Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.
Numerous real and successful mobile applications have emerged in recent times such as WAYZ http: www.wayz.com for real-time traffic navigation information and Wazer2 https: www.wazer2.co.il for real-time, location-based citizen journalism, context-aware open-mobile miner (CAROMM) @cite_19 among others. Mobile applications @cite_22 @cite_21 thrive on the data obtained from heterogeneous sets of smart phones owned and operated by humans. Until recently mobile sensing application such as activity recognition (, where people's activity (e.g. walking, talking, sitting) is classified and monitored, required specialised mobile devices @cite_11 @cite_6 . This has significantly changed with advent of smartphones equipped with on-board sensing capabilities. More recently, research efforts have focused on development of activity recognition, context-aware @cite_18 and data mining models for smartphones @cite_0 @cite_3 @cite_16 that leverage on smartphone's processing and on-board sensing capabilities.
@cite_18 @cite_22 @cite_21 @cite_6 @cite_3 @cite_0 @cite_19 @cite_16 @cite_11
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A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods. The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate realtime traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this talk, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges. The continual advancement in semiconductor technology has enabled vendors to integrate an increasing number of sensors, e.g., accelerometer, gyroscope, digital compass, high resolution cameras and others, in modern mobile phones. Combining with the ever expanding market penetration of mobile phones, the onboard sensors are driving the sensing community towards a new paradigm called participatory sensing in which ordinary citizens voluntarily collect and share information from their local environment using personal mobile devices. The inherent openness of this platform is a double-edged sword. While encouraging user participation on the one hand, it also makes it easy to contribute corrupted data. As such, data trustworthiness becomes a key issue which needs to be addressed to ensure sustainable development of this emerging paradigm. In this paper, we propose a reputation system in which a reputation score is calculated for each device as a reflection of the trustworthiness of its sensor data. We adopt the Gompertz function as a fundamental building block of our system, since it is better suited to deal with the unique characteristics of participatory sensing. We present an application-agnostic implementation of the system, which can work with a wide variety of participatory sensing applications. We evaluate the performance of our reputation system in the context of two real-world participatory sensing applications. Our results show that the proposed reputation system outperforms existing solutions by up to a factor of six. In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing. Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the rich sensory data that is available on today's smart phones and other wearable sensors. The state of the art in mobile activity recognition research has focused on traditional classification learning techniques. In this paper, we propose the Mobile Activity Recognition System (MARS) where for the first time the classifier is built on-board the mobile device itself through ubiquitous data stream mining in an incremental manner. The advantages of on-board data stream mining for mobile activity recognition are: i) personalisation of models built to individual users; ii) increased privacy as the data is not sent to an external site; iii) adaptation of the model as the user's activity profile changes. In our extensive experimental results using a recent benchmarking activity recognition dataset, we show that MARS can achieve similar accuracy when compared with traditional classifiers for activity recognition, while at the same time being scalable and efficient in terms of the mobile device resources consumption. MARS has been implemented on the Android platform for empirical evaluation. We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is easy to obtain, e.g., through contemporary smartphones carried by users as they go about their everyday activities.Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data.We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on a transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-coding framework demonstrates better performance than the state-of-the-art in supervised learning approaches. More importantly, we show the practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities of daily living. The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works. Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise. Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder. Emerging class of context-aware mobile applications, such as Google Now and Foursquare require continuous location sensing to deliver different location-aware services. Existing research, in finding location at higher abstraction, use GPS and WiFi location interfaces to discover places, which result in high power consumption. These interfaces are also not available on all feature phones that are in majority in developing countries. In this paper, we present a framework PlaceMap that discovers different places and routes, solely using GSM information, i.e., Cell ID. PlaceMap stores and manages all the discovered places and routes, which are used to build spatio-temporal mobility profiles for the users. PlaceMap provides algorithms that can complement GSM-based place discovery with an initial WiFi-based training to increase accuracy. We performed a comprehensive offline evaluation of PlaceMap algorithms on two large real-world diverse datasets, self-collected dataset of 62 participants for 4 weeks in India and MDC dataset of 38 participants for 45 weeks in Switzerland. We found that PlaceMap is able to discover up to 81 of the places correctly as compared to GPS. To corroborate the potential of PlaceMap in real-world, we deployed a life-logging application for a small set of 18 participants and observed similar place discovery accuracy. The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android-based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the application is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road. Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the device. Our recognition system is designed for everyday use: it allows flexible placement of the device (no assumed or fixed position), requires only minimal personalization effort from the user (1–3 minutes per activity) and is capable of detecting a high number of activities. The components of the service are shown in an evaluation scenario, in which recognition rates up to 97 can be achieved for ten activity classes. Mobile sensing and computing applications usually require time-series inputs from sensors, such as accelerometers, gyroscopes, and magnetometers. Some applications, such as tracking, can use sensed acceleration and rate of rotation to calculate displacement based on physical system models. Other applications, such as activity recognition, extract manually designed features from sensor inputs for classification. Such applications face two challenges. On one hand, on-device sensor measurements are noisy. For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice. Unfortunately, calculating target quantities based on physical system and noise models is only as accurate as the noise assumptions. Similarly, in classification applications, although manually designed features have proven to be effective, it is not always straightforward to find the most robust features to accommodate diverse sensor noise patterns and heterogeneous user behaviors. To this end, we propose DeepSense, a deep learning framework that directly addresses the aforementioned noise and feature customization challenges in a unified manner. DeepSense integrates convolutional and recurrent neural networks to exploit local interactions among similar mobile sensors, merge local interactions of different sensory modalities into global interactions, and extract temporal relationships to model signal dynamics. DeepSense thus provides a general signal estimation and classification framework that accommodates a wide range of applications. We demonstrate the effectiveness of DeepSense using three representative and challenging tasks: car tracking with motion sensors, heterogeneous human activity recognition, and user identification with biometric motion analysis. DeepSense significantly outperforms the state-of-the-art methods for all three tasks. In addition, we show that DeepSense is feasible to implement on smartphones and embedded devices thanks to its moderate energy consumption and low latency. Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications. Environmental and user-centric sensor data of unprecedented quantity and quality can be captured and reported by a possible user base of billions of mobile phone subscribers worldwide. The strong focus on the collection of detailed sensor data may however compromise user privacy in various regards, e.g., by tracking a user's current location. In this survey, we identify the sensing modalities used in current participatory sensing applications, and assess the threats to user privacy when personal information is sensed and disclosed. We outline how privacy aspects are addressed in existing sensing applications, and determine the adequacy of the solutions under real-world conditions. Finally, we present countermeasures from related research fields, and discuss their applicability in participatory sensing scenarios. Based on our findings, we identify open issues and outline possible solutions to guarantee user privacy in participatory sensing. Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm - participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus. The location of people, mobile terminals and equipment is highly desirable for operational enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflection, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest direct path between transmitter and receiver are the main sources of range measurement errors. Unreliable measurements of location metrics such as received signal strengths (RSS), angles of arrival (AOA) and times of arrival (TOA) or time differences of arrival (TDOA), result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the wide band neural network-locate (WBNN-locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses for line of sight (LOS) and non-line of sight (NLOS) situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90 and 80 of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface). Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations. To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy quality trade-offs. We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90 depending on the scenario) compared to a representative collection of existing approaches. The rapid deployment of sensing technology in smartphones and the explosion of their usage in people's daily lives provide users with the ability to collectively sense the world. This leads to a growing trend of mobile healthcare systems utilizing sensing data collected from smartphones with without additional external sensors to analyze and understand people's physical and mental states. However, such healthcare systems are vulnerable to user spoofing attacks, in which an adversary distributes his registered device to other users such that data collected from these users can be claimed as his own to obtain more healthcare benefits and undermine the successful operation of mobile healthcare systems. Existing mitigation approaches either only rely on a secret PIN number (which can not deal with colluded attacks) or require an explicit user action for verification. In this paper, we propose a user verification scheme leveraging unique gait patterns derived from acceleration readings in mobile healthcare systems to detect possible user spoofing attacks. Our framework exploits the readily available accelerometers embedded within smartphones for user verification. Specifically, our user spoofing attack mitigation scheme (which consists of three components, namely Step Cycle Identification, Step Cycle Interpolation, and Similarity Score Computation) is used to extract gait patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. Our experiments using 322 smartphone-based traces over a period of 6 months confirm that our scheme is highly effective for detecting user spoofing attacks. This strongly indicates the feasibility of using smartphone based low grade accelerometer to conduct gait recognition and facilitate effective user verification without active user cooperation. Activity recognition is fundamental to many of the services envisaged in pervasive computing and ambient intelligence scenarios. However, delivering sufficiently robust activity recognition systems that could be deployed with confidence in an arbitrary real-world setting remains an outstanding challenge. Developments in wireless, inexpensive and unobtrusive sensing devices have enabled the capture of large data volumes, upon which a variety of machine learning techniques have been applied in order to facilitate interpretation of and inference upon such data. Much of the documented research in this area has in the main focused on recognition across pre-segmented sensor data. Such approaches are insufficient for near real-time analysis as is required for many services, such as those envisaged by ambient assisted living. This paper presents a novel near real-time sensor segmentation approach that incorporates the notions of both sensor and time correlation. Mobile phone sensing has become increasingly popular since it can collect and analyze real-time data anywhere anytime, especially with the help of mobile phone users via crowdsourcing. In order to stabilize the mobile crowdsourcing at a massive scale, incentive mechanisms are needed not only to stimulate the users of mobile phones to participate in sensing, but also to incentivize the organizer of the sensing tasks with maximum service time and payoff. In this paper, we study a practical problem in mobile sensing, i.e., sensing time uncertainty, which may lead to failures of existing incentive mechanisms. In particular, we model this problem as a perturbed Stackelberg game in which mobile phone users may actually conduct sensing tasks during different periods of time rather than what they intend, like through a trembling hand. We find that there exist Trembling-Hand Perfect Equilibria (THP) given proper rewards. After characterizing THPs in this game with rigorous analysis, we design incentive mechanisms that can achieve a THP with the maximum system wide total sensing time and maximum platform utility. We finally verify the correctness and efficiency of our proposed incentive mechanisms and algorithms through extensive experiments.