Chengwen Luo

PiLoc: a self-calibrating participatory indoor localization system

While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths. We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.

SocialWeaver: collaborative inference of human conversation networks using smartphones

Understanding how people communicate with one another plays a very important role in many disciplines including social psychology, economics, marketing, and management science. This paper proposes and evaluates SocialWeaver, a sensing service running on smartphones that performs conversation clustering and builds conversation networks automatically. SocialWeaver uses a hybrid speaker classification scheme that exploits an adaptive histogram-based classifier to non-obtrusively bootstrap the in situ speaker model learning. The conversation clustering algorithm proposed is able to detect fine-grain conversation groups even if speakers are close together. Finally, to address energy constrain, a POMDP-based energy control scheme is incorporated. We evaluate the performance of each component in SocialWeaver using more than 100 hours of conversation data collected from conversation groups with sizes ranging from 2 to 13. Evaluation shows that accuracy of 71% to 92% can be achieved for various conversation modes and up to 50% of the energy consumption in SocialWeaver can be reduced through the POMDP-based scheme. Evaluations of SocialWeaver in both controlled and uncontrolled settings show promising results in realistic settings and potential to enable many future applications.

Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges

This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.

A Hybrid Path Planning Method in Unmanned Air/Ground Vehicle (UAV/UGV) Cooperative Systems

In this paper, we study the automatic ground map building and efficient path planning in unmanned aerial/ground vehicle (UAV/UGV) cooperative systems. Using the UAV, a ground image can be obtained from the aerial vision, which is then processed with image denoising, image correction, and obstacle recognition to construct the ground map automatically. Image correction is used to help the UGV improve the recognition accuracy of obstacles. Based on the constructed ground map, a hybrid path planning algorithm is proposed to optimize the planned path. A genetic algorithm is used for global path planning, and a local rolling optimization is used to constantly optimize the results of the genetic algorithm. Experiments are performed to evaluate the performance of the proposed schemes. The evaluation results show that our proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.

Pallas: Self-Bootstrapping Fine-Grained Passive Indoor Localization Using WiFi Monitors

Passive indoor localization for smartphones requires no explicit cooperation of the smartphone and enables a new spectrum of applications such as passive user tracking, mobility monitoring, social pattern analysis, etc. However, existing passive localization methods either achieve coarse-grained localization accuracy or require expensive infrastructure support. In this paper, we present Pallas, a self-bootstrapping system for fine-grained passive indoor localization using non-intrusive WiFi monitors. Pallas uses off-the-shelf access point hardware to opportunistically capture WiFi packets to infer the location of smartphones in the indoor environment. The key novelty of Pallas lies in that the passive fingerprint database for localization is automatically constructed and updated without any active participation of WiFi devices or manual calibration. To achieve this, Pallas first identifies passive landmarks that are present in WiFi RSS traces. Given the knowledge of the indoor floor plan and the location of WiFi monitors, Pallas statistically maps the collected RSS traces to specific indoor pathways. With sufficient mapping opportunistically detected, Pallas is able to bootstrap a fine-grained passive fingerprint database and build Gaussian processes for localization automatically without requiring any additional calibration effort.

Accuracy-aware wireless indoor localization

Fingerprint-based indoor localization has attracted extensive research efforts due to its potential for deployment without extensive infrastructure support. However, the accuracies of these different systems vary and it is difficult to compare and evaluate these systems systematically. In this work, we propose a Gaussian process based approach that takes the radio map and the localization algorithm as an input, and outputs the expected accuracy of the localization system. With an efficient error estimation algorithm, many applications such as landmark detection, localization algorithm selection and access point subset selection can be performed. Our evaluations show that our approach provides sufficient accuracy and can serve as a useful tool for system evaluation and performance tuning when developing fingerprint-based indoor localization systems.

iMap: Automatic inference of indoor semantics exploiting opportunistic smartphone sensing

Indoor environment inference is of great importance to mobile and pervasive computing. As high-level metadata of indoor environment, floor maps contain rich information and are widely required in many pervasive systems. However, despite significant research progress, automatic inference of indoor maps has been less studied. In this paper, we present iMap, a smartphone-based opportunistic sensing system that automatically constructs the indoor maps by merging crowdsourced walking trajectories from smart-phone users. Most importantly, indoor semantics, such as stairs, escalators, elevators and doors are also automatically detected and annotated to the constructed map in the same inference process. The evaluation result shows that iMap can accurately detect different indoor semantics and be applied to different indoor environments. With the capability of generating semantic-annotated indoor maps without requiring any prior knowledge of the indoor environment, iMap has the potential to be widely deployed in practice.

Seamless Streaming Data Delivery in Cluster-Based Wireless Sensor Networks With Mobile Elements

Exploiting mobility improves the energy efficiency of data collection in wireless sensor networks (WSNs) for many applications. As more and more multimedia sensor nodes, equipped with audio and video capture capabilities are employed to characterize a sensing environment, streaming data are becoming increasingly important in WSNs. However, the mobility of MEs poses challenges on how to efficiently provide uninterrupted message delivery for continuous data streams in WSNs. In this paper, we propose a seamless streaming data delivery (SSDD) protocol for multihop cluster-based WSNs with MEs. Different from existing works, we concentrate on the localized mobility support for the delivery of streaming data in hierarchical WSNs. By introducing a cross-cluster handover mechanism and a path redirection scheme, SSDD efficiently maintains the end-to-end connectivity between a source and a ME during data transmission while avoiding most of the overhead in broadcasting the location of the ME as it moves in the sensing field. Extensive evaluation results demonstrate the effectiveness and high scalability of SSDD in terms of both energy efficiency and delivery latency.

Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks

Abstract Wireless sensor networks (WSNs) play a very important role in realizing Internet of Things (IoT). In many WSN applications, flooding is a fundamental network service for remote network configuration, diagnosis or disseminating code updates. Despite a plethora of research on flooding problem in the literature, there has been very limited research on flooding tree construction in asynchronous low-duty-cycle WSNs. In this paper, we focus our investigation on minimum-delay and energy-efficient flooding tree construction considering the duty-cycle operation and unreliable wireless links. We show the existence of the latency-energy trade-off in flooding. We formulate the problem as a undetermined-delay-constrained minimum spanning tree (UDC-MST) problem, where the delay constraint is known a posteriori. Due to the NP-completeness of the UDC-MST problem, we design a distributed Minimum-Delay Energy-efficient flooding Tree (MDET) algorithm to construct an energy optimal tree with flooding delay bounding. Through extensive simulations, we demonstrate that MDET achieves a comparable delivery latency with the minimum-delay flooding, and incurs only 10% more transmission cost than the lower bound, which yields a good balance between flooding delay and energy efficiency.

Compressive sensing based data quality improvement for crowd-sensing applications

Crowd-sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of crowd-sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth an important issue of false data detection and correction in crowd-sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose Deco, a general framework to detect false values for crowd-sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, Deco is able to accurately detect the false data and estimate both false and missing values for data correction. Through comprehensive performance evaluations, we demonstrate the efficacy of Deco in achieving false data detection and correction for crowd-sensing applications with incomplete sensory data.