We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-user distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mobility level, and recognizes the group structure by applying the multi-dimensional scaling technique on the matrix of distance between pairwise users within the same group. We first validate the performance of between-user distance estimation via conducting extensive experiments in a three-floor campus building and a shopping center, and the results show that WiDE outperforms other machine learning based approaches for between-user distance estimation, with the average absolute error (AAE) of 0.69m and 1.14m for the campus building and shopping center, respectively, and the corridor identification accuracy for the campus building is over 99 percent. In addition, the experiments in the shopping center show that our approach can accurately detect groups, classify group mobility into fine-grained level and recognize the group structure.

In this article, we study the problem of passive human localization using an infrared (IR) thermal imaging camera which detects IR radiation emitted by human without carry-on devices and thereby generates a heat map of human body. Rather than directly using the heat map, we propose to exploit temperature of human body and design a lightweight approach for human localization using machine learning techniques. We observe that person-to-camera distance is closely related with the position and the size of a person’s head in the heat map, and several other features, such as variance, skewness, and kurtosis of temperatures in the head region are also good indicators of person-to-camera distance estimation. Accordingly, we propose a set of features and construct a model for inferring person-to-camera distance using machine learning techniques. With the estimated distance, we further compute human localization based on the relative position of the person in the heat map, the estimated person-to-camera distance, and the location and the DFoV of the IR thermal camera. Our experiments in real environments show that the proposed approach can accurately estimate person-to-camera distance and human localization with submeter errors.

Existing studies on indoor navigation often require such a pre-deployment as floor map, localization system and/or additional (customized) hardwares, or human motion traces, making them prohibitive when the situation deviates from these requirements (e.g., navigating a crowd of panicking people where no localization system or motion traces are available). The main observation inspiring our work without reliance on such pre-deployment is that when there are sufficient participants (e.g., a crowd of panicking people), the WiFi signatures collected by participants can serve as the fingerprints (referred to as location fingerprints) of their unknown locations. By computing relative positions of these location fingerprints we can connect them to form a global map. Such a map reflects the topology of the underlying walkable space and thus holds the potential of offering a navigation path for any intended users. Based on this observation, we design Fly-Navi, a crowdsourcing based indoor navigation system via on-the-fly map generation, and primarily designed for indoor environments with rectilinear and narrow corridors. Specifically, each participant uploads sensory data, and the server then generates a global map (on-the-fly map) through a series of operations such as local map generation, local map stitch and edge computation. On top of the global map, Fly-Navi computes a navigation path to the given destination and tracks the progress. We implement the prototype of Fly-Navi and our experiments show that Fly-Navi can quickly generate a correct global map with the 80-percentile of between-fingerprint distance error less than 3 meters, which is important for computing turning points of the map and hereon offering turn-by-turn instructions, and correctly navigate the intended users to their destinations.

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.

Accurately estimating the distance between persons with COTS mobile devices can benefit many applications (e.g., group activity analysis, indoor navigation, etc.). In this paper we present WiDE, a deep learning-based system for estimating person-to-person distance based on surrounding WiFi signals. Specifically, WiDE has two phases: offline learning and online prediction. In offline learning phase, we apply a stacked autoencoder (SAE) for pre-training the weights of a deep neural network (DNN), and establish a DNN-based classifier for predicting between-person discretized distance and corridor identity using WiFi signals. During online prediction phase, based on the trained DNN with the SAE and newly uploaded WiFi information, we estimate the corridor identities and the distance between pairwise persons. We validate our system by conducting extensive experiments in a three-floor campus building, and the results show that WiDE achieves the corridor identification accuracy over 98 % and the median ranging error of 0.9m and 3.0m for two persons on the same corridor and on different corridors, respectively, which outperforms the state-of-the-art proximity inferring system [1].

Offering the services of real-time tracking and arrival time prediction is a common welfare for bus riders and transit agencies, especially in urban environments. On the down side, the traditional GPS-based solutions work poorly in urban areas due to urban canyons, while the location systems based on cellular signal also suffer from inherent limitations. In this paper, we present a powerful tool named Signal Voronoi Diagram (SVD) to partition the radio-frequency (RF) signal space of WiFi Access Points (APs), distributed where a bus travels, into Signal Cells, and then into fine-grained Signal Tiles, tackling the problem of noisy received signal strength (RSS) readings and possible AP dynamics. On top of SVD, we present a novel framework so-called WiLocator, to track and predict the arrival time of an urban bus based on the surrounding WiFi information collected by the commodity off-the-shelf (COTS) smartphones of bus riders, the mobility constraint of a bus and the temporal consistency of travel time of buses on the overlapped road segments. We also show the WiLocator's power of generating an accurate and real-time traffic map with the predicted travel time on each road segment. We implement the prototype of WiLocator and conduct the in-situ experiment to demonstrate its accuracy.

The in-network data storage and retrieval are fundamental functions of sensor networks. Among many proposals, geographical hash table GHT is perhaps most appealing as it is very simple yet powerful with low communication cost, where the key is to correctly define the bounding box. It is envisioned that the skeleton has the power to facilitate computing a precise bounding box. In existing works, the focus has been on skeleton extraction algorithms targeting for 2D sensor networks, which usually deliver a 1-manifold skeleton consisting of 1D curves. It faces a set of non-trivial challenges when 3D sensor networks are considered, in order to properly extract the surface skeleton composed of a set of 2-manifolds and possibly 1D curves. In this paper, we study the problem of surface skeleton extraction in 3D sensor networks. We propose a scalable and distributed connectivity-based algorithm to extract the surface skeleton of 3D sensor networks. First, we propose a novel approach to identifying surface skeleton nodes by computing the extended feature nodes such that it is robust against boundary noise, etc. We then find the maximal independent set of the identified skeleton nodes and triangulate them to form a coarse-grained surface skeleton, followed by a refining process to generate the fine-grained surface skeleton. Furthermore, we design an efficient updating scheme to react to the network dynamics caused by node failure, insertion, etc. We also investigate the impact of boundary incompleteness and present a scheme to extract the surface skeleton under incomplete boundary. Finally, we apply the extracted surface skeleton to facilitate the design of data storage protocol and curve skeleton extraction algorithm. Extensive simulations show the robustness of the proposed algorithm to shape variation, node density, node distribution, communication radio model and boundary incompleteness, and its effectiveness for data storage and retrieval application with respect to load balancing.