Nam Pham

GreenGPS: a participatory sensing fuel-efficient maps application

This paper develops a navigation service, called GreenGPS, that uses participatory sensing data to map fuel consumption on city streets, allowing drivers to find the most fuel efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular fuel consumption sensors, available via the OBD-II interface standardized in all vehicles sold in the US since 1996. The interface gives access to most gauges and engine instrumentation. The most fuel-efficient route does not always coincide with the shortest or fastest routes, and may be a function of vehicle type. Our experimental study shows that a participatory sensing system can influence routing decisions of individual users and also answers two questions related to the viability of the new service. First, can it survive conditions of sparse deployment? Second, how much fuel can it save? A challenge in participatory sensing is to generalize from sparse sampling of high-dimensional spaces to produce compact descriptions of complex phenomena. We illustrate this by developing models that can predict fuel consumption of a set of sixteen different cars on the streets of the city of Urbana-Champaign. We provide experimental results from data collection suggesting that a 1% average prediction error is attainable and that an average 10% savings in fuel can be achieved by choosing the right route.

Privacy-aware regression modeling of participatory sensing data

Many participatory sensing applications use data collected by participants to construct a public model of a system or phenomenon. For example, a health application might compute a model relating exercise and diet to amount of weight loss. While the ultimately computed model could be public, the individual input and output data traces used to construct it may be private data of participants (e.g., their individual food intake, lifestyle choices, and resulting weight). This paper proposes and experimentally studies a technique that attempts to keep such input and output data traces private, while allowing accurate model construction. This is significantly different from perturbation-based techniques in that no noise is added. The main contribution of the paper is to show a certain data transformation at the client side that helps keeping the client data private while not introducing any additional error to model construction. We particularly focus on linear regression models which are widely used in participatory sensing applications. We use the data set from a map-based participatory sensing service to evaluate our scheme. The service in question is a green navigation service that constructs regression models from participant data to predict the fuel consumption of vehicles on road segments. We evaluate our proposed mechanism by providing empirical evidence that: i) an individual data trace is generally hard to reconstruct with any reasonable accuracy, and ii) the regression model constructed using the transformed traces has a much smaller error than one based on additive data-perturbation schemes.

Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing

The proliferation of sensors in devices of frequent use, such as mobile phones, offers unprecedented opportunities for forming self-selected communities around shared sensory data pools that enable community specific applications of mutual interest. Such applications have recently been termed participatory sensing. An important category of participatory sensing applications is one that construct maps of different phenomena (e.g., traffic speed, pollution) using vehicular participatory sensing. An example is sharing data from GPS-enabled cell-phones to map traffic or noise patterns. Concerns with data privacy are a key impediment to the proliferation of such applications. This paper presents theoretical foundations, a system implementation, and an experimental evaluation of a perturbation-based mechanism for ensuring privacy of location-tagged participatory sensing data while allowing correct reconstruction of community statistics of interest (computed from shared perturbed data). The system is applied to construct accurate traffic speed maps in a small campus town from shared GPS data of participating vehicles, where the individual vehicles are allowed to “lie” about their actual location and speed at all times. An extensive evaluation demonstrates the efficacy of the approach in concealing multi-dimensional, correlated, time-series data while allowing for accurate reconstruction of spatial statistics.

The Sparse Regression Cube: A Reliable Modeling Technique for Open Cyber-Physical Systems

Understanding the end-to-end behavior of complex systems where computing technology interacts with physical world properties is a core challenge in cyber-physical computing. This paper develops a hierarchical modeling methodology for open cyber-physical systems that combines techniques in estimation theory with those in data mining to reliably capture complex system behavior at different levels of abstraction. Our technique is also novel in the sense that it provides a measure of confidence in predictions. An application to green transportation is discussed, where the goal is to reduce vehicular fuel consumption and carbon footprint. First-principle models of cyber-physical systems can be very complex and include a large number of parameters, whereas empirical regression models are often unreliable when a high number of parameters is involved. Our new modeling technique, called the Sparse Regression Cube, simultaneously (i) partitions sparse, high-dimensional measurements into subspaces within which reliable linear regression models apply and (ii) determines the best reliable model for each partition, quantifying uncertainty in output prediction. Evaluation results show that the framework significantly improves modeling accuracy compared to previous approaches and correctly quantifies prediction error, while maintaining high efficiency and scalability.

Joint Optimization of Computing and Cooling Energy: Analytic Model and a Machine Room Case Study

Total energy minimization in data centers (including both computing and cooling energy) requires modeling the interactions between computing decisions (such as load distribution) and heat transfer in the room, since load acts as heat sources whose distribution in space affects cooling energy. This paper presents the first closed-form analytic optimal solution for load distribution in a machine rack that minimizes the sum of computing and cooling energy. We show that by considering actuation knobs on both computing and cooling sides, it is possible to reduce energy cost comparing to state of the art solutions that do not offer holistic energy optimization. The above can be achieved while meeting both throughput requirements and maximum CPU temperature constraints. Using a thorough evaluation on a real test bed of 20 machines, we demonstrate that our simple model adequately captures the thermal behavior and energy consumption of the system. We further show that our approach saves more energy compared to the state of the art in the field.

Virtual Battery: An Energy Reserve Abstraction for Embedded Sensor Networks

This paper introduces the abstraction of energy reserves for sensor networks that virtualizes energy sources. It gives each of several applications sharing a platform the illusion of having its own private energy source. Energy virtualization is the next logical step in embedded systems after visualizing communication links and CPU capacity. Energy virtualization has not been addressed in past sensor network literature because most current wireless sensor networks feature single-user applications. To amortize deployment costs, future sensor networks, deployed in remote or hard- to-access areas, will likely be leveraged by scientists from different disciplines, each having their independent application for their individual research purposes. Platforms, planned for such deployment, will befitted with the union of sensors needed, but independent applications will share the remaining resources such as in-field storage and communication bandwidth, calling for quotas and isolation mechanisms. The most expensive resource shared in sensor networks is energy. This paper provides an energy isolation mechanism, called the virtual battery, that logically divides energy among applications to provide each its private energy reserve. An application can manage its private energy independently as if it were running alone on the platform. The application is terminated when its reserve is depleted. We implement and evaluate this abstraction on MicaZ motes running LiteOS. Our results show that the virtual battery mechanism succeeds at exporting the private reserve abstraction accurately and at a low overhead.

On Bounding Data Stream Privacy in Distributed Cyber-physical Systems

This paper derives fundamental bounds on privacy achievable in future human-centric cyber-physical systems, where time-series sensor data are shared among individuals to compute aggregate information of mutual interest. For example, individual GPS-trajectories may be shared to compute average traffic speed at different locations. An optimal trade-off is explored between individual user privacy, achieved by perturbing data prior to sharing, and the corresponding accuracy of computed aggregate information. The work is motivated by an emergent category of cyber-physical applications that involves large-scale interaction between humans, networked engineered artifacts, and the physical world. These applications are brought about by the proliferation of personal sensing devices of everyday use, leading to unprecedented opportunities for sensory data collection and sharing. The collection of sensory data from large numbers of participants offers privacy as a major new cyber-physical system challenge. In this paper, we propose a novel privacy measure, based on mutual information, and derive a perturbation algorithm, to apply prior to data sharing, that guarantees a least upper bound on the privacy measure. The new algorithm effectively hides individual user data by optimally perturbing the time-series using knowledge of only the mean and the covariance of the original data. We evaluate it using both synthetic data and collected real application data. The results show that the method significantly improves the trade-off between privacy and the accuracy of reconstruction of aggregate information from shared perturbed data.

Robust Dynamic Human Activity Recognition Based on Relative Energy Allocation

This paper develops an algorithm for robust human activity recognition in the face of imprecise sensor placement. It is motivated by the emerging body sensor networksthat monitor human activities (as opposed to environmental phenomena) for medical, entertainment, health-and-wellness, training, assisted-living, or entertainment reasons. Activities such as sitting, writing, and walking have been successfully inferred from data provided by body-worn accelerometers. A common concern with previous approaches is their sensitivity with respect to sensor placement. This paper makes two contributions. First, we explicitly address robustness of human activity recognition with respect to changes in accelerometer orientation. We develop a novel set of features based on relative activity-specific body-energy allocation and successfully apply them to recognize human activities in the presence of imprecise sensor placement. Second, we evaluate the accuracy of the approach using empirical data from body-worn sensors.