Baoqi Huang

Inner Mongolia University · School of Computer Science (Software Engineering)

In multi-hop localization procedures where not every node at unknown positions (i.e. sensors) can directly measure distances to nodes at known positions (i.e. anchors), sensor localization errors normally propagate (i.e. increase) as sensors progressively more distant from anchors are localized. To understand error propagation, we consider a primit...

One-dimensional sensor networks can be found in many fields and demand node location information for various applications. Developing localization algorithms in one-dimensional sensor networks is trivial, due to the fact that existing localization algorithms developed for two- and three-dimensional sensor networks are applicable; nevertheless, anal...

Crowd counting aims to estimate the number of individuals in targeted areas. However, mainstream vision-based methods suffer from limited coverage and difficulty in multi-camera collaboration, which limits their scalability, whereas emerging WiFi-based methods can only obtain coarse results due to signal randomness. To overcome the inherent limitat...

Visual odometry (VO) is a critical component of simultaneous localization and mapping (SLAM) with extensive applications in robot navigation and beyond. However, prevalent VO methods often underperform in intricate environments with dynamic textures, insufficient lighting, and rapid rotational movements, primarily due to constrained feature selecti...

For Unmanned Aerial Vehicles (UAVs) with Tiny Machine Learning (TML), there is mutual exclusivity between the energy consumption for flight and the energy consumption to support their computation and processing. IoUAVs integrated with TML systems often consume substantial amounts of energy during flights, particularly when engaged in extended cover...

Real-time awareness of crowd counts and distribution is vital for applications like crowd management, traffic control, and urban planning. Compared to vision-based methods, passive Wi-Fi sensing-based approaches offer advantages such as lower deployment costs, broader coverage, and greater scalability, and thus have become prevalent for fine-graine...

Unmanned aerial vehicles (UAVs) have various advantages, but suffer from their limited energy in practical applications. Therefore, it is important to establish reasonable power consumption models for further efficient power. However, most existing works either establish theoretical power consumption models for fixed-wing UAVs and single-rotor UAVs...

Great efforts have been devoted to solving the crowd counting problem based on vision or other fine-grained measurements. Popular vision and WiFi channel state information based approaches, though are able to achieve relatively high accuracy, suffer from limited scalability. In contrast, passive WiFi sensing-based approaches are capable of supporti...

p>Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsit...

p>Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsit...

Online public transit ridership information is helpful to enhance the service quality of urban public transportation and the travel experiences of passengers. Passive WiFi sensing collects WiFi probe (request) frames sent by nearby mobile devices in a non-intrusive manner, and can thus be employed to monitor ridership. Compared with the existing no...

In comparison with capturing channel state information (CSI) measurements via a laptop or desktop, using a smartphone to collect CSI measurements incurs the restriction of working with a single access point and significant signal distortions, resulting in limited information for smartphone localization. Therefore, this paper intends to leverage as...

Fusing WiFi fingerprint localization and pedestrian dead reckoning (PDR) on smartphones is attractive because of their obvious complementarity in localization accuracy and energy consumption. Although fusion localization algorithms tend to improve localization accuracy, extra hardware and software involved will result in extra computations, such th...

Crowdsourcing dramatically benefits WiFi fingerprinting localization in reducing the costs of collecting received signal strength (RSS) data during offline site survey, and has gained much attention in the literature. This paper proposes a deep learning based indoor positioning system (IPS), termed SeqIPS, to sufficiently exploit the available info...

Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity...

Due to the complexities of indoor WiFi signal propagations, it is challenging to improve the performance of indoor fingerprint-based positioning techniques which is the main hot research in Internet of Things. Most existing methods have limited positioning accuracy, since they do not take the full advantage of the information available, i.e. timing...

The existing indoor location methods are mainly oriented towards the study of single Received Signal Strength Indication ( RSSI ), which does not make full use of the time information attached to RSSI, so the location accuracy is limited.In this paper, considering the correlation of RSSI in time and space, Temporal Convolutional Network (TCN) based...

Gesture recognition is an important step to realize ubiquitous WiFi-based human-computer interaction. However, most current WiFi-based gesture recognition systems rely on domain-specific training. To address this issue, we propose an attention-based cross-domain gesture recognition system using WiFi channel state information. In order to overcome t...

Training an accurate and up-to-date radio map has always been a primary concern for implementing a WiFi fingerprint-based localization system. This paper presents a novel radio map learning scheme for online learning a set of kernel density functions, which function like a traditional radio map in WiFi fingerprint-based localization systems. To be...

Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers fro...

Passive WiFi localization refers to determining the location of WiFi enabled mobile devices by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile devices and measure the corresponding Received Signal Strengths (RSSs) for use in localization. However, most existing studies fail to consider the effect of multiple...

Compared with the traditional Internet services, the services under the Internet of Things (IoT) expand the binary field of “user and information space” to the ternary field of “user, information space, and physical space”. How to aggregate all kinds of information, contents and applications, and how to filter services according to users’ demands,...

This paper deals with a key problem in WiFi fingerprint-based localization, namely how to sample a sufficient number of received signal strength (RSS) measurements during an offline site survey. To this end, a probabilistic framework is firstly presented to characterize the ability of distinguishing two fingerprints, and is then applied in both the...

Indoor automatic localization technology is very important for the Internet of Things. With the development of wireless technology and the diversification of location service requirements, especially in complex indoor scenarios, users are increasingly demanding location-based services. Traditional Global Positioning System (GPS) location technology...

Step counting is not only the key component of pedometers (which is a fundamental service on smartphones), but is also closely related to a range of applications, including motion monitoring, behavior recognition, indoor positioning and navigation. Due to the limited battery capacity of current smartphones, it is of great value to reduce the energy...

Nowadays, wireless communication techniques, such as WiFi, Bluetooth low energy (BLE), etc., have been pervasive in our daily lives, and not only provide convenient data transmission services, but also enable popular indoor positioning and navigation services. The placement of wireless infrastructures like WiFi access points (APs) and BLE beacons h...

Distance estimation is vital for localization and many other applications in wireless sensor networks (WSNs). Particularly, it is desirable to implement distance estimation as well as localization without using specific hardware in low-cost WSNs. As such, both the received signal strength (RSS) based approach and the connectivity based approach hav...

In public places, even if pedestrians do not have their mobile devices connected with any WiFi access point (AP), WiFi probe requests will be broadcast, so that WiFi sniffers can be employed to crowdsource these WiFi probe packets for use. This paper tackles the problem of exploiting the passive WiFi sensing approach for pedestrian flow analysis. T...

Motivated by the network tomography, in this paper, we present a novel methodology to estimate link travel time distributions (TTDs) using end-to-end (E2E) measurements detected by the limited traffic detectors at or near the road intersections. As it is not necessary to monitor the traffic in each link, the proposed estimator can be readily implem...

Acquiring the locations of WiFi access points (APs) not only plays a vital role in various WiFi related applications, such as localization, security and AP deployment, but also inspires the emergence of novel applications. Thus, many efforts have been invested in studying AP localization. Most existing studies adopt the well-known lognormal distanc...

Fingerprint-based localization relies on an accurate and up-to-date radio map, which is however cumbersome to obtain. In this paper, a novel scheme is proposed to online adapt radio maps to environmental dynamics by using low-cost crowdsourced received signal strength (RSS) measurements. To be specific, a coarse-grained radio map is initially estab...

The radio map built during the offline phase of any WiFi fingerprint-based localization system usually scales with the number of WiFi APs (access points) that can be detected at a single location by any mobile device. But, this number in practice can be as large as one hundred, only a few of which essentially contribute to localization. Simply invo...

Differently from most existing studies either directly eliminating redundant WiFi APs with trivial importance or adopting unsupervised dimension reduction methods, e.g. principal component analysis (PCA), this paper employs a supervised approach to take the full advantage of the information available for building radio maps, i.e. location labels at...

In practice, a wireless sensor network normally includes a small portion of nodes with known locations, termed anchors, and the other nodes with unknown locations, termed sensors, have to be localized through dedicated algorithms. Since not every sensor is directly neighboring with anchors, sensor locations are determined in a multi-hop localizatio...

Nowadays, WiFi infrastructures and WiFi-enabled mobile devices have been ubiquitous in our daily lives, and are promising to provide both network services and indoor positioning and navigation services due to its simplicity and low costs. But, it is evident that AP placement is critical to both localization and network coverage, so that it is helpf...

Sensor location plays an important role in wireless sensor networks (WSNs), so that developing sensor localization algorithms has gained much attention from both academia and industries. Among existing solutions, range-free localization algorithms, including the well-known DV-Hop algorithm, are a promising one due to its independence of any dedicat...

In wireless sensor networks, the problem of anchor (whose locations are a priori known) placement plays a vital role in improving the estimation accuracy of sensor (whose locations are unknown and need to be determined) locations. This paper deals with single-hop sensor localization from a novel perspective. On the one hand, unlike existing studies...

Distance estimation is vital for localization and many other applications in wireless sensor networks (WSNs). Particularly, it is desirable to implement distance estimation as well as localization without using specific hardware in low-cost WSNs. As such, both the received signal strength (RSS) based approach and the connectivity based approach hav...

Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously...

In recent years, mobile devices (e.g., smartphones, tablets and etc.) equipped with various inertial sensors have been increasingly popular in daily life, and a large number of mobile applications have been developed based on such built-in inertial sensors. In particular, many of these applications, such as healthcare, navigation, and etc., rely on...

Existing WiFi fingerprinting-based Indoor Positioning System (IPS) suffers from the vulnerability of environmental dynamics. To address this issue, we propose TKL-WinSMS as a systematic strategy, which is able to realize robust and adaptive localization in dynamic indoor environments. We developed a WiFi-based Non-intrusive Sensing and Monitoring S...

Pedestrian dead reckoning (PDR) is a promising complementary technique to balance the requirements on both accuracy and costs in outdoor and indoor positioning systems. In this paper, we propose a unified framework to comprehensively tackle the three sub problems involved in PDR, including step detection and counting, heading estimation and step le...

Currently, pedestrian dead reckoning (PDR) is widely used in indoor positioning. Its principle is to recursively update the location of pedestrians by using the step count, step length and heading. To estimate the pedestrian heading, a common method in PDR is to utilize magnetometer measurements to estimate the heading of a smartphone. However, mag...

This study proposes a novel shape matching algorithm through exploiting shape contexts. The contributions of the proposed algorithm are twofold: (i) a new framework is presented to deal with the shape matching problem based on shape contexts, but differently from existing methods, the authors exploit a polynomial fitting-based feature point extract...

In order to overcome the shortcomings of the Beihang University of Aeronautical and Astronautics inertial terrain-aided navigation II (BITAN II) algorithm, which is based on extended Kalman filtering, a novel algorithm combined with the terrain contour matching (TERCOM) algorithm and particle filter is proposed. Compared with BITAN II, the proposed...

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and posit...

This paper focuses on the problem of source localization using time-difference-of-arrival (TDOA) measurements in both 2-D and 3-D spaces. Different from existing studies where the variance of TDOA measurement noises is assumed to be independent of the associated source-to-sensor distances, we consider the more realistic model where the variance is...

Indoor Positioning System (IPS) has become one of the most attractive research fields due to the increasing demands on Location Based Services (LBSs) in indoor environments. Various IPSs have been developed under different circumstances, and most of them adopt the fingerprinting technique to mitigate pervasive indoor multipath effects. However, the...

This paper develops an efficient and distributed boundary detection algorithm to precisely recognize wireless sensor network (WSN) boundaries using only local connectivity information. Specifically, given any node in a WSN, the proposed algorithm constructs its 2-hop isocontour and locally makes a rough decision on whether this node is suspected to...

This paper focuses on the problem of source localization using time-of-arrival (TOA) measurements. Differently from the existing studies assuming that TOA measurement noises are independent and identically distributed, we deal with more practical TOA measurements suffering from heteroscedastic noises due to different physical distances between a so...

We propose a systematic framework for moving target positioning based on a distributed camera network. In the proposed framework, low-cost static cameras are deployed to cover a large region, moving targets are detected and then tracked using corresponding algorithms, target positions are estimated by making use of the geometrical relationships amo...

Sensor localization is a basic and important task of wireless sensor networks, and abundant localization algorithms have been proposed based on various ranging techniques, including time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS), angle-of-arrival (AOA) and etc. The accuracy of these ranging techniques rely...

Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum-likelihood estimator (MLE) to estimate distances between a pair of neighboring nodes in static wireless sensor networks using their local connectivity information, namely the numbers of the...

This paper tackles the problem of boundary detection by proposing a simple, distributed and connectivity-based algorithm. Our algorithm examines the 2-hop iso-contour of each node, and outperforms existing algorithms examining iso-contours. Specifically, the proposed algorithm makes a rough decision on a suspected boundary node by examining its 2-h...

In this paper, we study the Cramér–Rao Lower Bound (CRLB) in single-hop sensor localization using measurements derived from received signal strength (RSS), time of arrival (TOA) and bearing, respectively, from a novel perspective. Differently from the existing work, we use a statistical sensor–anchor geometry modeling method, with the result that t...