Air quality is known to be strongly correlated with respiratory diseases. Indoor air quality considerably affects human health, especially in spaces such as classrooms, where students gather and interact for long periods. Most schools are located in relatively old buildings, where suitable ventilation systems are difficult to implement. The consequent lack of a standard ventilation rate increases the risk of cluster infections in classrooms. Accordingly, this article proposes a classroom respiratory disease occurrence prediction method based on indoor air-quality data (CROP–IAQ). Early warnings provided by CROP–IAQ will enable authorities to implement measures such as ventilation and isolation that reduce the risk of cluster infections in school campuses. Data on indoor temperature, relative humidity, particulate matter (PM) concentrations (PM1.0, PM2.5, and PM10, referring to the concentrations of particles with diameters of \(\leq 1\) , \(\leq 2.5\) , and \(\leq 10\) micrometer, respectively), carbon dioxide concentration, total volatile organic compound concentration, and luminosity in classrooms were collected using a MAPS V6.0 airbox. The air-quality data corresponding to potential cluster infections were identified from the aforementioned data and records of student epidemic prevention leaves in each class. Because most of the collected air-quality data did not correspond to potential cluster infections (that is, the dataset was imbalanced), synthetic data samples were generated using a synthetic minority oversampling technique. Four neural network models were constructed for predicting the possibility of disease occurrence and alerting authorities to classrooms at the risk of cluster infections: a convolutional neural network model, the inception model, a residual network model, and a residual network with external transformations model. The predictive capabilities of these models were only slightly improved after implementing a squeeze-and-excitation (SE) module. Experimental results indicated that the inception model with the SE module achieved the best results among the four models, with an F1 score and sensitivity of 0.72 and 0.76, respectively.

Disease surveillance is essential for the control of flu and respiratory infectious diseases including the novel coronavirus disease (COVID-19). Indoor air quality monitoring has been shown effective in understanding the effectiveness of airflow and circulation indoors to reduce the risk of infectious diseases. In this project, we developed low-cost indoor air quality monitoring devices and systems to tackle the disease surveillance problem. The monitoring device consists of a set of air quality sensors. By strategic deployment and real-time data analysis, the system is able to yield insightful air circulation information indoors. The real-time data analysis is performed on air quality for the indoor ventilation using Long Short-Term Memory (LSTM) on sensed data. A series of user-friendly visualization interfaces and chatbot applications are designed to interact with users and ensure the successful delivery of infection control information. Finally, we work closely with the Taiwan Centers for Disease Control (CDC) and conduct field experiments in 15 locations including hospitals, long-term care centers, schools with total of 144 IAQ devices.

As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an anomaly detection framework (ADF) for large-scale, real-world environmental sensing systems. The framework is composed of four modules: 1) time-sliced anomaly detection (TSAD), which detects spatial, temporal, and spatio-temporal anomalies in the real-time sensor measurement data stream; 2) real-time emission detection, which detects potential regional emission sources; 3) device ranking, which provides a ranking for each sensing device; and 4) malfunction detection, which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.

Indoor localization systems aim to accurately and cost-effectively locate targets. While numerous indoor localization solutions based on Wi-Fi, Bluetooth, ZigBee and other technologies have been proposed, they fail to achieve satisfactory performance. The accuracy of these solutions is often affected by obstacles such as shelf, human etc; due to the hight frequency of the signals, which weaken the ability of signals to penetrate obstacles. Moreover, high accuracy comes at the expense of more hardware, labor-expensive deployment. To overcome these limitations, we propose an indoor localization system based on Software Defined Radio (SDR), using FM and DVB-T signals. Our system achieves sub-meter accuracy at low cost with low site survey overhead. Additionally, we investigate the temporal and window effect on different sizes of training data.

In the last few years, we have witness many disasters which destroyed communication networks to the outside world. Wireless mesh network (WMN) is one of the best choices for establishing a network during disaster recovery. In most disaster area, the reliability of WMNs decreased rapidly due any unpredictable environment changes. The disruption of services are often introducing delay and dropping of important and live saving messages. Thus, it is desirable to have a disaster resilient network that can temporarily withstand one or multiple of failed mesh nodes. In this paper, we introduce Disaster Resilient Network (DRN) for tunnels and bridges that allows mesh clients to contribute and retain the connectivity of the network. In DRN, mesh clients are able to establish and participate in data forwarding. We demonstrated that DRN is able to retain the capacity of original network under number of mesh node failures.

Knowing link characteristics of a network connection is essential for the efficient design, management, and usage of a network. In view of the proliferation of power line communication systems, in this work, we propose a novel measurement approach, called PLC-Probe, to concurrently measure link capacity and available bandwidth of PLC links. PLC-Probe uses `train-chirps' to probe the network, and it combines the strengths of the packet train and packet chirp approaches for estimation of link characteristics. Using testbed experiment, we evaluate the proposed approach and show that PLC-Probe is accurate, stable, and robust against cross traffic. Moreover, PLC-Probe is simple, effective, and applicable to other multi-rated PLC-like networks.

Location-based applications are becoming increasingly popular due to the wide spread of global positioning devices. One major concern in location-based applications is how to protect user privacy. A typical solution consists of three tiers: mobile users, trusted anonymizer, and service provider. The trusted anonymizer first needs to cloak the queries received from mobile users, then sends the cloaked queries to the service provider. The service provider then answers the cloaked query, which is a challenge if the underlying environment is a road network. In this paper, we propose to use Road Network Embedding (RNE) to answer cloaked queries in a road network environment. We first give an algorithm to answer k-nearest neighbor queries, then extend the algorithm to answer range queries and queries over private objects. Extensive simulation studies are preformed to show the effectiveness of the proposed technique.

In many metropolitan areas, traffic congestion is an escalating problem which causes a significant waste of money and time. Nowadays, cars equipped with GPS devices become widespread. The location information of those cars is very useful for estimate traffic condition in the complex city road network. Using the accurate and real time traffic condition, we can provide dynamic route guidance to ease traffic congestion. In this paper, we proposed a speed pattern model, called two phase piecewise linear speed model (2PEED), to estimate traffic condition and represent speed pattern in a road network using GPS data collected vehicles. With the estimated traffic condition and speed pattern, a proposed classification-based route guidance approach using machine learning technique provides dynamic routing for drivers. Using both current traffic data and the experience learned from history data, our route guidance approach is able to accurately predict the future traffic condition and selects a best route. We give simulation results to show that the proposed approach is able to select and dynamically update a route to prove drivers a best (e.g., less traffic and shortest travel time) route to their destination.

Supporting high mobility is essential to mobile ad hoc networks in a wide range of emerging applications such as vehicular networks. Communication links of an established communication path that extends between source and destination nodes are often broken under a high mobility environment. Although a new communication route can be established when a break in the communication path occurs, repeatedly reestablishing new routes incurs delay and substantial overhead. To address this limitation, we introduce the Communication Path abstraction in this paper. A communication path is a dynamically-created geographical area that connects the source and destination nodes. The routing functionality of a communication path is provided by the physical nodes (i.e., mobile devices) currently within the geographical region served by the path. These physical nodes take turns in forwarding data packets for the path. Since a path can be supported by many alternative nodes, this scheme is much less susceptible to node mobility. Our simulation results show the Communication Path approach can achieve several times better performance than traditional approach based on a fixed sequence of physical links.

Abstract In this work, we study two cross‐layer techniques, the Patching‐based scheme (PS) and the Caching‐based scheme (CS), to provide video‐on‐demand (VoD) in the multi‐hop WiMax mesh networks. PS consists of an efficient and lightweight multicast routing technique as well as the Patching technique in the application layer. Although PS improves the scalability of the system, the server has to launch extra Patching streams for later users in the multicast group. CS caches the video content in the subscribe stations of the mesh network. The cached video data can be used to accommodate later request. Both approaches employ a novel joint solution of admission control and channel scheduling for video streams in the lower layer. This joint solution guarantees the data rate for the admitted video streams, which is crucial for real‐time video streaming application. We used simulation to study the performance of PS and CS under real system settings. Copyright © 2010 John Wiley & Sons, Ltd.

User Relevance feedback techniques based on learning methods such as Artificial Neural Networks and kernel machines have been widely used in content-based image retrieval. However, the traditional relevance feedback framework for existing techniques still suffers from: (1) high learning cost incurs substantial delay in responding to user relevance feedback, (2) the classifiers may be biased when the negative feedback samples out-number the positive feedback samples, and (3) The high feature dimensions compared to the size of the training set causes over fitting. We propose a new relevance feedback approach based on a lazy processing framework. This approach combines random sampling, data clustering, and ensembles of classifiers to address the aforementioned problems. Our experimental studies show that the proposed framework provides a responsive user feedback environment that is capable of outperforming the traditional approach.

Vehicular Ad-Hoc Network (VANET) has been one of the most transforming technologies for Intelligent Transportation Systems (ITS) in recent years. In particular, wireless mesh network is widely used for VANETs due to its scalability, flexibility, and low maintenance cost. When a mesh node fails, one can simply replace it with a new mesh node, and the network automatically reconfigures itself. However, due to the environment or limited resources, it may not be feasible to replace the failed node immediately. In this work, we introduce a resilient VANET that can effectively retain the coverage and capacity of the original network until the failed mesh nodes can be repaired or replaced. We give simulation results to show that this desirable property can be achieved using a virtual router concept with minimal overhead.

It has long been acknowledged that TCP performance suffers in hybrid networks. A Split TCP approach has been proposed to address this problem. In this approach, a proxy is employed at the boundary of wired networks (e.g. the Internet) and wireless networks (e.g. cellular networks, WLan or mobile ad hoc networks). This strategy significantly improves TCP performance. However, it does not maintain the end-to-end semantics of TCP. The loss of end-to-end semantics may cause problems to applications that rely on such a guarantee provided by TCP. In this paper, we introduce a novel technique called Semi-Split TCP to address the problem without sacrificing the benefits of Split TCP. We present the architecture and algorithms of the proposed technique; and give implementation, simulation, and analytical results to illustrate the feasibility of this method. Our simulation results demonstrate that Semi-Split TCP can achieve similar performance as Split-TCP without sacrificing the end-to-end semantics.

Live video streaming over vehicular ad hoc networks (VANET) is an attractive feature to many applications, such as emergency live video transmission, road-side video advertisement broadcasting and inter-vehicle video conversation. Though vehicles have ample bandwidth, computation and storage capacity to support data intensive communication, the high mobility may cause persistent network partition. The performance of video streaming suffers from the delay and packet loss incurred by the long time disconnection. Although many solutions have been proposed to handle the high mobility problem, few of them addressed the problem in the context of video transmission. In this paper, we focus on video streaming between vehicles in highway, where the traffic density is adequate to mitigate frequent link disconnections and persistent network partitions. We study the performance of video streaming under different data forwarding and buffer management schemes. Real video data and the dedicated short range communications (DSRC) are used in the simulation study to better match the reality. The result of this paper gives insight for the design of the future inter-vehicle video streaming system.

Today's communication techniques for mobile ad hoc networks take a connection-oriented approach. Mobile nodes need to discover routes and establish a connection before they can communicate. This strategy is not robust as it cannot adapt to frequent unpredictable topology changes due to high mobility. Constant reconnections incur significant overhead making these schemes unsuitable for applications such as voice and video. To address these issues, we explore a connectionless paradigm in this paper. We leverage technology such as GPS (global positioning system) to allow a source node to discover a grid path to the destination node. Data packets are relayed along this path toward the destination using different intermediate nodes at different times without having to first establish connections between them. The performance of this new solution is essentially unaffected by node mobility. As a result, it is suitable for a wide range of mobile applications.

In this paper, we present the Range Multicast protocol and the implemented prototype. We propose to demonstrate the system at the 2004 ACM Multimedia Conference.