Yuanchao Shu

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

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 × less energy in tracking compared to WiFi-based tracking.

TOC: Localizing Wireless Rechargeable Sensors with Time of Charge

Wireless rechargeable sensor network is a promising platform for long-term applications such as inventory management, supply chain monitoring and so on. For these applications, sensor localization is one of the most fundamental challenges. Different from traditional sensor node, wireless rechargeable sensor has to be charged above a voltage level by the wireless charger in order to support its sensing, computation and communication operations. In this work, we consider the scenario where a mobile charger stops at different positions to charge sensors, and propose a novel localization design that utilizes the unique Time of Charge (TOC) sequences among wireless rechargeable sensors. Specifically, we introduce two efficient region dividing methods, Inter-node Division and Inter-area Division, to exploit TOC differences from both temporal and spatial dimensions to localize individual sensor nodes. To further optimize the system performance, we introduce both an optimal charger stop planning algorithm for single sensor case and a suboptimal charger stop planning algorithm for the generic multisensor scenario with a provable performance bound. We have extensively evaluated our design by both testbed experiments and large-scale simulations. The experiment and simulation results show that by as less as 5 stops, our design can achieve sub-meter accuracy and the performance is robust under various system conditions.

Gradient-Based Fingerprinting for Indoor Localization and Tracking

Of the different branches of indoor localization research, WiFi fingerprinting has drawn significant attention over the past decade. These localization systems function by comparing WiFi received signal strength indicator (RSSI) and a pre-established location-specific fingerprint map. However, due to the time-variant wireless signal strength, the RSSI fingerprint map needs to be calibrated periodically, incurring high labor and time costs. In addition, biased RSSI measurements across devices along with transmission power control techniques of WiFi routers further undermine the fidelity of existing fingerprint-based localization systems. To remedy these problems, we propose GradIent FingerprinTing (GIFT) which leverages a more stable RSSI gradient. GIFT first builds a gradient-based fingerprint map (Gmap) by comparing absolute RSSI values at nearby positions, and then runs an online extended particle filter (EPF) to localize the user/device. By incorporating Gmap, GIFT is more adaptive to the time-variant RSSI in indoor environments, thus effectively reducing the overhead of fingerprint map calibration. We implemented GIFT on Android smartphones and tablets, and conducted extensive experiments in a five-story campus building. GIFT is shown to achieve an 80 percentile accuracy of 5.6 m with dynamic WiFi signals.

Privacy and performance trade-off in cyber-physical systems

The increasing number of instances of privacy leakage in CPSs and the corresponding serious consequences have caused great worry in our society. In most privacy preserving mechanisms proposed to protect sensitive individual information, system performance is compromised at the same time. In this article, we consider the trade-off between individual privacy and system performance in CPSs. After introducing the CPS architecture and the basic definition of differential privacy, we formulate the performance optimization problem subject to a given differential privacy requirement. For a simplified system, we derive the closed-form optimal system performance under the desired privacy requirement. Simulation results are provided to verify the proposed mechanism, which balances the trade-off between system performance and privacy. We also identify future research topics on the privacy preserving problem in CPSs.

Dynamic Authentication with Sensory Information for the Access Control Systems

Access card authentication is critical and essential for many modern access control systems, which have been widely deployed in various government, commercial, and residential environments. However, due to the static identification information exchange among the access cards and access control clients, it is very challenging to fight against access control system breaches due to reasons such as loss, stolen or unauthorized duplications of the access cards. Although advanced biometric authentication methods such as fingerprint and iris identification can further identify the user who is requesting authorization, they incur high system costs and access privileges cannot be transferred among trusted users. In this work, we introduce a dynamic authentication with sensory information for the access control systems. By combining sensory information obtained from onboard sensors on the access cards as well as the original encoded identification information, we are able to effectively tackle the problems such as access card loss, stolen, and duplication. Our solution is backward-compatible with existing access control systems and significantly increases the key spaces for authentication. We theoretically demonstrate the potential key space increases with sensory information of different sensors and empirically demonstrate simple rotations can increase key space by more than 1,000,000 times with an authentication accuracy of 90 percent. We performed extensive simulations under various environment settings and implemented our design on WISP to experimentally verify the system performance.

Group-based discovery in low-duty-cycle mobile sensor networks

Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each others vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%~8.8% increase in energy consumption.

Mobility Modeling and Prediction in Bike-Sharing Systems

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users and operators. The primary issue among these problems is the uneven distribution of bicycles caused by the ever-changing usage and (available) supply. This bicycle imbalance issue necessitates efficient bike re-balancing strategies, which depends highly on bicycle mobility modeling and prediction. In this paper, for the first time, we propose a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devise a traffic prediction mechanism on a per-station basis with sub-hour granularity. We extensively evaluated the performance of our design through a one-year dataset from the worlds largest public bike-sharing system (BSS) with more than 2800 stations and over 103 million check in/out records. Evaluation results show an 85 percentile relative error of 0.6 for both check in and check out prediction. We believe this new mobility modeling and prediction approach can advance the bike re-balancing algorithm design and pave the way for the rapid deployment and adoption of bike-sharing systems across the globe.

Joint Energy Replenishment and Operation Scheduling in Wireless Rechargeable Sensor Networks

Wireless charging is a promising way to solve the energy constraint problem in sensor networks. While extensive efforts have been made to improve the performance of charging and communication in wireless rechargeable sensor networks (WRSNs), little has been done to address the operation scheduling problem. To fill this void, we propose a joint energy replenishment and scheduling mechanism so as to maximize the network lifetime while making strict sensing guarantees in the WRSN. We first formulate the problem in a general 2-D space and prove its NP-completeness. We then devise an f-approximate scheduling mechanism by transforming the classical minimum set cover problem and develop an optimal energy-replenish strategy based on the energy consumption of nodes returned by the scheduling mechanism. Large-scale simulation results validate our design and show a

Sensory-data-enhanced authentication for RFID-based access control systems

39.2\%

Demo: WISP-based access control combining electronic and mechanical authentication

improvement of network lifetime over a baseline method.