Ajay A. Deshpande

The dubuque electricity portal: evaluation of a city-scale residential electricity consumption feedback system

This paper describes the Dubuque Electricity Portal, a city-scale system aimed at supporting voluntary reductions of electricity consumption. The Portal provided each household with fine-grained feedback on its electricity use, as well as using incentives, comparisons, and goal setting to encourage conservation. Logs, a survey and interviews were used to evaluate the user experience of the Portal during a 20-week pilot with 765 volunteer households. Although the volunteers had already made a wide range of changes to conserve electricity prior to the pilot, those who used the Portal decreased their electricity use by about 3.7%. They also reported increased understanding of their usage, and reported taking an array of actions - both changing their behavior and their electricity infrastructure. The paper discusses the experience of the systems users, and describes challenges for the design of ECF systems, including balancing accessibility and security, a preference for time-based visualizations, and the advisability of multiple modes of feedback, incentives and information presentation.

Urban Street Lighting Infrastructure Monitoring Using a Mobile Sensor Platform

We present a system for collecting and analyzing information on street lighting infrastructure. We develop a car-mounted sensor platform that enables collection and logging of data on street lights during night-time drive-bys. We address several signal processing problems that are key to mapping street illumination levels, identifying street lamps, estimating their heights, and geotagging them. Specifically, we highlight an image recognition algorithm to identify street lamps from the video data collected by the sensor platform and its subsequent use in estimating the heights of street lamps. We also outline a framework to improve vehicle location estimates by combining sensor observations in an extended Kalman filter framework. Our eventual goal is to develop a semi-live virtual 3-D street lighting model at urban scale that enables citizens and decision makers to assess and optimize performance of nighttime street lighting.

A pseudopolynomial time O (log n )-approximation algorithm for art gallery problems

In this paper, we give a O(log copt)-approximation algorithm for the point guard problem where copt is the optimal number of guards. Our algorithm runs in time polynomial in n, the number of walls of the art gallery and the spread Δ, which is defined as the ratio between the longest and shortest pairwise distances. Our algorithm is pseudopolynomial in the sense that it is polynomial in the spread Δ as opposed to polylogarithmic in the spread Δ, which could be exponential in the number of bits required to represent the vertex positions. The special subdivision procedure in our algorithm finds a finite set of potential guard-locations such that the optimal solution to the art gallery problem where guards are restricted to this set is at most 3copt. We use a set cover cum VC-dimension based algorithm to solve this restricted problem approximately.

Distributed coverage control for mobile sensors with location-dependent sensing models

This paper addresses the problem of coverage control of a network of mobile sensors. In the current literature, this is commonly formulated as a locational optimization problem under the assumption that sensing performance is independent of the locations of sensors. We extend this work to a more general framework where the sensor model is location-dependent. We propose a distributed control law and coordination algorithm. If the global sensing performance function is known a priori, we prove that the algorithm is guaranteed to converge. To validate this algorithm, we conduct experiments with indoor and outdoor deployments of Cyclops cameras and model its sensing performance. This model is used to simulate deployments on 1D pathways and study the coverage obtained. We also examine the coverage in the case when the global sensing function is not known and is estimated in an online fashion.

Optimal coverage of an infrastructure network using sensors with distance-decaying sensing quality

Motivated by recent applications of wireless sensor networks in monitoring infrastructure networks, we address the problem of optimal coverage of infrastructure networks using sensors whose sensing performance decays with distance. We show that this problem can be formulated as a continuous p-median problem on networks. The literature has addressed the discrete p-median problem on networks and in continuum domains, and the continuous p-median problem in continuum domains extensively. However, in-depth analysis of the continuous p-median problem on networks has been lacking. With the sensing performance model that decays with distance, each sensor covers a region equivalent to its Voronoi partition on the network in terms of the shortest path distance metric. Using Voronoi partitions, we define a directional partial derivative of the coverage metric with respect to a sensors location. We then propose a gradient descent algorithm to obtain a locally optimal solution with guaranteed convergence. The quality of an optimal solution depends on the choice of the initial configuration of sensors. We obtain an initial configuration using two approaches: by solving the discrete p-median problem on a lumped network and by random sampling. We consider two methods of random sampling: uniform sampling and D^2-sampling. The first approach with the initial solution of the discrete p-median problem leads to the best coverage performance for large networks, but at the cost of high running time. We also observe that the gradient descent on the initial solution with the D^2-sampling method yields a solution that is within at most 7% of the previous solution and with much shorter running time.

Smartphone-Based Wheel Imbalance Detection

Onboard sensors in smartphones present new opportunities for vehicular sensing. In this paper, we explore a novel application of fault detection in wheels, tires and related suspension components in vehicles. We present a technique for in-situ wheel imbalance detection using accelerometer data obtained from a smartphone mounted on the dashboard of a vehicle having balanced and imbalanced wheel conditions. The lack of observable distinguishing features in a Fourier Transform (FT) of the accelerometer data necessitates the use of supervised machine learning techniques for imbalance detection. We demonstrate that a classification tree model built using Fourier feature data achieves 79% classification accuracy on test data. We further demonstrate that a Principal Component Analysis (PCA) transformation of the Fourier features helps uncover a unique observable excitation frequency for imbalance detection. We show that a classification tree model trained on randomized PCA features achieves greater than 90% accuracy on test data. Results demonstrate that the presence or absence of wheel imbalance can be accurately detected on at least two vehicles of different make and model. Sensitivity of the technique to different road and traffic conditions is examined. Future research directions are also discussed.Copyright

Vehicular engine oil service life characterization using On-Board Diagnostic (OBD) sensor data

Standardized vehicular On-Board Diagnostics (OBD) systems offer access to information commonly used for fault notification and reactive diagnostic services. Recently, there have been efforts to use OBD data to diagnose and predict faults prior to catastrophic failure events. Engine oil service life, a parameter directly related to engine longevity, is difficult to measure conventionally. We show that the rate of engine coolant temperature rise, readily obtainable through the OBD suite, can serve as a proxy to indicate the remaining engine oil life. We demonstrate consistent results for one vehicle under similar environmental and unloaded engine operating conditions. We also examine the validity of this approach under varying environmental and engine loading conditions with tests on a second vehicle.

Smartphone-Based Vehicular Tire Pressure and Condition Monitoring

Proper tire maintenance is key to the safe and efficient operation of vehicles. Without frequent inspection, pressure can drop and tread can wear down, increasing drag and reducing traction. While modern cars make use of Tire Pressure Monitoring Systems (TPMS), these systems may only identify differential changes in pressure and older vehicles lack sensing entirely. To address this problem, we applied mobile phone accelerometer and GPS data to calculate predicted and true wheel rotational frequencies, and therefore infer tire circumference and related pressure or tread depth. Through tree-based classification, we were able to identify from among five tire states with 20% increases or decreases in tire pressure with 80% accuracy using only a mobile phone. The models demonstrated are robust to road surface and the main differentiating element, the GPS/accelerometer velocity ratio, has proven generalizable across vehicle makes, models, and tire sizes.

Partner-marketing using geo-social media data for smarter commerce

As a result of significantly growing competition from online-only retailers, physical store retailers have resorted to measures such as discount coupons, online price matching, sale events, and a seamless omnichannel customer experience. One important, often overlooked method is partner-marketing, where a retailer promotes products or services offered by other merchants. The main challenge in partner-marketing is finding partners, i.e., determining whom a retailer should choose as partners among many choices. In this paper, we discuss how a retailer can make use of rich information from mobile and geo-social media to find potential partners in a geographic region. The explosion of mobile and geo-social media in recent years has led to increasing insights about consumers’ footprintsVwhich shops they visit, where they travel, and where they live and work. In addition, geo-social data also reveals which regional merchants are popular and why. We propose a series of partner-finding techniques that leverage geo-social data at increasing granularity and discuss analytics to rank potential partners. We present compelling business case studies using real data from Foursquare, a social networking website. We also discuss how retailers can combine geo-social data with traditional data such as demographics, weather information, and their own marketing databases to personalize partner-marketing.

Stable arrangements of mobile sensors for sampling physical fields

Todays wireless sensor nodes can be easily attached to mobile platforms such as robots, cars and cell phones enabling pervasive sensing of physical fields (say of temperature, vibrations, air quality and chemicals). We address the sensor arrangement problem, i.e. when and where sensors should take samples to obtain a good estimate of a field using mobile sensors. In particular, we focus on incidentally mobile sensors that move passively under the influence of the environment (e.g. sensors attached to floating buoys, cars and smartphones carried by humans). We model the field as a linear combination of known basis functions. Given the samples, we use a linear estimator to find unknown coefficients of the basis functions. We formulate the sensor arrangement problem as one of finding suitably characterized classes of sensor arrangements that lead to a stable reconstruction of the field. We consider a family of multidimensional δ-dense sensor arrangements, where any square disc of size δ contains at least one sample, and derive sufficiency conditions for the arrangement to be stable. δ-dense sensor arrangements are geometrically intuitive and are easily compatible with the incidental mobility of sensors in many situations. We present simulation results on the stability of such arrangements for two-dimensional basis functions. We also present an example for constructing basis functions through proper orthogonal decompositions for a one-dimensional chemical diffusion field in a heterogeneous medium, which are later used for field estimation through δ-dense sampling.