiently recovered. To address these challenges, we propose MetroTrack, a mobile-event tracking system based on off-the-shelf mobile phones. MetroTrack is capable of tracking mobile targets through collaboration among local sensing devices that track and predict the future location of a target using a distributed Kalman-Consensus filtering algorithm. We present a proof-of-concept implementation of MetroTrack using Nokia N80 and N95 phones. Large scale simulation results indicate that MetroTrack prolongs the tracking duration in the presence of varying mobile sensor density. <s> BIB006 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> Bicycling is an affordable, environmentally friendly alternative transportation mode to motorized travel. A common task performed by bikers is to find good routes in an area, where the quality of a route is based on safety, efficiency, and enjoyment. Finding routes involves trial and error as well as exchanging information between members of a bike community. Biketastic is a platform that enriches this experimentation and route sharing process making it both easier and more effective. Using a mobile phone application and online map visualization, bikers are able to document and share routes, ride statistics, sensed information to infer route roughness and noisiness, and media that documents ride experience. Biketastic was designed to ensure the link between information gathering, visualization, and bicycling practices. In this paper, we present architecture and algorithms for route data inferences and visualization. We evaluate the system based on feedback from bicyclists provided during a two-week pilot. <s> BIB007 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> Digital wireless radios broadcast identification numbers that uniquely identify them. As has been previously observed, given the ubiquity with which people carry smartphones with their embedded WiFi radios powered on, comes the ability to track individuals' movements. The ability to use wireless radios for positioning has been previously observed and developed in to useful products. In these systems a user willingly geolocates themselves by providing identifiers to infrastructure hardware. In this paper we consider the converse question: what rates of monitoring by smartphones devices in a given metropolitan area are necessary to achieve different levels of involuntary geolocation. While previous work has looked at countermeasure that attempt to maintain privacy, no work has attempted to quantify the problem and risks. Using appropriate simulations we give the first quantitative support for the number and conditions of tracking devices necessary to track the locations of non-participant individuals in urban environments. We provide evidence that a small, but not insignificant, number of mobile devices can be used to track a majority of users during a significant fraction of their travel with current devices. We conclude that in the immediate future, malnets would require relatively high infection rates to pose a significant threat, but that voluntary networks, with perceived benefit can probably achieve the usage rates necessary to track individual movements of non-subscribed users to a high-degree of accuracy. Our results also suggest ubiquitous deployment of 802.11n in smartphones would make geolocation feasible by malnets <s> BIB008 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> Mobile advertising will become more pervasive and profitable, but not before addressing key technical and business challenges. <s> BIB009 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> Planning travel to unfamiliar regions is a difficult task for novice travelers. The burden can be eased if the resident of the area offers to help. In this paper, we propose a social itinerary recommendation by learning from multiple user-generated digital trails, such as GPS trajectories of residents and travel experts. In order to recommend satisfying itinerary to users, we present an itinerary model in terms of attributes extracted from user-generated GPS trajectories. On top of this itinerary model, we present a social itinerary recommendation framework to find and rank itinerary candidates. We evaluated the efficiency of our recommendation method against baseline algorithms with a large set of user-generated GPS trajectories collected from Beijing, China. First, systematically generated user queries are used to compare the recommendation performance in the algorithmic level. Second, a user study involving current residents of Beijing is conducted to compare user perception and satisfaction on the recommended itinerary. Third, we compare mobile-only approach with Mobile+Cloud architecture for practical mobile recommender deployment. Lastly, we discuss personalization and adaptation factors in social itinerary recommendation throughout the paper. <s> BIB010 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Bird@?s Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the users@? location and activity histories as a user-location-activity rating tensor. Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms based on collaborative filtering. The first algorithm merges all the users@? data together, and uses a collective matrix factorization model to provide general recommendation (Zheng et al., 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (Zheng et al., 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w.r.t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations/activities for recommendations. For these three algorithms, we also exploit some additional information, such as user-user similarities, location features, activity-activity correlations and user-location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2.5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms. <s> BIB011 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> C. Transportation and Location <s> We introduce our vision for mining fine-grained urban traffic knowledge from mobile sensing, especially GPS location traces. Beyond characterizing human mobility patterns and measuring traffic congestion, we show how mobile sensing can also reveal details such as intersection performance statistics that are useful for optimizing the timing of a traffic signal. Realizing such applications requires co-designing privacy protection algorithms and novel traffic modeling techniques so that the needs for privacy preserving and traffic modeling can be simultaneously satisfied. We explore privacy algorithms based on the virtual trip lines (VTL) concept to regulate where and when the mobile data should be collected. The traffic modeling techniques feature an integration of traffic principles and learning/optimization techniques. The proposed methods are illustrated using two case studies for extracting traffic knowledge for urban signalized intersection. <s> BIB012
Location-based sensing BIB009 aims at tracking people over a period of time by recognizing their activities in terms of specifying transportation modes (e.g., walking, running, vehicle etc. when user is outside). Especially, since GPS receivers have been an integral hardware component in smartphones, data collected by GPS becomes handy to be used for network connected applications. Thereby, GPS is employed as an instrument for location-based sensing in order to inspect for the habits and general behaviors of individuals and communities BIB010 - BIB003 . The localization is inferred with the help by GPS delivered speed and location information as well as a large amount of available data (e.g., street maps). GPS provides 2D data by setting a resolution value (e.g., generally 10 m) per certain distance within two successive data points (i.e., unit difference). Hence, consecutive GPS readings are grouped based on their spatial relationships in order to create distinctive segmentations among GPS traces. Then, GPS traces are associated with available street maps, which are represented as directed graphs where an edge represents a street and a vertex represents the intersection of streets. GPS cannot penetrate through walls, and thereby the received data gets degraded. Thus, the usage of GPS for location-based sensing is valid for outdoors. Once GPS times out because of the lost satellite signals, Wi-Fi scan can be performed for indoors by checking for surrounding wireless access points. Wi-Fi could be used for outdoors either since it covers a range of 20-30 m as radius. Indeed, smartphones apply a hybrid localization scheme by using GPS with network-based triangulation by leveraging wireless access points for achieving coarse positioning BIB001 . The network-based triangulation collects information from RF signal beacons around reachable wireless cell towers, from Wi-Fi access points or even from Bluetooth (which is not effective but could be used indoor environment in presence of multiple users around), and then it uses received RF signal strength to measure relative distance through the physics of signal propagation among network nodes (e.g., utilization of local and mobile base stations). Hence, by measuring sequential RSSI data, transportation modes for users can be identified. In addition, during the Wi-Fi scan, MAC address (i.e., BSSID) of wireless access points might have already been tagged as a point of interest, which yields to retrieve automatically that user is in a familiar environment (e.g., office, home, gym etc.). Although GPS could detect some postures such as sitting or standing, the accelerometer sensor is rather used for such static activities due to GPS may not provide a concise solution for differentiation of user state classes at similar speed. Besides, the efficiency in power consumptions would be more healed in case where the accelerometer sensor is used. Investigation of mobility patterns to extract places and activities from GPS traces have been generally implemented in a hierarchical structure BIB011 - BIB012 . According to the structure, the lower level begins with association of GPS traces with street maps, and the structure rises up by inferring activity sequences; and eventually, the structure ends up with discovering significant places from activity pattern with the help of spent time within each activity. By taking a log of recent history of transportation modes belonging to individuals throughout the daily life as well as mapping their location history, a general physical activity report can be documented, and also the goals of future activity plan can be reconfigured for the purpose of health and fitness monitoring. For instance; from physiological perspective, driving behaviors are investigated in BIB002 by taking consideration of trip destinations, trip times and driving efficiency. By actuating community sensing, it could be possible to monitor highways for real-time traffic conditions, and forecast probabilistic traffic congestions, thereby the traffic flow could be re-routed in such cases BIB012 ,  - BIB005 . This scenario can also be applied into biking BIB007 , BIB004 , thereby bikers can share their routes and let noisiness of the bike trails be known, and also they can take ride statistics for fitness documentation. Besides, most significantly, crowdedness level of metropolitan areas can be investigated in terms of daily visitor density BIB008 , BIB006 . Meanwhile, existence of multiple users in a specific area could also give a help to track and notify air pollution level for environmental monitoring.