Large-scale micro-aerial vehicle (MAV) swarms provide promising solutions for situational awareness in applications such as environmental monitoring, urban surveillance, search and rescue, and so on. However, these scenarios do not provide localization infrastructure and limit cost and size of on-board capabilities of individual nodes, which makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this article, we present H-DrunkWalk , a collaborative and adaptive technique for heterogeneous MAV swarm navigation in environments not formerly preconditioned for operation. Working with heterogeneous MAV swarm, the H-DrunkWalk achieves high accuracy through collaboration but still maintains a low cost of the entire swarm. The heterogeneous MAV swarm consists of two types of nodes: (1) basic MAVs with limited sensing, communication, computing capabilities and (2) advanced MAVs with premium sensing, communication, computing capabilities. The key focus behind this networked MAV swarm research is to (1) rely on collaboration to overcome limitations of individual nodes and efficiently achieve system-wide sensing objectives and (2) fully take advantage of advanced MAVs to help basic MAVs improve their performance. The evaluations based on real MAV testbed experiments and large-scale physical-feature-based simulations show that compared to the traditional non-collaborative and non-adaptive method (dead reckoning with map bias), our system achieves up to 6× reductions in location estimation errors, and as much as 3× improvements in navigation success rate under the given time and accuracy constraints. In addition, by comprehensively considering the environment, heterogeneous structure, and quality of location estimation, our H-DrunkWalk brings 2× performance improvement (on average) as that of a hardware upgrade.

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. Static sensor networks that are pre-deployed or manually deployed have been proposed but are less practical due to need for large infrastructure, lack of adaptivity, and limited coverage. Controlled-mobility in sensor networks, that is, the capability of nodes to move as per network needs can provide the desired autonomy to overcome these limitations. In this article, we present SensorFly, a controlled-mobile aerial sensor network platform for indoor emergency response application. The miniature, low-cost sensor platform has capabilities to self deploy, achieve three-dimensional sensing, and adapt to node and network disruptions in harsh environments. We describe hardware design trade-offs, the software architecture, and the implementation that enables limited-capability nodes to collectively achieve application goals. Through the indoor fire monitoring application scenario, we validate that the platform can achieve coverage and sensing accuracy that matches or exceeds static sensor networks and provide higher adaptability and autonomy.

Micro-aerial vehicle (MAV) swarms are a new class of mobile sensor networks with many applications, including search and rescue, urban surveillance, radiation monitoring, etc. These sensing applications require autonomously navigating a high number of low-cost, low-complexity MAV sensor nodes in hazardous environments. The lack of preexisting localization infrastructure and the limited sensing, computing, and communication abilities of individual nodes makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this paper, we present a collaborative and adaptive algorithm for resource-constrained MAV nodes to quickly and efficiently navigate to preassigned locations. Using radio fingerprints between flying and landed MAVs acting as radio beacons, the algorithm detects intersections in trajectories of mobile nodes. The algorithm combines noisy dead-reckoning measurements from multiple MAVs at detected intersections to improve the accuracy of the MAVs' location estimations. In addition, the algorithm plans intersecting trajectories of MAV nodes to aid the location estimation and provide desired performance in terms of timeliness and accuracy of navigation. We evaluate the performance of our algorithm through a real testbed implementation and large-scale physical feature based simulations. Our results show that, compared to existing autonomous navigation strategies, our algorithm achieves up to 6X reduction in location estimation errors, and as much as 3X improvement in navigation success rate under the given time and accuracy constraints.

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.

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.

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.

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.

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.

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.

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.

This demo presents PANDAA, a zero-configuration automatic spatial localization technique for networked devices based on ambient sound sensing. We will demonstrate that after initial placement of the devices, ambient sounds, such as human speech, music, footsteps, finger snaps, hand claps, or coughs and sneezes, can be used to autonomously resolve the spatial relative arrangement of devices, such as mobile phones, using trigonometric bounds and successive approximation.

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 ~$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.