Aveek Purohit

Spartacus: spatially-aware interaction for mobile devices through energy-efficient audio sensing

Recent developments in ubiquitous computing enable applications that leverage personal mobile devices, such as smartphones, as a means to interact with other devices in their close proximity. In this paper, we propose Spartacus, a mobile system that enables spatially-aware neighboring device interactions with zero prior configuration. Using built-in microphones and speakers on commodity mobile devices, Spartacus uses a novel acoustic technique based on the Doppler effect to enable users to accurately initiate an interaction with a neighboring device through a pointing gesture. To enable truly spontaneous interactions on energy-constrained mobile devices, Spartacus uses a continuous audio-based lower-power listening mechanism to trigger the gesture detection service. This eliminates the need for any manual action by the user. Experimental results show that Spartacus achieves an average 90% device selection accuracy within 3m for most interaction scenarios. Our energy consumption evaluations show that, Spartacus achieves about 4X lower energy consumption than WiFi Direct and 5.5X lower than the latest Bluetooth 4.0 protocols.

Polaris: getting accurate indoor orientations for mobile devices using ubiquitous visual patterns on ceilings

Ubiquitous computing applications commonly use digital compass sensors to obtain orientation of a device relative to the magnetic north of the earth. However, these compass readings are always prone to significant errors in indoor environments due to presence of metallic objects in close proximity. Such errors can adversely affect the performance and quality of user experience of the applications utilizing digital compass sensors. In this paper, we propose Polaris, a novel approach to provide reliable orientation information for mobile devices in indoor environments. Polaris achieves this by aggregating pictures of the ceiling of an indoor environment and applies computer vision based pattern matching techniques to utilize them as orientation references for correcting digital compass readings. To show the feasibility of the Polaris system, we implemented the Polaris system on mobile devices, and field tested the system in multiple office buildings. Our results show that Polaris achieves 4.5° average orientation accuracy, which is about 3.5 times better than what can be achieved through sole use of raw digital compass readings.

SensorFly: a controlled-mobile aerial sensor network

The SensorFly system is a novel, low-cost, miniature controlled-mobile aerial sensor network. Mobility permits the network to be autonomous in deployment, maintenance and adapting to the environment, overcoming the reliance of traditionally fixed networks on human intervention, large infrastructure or inefficient random methods. We wish to demonstrate the novel hardware design, flight control and collaborative localization capabilities of the SensorFly system. To the best of our knowledge, this is the lightest realized flying sensor system in existence, with a weight of 30g and low mass production cost of ~

PANDAA: physical arrangement detection of networked devices through ambient-sound awareness

100. Developed under severe weight, cost, energy and processing power constraints, we show that this system is a viable and capable mobile sensor network platform.

SugarTrail: Indoor navigation in retail environments without surveys and maps

Future ubiquitous home environments can contain 10s or 100s of devices. Ubiquitous services running on these devices (i.e. localizing users, routing, security algorithms) will commonly require an accurate location of each device. In order to obtain these locations, existing techniques require either a manual survey, active sound sources, or estimation using wireless radios. These techniques, however, need additional hardware capabilities and are intrusive to the user. Non-intrusive, automatic localization of ubiquitous computing devices in the home has the potential to greatly facilitate device deployments. This paper presents the PANDAA system, a zero-configuration spatial localization system for networked devices based on ambient sound sensing. After initial placement of the devices, ambient sounds, such as human speech, music, foot- steps, finger snaps, hand claps, or coughs and sneezes, are used to autonomously resolve the spatial relative arrangement of devices using trigonometric bounds and successive approximation. Using only time difference of arrival measurements as a bound for successive estimations, PANDAA is able to achieve an average of 0.17 meter accuracy for device location in the meeting room deployment.

SugarMap: location-less coverage for micro-aerial sensing swarms

A system that helps people navigate in indoor environments on a fine-grained level can enable a variety of pervasive computing applications in retail environments. Existing indoor navigation systems rely on extensive RF tagging surveys and accurate floor plans. These prerequisites are often impractical in indoor environments. In this paper, we present SugarTrail, a system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require existing maps. By leveraging the structured movement patterns of shoppers in retail store environments, the system provides higher accuracy than existing radio finger-printing approaches. With minimal setup and active user participation, the system automatically learns user movement pathways in indoor environments from radiofrequency and magnetic signatures. These pathways are clustered and used to automatically build a navigable virtual roadmap of the environment. We present results from a campus testbed and from actual radio measurements collected in an operational supermarket to show that SugarTrail system can navigate users with a success rate of > 85% and an average accuracy of 0.7m.

Controlled-mobile sensing simulator for indoor fire monitoring

Micro-aerial vehicle (MAV) swarms are emerging as a new class of mobile sensor networks with many potential applications such as urban surveillance, disaster response, radiation monitoring, etc., where the swarm is tasked with collaboratively covering a hazardous unknown environment. However, efficient collaborative coverage is challenging due to limited individual sensing, computing and communication resources of MAV sensor nodes, and lack of location infrastructure in the unknown application environment. We present SugarMap, a novel system that enables such resource-constrained MAV nodes to achieve efficient sensing coverage. The self-establishing system uses approximate motion models of mobile nodes in conjunction with radio signatures from self-deployed stationary anchor nodes to create a common coverage map. Consequently, the system coordinates node movements to reduce sensing overlap and increase the speed and efficiency of coverage. The system uses particle filters to account for uncertainty in sensors and actuation of MAV nodes, and incorporates redundancy to guarantee coverage. Through large-scale simulations and a real implementation on the SensorFly MAV sensing platform, we show that SugarMap provides better coverage than the existing coverage approaches for MAV swarms.

Deployment of swarms of micro-aerial vehicles: From theory to practice

Indoor emergency response situations, such as urban fire, are characterized by dangerous constantly-changing operating environments with little access to situational information for first responders. In situ information about the conditions, such as the extent and evolution of an indoor fire, can augment rescue efforts and reduce risk to emergency personnel. Cyber-physical controlled-mobile sensor networks have been proposed for emergency response situations. However, cost-effective development, analysis and evaluation of such cyber-physical systems require simulation frameworks that simultaneously model its many computational and physical components. Existing multi-sensor/robot simulation environments are inadequate for this purpose. This paper presents a simulator that incorporates a realistic indoor fire growth model (CFAST), with a radio path loss model, wireless network model, and mobility model of a controlled-mobile sensor network, to achieve a more comprehensive representation of such cyber-physical systems. A detailed example simulation scenario is presented along with analysis to illustrate the capabilities of the framework.

Collaborative indoor sensing with the sensorfly aerial sensor network

We study the problem of deploying a high number of low-cost, low-complexity robots inside a known environment with the objective that at least one robotic platform reaches each of N preassigned goal locations. Our study is inspired by SensorFly, a micro-aerial vehicle successfully used for mobile sensor network applications. SensorFly nodes feature limited on-board sensors, so one has to rely on simple navigation strategies and increase performance through redundance in the team. We introduce a simple, fully scalable deployment algorithm exploiting the limited capabilities offered by the SensorFly platform, and we explore its performance by feeding the simulation system with parameters extracted from the real SensorFly platform.

Design Experiences in Minimalistic Flying Sensor Node Platform through SensorFly

The SensorFly is a novel, low-cost, miniature (29g) controlled-mobile aerial sensor networking platform. Mobility permits a network of SensorFly nodes, unlike fixed networks, to be autonomous in deployment, maintenance and adapting to the environment, as required for emergency response situations such as fire monitoring or survivor search. We demonstrate the ability of the SensorFly system to collaboratively sense the environment (floor temperature) in a demonstration scenario. The SensorFly nodes are tasked to explore the area and transmit sensed data back to a base station. The system partitions tasks among SensorFly nodes based on their capabilities (location, sensors, energy) to achieve concurrent and faster coverage. The real-time sensor data is presented to the user on a display terminal at the base station.