Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 $\times$ less energy in tracking compared to WiFi-based tracking.

Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing

A growing number of sensors on smart mobile devices has led to rapid development of various mobile applications using location-based or context-aware services. Typically, outdoor localization techniques have relied on GPS or on cellular infrastructure support. While GPS gives high positioning accuracy, it can quickly deplete the battery on the device. On the other hand, base station based localization has low accuracy. In search of alternative techniques for outdoor localization, several approaches have explored the use of data gathered from other available sensors, like accelerometer, microphone, compass, and even daily patterns of usage, to identify unique signatures that can locate a device. Signatures, or fingerprints of an area, are hidden cues existing around a user's environment. However, under different operating scenarios, fingerprint-based localization techniques have variable performance in terms of accuracy, latency of detection, battery usage. The main contribution of this survey is to present a classification of existing fingerprint-based localization approaches which intelligently sense and match different clues from the environment for location identification. We describe how each fingerprinting technique works, followed by a review of the merits and demerits of the systems built based on these techniques. We conclude by identifying several improvements and application domain for fingerprinting based localization.

A Survey of Fingerprint-Based Outdoor Localization

Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as wireless sensor networks (WSNs) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location-based services. The development of sensor technology, processing techniques, and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization, namely self-determining and training dependent approaches. Finally, various open research issues related to localization schemes for IoT are compared and various directions for future research are proposed.

Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure

Detecting how a vehicle is steered and then alarming drivers in real time is of utmost importance to the vehicle and the driver's safety, since fatal accidents are often caused by dan- gerous steering. Existing solutions for detecting dangerous maneuvers are implemented either in only high-end vehicles or on smartphones as mobile applications. However, most of them rely on the use of cameras, the performance of which is seriously constrained by their high visibility requirement. Moreover, such an over/sole-reliance on the use of cameras can be a distraction to the driver. To alleviate these problems, we develop a vehicle steering detection middleware called V-Sense which can run on commodity smartphones without additional sensors or infrastructure support. Instead of using cameras, the core of V-Sense/ senses a vehicle's steering by only utilizing non-vision sensors on the smartphone. We design and evaluate algorithms for detecting and differentiating various vehicle maneuvers, including lane-changes, turns, and driving on curvy roads. Since V-Sense does not rely on use of cameras, its detection of vehicle steering is not affected by the (in)visibility of road objects or other vehicles. We first detail the design, implementation and evaluation of V-Sense and then demonstrate its practicality with two prevalent use cases: camera-free steering detection and fine-grained lane guidance. Our extensive evaluation results show that V-Sense is accurate in determining and differentiating various steering maneuvers, and is thus useful for a wide range of safety-assistance applications without additional sensors or infrastructure.

Invisible Sensing of Vehicle Steering with Smartphones

Mobile navigation services are used by billions of users around globe today. While GPS spoofing is a known threat, it is not yet clear if spoofing attacks can truly manipulate road navigation systems. Existing works primarily focus on simple attacks by randomly setting user locations, which can easily trigger a routing instruction that contradicts with the physical road condition (i.e., easily noticeable).

In this paper, we explore the feasibility of a stealthy manipulation attack against road navigation systems. The goal is to trigger the fake turn-by-turn navigation to guide the victim to a wrong destination without being noticed. Our key idea is to slightly shift the GPS location so that the fake navigation route matches the shape of the actual roads and trigger physically possible instructions. To demonstrate the feasibility, we first perform controlled measurements by implementing a portable GPS spoofer and testing on real cars. Then, we design a searching algorithm to compute the GPS shift and the victim routes in real time. We perform extensive evaluations using a trace-driven simulation (600 taxi traces in Manhattan and Boston), and then validate the complete attack via real-world driving tests (attacking our own car). Finally, we conduct deceptive user studies using a driving simulator in both the US and China. We show that 95% of the participants follow the navigation to the wrong destination without recognizing the attack. We use the results to discuss countermeasures moving forward.

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All your GPS are belong to us: towards stealthy manipulation of road navigation systems

We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.

SmartLoc: push the limit of the inertial sensor based metropolitan localization using smartphone

In a wireless body area sensor network (WBAN), biosensors are implanted or worn on an individual to acquire medical data for a clinical diagnosis or physical monitoring. Traditionally, authentication and cryptography techniques are used to achieve the data confidentiality and privacy of a WBAN system. However, for WBAN systems, besides data itself, the pattern of data transmission may leak critical information about the user. For example, various medical sensors deliver data in different patterns and these patterns leak many critical facts, e.g., what sensors are used (which implies diseases the individual may have), and how frequent the sensor is sampling (which implies how urgent some abnormal observation is about a patient). In this work, to address this challenge, we design a regulator, which packs real data session into a type-independent transmission model at transmission layer. All valid data packets are equably sent at the frequency defined by the regulator and at the same length to clutter the inherent pace of valid data transmission and other parameters.We also propose a strategy PAS to minimize the overhead while preventing attackers from locating the patients. Our extensive experiments validate our regulator and PAS design.

Lan Yao

Papers

SmartLoc: push the limit of the inertial sensor based metropolitan localization using smartphone

Pattern Regulator: Protecting Temporal Usage Privacy for Wireless Body Area Sensor Networks