Towards System Implementation and Data Analysis for Crowdsensing Based Outdoor RSS Maps

© 2013 IEEE. With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., Wi-Fi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed, this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS) paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users could cause inherent noises and blank spots in collected data set. In this paper, we study how to: 1) tame the sensing noises from heterogenous mobile devices and 2) construct accurate and complete RSS maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called i Map to collect RSS measurements with heterogeneous mobile devices. Second, through observing experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate system and polar coordinate system, respectively

Evaluating Sensor Data in the Context of Mobile Crowdsensing

With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach

Mobile Edge Computing: From Task Load Balancing to Real-World Mobile Sensing Applications

University of Technology Sydney. Faculty of Engineering and Information Technology.With the rapid development of mobile computing technologies and the Internet of Things, there has been an increasing rise of capable and affordable edge devices that can provide in-proximity computing services for mobile users. Moreover, a massive amount of mobile edge computing (MEC) systems have been developed to enhance various aspects of people's daily life, including big mobile data, healthcare, intelligent transportation, connected vehicles, smart building control, indoor localization, and many others. Although MEC systems can provide mobile users with swift computing services and conserve devices' energy by processing their tasks, we confront significant research challenges in several perspectives, including resource management, task scheduling, service placement, application development, etc. For instance, computation offloading in MEC would significantly benefit mobile users and bring new challenges for service providers. Unbalance and inefficiency are the two challenging issues when making decisions on computation offloading among MEC servers. On the other hand, it is unprecedented to design and implement novel and practical applications for edge-assisted mobile computing and mobile sensing. The power of mobile edge computing has not been fully unleashed yet from theoretical and practical perspectives. In this thesis, to address the above challenges from both theoretical and practical perspectives, we present four research studies within the scope of MEC, including load balancing of computation task loading, fairness in workload scheduling, edge-assisted wireless sensing, and cross-domain learning for real-world edge sensing. The thesis consists of two major parts as follows. In the first part of this thesis, we investigate load balancing issues of computation offloading in MEC. First, we present a novel collaborative computation offloading mechanism for balanced mobile cloudlet networks. Then, a fairness-oriented task offloading scheme for IoT applications of MEC is further devised. The proposed computation offloading mechanisms incorporate algorithmic theories with the random mobility and opportunistic encounters of edge servers, thereby processing computation offloading for load balancing in a distributed manner. Through rigorous theoretical analyses and extensive simulations with real-world trace datasets, the proposed methods have demonstrated desirable results of significantly balanced computation offloading, showing great potential to be applied in practice. In the second part of this thesis, beyond theoretical perspectives, we further investigate two novel implementations with mobile edge computing, including edge-assisted wireless crowdsensing for outdoor RSS maps, and urban traffic prediction with cross-domain learning. We implement our ideas with the iMap system and the BuildSenSys system, and further demonstrate demos with real-world datasets to show the effectiveness of proposed applications. We believe that the above algorithms and applications hold great promise for future technological advancement in mobile edge computing

CrowdFusion: Multi-Signal Fusion SLAM Positioning Leveraging Visible Light

With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field and WiFi to achieve sub-meter accuracy. We propose a CrowdFusion Simultaneous Localization and Mapping (SLAM) comprised of coarse-grained and fine-grained trace merging respectively based on the Iterative Closest Point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67m for the merged traces and 0.77m for positioning, merely replying on crowdsourcing traces without professional calibration

Robustness, Security and Privacy in Location-Based Services for Future IoT : A Survey

Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.Peer reviewe

Design and Implementation of a Scalable Crowdsensing Platform for Geospatial Data

In the recent years smart devices and small low-powered sensors are becoming ubiquitous and nowadays everything is connected altogether, which is a promising foundation for crowdsensing of data related to various environmental and societal phenomena. Very often, such data is especially meaningful when related to time and location, which is possible by already equipped GPS capabilities of modern smart devices. However, in order to gain knowledge from high-volume crowd-sensed data, it has to be collected and stored in a central platform, where it can be processed and transformed for various use cases. Conventional approaches built around classical relational databases and monolithic backends, that load and process the geospatial data on a per-request basis are not suitable for supporting the data requests of a large crowd willing to visualize phenomena. The possibly millions of data points introduce challenges for calculation, data-transfer and visualization on smartphones with limited graphics performance. We have created an architectural design, which combines a cloud-native approach with Big Data concepts used in the Internet of Things. The architectural design can be used as a generic foundation to implement a scalable backend for a platform, that covers aspects important for crowdsensing, such as social- and incentive features, as well as a sophisticated stream processing concept to calculate incoming measurement data and store pre-aggregated results. The calculation is based on a global grid system to index geospatial data for efficient aggregation and building a hierarchical geospatial relationship of averaged values, that can be directly used to rapidly and efficiently provide data on requests for visualization. We introduce the Noisemap project as an exemplary use case of such a platform and elaborate on certain requirements and challenges also related to frontend implementations. The goal of the project is to collect crowd-sensed noise measurements via smartphones and provide users information and a visualization of noise levels in their environment, which requires storing and processing in a central platform. A prototypic implementation for the measurement context of the Noisemap project is showing that the architectural design is indeed feasible to realize

Design and implementation of an indoor modeling method through crowdsensing

While automatic modeling and mapping of outdoor environments is well-established, the indoor equivalent of automated generation of building floor plans poses a challenge. In fact, outdoor localization is commonly available and inexpensive through the existing satellite positioning systems, such as GPS and Galileo. However, these technologies are not applicable in indoor environments, since a direct line of sight to the satellites, orbiting the globes, is required. As a substitution, the technical literature comprises several proposals for the development of simultaneous indoor localization and mapping (SLAM). In these approaches, the authors mostly exploit indoor resources such as the WiFi access points and the mobile smart devices carried by individuals in the indoor environment. Collecting data from several mobile devices is referred to as crowdsensing. To enable the generation of two-dimensional (2D) as well as three-dimensional (3D) maps, we propose crowdsensing of point clouds, which are 3D data structures of points in space. For localization, we integrate two features of a recently developed mobile device, called Project Tango. Specifically, the Tango platform provides two main technologies for reliable localization, namely motion tracking and area learning. Moreover, Tango-powered devices provide us with the ability to collect point clouds though a third technology, called depth perception. In the past few years, spatial data obtained from range imaging was used to generate indoor maps. Nevertheless, range images are expensive and not always available. The required equipment, e.g. laser range scanners, are both expensive in procurement and require trained personnel for proper setup and operation. In this thesis, we aim for obtaining spatial point clouds via crowdsensing. The main idea is to use sensor data which can be scanned by volunteering individuals using easy to handle mobile devices. Specifically, we depend on depth perception capabilities as provided by Google Tango-powered tablet computers. A crowdsensing infrastructure assigns scanning tasks to individuals carrying a Tango device. Execution of such a task consists of taking scans of e.g. offices in a public building. The scanning results contain both spatial information about the room layout and its position. Energy consumption on the mobile device is reduced by applying Octree compression to the scanned point clouds, which results in a significant reduction of the amount of data, which has to be transferred to a back-end server. Afterwards, the back-end is responsible for assembling the received scans and the extraction of an indoors model. The modeling process - developed in this thesis - comprises two-phases. First, we extract a basic model from the obtained point clouds, which may contain outliers, inaccuracies and gaps. In the second phase, we refine the model by exploiting formal grammars. It is worth to mention here that we are the first to exploit formal grammars as a model fitting tool. We feed the information obtained in the first phase to an indoors grammar, which has been developed in the ComNSense project, University of Stuttgart. The resultant model both contains much less deviations from the ground truth and provides improved robustness against aberrations with respect to localization during the scanning process. Thus, instead of scanning multiple point clouds per room, we need only one scan to be able to construct an indoor map. During evaluation of this process, using scans of offices of our department, we were able to reproduce a model which is very close to the ground truth