Xinlei Chen

DrunkWalk: Collaborative and Adaptive Planning for Navigation of Micro-Aerial Sensor Swarms

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.

Device-to-Device Communications Enabled Energy Efficient Multicast Scheduling in mmWave Small Cells

To keep pace with the rapid growth of mobile traffic demands, dense deployment of small cells in millimeter wave (mmWave) bands has become a promising candidate for next-generation wireless communication systems. With a greatly increased data rate from huge bandwidth of mmWave communications, energy consumption should be mitigated for higher energy efficiency. Due to content popularity, many content-based mobile applications can be supported by the multicast service. mmWave communications exploit directional antennas to overcome high path loss, and concurrent transmissions can be enabled for better multicast service. On the other hand, device-to-device (D2D) communications in physical proximity should be exploited to improve multicast performance. In this paper, we propose an energy-efficient multicast scheduling scheme, referred to as EMS, which utilizes both D2D communications and concurrent transmissions to achieve high energy efficiency. In EMS, a D2D path planning algorithm establishes multi-hop D2D transmission paths, and a concurrent scheduling algorithm allocates the links on the D2D paths into different pairings. Then, the transmission power of links is adjusted by the power control algorithm. Furthermore, we theoretically analyze the roles of D2D communications and concurrent transmissions in reducing energy consumption. Extensive simulations under various system parameters demonstrate the superior performance of EMS in terms of energy consumption compared with the state-of-the-art schemes. Furthermore, we also investigate the choice of the interference threshold to optimize network performance.

Social Trust Aided D2D Communications: Performance Bound and Implementation Mechanism

In a device-to-device (D2D) communications underlaying cellular network, any user is a potential eavesdropper for the transmissions of others that occupy the same spectrum. The physical-layer security mechanism of theoretical secure capacity, which maximizes the rate of reliable communication from the source user to the legitimate receiver and ensure unauthorized users learn as little as information as possible, is typically employed to guarantee secure communications. As hand-held devices are carried by human beings, we may leverage their social trust to decrease the number of potential eavesdroppers. Aiming to establish a new paradigm for solving the challenging problem of security and efficiency tradeoff, we propose a social trust-aware D2D communication architecture that exploits the social-domain trust for securing the physical-domain communication. In order to understand the impact of social trust on the security of transmissions, we analyze the system ergodic rate of social trust aided communications via stochastic geometry, and our result based on a real data set shows that the proposed social trust aided D2D communication increases the system secrecy rate by about 63% compared with the scheme without considering social trust relation. Furthermore, in order to provide implementation mechanism, we utilize matching theory to implement efficient resource allocation among multiple users. Numerical results show that our proposed mechanism increases the system secrecy rate by 28% with fast convergence over the social oblivious approach.

Design Experiences in Minimalistic Flying Sensor Node Platform through SensorFly

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.

Collaborative Localization and Navigation in Heterogeneous UAV swarms: Demo Abstract

Resilient localization and navigation for autonomous Unmanned Aerial Vehicles (UAVs) still remains a challenge in certain scenarios, like GPS-deprived environments such as indoors or urban canyons. In this work, we explore a heterogeneous UAV swarm design, in which a small number of sensor and computationally powerful UAVs collaborate with the remaining resource-constrained UAVs to guarantee optimal localization accuracy.

Gotcha II: Deployment of a Vehicle-based Environmental Sensing System: Poster Abstract

According to the World Health Organization (WHO), outdoor air pollution led to an estimated 3.7 million premature deaths worldwide in 2012. To address this problem, it is necessary for both residents and city administrations to understand air quality in their immediate environment with fine-grained temporal-spatial resolution. Currently both fixed and mobile systems are used to attempt to sense the pollution field. However, they generally are expensive, cover small areas and thus result in lower accuracy. To address this problem, we present Gotcha II, our environmental sensing system that utilizes data from both official sites and mobile sensing devices deployed on vehicles to infer air pollution. We present our experiences and calibration methods to improve data accuracy through the deployment on 100 vehicles in the greater Shenzhen area.

HAP: Fine-Grained Dynamic Air Pollution Map Reconstruction by Hybrid Adaptive Particle Filter: Poster Abstract

This paper presents a hybrid adaptive particle filter (HAP) with online feedback to dynamically reconstruct high spatial-temporal resolution air pollution information from sparse vehicular based sensors. To deal with data sparsity, we apply both spatial and temporal correlation of air dispersion to reduce data dimension requirement. HAP adaptively predicts when the accumulated prediction error is low and then uses data compensation for correction whenever the prediction error becomes high. The preliminary results based on the city scale deployments with 10 taxis show that our system achieves up to 50% reduction on system errors.

QoE-Aware wireless video communications for emotion-aware intelligent systems: A multi-layered collaboration approach

Abstract With the ever increasing demand on high-quality visual information for emotion-aware intelligent systems, wireless video traffic explosively grows and causes great energy consumption. Therefore, providing high quality of experience (QoE) for connected users becomes increasingly important. Aiming to establish a new paradigm to solve this challenging problem, in this article we propose a multi-layered collaboration approach to provide energy-efficient QoE-aware wireless video communications by efficiently utilizing the limited transmission resources of wireless networks for 5G. We first investigate the emotion-aware intelligent system QoE measurement based on objective metrics of quality of service (QoS). Then, we utilize the multi-layered collaborations of physical, network and application layers among the connected users to achieve energy-efficient QoE-aware video communications. By developing a profound understanding of the interplay between the video applications and wireless networks, we qualitatively analyze how QoE can benefit from the multi-layered collaborations, and quantitatively assess the achievable gains in a typical wireless-connected emotion-aware application scenario.

Locally Differentially Private Participant Recruitment for Mobile Crowdsourcing

Location-aware mobile crowdsourcing tasks like urban sensing always require exposing users location, which lead to serious privacy breaches. In this poster, we propose a locally differentially private participants recruitment system to maximize spatial coverage of the mobile crowdsourcing task while preserving location privacy. Based on the mechanism of randomized response, our system preserves the privacy in a local way, which eliminates the need for a trusted server. With guaranteed location privacy protection, a heuristic algorithm is proposed to solve the maximum spatial coverage problem efficiently given the obfuscated reports. Extensive experiments on real-world user trajectories demonstrate the feasibility of our proposed system, which improves the spatial coverage by more than 10% on average compared with the state-of-the-art solutions.

CTS: A Cellular-based Trajectory Tracking System with GPS-level Accuracy

GPS has been widely used for locating mobile devices on the road map. Due to its high power consumption and poor signal penetration, GPS is unfortunately unsuitable to be used for continuously tracking low-power devices. Compared with GPS-based positioning, cellular-infrastructure-based positioning consumes much less energy, and works in any place covered by the cellular networks. However, the challenges of cellular positioning come from the relatively low accuracy and sampling rate. In this paper, we propose a novel cellular-based trajectory tracking system, namely CTS. It achieves GPS-level accuracy by combining trilateration-based cellular positioning, stationary state detection, and Hidden-Markov-Model-based path recovery. In particular, CTS utilizes basic characteristics of cellular sectors to produce more credible inferences for device locations. n nTo evaluate the performance of CTS, we collaborated with a mobile operator and deployed the system the city of Urumchi, Xinjiang Province of China. We collected the location data of 489,032 anonymous mobile subscribers from cellular networks during 24 hours, and retrieved 201 corresponding GPS trajectories. Our experimental results show that CTS achieves GPS-level accuracy in 95.7% of cases, which significantly outperforms the state-of-the-art solutions.