Bus networks in cities have distinctive features such as wide coverage and fixed bus routes so that they show the potential of forming the communication backbone in vehicular ad hoc networks ...(VANETs). This paper focuses on the geocast in bus-based VANETs and presents a geocast routing mechanism named Vela. Specifically, Vela analyzes and mines historical bus trajectories and characterizes spatial-temporal patterns (i.e., bus travel-time patterns and bus spatial encounter patterns) in a moderate granularity of road segments, which makes the mined patterns both accurate and steady. Furthermore, Vela exploits these acquired patterns to build a probabilistic spatial-temporal graph model and provides the available routing paths with the best possible quality-of-service levels for data delivery requests. Moreover, Vela also employs a two-hop aware strategy that utilizes the real-time spatial-temporal relationships between buses to increase the chances of forwarding the data. The results of the experiments on the real and synthetic trajectories show that Vela performs much better in terms of delivery ratio and delay and has stronger scalability than the other solutions.
Recent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with motor ...neurone disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most of the patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus, we propose to develop a Morse code-based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the channel state information (CSI) between two antennas to enhance the signal to noise ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity.
WiFi based contactless sensing systems use pervasive wireless communication signals in the environment to sense human activities in a natural way, enabling many promising applications. From ...fine-grained activity sensing to coarse-grained activity recognition, existing work have done a great deal of exploration. However, there is lack of understanding and tackling the serious unstable sensing performance problem. While changing the human target, the position of transceivers, and test environment, the system performance is severely degraded. The reason behind the instability of WiFi-based sensing system is that human activities induce the inconsistent signal patterns inherently at different positions. This paper proposes the Fresnel zone-based diffraction and reflection sensing model, which can be used to accurately quantify the relationship between the target's position with respect to the transceiver, movement trajectory and the signal variation pattern. By illustrating two application examples, i.e., fine-gr
With the development of global positioning system and radar technology, more and more trajectory data can be collected. Among them, the trajectories generated by aircraft, ships, migratory birds and ...other objects are complex and changeable, with greater degrees of freedom. In order to help identify the behavior and intention of flying objects, track type recognition plays an important role. A method of track classification based on frequent route patterns is proposed in this paper. The method includes a frequent route extraction algorithm and a convolutional neural network model. The algorithm first compresses the trajectory to obtain the key points; then extracts the closed route by finding the self-intersecting points of the trajectory, and then finds the frequent route patterns in the closed route as the basis for the classification of the model; finally, the identification of the track type is completed by image processing. In this paper, a large number of experiments are carried out using the real track
Low delivery latency and high delivery ratio are two key goals in the design of routing schemes in Vehicular Ad Hoc Networks (VANETs). The existing routing schemes utilize real-time information ...(e.g., Geographical position and vehicle density) and historical information (e.g., Contacts of vehicles), which usually suffer from a long delivery latency and a low delivery ratio. Inspired by the unique features of bus systems such as wide coverage, fixed routes and regular service, we propose to use the bus systems as routing backbones of VANETs. In this work, we present a Community-based Bus System (CBS) which consists of two components: a community-based backbone and a routing scheme over the backbone. We collect real traces of 2515 buses in Beijing and build a community-based backbone by applying community detection techniques in the Beijing bus system. A two-level routing scheme is proposed to operate over the backbone. The proposed routing scheme performs sequentially in the inter-community level and the intra-community level, and is able to support message delivery to both mobile vehicles and specific locations/areas. Extensive experiments are conducted on the real trace data of the Beijing bus system and the results show that CBS can significantly lower the delivery latency and improve the delivery ratio. CBS is applicable to any bus-based VANETs.
Vehicles on the roads have high heterogeneity in vehicle types. Real-time and full-coverage vehicle classification has always been a challenge. Existing intrusive and non-intrusive methods cannot ...meet the requirements with satisfaction. Considering that signaling data from mobile operators have the advantages such as the wide coverage and the low cost, a new approach named Lepus, which analyzes the signaling stream to achieve the real-time multi-class classification of vehicles on highways, is proposed. Following the Lepus, the historical GPS trajectories with vehicle types and the signaling trajectories occurring at the same time and space are first examined to establish the relation among signaling trajectories, vehicles and vehicle types and then identify signaling-recognizable vehicles. Further, the driving characteristics of these labeled signaling-recognizable vehicles are analyzed so as to determine vehicle classification features. Finally, the vehicle classification model is established and used to analyze the incoming signaling stream and classify the vehicles in real time. Extensive experiments are conducted on real data and the results show that the Lepus approach is effective in real time vehicle classification.
In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these ...systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable.
With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.
Analyzing and mining trajectories of moving objects (such as persons or vehicles) in the cities bring a promising way to discover the potential knowledge and therefore can foster diversified ...applications, including personalized travel services, intelligent transportation systems (ITSs), and etc. For increasing the intelligence of current public transit systems, the paper proposes to discover and utilize the patterns in passenger trajectory streams to optimize bus scheduling. More specifically, the paper first analyzes the real world data from bus smart cards so as to fully understand the nature of passenger trajectories and bus operations. Based on it, the paper defines a new trip pattern, i.e., the frequent bus passenger trip pattern for bus scheduling (the FBPT4BS pattern in short). Then, the paper proposes an approach. The approach gives the procedure of discovering FBPT4BS patterns from passenger trajectory streams and finds the bus lines whose capacities are not enough to satisfy the passengers' travel demands. Further, the approach gives the suggestion on the corresponding scheduling adjustment strategy for bus lines. Experiments are conducted on the data from the Beijing Public Transport Group. The experimental results show that the proposed approach can efficiently decrease the travel times of passengers.
Enabling pervasive WiFi devices with non-contact sensing capability is an important topic in the field of integrated sensing and communication. Doppler effect has been widely exploited to estimate ...targets' velocity from wireless signals. However, the separation of signal sources and receivers complicates the relationship between Doppler frequency shift (DFS) and target velocity in WiFi-based non-contact sensing systems. In contrast to existing works that rely on either approximated relations or coarse-grained information such as whether a target is moving toward or away from WiFi transceivers, this paper investigates rigorously the dependency of velocity estimation accuracy on target locations and headings in WiFi sensing systems. The theoretical insights allow us to derive a closed-form solution and understand the fundamental limitation of velocity estimation. To optimize velocity estimation performance, we devise a receiving device selection scheme that dynamically chooses the optimal set of receivers among multiple available WiFi devices. A prototype real-time target tracking system has been implemented using commodity WiFi devices. Extensive experimental results show that the proposed system outperforms state-of-the-art approaches in velocity estimation and tracking, and is able to achieve <inline-formula> <tex-math notation="LaTeX">9.38cm/s </tex-math></inline-formula>, 13.42°, <inline-formula> <tex-math notation="LaTeX">31.08cm </tex-math></inline-formula> median errors in speed, heading and location estimation amongst experiments conducted in three indoor environments with three device placements and eight human subjects over 15 trajectories.
Exploring LoRa for Long-range Through-wall Sensing Zhang, Fusang; Chang, Zhaoxin; Niu, Kai ...
Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies,
06/2020, Volume:
4, Issue:
2
Journal Article
Peer reviewed
Open access
Wireless signals have been extensively utilized for contactless sensing in the past few years. Due to the intrinsic nature of employing the weak target-reflected signal for sensing, the sensing range ...is limited. For instance, WiFi and RFID can achieve 3-6 meter sensing range while acoustic-based sensing is limited to less than one meter. In this work, we identify exciting sensing opportunities with LoRa, which is the new long-range communication technology designed for IoT communication. We explore the sensing capability of LoRa, both theoretically and experimentally. We develop the sensing model to characterize the relationship between target movement and signal variation, and propose novel techniques to increase LoRa sensing range to over 25 meters for human respiration sensing. We further build a prototype system which is capable of sensing both coarse-grained and fine-grained human activities. Experimental results show that (1) human respiration can still be sensed when the target is 25 meters away from the LoRa devices, and 15 meters away with a wall in between; and (2) human walking (both displacement and direction) can be tracked accurately even when the target is 30 meters away from the LoRa transceiver pair.
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