Avinash Kalyanaraman

GFFS — THE XSEDE GLOBAL FEDERATED FILE SYSTEM

Federated, secure, standardized, scalable, and transparent mechanism to access and share resources, particularly data resources, across organizational boundaries that does not require application modification and does not disrupt existing data access patterns has been needed for some time in the computational science community. The Global Federated File System (GFFS) addresses this need and is a foundational component of the NSF-funded eXtreme Science and Engineering Discovery Environment (XSEDE) program. The GFFS allows user applications to access (create, read, update, delete) remote resources in a location-transparent fashion. Existing applications, whether they are statically linked binaries, dynamically linked binaries, or scripts (shell, PERL, Python), can access resources anywhere in the GFFS without modification (subject to access control). In this paper we present an overview of the GFFS and its most common use cases: accessing data at an NSF center from a home or campus, accessing data on a camp...

Peripheral WiFi Vision: Exploiting Multipath Reflections for More Sensitive Human Sensing

A large amount of energy could be saved by detecting home occupancy and automatically controlling the lights, and HVAC. Existing occupancy sensors can detect the motion of people but cannot detect people when they are stationary. In this paper, we present a system called Peripheral WiFi Vision (PeriFi), which exploits multipath reflections as individual spatial sensors to increase the sensitivity of the conventional approaches. PeriFi analyzes each multipath component independently, increasing sensitivity so it can directly sense both moving and non-moving occupants. Our evaluations for 6 physical configurations with 11 different occupancy states show that PeriFi can achieve 96.7% accuracy, which translates to nearly 30% improvement over the conventional approaches.

Poster: Occupancy State Detection using WiFi Signals

A large amount of energy could be saved by detecting home occupancy and automatically controlling the lights, HVAC, water heating, and other mechanical systems. Existing systems rely on motion information, which usually fail to detect occupied rooms with stationary people. In this project, we study the possibility of converting commodity WiFi access points to occupancy sensors by exploiting multipath reflections as individual spatial sensors. The proposed method measures fine-grained distortions caused by human body on phase and amplitude of WiFi signals. Our initial results suggest that formulating WiFi parameters into angle of arrival provides a more sensitive metric to measure occupancy.

TransTrack: Tracking Multiple Targets by Sensing Their Zone Transitions

In this paper, we consider a variant of the multi-target tracking problem in which the tracking region is divided into zones and targets can only be monitored as they transition between these zones. We call this the transition tracking problem. The key challenge in Transition Tracking is to estimate the number of targets in the tracking region without being able to sense all targets simultaneously. In this paper, we propose an approach to the Transition Tracking problem called TransTrack. Unlike most other tracking algorithms that maximize the likelihood of the sensor data, TransTrack applies penalty functions to find the minimum number of targets that can explain the sensor data. These penalties allow tracks with larger numbers of targets only ifthey have sufficiently fewer errors than other, alternative tracks. To evaluate this approach, we apply TransTrack to a data set containing 3275 transitions between rooms in a home. We observe an average room tracking accuracy of up to 94.5%.

An event-based data fusion algorithm for smart cities

The last decade has seen a considerable increase in the number of sensors we interact with on a daily basis. However, it is not always possible for a single sensing system to capture the complete story. While statically mounted infrastructure sensors typically capture the what, where, how much etc aspects of a detected event, e.g. (what appliance was used, how much energy did it consume), they do not always answer the who question. On the other hand, the advent of wearables has helped answer the what and who aspects - e.g. (who used the appliance). Fusing such sensor streams that observe the same event but different attributes of it, opens up many interesting applications. In this paper, we present a globally optimal data fusion algorithm for such pairs of systems, and show why traditional bipartite algorithms do not work. We evaluate our algorithm against two greedy baselines and show that our algorithm has lesser variance in the presence of time skew, false positives and false negatives.

Forma Track: Tracking People based on Body Shape

Knowledge of a person’s whereabouts in the home is key to context-aware applications, but many people do not want to carry or wear a tag or mobile device in the home. Therefore, many tracking systems are now using so-called weak biometrics such as height, weight, and width. In this paper, we propose to use body shape as a weak biometric, differentiating people based on features such as head size, shoulder size, or torso size. The basic idea is to scan the body with a radar sensor and to compute the reflection profile: the amount of energy that reflects back from each part of the body. Many people have different body shapes even if they have the same height, weight, or width, which makes body shape a stronger biometric. We built a proof-of-concept system called FormaTrack to test this approach, and evaluate using eight participants of varying height and weight. We collected over 2800 observations while capturing a wide range of factors such as clothing, hats, shoes, and backpacks. Results show that FormaTrack can achieve a precision, recall, direction and identity accuracy (over all possible groups of 2 people) of 100%, 99.86%, 99.7% and 95.3% respectively. Results indicate that FormaTrack can achieve over 99% tracking accuracy with 2 people in a home with 5 or more rooms.

Automatic rock climbing route inference using wearables

Rock climbing is a relatively new, but highly popular sport. In the current state of the art, there is no technology that can record climbing routes and statistics about a climbers exercises. Taking photographs of routes is the state of art method of sharing descriptions of climbing routes with other climbing enthusiasts. We propose to automatically track climbing routes using wearable devices placed on the hands and feet of a climber. Each wearable device has an IMU sensor and a barometer on it. In addition, the devices on the hands have EMG sensors on them. The combination of these sensors can detect when a person makes contact with a hold, and the relative position of the hold contacted with respect to the current hold, and the type of hold. A map of the route, including annotations of the hand hold and foot holds used by the climber, can be inferred by performing dead reckoning based navigation for each of the devices.

An Empirical Design Space Analysis of Doorway Tracking Systems for Real-World Environments

Doorway tracking systems track people’s room location by instrumenting the doorways rather than instrumenting the rooms themselves—resulting in fewer sensors and less monitoring while still providing location information on occupants. In this article, we explore what is required to make doorway tracking a practical solution. We break a doorway tracking system into multiple independent design components, including both sensor and algorithmic design. Informed by this design, we construct a doorway tracking system and analyze how different combinations of these design components affect tracking accuracy. We perform a six-day in situ study in a ten-room house with two volunteers to analyze how these design components respond to the natural types and frequencies of errors in a real-world setting. To reflect the needs of different application classes, we analyze these design components using three different evaluation metrics: room accuracy, duration accuracy, and transition accuracy. Results indicate that doorway tracking can achieve 99.5% room accuracy on average in controlled settings and 96% room accuracy in in situ settings. This is contrasted against the 76% in situ setting room accuracy of Doorjamb, a doorway tracking system whose design implements only a limited number of components in our proposed doorway tracking system design space. We describe the differences between the data in the in situ and controlled settings, and provide guidelines about how to design a doorway tracking system for a given application’s accuracy requirements.

Multipath Triangulation: Decimeter-level WiFi Localization and Orientation with a Single Unaided Receiver

Decimeter-level localization has become a reality, in part due to the ability to eliminate the effects of multipath interference. In this paper, we demonstrate the ability to use multipath reflections to enhance localization rather than throwing them away. We present Multipath Triangulation, a new localization technique that uses multipath reflections to localize a target device with a single receiver that does not require any form of coordination with any other devices. In this paper, we leverage multipath triangulation to build the first decimeter-level WiFi localization system, called MonoLoco, that requires only a single access point (AP) and a single channel, and does not impose any overhead, data sharing, or coordination protocols beyond standard WiFi communication. As a bonus, it also determines the orientation of the target relative to the AP. We implemented MonoLoco using Intel 5300 commodity WiFi cards and deploy it in four environments with different multipath propagation. Results indicate median localization error of 0.5m and median orientation error of 6.6 degrees, which are comparable to the best performing prior systems, all of which require multiple APs and/or multiple frequency channels. High accuracy can be achieved with only a handful of packets.

Poster: Improving Multipath Resolution with MIMO Smoothing

Super-resolution subspace methods are popular in estimating multipath parameters such as angle of arrival and time of flight. However, they require decorrelation techniques to resolve coherent multipath components. The conventional decorrelation techniques reduce the effective aperture of the MIMO array, thus reducing the resolution and number of resolved paths. In this paper, we introduce MIMO smoothing as a new technique to bring decorrelation effect by leveraging the spacial and frequential diversity in MIMO transmitters and receivers. Via extensive experiments on WiFi links, we show that MIMO smoothing can increase the accuracy of multipath resolution.