This work considers the inpainting of missing data in an indoor field, such as geomagnetism and WiFi fingerprints. As opposed to typical image/video inpainting problems, this problem poses several new challenges. First, unlike images with rectangular shapes and fixed RGB channels, indoor geographic data are multi-channeled and highly influenced by building structures. Second, unlike natural objects with fixed shapes, each geographic field is distinct and geographic data are environmentsensitive, following complex physical laws. Consequently, learning from data in other fields is difficult. Third, such data may be obtained from manual surveys and crowdsourcing, which often results in weakly-labeled and noisy datasets. We model our field data as (i) manually surveyed labeled data with holes and (ii) crowdsourced weakly-labeled data without holes. We propose a two-level adversarial regularization inpainting model to conquer these challenges and validate our results with real field data.

Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting scene illumination, (2) a Laplacian decomposition technique that preserves checker structure while allowing illumination-dependent color adaptation, and (3) a mask-based data augmentation strategy for handling imprecise color checker annotations. By harnessing rich priors from pre-trained diffusion models, GCC demonstrates strong robustness in challenging cross-camera scenarios. These results highlight our method’s effective generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile and practical solution for real-world applications.

We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic scenes have struggled to represent specular surfaces accurately. Our method addresses this limitation by introducing a residual correction technique for accurate surface normal computation during deformation, complemented by a deformable environment map that adapts to time-varying lighting conditions. We implement a coarse-to-fine training strategy significantly enhancing scene geometry and specular color prediction. It is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes, outperforming state-of-the-art methods in rendering complex, dynamic, and specular scenes. Please see our project page at cdfan0627.github.io/spectromotion.

When users give natural language instructions to service robots, positional information is often referenced relative to objects in the environment rather than absolute coordinates. However, humans naturally use relative references. For example, in“Go to the chair and pick up empty bottles”, where the positional reference is the chair, ambiguity arises when multiple similar objects co-exist in the environment or when the robot’s view is limited, resulting in multiple possible interpretations of the same command and affecting navigation decisions. To address this issue, we propose a two-level framework that integrates a large language model (LLM) and a vision-language model (VLM), allowing the robot to engage in multi-turn dialogues for spatial disambiguation. Our method first utilizes a VLM to map the semantic meanings of dialogues to a unique object ID in images and then further maps this object ID to a 3D depth map, enabling the robot to accurately determine its navigation target. To the best of our knowledge, this is the first work leveraging foundation models to address spatial ambiguity.

WiFi fingerprint-based localization has been studied intensively. Point-based solutions rely on position annotations of WiFi fingerprints. Trajectory-based solutions, however, require end-position annotations of WiFi trajectories, where a WiFi trajectory is a multivariate time series of signal features. A trajectory dataset is much larger than a pointwise dataset as the number of potential trajectories in a field may grow exponentially with respect to the size of the field. This work presents a semi-self representation learning solution, where a large dataset$C$of crowdsourced unlabeled WiFi trajectories can be automatically labeled by a much smaller dataset$\tilde{C}$of labeled WiFi trajectories. The size of$\tilde{C}$only needs to be proportional to the size of the physical field, while the unlabeled$C$could be much larger. This is made possible through a novel “cut-and-flip” augmentation scheme based on the meet-in-the-middle paradigm. A two-stage learning consisting of trajectory embedding followed by endpoint embedding is proposed for the unlabeled$C$. Then the learned representations are labeled by$\tilde{C}$and connected to a neural-based localization network. The result, while delivering promising accuracy, significantly relieves the burden of human annotations for trajectory-based localization.

With the advancement of Internet of Things (IoT) applications, it is essential to utilize an IoT platform to facilitate data exchange and application deployment. Existing platforms are typically either data-cloud-based or data-centralized, relying on servers as repeaters to exchange data. However, these architectures often face limitations related to triangle routing, network bottlenecks, and data scalability challenges, particularly in AIoT (Artificial Intelligence of Things) applications that require the fusion of numerous high-volume data streams. These challenges can be significantly mitigated through data-decentralized direct sender-to-receiver exchanges with a remote 'Agent', which is responsible for connectivity management. This work presents a prototype data-decentralized AIoT platform (DAIoTtalk) featuring peer-to-peer communications empowered by customized gRPC remote procedure calls based on the publish-subscribe (pub-sub) paradigm. An extension of IoTtalk, DAIoTtalk ensures device management with more adaptable node networking and offers a test bed for low-code development with decentralized communications. We demonstrate through extensive experiments that our design achieves at least 3 times more efficiency than a data-centralized approach. We also develop a case study to showcase the flexibility of our platform.

Traditional crowd-counting networks suffer from information loss when feature maps are reduced by pooling layers, leading to inaccuracies in counting crowds at a distance. Existing methods often assume correct annotations during training, disregarding the impact of noisy annotations, especially in crowded scenes. Furthermore, using a fixed Gaussian density model does not account for the varying pixel distribution of the camera distance. To overcome these challenges, we propose a Scale-Aware Crowd Counting Network (SACC-Net) that introduces a scale-aware loss function with error-compensation capabilities of noisy annotations. For the first time, we simultaneously model labeling errors (mean) and scale variations (variance) by spatially varying Gaussian distributions to produce fine-grained density maps for crowd counting. Furthermore, the proposed scale-aware Gaussian density model can be dynamically approximated with a low-rank approximation, leading to improved convergence efficiency with comparable accuracy. To create a smoother scale-aware feature space, this paper proposes a novel Synthetic Fusion Module (SFM) and an Intra-block Fusion Module (IFM) to generate fine-grained heat maps for better crowd counting. The lightweight version of our model, named SACC-LW, enhances the computational efficiency while retaining accuracy. The superiority and generalization properties of scale-aware loss function are extensively evaluated for different backbone architectures and performance metrics on six public datasets: UCF-QNRF, UCF CC 50, NWPU, ShanghaiTech A, ShanghaiTech B, and JHU. Experimental results also demonstrate that SACC-Net outperforms all state-of-the-art methods, validating its effectiveness in achieving superior crowd-counting accuracy. The source code is available at https://github.com/Naughty725.

WiFi-based indoor positioning has been extensively studied. A fundamental issue in such solutions is the collection of WiFi fingerprints. However, due to real-world constraints, collecting complete fingerprints at all intended locations is sometimes prohibited. This work considers the WiFi fingerprint inpainting problem. This problem differs from typical image/video inpainting problems in several aspects. Unlike RGB images, WiFi field maps come in any shape, and signal data may follow certain distributions. Therefore, it is difficult to forcefully fit them into a fixed-dimensional matrix, as done with processing images in RGB format. As soon as a map is changed, it also becomes difficult to adapt it to the same model due to scale issues. Furthermore, such models are significantly constrained in situations requiring outward inpainting. Fortunately, the spatial relationships of WiFi signals and the rich information provided among channels offer ample opportunities for this generative model to accomplish inpainting. Therefore, we designed this model to not only retain the characteristic of regression models in generating fingerprints of arbitrary shapes but also to accommodate the observational outcomes from densely deployed APs. This work makes two major contributions. Firstly, we delineate the distinctions between this problem and image inpainting, highlighting potential avenues for research. Secondly, we introduce novel generative inpainting models aimed at capturing both inter-AP and intra-AP correlations while preserving latent information. Additionally, we incorporate a specially designed adversarial discriminator to enhance the quality of inpainting outcomes.

Wi-Fi fingerprint-based indoor localization methods rely on establishing a complete fingerprint database of the target area. However, fingerprint collection usually requires a significant amount of labor and time. We propose that a target area with only partially labeled fingerprints can be expanded to become completely labeled through human walking trajectories. Data collectors (trainers) are requested to move within the field to obtain unlabeled fingerprint trajectories. To enable label expansion, a series of corrections is made to ensure that the walking trajectories of trainers are correctly predicted and, consequently, labeled. This work greatly reduces the manual cost of fingerprint labeling.

Health monitoring of wind turbines (WTs) has gained a lot of attention recently. Prevalent solutions mainly rely on the status data from Supervisory Control And Data Acquisition (SCADA), which is widely installed on modern WTs, to detect failures. However, the sensor data from SCADA may not be sufficient to identify defects of blades. One possible approach is to collect audio signals from the target WTs. Nonetheless, lacking spatial information, audio signals are unable to pinpoint the locations of anomalies. In this work, we propose to employ acoustic imaging for WTs anomaly detection. A reconstruction-based anomaly detection model with a Spatial-Temporal Convolutional Autoencoder is developed. The core idea is to learn the visual representations of acoustic images from a healthy state and therefore a poor reconstruction result would indicate an anomaly. To the best of our knowledge, this is the first attempt to adopt acoustic imaging to handle anomaly detection in the field of WTs. Experimental results demonstrate the effectiveness and robustness of the proposed method across various anomalous conditions.

The Artificial Intelligence of Things (AIoT) plays a crucial role in shrimp farming by enabling automated and real-time monitoring of shrimp counting, especially larvae. With this counting information, proper feeding control can be maintained by equipping IoT devices to capture real-time data on water quality, temperature, and other environmental factors, ensuring healthy shrimp growth and increasing production. Taking advantage of the power of AIoT, this article proposes a scaled multilayer feature fusion network (SMILES-Net) to allow farmers to remotely and automatically manage the counting of shrimps, effectively reducing the need for manual labor, and enabling swift interventions to sustain shrimp well-being. Since shrimp larvae are extremely small, we frame this counting problem as a density prediction problem, where the sum of the constructed density map is the total number of shrimps predicted. The pooling operation used in convolutional neural networks scales each feature map to 1/4 and causes the rich features of small shrimps to disappear dramatically. To tackle this truncation problem, we propose a novel synthetic fusion module (SFM) and an intrablock fusion module (IFM) to create a smoother scale space, generating better heat maps with fine-grained features for shrimp counting. Furthermore, we introduce a lightweight version of SMILES-Net (LW-SMILES-Net) that enables real-time shrimp counting without compromising accuracy. This method is evaluated on different data sets for shrimp counting and outperforms all State-of-The-Art methods. Overall, integrating SMILES-Net with IoT devices can provide a powerful solution for real-time shrimp larvae counting in shrimp farming, contributing to sustainable increases in shrimp production. The data set is available athttps://github.com/Naughty725/shrimp.

With the advance of deep learning, several indoor fingerprint-based localization models have been proposed. While being able to learn the relationships between fingerprints and locations well, such models may suffer from environment change and data aging problems. To avoid a bad user experience, an uncertainty value can be provided to indicate the reliability of a localization estimate. We thus propose a multi-branch neural network that can conduct magnetic-based indoor localization and uncertainty estimation simultaneously. The main idea is to duplicate the main localization branch multiple times with different depths. A loss function is proposed to balance local-ization accuracy and uncertainty estimation. Through extensive experiments, we show that the proposed method outperforms Monte Carlo dropout approaches in AUCO by 72.9% and precision-recall AUC by more than 100%. Besides, the model uses much less parameters than the deep ensemble approach due to our shared-weight multi-branch design.

Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-resolution thermal images as conditions to guide the synthesis of RGB images, bringing a promising solution for real-time video conferencing when privacy leakage is a concern. SPADE-SR [1] (Spatially-Adaptive De-normalization with Self Resampling), a variant of SPADE, is adopted to incorporate the spatial property of a thermal heatmap and the non-thermal property of a normal, privacy-free pre-recorded RGB image provided in a form of latent code. We create a PAIR-LRT-Human (LRT = Low-Resolution Thermal) dataset to validate our claims. The result enables a convenient way of video conferencing where users no longer need to groom themselves and tidy up backgrounds for a short meeting. Additionally, it allows a user to switch to a different appearance and background during a conference.

As robots are widely used in assisting manual tasks, an interesting challenge is: Can mobile robots help create a labeled knowledge dataset that can be used for efficiently creating deep learning models for other sensors? This paper proposes an Unsupervised Person Labeling and Identification (UPLIFT) framework to automatically enlarge the labeled knowledge dataset. Typically, manual data labeling is very costly, especially when the user population is large and dynamic. To reduce the cost, we use a mobile robot to serve as a knowledge seed and to provide the pseudo-ground-truth for the system so that unlabeled images from other fixed surveillance cameras can be paired with the pseudo-ground-truth. Ultimately, the knowledge dataset can be generated via a system-to-system knowledge transfer process from the former to the latter and gradually expanded as the system operates longer. Experimental results in two environments indicate that UPLIFT achieves an accuracy of 94.1% on average to detect pedestrians' IDs every 10 seconds.

With the rapid development of smart devices and wireless communication technologies, IEEE 802.11ah (WiFi HaLow) is designed to solve one of the major problems of Internet of Things (IoT): high collision probability in dense networks. It proposes the Restricted Access Window (RAW) mechanism, where stations (sensors) are partitioned into groups for time-division channel access. The grouping strategy, which highly influences network performance, needs to consider factors including the number of stations per group, and stations’ data rates, and locations. With the advance of artificial intelligence technologies, we ponder whether deep learning can help solving this station grouping problem. In this paper, we propose a multilayer perceptron (MLP) model to predict RAW performance. More precisely, the model predicts the corresponding throughputs and packet loss rates of a given set of RAW configurations. Thus, based on the predicted results, we can determine proper RAW parameters. We have validated the proposed method by ns-3 simulations.

Video prediction has been studied in several earlier works. However, there are two inherent limitations in existing solutions, one being the ambiguity concern and the other being the degradation problem in lengthy predictions. To overcome these limitations, this work studies using wearable inertial sensors to guide video prediction. We create a data set called Pedestrians with IMU (Ped-IMU) that records people walking around with the wearable devices to collect the relation between inertial measurement unit (IMU) and video data and propose a SensePred model to conquer these limitations. The model takes full wearable sensor information and partial video information as inputs and predicts the missing video information. Simulation results validate the effectiveness of our model.

Generatinga detailed 4D medical image usually accompanies with prolonged examination time and increased radiation exposure risk. Modern deep learning solutions have exploited interpolation mechanisms to generate a complete 4D image with fewer 3D volumes. However, existing solutions focus more on 2D-slice information, thus missing the changes on the z-axis. This article tackles the 4D cardiac and lung image interpolation problem by synthesizing 3D volumes directly. Although heart and lung only account for a fraction of chest, they constantly undergo periodical motions of varying magnitudes in contrast to the rest of the chest volume, which is more stationary. This poses big challenges to existing models. In order to handle various magnitudes of motions, we propose a Multi-Pyramid Voxel Flows (MPVF) model that takes multiple multi-scale voxel flows into account. This renders our generation network rich information during interpolation, both globally and regionally. Focusing on periodic medical imaging, MPVF takes the maximal and the minimal phases of an organ motion cycle as inputs and can restore a 3D volume at any time point in between. MPVF is featured by a Bilateral Voxel Flow (BVF) module for generating multi-pyramid voxel flows in an unsupervised manner and a Pyramid Fusion (PyFu) module for fusing multiple pyramids of 3D volumes. The model is validated to outperform the state-of-the-art model in several indices with significantly less synthesis time.

How a robot explores and interacts with the real world by itself is a major research challenge. On the other hand, causal reasoning and combinatorial generalization are indispensable parts of human intelligence for exploration and survival. This paper presents SelfSVT, a self-supervised solution for the switch-toggling visual task, of which the goal is to infer causalities of visual combinatorial effects on the environment. Specifically, when a robot takes over a new place with a set of light switches and knows nothing about the switches’ functions, it has to figure out how to transfer the environment from the current visual state to a goal visual state by toggling these switches by itself. SelfSVT trains efficient learning models that are able to perform the goal-conditioned visual task by directly reasoning the causalities of different visual states or inferring the switch states from its observations. In particular, we use the switch state to directly represent the combinatorial effect to make self-supervised learning possible and our framework adopts a siamese network with a discrete contrastive loss. It can perform causal induction and combinatorial generalization in a new environment with a few interactions. Our solution outperforms previous methods in both simulated and real-world environments and both static and dynamic environments. SelfSVT could achieve 100% success reasoning rates in most cases when there are sufficient interactions with the environment.

Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence of latent codes are injected into an LSTM network to identify location. To assess the accuracy and effectiveness of our model, we perform extensive real-life experiments.

A highly efficient vehicle counting approach based on timespatial images with deep learning is proposed in this paper. Most vehicle counting solutions are based on frame-by frame object detection and tracking to calculate the number of cars that cross a counting line. However, these approaches incur a great deal of redundancy because they track vehicles in a large area though it matters only when vehicles cross the counting line. In this work, we use time-spatial images to focus only on the information happening along the counting lines, instead of whole images, to reduce redundancy. Due to the nature of time-spatial images, vehicle counting can be achieved by object detection in such images without frame-by-frame tracking. We propose Foreground Favorable Model to conquer occlusion, congestion, and lighting change problems and Cross-Image Object Linking to conquer the distortion problem of nearly static vehicles. We also present an automatic time-spatial image dataset generation flow and the first time-spatial image dataset, called DRIVE-TSI, for vehicle counting tasks. Our vehicle counting accuracy beats state-of-the-art solutions in accuracy and is proved to be much more efficient because it only focuses on a small number of pixels. Our model achieves a 97.95% counting accuracy at 2.91 ms per frame in day time urban scenarios.

With the maturity of 5G networks and V2X technologies, utilizing V2X to increase driving safety becomes feasible. Road side units (RSU) recognize dangerous events and notify the related vehicles using 5G V2X communications. The alert latency between RSUs and related vehicles may cause car accidents. Besides, the V2X emergency alarms are broadcasted by dipole antennas. Many unrelated vehicles will receive unnecessary alerts and distract drivers. This paper proposes an accurate vehicle-to-vehicle instant alert system (V2V-IAS) to detect potential dangerous events and alert target vehicles by directional antennas to reduce transmission delay and interference. In V2V-IAS, a vehicle can fuse camera information and cooperative awareness messages (CAM) messages from surrounding vehicles carried to predict dangerous events without relying on RSUs. Once an event is detected, the vehicle will send an alert to specific cars in real time using directional antennas to increase the transmission success rate and reduce disturbing unrelated vehicles. We also use two sets of Qualcomm MDM9150 to conduct real road tests and simulate multi-vehicle environments on Matlab to validate our idea. From experiments, directional antennas can reduce the possibility of packet collisions up to 21% and transmit at the same distance with less output power compared to dipole antennas.

In this paper, an edge-computed and controlled outdoor autonomous UA V system is proposed to monitor the safety helmet wearing of workers in construction sites. Detection and counting of the workers with safety helmets of specified colors and those without safety helmets is the main focus of this work. Five standard safety helmet colors including blue, orange, red, white, and yellow are considered. The novelties of the work are 1) the design of a modularized software architecture running on an Android smartphone as an edge device for outdoor autonomous UA V navigation, 2) the implementation of realtime colorwise detection and counting of workers with and without safety helmets from UAV's first-person view (FPV), 3) the implementation of a simple upper-side cropping and hue, saturation, value (HSV) filtering method for color decision. The resulting average safety helmet detection accuracy for 10 different cases is 81.02%.

Multisensor data fusion combines various information sources to produce a more accurate or complete description of the environment. This article studies an object identification (OID) system using multiple distributed cameras and Internet-of-Things (IoT) devices for better visualizability and reconfigurability. We first propose a data processing and fusing method to merge the detection results of different IoT devices and video cameras, in order to locate, identify, and track target objects in the monitored area. Then, we develop the FusionTalk system by integrating the data fusion techniques with IoTtalk, an IoT device management platform. FusionTalk is designed with flexibility, modularity, and expansibility, where cameras, IoT devices, and network applications are modularized and can be conveniently plugged in/out, reconfigured, and reused through graphical user interfaces. In FusionTalk, the scope and the target of surveillance can be flexibly configured and associated, and administrators can be warned and easily visualize the movement and behavior of specific objects. Our experimental evaluation of the data fusion algorithm in various scenarios shows an identification accuracy above 95%. Finally, theoretical and numerical analyses on the failure probability of pairing IoT devices with video objects by FusionTalk are presented. Extensive experiments are performed to demonstrate the pairing effectiveness in real-world scenarios with failure probability less than 0.01%.

Sensing data fusion is one of the most important technologies in autonomous driving. Its performance depends on advance communication technology. Cellular-Vehicle to Everything (C-V2X) initially defined as LTE V2X in 3GPP Release 14 is a solution for vehicle communication that includes Vehicle-to-Infrastructure (V2I), Vehicle-to-person (V2P), and Vehicle-to-Vehicle (V2V). Although 4G LTE and 5G provides high-speed transmission, packet loss and delay are still inevitable. Packet loss and delay affect the safety of autonomous driving, especially for the judgment of emergency. In this paper, we compare the accuracy of data fusion under different rate of packet loss and broadcast frequency on the simulated platform CALAR. And we propose a skill to improve accuracy. Experiments show that the proposed skill significantly alleviates the effect of communication packet loss and delay on the accuracy of V2X data fusion.

To achieve ultra-low energy consumption and decade-long battery lifetime for Internet of things (IoT) networks, wake-up radio (WuR) appears as an eminent solution. While keeping devices in deep sleep for most of the time, a WuR enabled IoT device can be woken up for data transmission at any time by a wake-up call (WuC). However, collisions happen among WuCs for transmitter-initiated data reporting and among data packets for receiver-initiated data collection. In this paper, we propose two novel hashing-based schemes in order to achieve collision-free data transmissions for receiver-initiated data collection. We consider first a simple scenario in which all devices in a region of interest are covered by a data collector and propose a scheme which facilitates a scheduled time for data uploading of each device. Then we extend our scheme to cover a more realistic scenario where IoT devices are distributed across a larger region that cannot be covered by a single data collector. In this case, we propose a partitioning algorithm for data collection across multiple partitions. Both analysis and simulations are performed to demonstrate the effectiveness of the proposed schemes.

This paper introduces an innovative model which incorporates vehicle On-Board Unit (OBU) data and roadside video information to provide instant alert messages to drivers. We apply computer vision techniques to perform real-time danger event detection and to identify specific surrounding vehicles that should be alerted. Different from traditional broadcast-based alerting, we propose to send these instant alert messages to the target vehicles by unicast and geocast. To do so, an accurate method is required to analyze the spatial relation of vehicles. Also, to confine our alert messages to only those target vehicles, we rely on roadside cameras and apply a sensor fusion technique that can link a video object with its communication MAC address. Through this innovative idea, we integrate computer vision with 5G networks and enable transmitting instant alerts to precise vehicles without interfering irrelevant vehicles. How to incorporate our system with 3GPP V2X by setting proper transmission parameters is also addressed. To validate our idea, we present four common road danger events and show how our model works. To the best of our knowledge, this is the first work bringing computer vision to instant messaging.

The emergence of voice over LTE enables voice traffic transmissions over 4G packet-switched networks. Since voice traffic is characterized by its small payload and frequent transmissions, the corresponding control channel overhead would be high. Semi-persistent scheduling (SPS) is hence proposed in LTE-A to reduce such overhead. However, as wireless channels typically fluctuate, tremendous retransmissions due to poor channel conditions, which are still scheduled dynamically, would lead to a large overhead. To reduce the control message overhead caused by SPS retransmissions, we propose a new SPS retransmission protocol. Different from traditional SPS, which removes the downlink control indicators (DCI) directly, we compress some key fields of all retransmissions' DCIs in the same subframe as a fixed-length hint. Thus, the base station does not need to send this information to different users individually but just announces the hint as a broadcast message. In this way, we reduce the signaling overhead and at the same time, preserve the flexibility of dynamic scheduling. Our simulation results show that, by enabling DCI compression, our design improves signaling efficiency by 2.16×, and the spectral utilization can be increased by up to 60%.

Computer vision based object tracking has been used to annotate and augment sports video. For automatically and systematically competition data collection and tactical analysis. The proposed project also includes research of data visualization, connected training auxiliary devices, and data warehouse. Deep learning techniques will be used to develop video-based real-time microscopic competition data collection based on broadcast competition video. Machine learning techniques will be used to develop tactical analysis. In addition, training auxiliary devices including smart badminton rackets and connected serving machines will be developed based on the IoT technology to further utilize competition data and tactical data and boost training efficiency. Especially, the connected serving machines will be developed to perform specified tactics and to interact with players in their training.

Collecting surrounding vehicles' motion information is one of the key issues for accident prevention and autonomous driving. Although multi-vehicle simulation frameworks are widely provided, We need a platform that enable inter-vehicle V2X communications. In this work, based on the open source simulation platform, CARLA, we extend and implement several modules to build a V2X simulation framework. In the proposed framework, vehicles are allowed to share their profiles and sensory data through V2X communications. With the motion information of other vehicles, a car can thus make more intelligent decisions. To validate the effectiveness of the framework, we run simulations in variose scenarios. Each time, a primary vehicle is selected and then both its sensory data and received surrounding vehicles' information are output and recorded in a simulated dataset. It is shown that with the dataset and our multi-vehicle data fusion algorithm, the primary vehicle can visually see the driving status of surrounding cars, which can greatly help a vehicle to choose a better driving strategy. This work not only proposes a V2X communication-enabled multi-vehicle simulation framework based on CARLA, but also provides a low cost way to generate simulated V2X datasets.

Over the decades, fitness activities and extreme endurance events are expanding throughout the world. The number of available public skeletal repositories and recognition/evaluation benchmarks has grown rapidly since Microsoft manufactured a motion sensing device called Kinect. Kinect RGBD data has become a very useful representation of an indoor scene for solving activity/fitness recognition problems. The other alternative sensor which has been utilized widely in this area is the wearable inertial measurement unit (IMU) sensor. With numerous advance sensors with mass adoption, this technology represents a possible approach to surpass current activity recognition and evaluation research solutions. Nevertheless, there is a limited number of publicly available datasets where depth camera, inertial sensor, and RGB image data are captured at the same time. In this paper, we introduce NCTU-MFD (National Chiao Tung University Multisensor Fitness Dataset), a comprehensive, diverse multisensor dataset collected using Kinect RGBD sensor, wearable inertial sensors, and web cameras. The dataset contains 47131 RGB images, 47131 depth images, and 100 csv files including 47131 skeletal data (from 25 joints) collected from Kinect sensor. In addition, our dataset also contains acceleration and gyroscope data from IMU sensors, and 94262 RGB images (47131 images from each web camera). To demonstrate the possible use of our dataset, we conduct an experiment on evaluation of depth maps.

Advanced machine learning and deep learning techniques have increasingly improved accuracy of image classification. Most existing studies have investigated the data imbalance problem among classes to further enhance classification accuracy. However, less attention has been paid to data imbalance within every single class. In this work, we present AC-GAN (Actor-Critic Generative Adversarial Network), a data augmentation framework that explicitly considers heterogeneity of intra-class data. AC-GAN exploits a novel loss function to weigh the impacts of different subclasses of data in a class on GAN training. It hence can effectively generate fake data of both majority and minority subclasses, which help train a more accurate classifier. We use defect detection as an example application to evaluate our design. The results demonstrate that the intra-class distribution of fake data generated by our AC-GAN can be more similar to that of raw data. With balanced training for various subclasses, AC-GAN enhances classification accuracy for no matter uniformly or non-uniformly distributed intra-class data.

Advanced Internet of Things (IoT) techniques have made human-environment interaction much easier. Existing solutions usually enable such interactions without knowing the identities of action performers. However, identifying users who are interacting with environments is a key to enable personalized service. To provide such add-on service, we propose WTW (who takes what), a system that identifies which user takes what object. Unlike traditional vision-based approaches, which are typically vulnerable to blockage, our WTW combines the feature information of three types of data, i.e., images, skeletons and IMU data, to enable reliable user-object matching and identification. By correlating the moving trajectory of a user monitored by inertial sensors with the movement of an object recorded in the video, our WTW reliably identifies a user and matches him/her with the object on action. Our prototype evaluation shows that WTW achieves a recognition rate of over 90% even in a crowd. The system is reliable even when users locate close by and take objects roughly at the same time.

Person identification and tracking (PIT) is an essential issue in computer vision and robotic applications. It has long been studied and achieved by technologies such as RFID or face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition accuracy highly depends on use scenarios. In this work, we propose RCU (Robot Catch yoU), an accompanyist robot system that provides follow-me or guide-me services. Such robots are capable of distinguishing users' profiles in front of them and keep tracking a specific target person. We study a more challenging scenario where the target person may be under occlusion from time to time. To enable robust PIT, we develop a data fusion technique that integrates two types of sensors, an RGB-D camera and wearable inertial sensors. Since the data generated by these sensors share common features, we are able to fuse them to achieve identity-aware tracking. Practical issues, such as time synchronization and coordinate calibration, are also addressed. We implement our design on a robotic platform and show that it can track a target person even when no biological feature is captured by the RGB-D camera. Our experimental evaluation shows a recognition rate of 95% and a following rate of 88%.

Both cameras and IoT devices have their particular capabilities in tracking moving objects. Their correlations are, however, unclear. In this work, we consider using a drone to track ground objects. We demonstrate how to retrieve IoT data from devices, which are attached on human objects, and correctly tag them on the human objects captured by a drone view. This is the first work correlating IoT data and computer vision from a drone camera. Potential applications of this work include aerial surveillance, people tracking, and intelligent human-drone interaction.

This paper presents a robot for people identification and tracking developed on robot operating system (ROS). It achieves modulized, light-weight, low-cost, and high-performance design goals even with the existence of distributed sensors. The key idea is to utilize wearable devices to enhance the people tracking capability of a robot through instant wireless communications and multi-sensory data fusion. Experimental results in a realistic environment demonstrate that our robot can keep tracking a specific person at a safe distance even without seeing the biological features of the person, who walks in a crowd with complex trajectories.

Emerging applications require the location information of clients to enable human-environment interactions or personalized services. With an increasing number of antennas equipped in today's wireless devices, recent research has shown possibility of sub-meter level localization based only on the angle of arrival (AoA) of WiFi sig- nals. While most existing work provides promising median accu- racy, their tail performance however is usually far worse. We ob- serve from measurements that the root cause is due to unequal AoA estimation reliability. In some critical areas, a small variation in the channel state information of signals could introduce an extremely large AoA estimation error. With this observation, we propose UAT (Unequal Angle Tracking), a confidence-aware AoA-based localiza- tion system. We show that unequal reliability of AoA measures can be mathematically quantified, allowing a system to weigh the de- cisions of different APs according to their confidence. Our testbed evaluation shows that UAT's confidence-aware design provides reli- able decimeter level localization for around 90% of locations. UAT is especially effective for risky areas and can reduce their localiza- tion errors by 27.5%, as compared to reliability-oblivious designs.

Collecting vehicle surrounding information is a key issue for accident prevention and autonomous driving applications. Although GPS and 4G/LTE are widely accepted, it is still a challenge for a vehicle to get complete information of its surrounding vehicles. In this work, we consider the integration of multi-sensory data through V2V communications to help a vehicle to understand its complex surroundings. We propose a fusion algorithm that can integrate four types of sensory inputs: V2V communications, GPS, camera, and inertial data. We show that through such fusion, it is possible for a vehicle to visually see the driving states of its surrounding vehicles.

The surge of M2M devices imposes new challenges for the current cellular network architecture, especially in radio access networks. One of the key issues is that the M2M traffic, characterized by small data and massive connection requests, makes significant collisions and congestion during network access via the random access (RA) procedure. To resolve this problem, in this paper, we propose a paging cycle-based protocol to facilitate the random access procedure in LTE-A. The high-level idea of our design is to leverage a UE's paging cycle as a hint to preassign RA preambles so that UEs can avoid preamble collisions at the first place. Our rpHint has two modes: (1) collision-free paging, which completely prevents cross-collision between paged user equipment (UEs) and random access UEs, and (2) collision-avoidance paging, which alleviates cross-collision. Moreover, we formulate a mathematical model to derive the optimal paging ratio that maximizes the expected number of successful UEs. This analysis also allows us to adapt dynamically to the better one between the two modes. We show via extensive simulations that our design increases the number of successful UEs in an RA procedure by more than 3× as compared to the legacy RA scheme of the LTE.

Person identification (PID) is a key issue in many IoT applications. It has long been studied and achieved by technologies such as RFID and face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition rates highly depend on use scenarios. To enable reliable and remote PID, in this work, we present EOY (Eye On You)1, a data fusion approach that combines two kinds of sensors, a 3D depth camera and wearable sensors embedded with inertial measurement unit (IMU). Since these two kinds of data share common features, we are able to fuse them to conduct PID. Further, the result can be transferred to a mobile platform (such as robot) since we have less constraints on devices. To realize EOY, we develop fusion algorithms to address practical challenges, such as asynchronous timing and coordinate calibration. The experimental evaluation shows that EOY can achieve the recognition rate of 95% and is very robust even in crowded areas.

The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency.

With the increasing popularity of machine-type communication (MTC) devices, several new challenges are encountered by the legacy long term evolution (LTE) system. One critical issue is that a massive number of MTC devices trying to conduct random access procedures may cause significant collisions and long delays. In this work, we present a new random access mechanism by splitting the contention-based preambles in LTE into two logically disjoint parts, one for the user equipment (UE) being paged and the other for the UEs not being paged. Since the IDs of paged UEs are known by the base station, a novel hash-based random access, which we call hint, is possible. The main idea is to pre-allocate preambles to paged UEs in a contention-free manner and confines non-paged UEs to contend in a separate region. We further build a mathematical model to find the optimal ratio of pre-allocated preambles. Extensive simulations are conducted to validate our results.

Smart home is one of the major Internet of Things (IoT) applications. When realizing a smart home, designing an intuitive interaction for appliance control has become a crucial issue. A control intention to an appliance comprises two main meanings, identification and control command. But, previous works seldom deliver these two meanings at the same time. To fully convey the control intention, this paper proposes a scheme of intuitive appliance control that tracks and recognizes hand moving trajectory and hand gestures respectively by exploiting acoustic information. We utilize a 3D positioning method based on time difference of arrival (TDoA) of acoustic signals to detect the trajectory of a hand movement for appliance identification and design a gesture recognition method by Doppler effect for control command. We validate the effectiveness of the proposed scheme via real-life experiments. The results show that average locating errors to the targets are 5.24° and 10.35° in the horizontal and vertical planes respectively and the accuracy of gesture recognition is 96.66%. Since human's eyesight is horizontal to the ground, locating an object in the vertical view is error-prone. We also provide a scroll gesture to assist the appliance selection.

The 3GPP Narrowband Internet of Thing (NB-IoT) is the promising technology that can provide multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and transmission reliability. Since the IoT devices need to operate for a very long time, the energy consumption becomes a critical issue. In this paper, we study how to guarantee the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem and then propose an energy-efficient scheme, which consists of two stages. The first stage tries to incur the lowest energy consumption of devices and satisfy their QoS requirement. The second stage determines the scheduling order to ensure the delay constraint while maintaining energy efficiency. Simulation results show that our scheme can serve more devices while saving their energy.

For next generation mobile communications, Cloud-RAN (C-RAN) is an emerging network architecture to provide broadband services. C-RAN separates computation entities, i.e., Baseband Units (BBUs), from base stations (BSs) and puts BBUs in a cloud located in a centralized network. With C-RAN, UEs can receive data from multiple collaborative cells and thus can leverage the dynamic point selection (DPS) technology to improve network efficiency. When user equipments (UEs) enter a hotspot and can not be served due to congestion, data offloading from the hotspot to its neighboring cells may take place to balance heterogeneous cells' loads. This work shows how to integrate such DPS offloading with the Discontinuous Reception (DRX) mechanism, which allows UEs to turn off their radio interfaces in a periodical manner. We address the resource allocation problem in heterogeneously-loaded C-RAN by optimizing UEs' energy consumption based on DRX while reserving sufficient bandwidths for UEs considering their quality-of-service (QoS) through DPS. We propose an offloading-based DPS scheduling scheme by exploiting not only maximal instantaneous throughputs but also minimal energy cost. Simulation results show that our scheme can improve throughput, resource utilization, and energy consumption as compared to existing schemes.

Indoor localization systems have attracted considerable attention recently. A lot of works have used wireless signals from existing base stations to track users' locations. The major challenge to such systems is the signal-drifting problem. A promising direction to conquer this problem is to fuse the tracked wireless signals with inertial sensing data. In this work, we consider location tracking in a multi-floor building, which we call a 2.5-D space, by taking wireless signals, inertial sensing data, and indoor floor plans of a 2.5-D space as inputs and building a SPF (sensor-assisted particle filter) model to fuse these data. Inertial sensors are to capture human mobility, while particles reflect our belief of the user's potential locations. Our work makes the following contributions. First, we propose a model to partition a 2.5-D space into multiple floors connected by stairs and elevators and further partition each floor, according to its floor plan, into logical units connected by passages. Second, based on the 2.5D space model, we then propose particle sampling and resampling mechanisms over the logical units using wireless signal strengths and inertial sensing data to adjust our beliefs of the user's potential locations. Third, to conquer the signal-drifting problem, we propose a weighting mechanism to control the distribution of particles based on user's activities of walking on grounds/stairs and taking elevators. A prototype has been developed and tested to verify the model and its accuracy.

Software defined network (SDN) provides flexible and scalable routing by separating control plane and data plane. With centralized control, SDN has been widely used in traffic engineering, link failure recovery, and load balancing. This work considers the flow update problem, where a set of flows need to be migrated or rearranged due to change of network status. During flow update, efficiency and consistency are two main challenges. Efficiency refers to how fast these updates are completed, while consistency refers to prevention of blackholes, loops, and network congestions during updates. This paper proposes a scheme that maintains all these properties. It works in four phases. The first phase partitions flows into shorter routing segments to increase update parallelism. The second phase generates a global dependency graph of these segments to be updated. The third phase conducts actual updates and then adjusts dependency graphs accordingly. The last phase deals with deadlocks, if any, and then loops back to phase three if necessary. Through simulations, we validate that our scheme not only ensures freedom of blackholes, loops, congestions, and deadlocks during flow updates, but is also faster than existing schemes.

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB‐IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB‐IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra‐reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP‐complete. Then, we propose an energy‐efficient, ultra‐reliable, and low‐complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

The use of the badminton stroke strategy in the evenly matched game is often the key to victory. In this work, a smart racket based on wearable sensors is proposed to collect the data of swing of badminton. A cell phone APP with machine learning techniques is implemented to record stroke types automatically. In each stroke hit event, this prototype system uses Bluetooth earphone to collect the sound for detecting the accuracy time. It uses the data of IMU in each stroke for determining stroke type. Compared to EMU only solution, the system will reduce the false count of stroke hit. Using cloud techniques could record the training and game record in a long period. Overall the accuracy of stroke hit event is almost 100% by using voice print. The data of EMU is classified by Random Forest or SMO. The accuracy for personal model is 95.91%, and it is 7932% for general model. We develop a stroke record system which is combined with Wearable sensor, Mobile platform and Cloud service.

We can find various Internet of Things (IoT) products available in the market to accommodate users needs. Current IoT applications, however, built delicately for their usage with their own sensor devices and Apps. This limitation thus prevents users from handling flexible situations. In this paper, we propose iToy, an LEGO-like solution to integrate various off-the-shelf sensors for different IoT applications. In iToy, the off-the-shelf sensors are like LEGO plastic bricks, which can be assembled and connected to construct IoT applications. Any sensors constructed in an IoT application can be taken apart again and then used to make other IoT applications. We demonstrate through proof-of-concept prototype that iToy is user-friendly and suitable for forming different small scale IoT applications.

In wireless sensor networks (WSNs), the sensing data of nodes have spatial similarity, so the network fault diagnosis can be done by comparing the data of neighbor nodes. When sending data, the node may send erroneous data because of the interference of the signal, thereby affecting the diagnostic accuracy of the network. This paper presents a parallel and local diagnostic algorithm (PLD) for WSN. In order to avoid the problem of signal collision, this paper constructs a special diagnosis structure, which effectively avoids the influence of signal collision on node diagnosis. The algorithm can be divided into three parts: generate the candidate sub-node set, establish the fault diagnosis structure and the diagnostic test. Diagnostic test contains four rounds. The first three rounds quickly compare perceived data of adjacent nodes in parallel, greatly reduces the time of diagnosis. In the fourth round, the most reliable node is tested with the first three rounds. Simulation results show that the proposed algorithm can guarantee higher diagnostic accuracy.

The paradigm shift toward SDN has exhibited the following trends: (1) relying on a centralized and more powerful controller to make intelligent decisions, and (2) allowing a set of relatively dumb switches to route packets. Therefore, efficiently looking up the flowtables in forwarding switches to guarantee low latency becomes a critical issue. In this paper, following the similar paradigm, we propose a new routing scheme called KeySet which is flowtable-free and enables constant-time switching at the forwarding switches. Instead of looking up long flowtables, KeySet relies on a residual system to quickly calculate routing paths. A switch only needs to do simple modular arithmetics to obtain a packet's forwarding output port. Moreover, KeySet has a nice fault- tolerant capability because in many cases the controller does not need to update flowtables at switches when a failure occurs. We validate KeySet through extensive simulations by using general as well as Facebook fat-tree topologies. The results show that the KeySet outperforms the KeyFlow scheme [1] by at least 25% in terms of the length of the forwarding label. Moreover, we show that KeySet is very efficient when applied to fat-trees.

Queuing recognition is a recently new raised research topic, which uses sensors of smartphones to automatically recognize human queuing behaviors. However, existing collaborative approaches need to exchange sensor data among nearby smartphones, causing extra communication overheads and even delay. In view of this, this work proposes a new framework called Qnalyzer for queuing recognition using accelerometer and Wi-Fi signals. It consists of three tiers. The first tier is run by each individual smartphone to identify each user's context without exchanging data with nearby smartphones. A new algorithm called QCF (Queuer and non-queuer ClassiFier) is proposed, which considers mixture features of accelerometer and Wi-Fi signals to effectively identify whether the user is queuing or not. The second tier is an algorithm called QCT (Queuers ClusTering) running at the server side to effectively identify which queuers belong to which queues based on users' movement features. The third tier is an estimation model called QPE (Queue Property Estimation) for measuring waiting time, service time, and queue lengths. The Qnalyzer prototype on Android smartphones and the corresponding performance evaluations under real- life queuing scenarios are implemented. The extensive experiment results show that Qnalyzer achieves good performance with high accuracy.

Massive Machine Type Communication (mMTC) has attracted increasing attention due to the explosive growth of IoT devices. Random Access (RA) for a large number of mMTC devices is especially difficult since the high signaling overhead between User Equipments (UEs) and an eNB may overwhelm the available spectrum resources. To address this issue, we propose “respond by hint” (r-Hint), an ID-free handshaking protocol for contention-based RA in mMTC. The core idea of r-Hint is to avoid sequentially notifying contending UEs of their IDs by broadcasting a hint in the RA Response (RAR). To do so, we exploit the concept of prime factorization and hashing to encode the hint such that UEs can extract their required information accordingly. Our simulation results show that r-Hint reduces the RAR message size by 20%-40%. Such reduction can be translated to around 50% improvement of spectrum efficiency in LTE-M.

Recent advances in technology have led to the rapid development of the Internet of Things (IoT) and the use of smartphones. The phenomenon has been widely influenced by the use of smart sensors, the accuracy of which is critical to the quality of service provided. Hence, it is vital that sensors are calibrated at both the device and software levels. Some applications even rely on environment-dependent sensing results. Floor detection by barometers is one example, where the pressure of a floor actually depends on the base reference pressure. This observation motivates us to study the floor detection problem by using barometers with device-to-device (D2D) communications. In this paper, we propose the Floor Calibration Protocol (FCP), which enables a mobile device to be calibrated with other mobile devices and anchor devices by considering both temporal and spatial factors. We conducted trace- based simulations to evaluate the proposed scheme and compared its performance with that of state- of-the-art approaches to validate the efficacy of our protocol.

IoT (Internet of Things) has attracted a lot of attention recently. IoT devices need to report their data or status to base stations at various frequencies. The IoT communications observed by a base station normally exhibit the following characteristics: (1) massively connected, (2) lightly loaded per packet, and (3) periodical or at least mostly predictable. The current design principals of communication networks, when applied to IoT scenarios, however, do not fit well to these requirements. When a large number of devices contend to send small packets, the signaling overhead is not cost-effective. To address this problem, our previous work [1] proposes the Hint protocol, which is slot-based and schedule- oriented for uploading IoT devices' data. In this work, we extend [1] to support data transmissions for multiple resource blocks. We assume that the uplink payloads from IoT devices are small, each taking very few slots (or resource blocks), but devices are massive. The main idea is to "encode" information in a tiny broadcast that allows each device to "decode" its transmission slots, thus significantly reducing transmission overheads and contention overheads. Our simulation results verify that the protocol can significantly increase channel utilization compared with traditional schemes.

Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or event-driven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, the widespread deployment of IoT raises the concurrent massive connectivity of IoT devices. How to solve these two problems is a critical issue. In this paper, we investigate an uplink resource allocation problem which considers the periodic, event-driven, and query-based IoT traffic behaviors over LTE-M. The proposed approach takes advantage of data aggregation and both spatial and temporal reuse. Our solution exploits long-term static scheduling for periodic data to ensure the latency and data rate, and employs short-term dynamic scheduling for event-driven, query-based data to improve transmission efficiency. Therefore, both small data and massive connectivity problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency and enlarge network capacity effectively.

Recently, elder care services, especially for those with chronic diseases, have become an important application in the field of IoT. Several studies have reported that the progress of chronic diseases can be slowed down effectively by moderate physical activities. In this paper, we propose an activity intensity monitoring system to support continuous monitoring of indoor calorie consumption. The system uses wireless signals to infer calorie burned in walking. In the proposed system, Bluetooth smart beacons are deployed in various spaces of a house. The elders who wear smart wristbands are continuously monitored for their locations by received wireless signals. The system has the following features. First, it utilizes wireless signals as location and activity intensity indicators; inertial sensors are only used in the training stage. Second, it is semi-training free and only needs relatively short training time to start using the monitoring services. Finally, the system is map-free, in the sense that it can be easily deployed without constructing the interior map of the house. A prototype system is implemented on a smartphone to evaluate the feasibility of the proposed system.

For next generation wireless communications, Cloud-RAN (C-RAN) has become an emerging network architecture of mobile communications, which separates computation entities-Baseband Units (BBUs) from original base stations (BSs), and puts BBUs in the cloud then forms a centralized network architecture. With C-RAN architecture, user equipments (UEs) can receive data from multiple collaborative cells and thus can leverage dynamic point selection (DPS) technology to improve network efficiency. Note that since the UEs are powered by batteries, energy saving is always a critical issue under C-RAN architecture. In current standard of 3GPP LTE-A, it has defined Discontinuous Reception (DRX) mechanism to allow UEs to turn off their radio interfaces and go to sleep to save energy. However, how to save UEs' energy under DPS in C-RAN is still an open issue. Therefore, this paper addresses the resource allocation problem by asking how to optimize the energy conservation of UEs based on DRX while serving UEs as more as possible under the consideration of UEs' quality of service (QoS) in C-RAN with DPS. To solve this problem, we propose an energy-efficient DPS (EE-DPS) scheduling scheme. The key idea of our scheme is to serve the UEs in the intersection of cells continuously and allocate resource tightly to avoid additional wake-up intervals. Extensive simulation results show that our scheme can serve most number of UEs while achieving high throughput as well as lower energy consumption, compared to the existing schemes.

In this paper, we identify two inefficiencies in the counting procedure of the evolved Multimedia Broadcast Multicast Service (eMBMS) in 3GPP: (1) it may consume too much network resources in determining the number of User Equipments (UEs) which are interested in receiving MBMS services, and (2) it is too conservative in that the counting results may be smaller than actual numbers because those UEs in the RRC_IDLE state are ignored (for avoiding RACH congestion). We then propose a new dynamic method by combining the theory of Geometric Splitting-based Estimation (GSE) and the theory of probability to accelerate the eMBMS counting procedure. Compared to the original eMBMS counting procedure, which only counts the UEs in the RRC_CONNECTED state, our scheme also counts the UEs in the RRC_IDLE state. However, our method still improves the accuracy of counting results and saves radio resources.

Detecting students' attention in class provides key information to teachers to capture and retain students' attention. Traditionally, such information is collected manually by human observers. Wearable devices, which have received a lot of attention recently, are rarely discussed in this field. In view of this, we propose a multimodal system which integrates a head-motion module, a pen-motion module, and a visual-focus module to accurately analyze students' attention levels in class. These modules collect information via cameras, accelerometers, and gyroscopes integrated in wearable devices to recognize students' behaviors. From these behaviors, attention levels are inferred for various time periods using a rule-based approach and a data-driven approach. The former infers a student's attention states using user-defined rules, while the latter relies on hidden relationships in the data. Extensive experimental results show that the proposed system has excellent performance and high accuracy. To the best of our knowledge, this is the first study on attention level inference in class using wearable devices. The outcome of this research has the potential of greatly increasing teaching and learning efficiency in class.

IoT (Internet of Things) and M2M (machine to machine) have attracted a lot of attention since more and more devices are expected to connect to the Internet for special purposes such as environment monitoring, home automation, industrial surveillance, and e-Health care. Currently, the OM2M (Open source platform for M2M communication) is a promising project which implements oneM2M and SmartM2M standards as an open-source platform for integrating various M2M services, applications, and devices. However, the individual data generated from those IoT/M2M devices is usually quite small, which incurs a lot of control overhead and thus decreases network performance significantly. Therefore, in this work we design and implement an OM2M 'plugin' that can aggregate small IoT data effectively. We will show how the plugin works and verify the effectiveness on the network bandwidth in this demonstration.

Smart video surveillance systems are essentials in modern environments to ensure safety and security for lives and property. In order to capture critical clues of reconnaissance, it is important to automatically and dynamically control the surveillance sensitivity and quality for detecting abnormal/suspicious events to perform high quality monitoring. In this work, we design a smart video surveillance system, which fully utilizes pan-tilt-zoom (PTZ) cameras and well integrates with environment sensors (such as fire/motion/door sensors) while cooperating with wearable devices that can adaptively monitor events and ensure the surveillance quality in terms of pixel-per-foot (PPF), viewing-angles, timeliness, and accuracy. In addition, this system also supports to define the special monitoring tasks and surveillance requirements, such as the surveillance timeliness and duty cycle for particular moving objects to be tracked per time unit. Then, a smart camera dispatch algorithm will calculate and determine the best cameras set with the corresponding configurations to accomplish the surveillance tasks effectively and precisely. We will show how our system realizes smart detecting and monitoring in this demonstration by leveraging PTZ cameras, environmental sensors and wearable devices.

Recently, many Internet of Things (IoT) or wireless sensor network (WSN) applications adopt ZigBee as their communication protocol. In these applications, messages may need to be disseminated to some specific objects or nodes using multicast transmissions. However, we observe that the original ZigBee multicast protocol causes extremely high packet overhead and energy consumption. In this work, we propose a ZigBee-compatible energy efficient multicast protocol, which allows nodes to execute the designed procedures in a distributed manner. By our scheme, each node uses the designed maintenance module to manage its neighbors. When multicasting, a node utilizes the designed multicasting module to compete for being a relay node by the proposed backoff mechanism. In addition, each node overhears its neighbors' broadcasts to avoid unnecessary transmissions and to ensure that the transmitted multicast packets are successfully received. Our results indicate that the proposed protocol can indeed lengthen network lifetime, reduce redundant packets, and preserve network reliability.

The demands on indoor localization services have grown explosively due to increasing popularity of mobile devices. Though various techniques based on wireless signals have been proposed, the new Bluetooth 4.0 and 4.1 technologies introduce both opportunities and challenges for localization. Like all wireless signals, the fluctuations caused by hardware conditions and environmental dynamics may deteriorate the accuracy of localization. Therefore, we propose to use adaptive ranging, which utilizes inter-beacon measurements to “sense” the transient device and environmental conditions, and adjust the parameters of signal propagation model. In this research, we apply this concept to multi-lateration and mobile encountering for localization using Bluetooth advertisements. We also validate that with the proposed adaptive ranging techniques, the impacts of signal fluctuations caused by hardware and environmental conditions can be greatly decreased. Furthermore, we combine the adaptive multi-lateration, encountering mechanism and pedestrian dead-reckoning with a particle filter (PF) framework to generate our localization results. Finally, two sets of experiments are performed in a department building. The overall improvement in accuracy is approximately 19.99% using our adaptive localization when comparing to the conventional non-adaptive methods.

A lot of personal daily contexts and activities may be inferred by analyzing acoustic signals in vicinity. Conversations play an important role in one's social communications. In this work, we consider the inference of conversation partners via acoustic sensing conducted by a group of smartphones in vicinity. By considering the continuity and overlap of speeches, we propose novel inference methods to identify conversational relationships among co-located users. In our system, each smartphone individually processes the acoustic data to understand its owner's talking turns and emotions. Via direct wireless communications, smartphones then cooperatively conduct the inference to retrieve conversational groups. Compared to existing work, which only exploits peer-to-peer conversational relationships, our approach is able to capture group conversational relationships in a more real-time manner. A prototype on Android smartphones is demonstrated to verify the feasibility of our approach. We also collect conversation data from movie clips and real life with 2 to 14 speakers to validate our result, which shows promising performance.

For next-generation wireless communications, the 3GPP Long Term Evolution-Advanced (LTE-A) is the most promising technology which provides transmission rate up to 1 Gbps and supports various broadband multimedia services, such as IPTV and Voice/Video-over-IP services. To reduce the energy consumption of user equipments (UEs), the LTE-A standard defines the Discontinuous Reception Mechanism (DRX) to allow UEs to turn off their radio interfaces and go to sleep when no data needs to be received. However, how to optimally configure DRX for UEs is still left as an open issue. In this paper, we address the DRX optimization problem for multicast services. This problem asks how to guarantee the quality of service (QoS) of the multicast streams under the Evolved Node B (eNB) while minimizing the UEs' wake-uptime. We prove this problem to be NP-complete and propose an energy-efficient heuristic. This heuristic consists of two phases. The first phase tries to aggregate the required bandwidth of the multicast streams for UEs to reduce their wake-up periods. The second phase further minimizes UEs' unnecessary wake-up periods by optimizing their DRX configurations. Extensive simulation results show that our scheduling is close to the optimum in most cases.

Two-tiered wireless sensor and actor networks (WSANs) have been proposed to enhance network capabilities, where a set of resource-rich mobile nodes (termed actors ) form a connected backbone to relay sensing data from static sensors to the sink and sometimes are requested by sensors to perform a particular action. Such a two-tiered WSAN facilitates scalability and can efficiently reduce the energy consumption incurred by conventional hop-by-hop relaying via only sensors. However, relocating actors to achieve both connectivity and load balance is a challenge, especially when there is no location information of the nodes. Connectivity ensures that the actors are connected, whereas load balance ensures that actors collect and originate a similar amount of sensory data from the sensors. In this article, we formulate the Connected and Balanced Mobile Actor Relocation (CBMAR) optimization problem to address both connectivity and load balance and prove that the problem is NP-hard. We thus propose a dual-mode distributed actor relocation protocol that does not rely on any location information of nodes to relocate actors. The idea is to locally form virtual Voronoi cells of actors (termed covering cells ) based on the lower-tiered topology, where each actor locally recruits its own sensor members to form its own covering cell. By maintaining the covering cell, each actor locally relocates itself toward a sensor along the lower-tiered topology. Extensive simulation results show that the protocol can achieve both objectives of connectivity and load balance with low moving and communication overheads.

This paper proposes a load-balancing framework for distributed emergency guiding based on wireless sensor networks. A load-balancing guiding scheme is designed and an analytical model is derived to reduce the total evacuation time of people indoors. The guiding scheme can provide the fastest path for people to reach an exit according to the evacuation time estimated using the analytical model. Based on thorough research, this is the first distributed solution in which corridor capacity and length, exit capacity, and the concurrent movement and distribution of people are considered in estimating the evacuation time and planning escape paths. Using the proposed framework, congestion in corridors and at exits can be eased to substantially reduce the total evacuation time. Analytical and simulation results show that this approach outperforms existing schemes and can prevent people from following localoptimal guiding directions that increase the evacuation time. A prototype called the Load-balancing Emergency Guiding System (LEGS) is implemented; this system can be used to compare the evacuation times and guiding directions provided by existing schemes and the proposed scheme for various distributions of people.

3GPP introduced the key management mechanism (KMM) in evolved multimedia broadcast/multicast service (eMBMS) to provide forward security and backward security for multicast contents. In this paper, we point out that KMM may lead to frequent rekeying and re-authentication issues due to eMBMS's characteristics: 1) massive group members; 2) dynamic group topology; and 3) unexpected wireless disconnections. Such issues expose extra load for both user equipment (UE) terminals and mobile operators. It seems prolonging the rekeying interval is an intuitive solution to minimizing the impact of the issues. However, a long rekeying interval is not considered the best operational solution due to revenue loss of content providers. This paper quantifies the tradeoff between the load of the UEs and the operators as well as the revenue loss of the content providers. Moreover, we emphasize how essential this rekeying interval has impacts on the problems. Using our proposed tradeoff model, the operators can specify a suitable rekeying interval to best balance the interest between the above three parties. The tradeoff model is validated by extensive simulations and is demonstrated to be an effective approach for the tradeoff analysis and optimization on eMBMS.

Since static pricing models (such as flat-rate or tiered-rate models) can not improve user utility for subscribers and ease network congestion for operators during peak time, Smart Data Pricing (SDP) has become an important incentive for mobile data markets. Paris Metro Pricing (PMP), which is a static pricing mode inspired by the pricing model for the Paris metro system, uses differentiated prices to motivate users to choose different train classes. Before choosing a class, people will consider their expected quality of service (QoS) versus the prices that they are willing to pay. Even though PMP can not guarantee the actual QoS during service time, a balance between users' utilities and operators' revenue is achieved. In this paper, we propose a dynamic PMP scheme, so-called DPMP, which determines the prices and capacities of different classes for the next 24 hours. The prices should optimize the revenues and utilities for operators and subscribers, respectively. Our simulation results show that DPMP can better balance those two factors and determine the appropriate log period for operators.

E-healthcare games are a new patient care tool for providing fun, on-line monitoring enabled lifestyle, and a way for redefining traditional doctor-patient relationship. In this work, we demonstrate the use of intelligent sensors to capture patient behaviors for home healthcare. We design a new e-healthcare game user interface, iCube, for monitoring and recording patient activities. The iCube is a 3D game which has six display panels on the surfaces of a 3D cube embedded with a zigbee interface and some sensors for behavior monitoring. To prove its concept, we implement a Spot the Difference game. When a player is playing the game, the iCube can capture the player's hand shaking, rolling response, and visual capability. Those collected data are then sent to a database for spotting the progress/difference of the player over a long period time.

Video surveillance systems are commonly used to monitor environments, such as factories, shopping malls, offices, and schools, for safety and security. However, traditional closed-circuit television cameras can only capture static scenes. When unexpected events happen, such as fire accidents or stranger intrusion, the recorded video data cannot provide immediate and precise information. In this work, we design a camera dispatch system for video surveillance with pan-tilt-zoom (PTZ) cameras, which can monitor event-areas/targets real-timely and flexibly. Users can dynamically define the monitoring requirements, such as pixel-per-foot and viewing-angle of the event-areas/targets. Then, a dispatch algorithm will decide the most necessary cameras and corresponding settings to monitor the event-areas/targets immediately. The control panel is implemented as a web-based service. The demo shows that users can easily monitor potential event-areas/targets anywhere, anytime through a web browser.

For simplifying billing and limiting data usage, Family Shared Plan (FSP) is now a popular plan provided by telecommunications operators. Based on the 3rd Generation Partnership Project (3GPP) specifications, Online Charging System (OCS) has to determine the Granted Unit (GU) and create reservations per session before a service is delivered by the serving network to a User Equipment (UE). However, fixed GUs cannot adapt to dynamic consumer behaviors, which usually require dynamic GUs. Clearly, giving small GUs will incur too many signaling messages for making reservations, but giving excessive GUs will cause unbalanced distribution of resources, especially in FSP cases. How to determine GUs is an open issue that can be self-defined by telecommunications operators. In this paper, we propose a new scheme for dynamically assigning GUs to UEs belonging to the same FSP, based on their historical data usage and total monthly data allowance. Simulation results show that our scheme can substantially save signalings by at least 22% compared to fixed scheme under unpredictable behaviors.

The cycle lengths and signal transition time ofTraffic Light Systems(TLS’s), or known as thePhase Timing Information(PTI), play a key role in modern transportation systems. However, such information is not always available to the public. In this paper, we propose acrowdsourcingapproach to solve this problem by exploiting thestop and goevents, abbreviated bySGevents, of vehicles on roads happening in front of target traffic lights. The PTI discovery problem is formulated by allowing only part of the vehicles participating in the discovery process. The proposed framework starts with discovering SG events, followed by collapsing these events over multiple signal cycles into one and calculating PTI information through ashockwavetechnique. The crowdsourcing part may be directly implemented on smartphones. The proposed framework was verified via field trials and simulations. Our simulation results showed that, even with a low penetration rate around$3.8$percent, the root mean square errors of the cycle length, green light and red light signal transition time of a TLS are$0.04$,$1.3$and$5.8$seconds, respectively. The achieved accuracy can be helpful in many PTI-enabled applications.

This paper proposes an individual-based framework for emergency guiding. The spatial-temporal mobility of all people is modeled to determine a dedicated path that provides the shortest evacuation time for each person. According to our review of relevant research, this is the first optimal solution without using time-expanded graphs, and corridor capacities and lengths, exit capacities, concurrent motion, and distribution of people are considered to minimize evacuation time. We prove that the proposed path planning algorithm is optimal and analyze its time and space complexity. The proposed framework can be used to estimate the evacuation time for each person accurately and evenly distribute evacuation load among exits to achieve the most efficient load balance. In the proposed framework, the congestion in all corridors and exits can be alleviated to maximally reduce the total evacuation time. Simulation results show that our approach outperforms existing schemes, and can be used to determine an optimal escape path for each person and, thus, achieve the shortest total evacuation time.

Long Term Evolution (LTE) has been widely adopted by many 4G network operators. Many countries in America, Europe, and Asia have started LTE services since 2010. In the meantime, some countries (such as Taiwan and China) are in the process of transferring from 3G or Worldwide Interoperability for Microwave Access (WiMAX) to 4G services. In this paper, we investigate the tariff plans of the nine telecommunications operators which own top-three market shares in Singapore, Japan, and Hong Kong, respectively. Several issues, such as market share, tariff strategy, and even customs are discussed. Also, Tier-pricing strategies of LTE, such as Fair Use Policy (FUP), monthly data allowance, and data overage allowance, are discussed and compared. Moreover, we compare different charging plans and examine their price-performance ratios.

Indoor positioning has been intensively studied recently due to the exploding demands of indoor mobile applications. While numerous works have employed wireless signals or dead-reckoning techniques, wearable computing poses new opportunities as well as challenges to the localization problem. This research studies the wearable localization problem by proposing a particle filter-based scheme to fuse the inputs from wearable inertial and visual sensors on human body. Specifically, the filter takes inertial measurements, wireless signals, visual landmarks, and indoor floor plans as inputs for location tracking. The inertial signals imply human body movements, the wireless signals indicate a rough absolute region inside a building, while the visual landmarks provide relative angles viewed from particular positions to these markers. Furthermore, a head-mounted display provides intuitive and friendly interfaces to users. The proposed system has also been prototyped and tested in our campus, and the experiments demonstrate an average localization error of about one meter.

IoT (Internet of Things) has been predicted as the next wave of information and communications technologies (ICT) after the widely accepted smartphones market. Some has estimated that more than 50 billions of IoT devices may be deployed by 2020. The success of IoT will heavily rely on the virtuous cycle of innovations from cloud computing, to broadband networks, to big data, and to smart but lightweight devices. Our future life might be surrounded by a lot of IoT devices. The service model of IoT that differs from others of ICT lies in its capability of "situating" itself in its serving environment, therefore being able to learn the context and collaborate and interact with the environment. This kind of "situated computing" has several interesting properties: (i) put-in-place computing, (ii) collaborative context learning, (iii) M2M opportunistic communications, (iv) fast developable and deployable, and (v) massive connected devices. We then look at these features and raise several examples to show why IoT has big potentials and how IoT can be smarter. We will then discuss recent efforts of the community in building common hardware and software platforms and open M2M standards to achieve this situated service-oriented computing.

Recently, mobile social networks (MSNs) have been widely discussed due to the rapid growth of smart mobile devices. This work focuses on mobile D2D social networks (MDSNs), where users in an MSN are physical neighbors. An important social application of MDSNs is common profile matching (CPM), which refers to the scenario where a group of smartphone users meet in a small region (such as a ball room) and these users are interested in identifying the common attributes among them from their personal profiles efficiently via short-range (such as D2D) communications. For example, a group of strangers may want to find common hobbies, friends, or countries they visited before, and a group of students may want to know the common courses they have ever taken. Assuming that users in an MDSN form a fully connected network, we formulate three versions, namely all-common, β-common, and top-γ-popular, of the CPM problem. The first problem is an extension of an earlier work, while the latter two problems are newly defined. We present solutions based on the basic and the iterative Bloom filters. Evaluation results show that our mechanisms are quite communication-efficient.

Smart devices have become essential in modern world. Nowadays, people conduct various social activities on smart devices, such as exchanging name cards, slides, or files. Traditionally, it would require URLs or flash drives to achieve this goal. However, these conventional means lack intuitive physical interaction and are sometimes hard to perform on smart devices, especially when people wish to interact with a target of physical meanings, e.g., “the man sitting on the opposite side of the table”. This paper proposes to integrate file sharing with localization of the target devices. A device-to-device (D2D) relative positioning method is introduced. The basic idea is to combine the orientations of devices with an acoustic ranging mechanism to determine their relative locations. The result allows users to drag files instinctively on their screens to destined receivers, and then release to initiate the file sharing in a network group. A prototype system is developed to verify the feasibility of our approach.

Mobile Augmented Reality (MAR) is generally defined as the service that is capable of enhancing the real-world camera views of a smartphone with extra information on top of the screen. Thus, MAR has triggered strong interests in mobile e-commerce, location-based service, etc. However, MAR is usually constrained by the local storage and computing power of the device as well as the latency and bandwidth of the underlying wireless channel. The computation demand is high because a targeted object needs to be compared to lots of images in the database. The communication demand is high because potential objects need to be continuously transmitted to the server and the augmented information needs to be downloaded from the server. This paper investigates the Local Cache Management (LCM) problem to manage the computation and communication work of a mobile device and harmonize the local and remote workloads. We present strategies to pre-fetch higher-priority objects from the server and replace lower-priority objects in the local cache based on temporal and spatial access locality. We verify the effectiveness of our strategies in term of cache hit ratio, response latency, and remote requests via simulations.

With the design of data communications in mind, 3GPP LTE-Advanced is probably the most promising technology for next generation mobile communications. For mobile applications, continuous communications at the user equipments (UEs) over a long period of time, imposing stringent requirements on power saving. To manage power consumption, 3GPP LTE-Advanced has defined the Discontinuous Reception (DRX) mechanism to allow UEs to turn off their radio interfaces and go to sleep in various patterns. Existing literature has paid much attention to evaluate the performance of DRX; however, how to tune DRX parameters to optimize energy cost is still left open. This paper addresses the optimization problem of the DRX mechanism, by asking how to minimize the wake-up periods of the UEs while guarantee their QoS, especially on the aspects of traffic bit-rate, packet delay, and packet loss rate in mobile applications. Efficient schemes to optimize DRX parameters and schedule UEs' packets at the evolved Node B (eNB) are proposed. The key idea of these schemes is to analyze and balance the impacts between QoS parameters and DRX configurations. Simulation results show that our scheme can fully satisfy QoS requirements of the UEs while save considerable energy, compared to the existing schemes.

No abstract available.

Addressing in wireless sensor networks is to assign each newly-joining device a unique address. However, to allot the naming space in a large-scale distributed wireless sensor network is not an easy task. ZigBee is a popular communication standard for wireless sensor networks. It suggests a distributed address assignment mechanism. A parent device can calculate network addresses for its child devices without communicating with other devices. However, the parameter configuration of this mechanism strictly restricts the number of children of a device and the depth of the network. ZigBee does not recommend suitable parameters. The improper parameter configuration usually makes many devices isolated from the network which become orphan devices. In this paper, we propose two automatic parameter selection schemes for ZigBee address assignment scheme by probing the network and then selecting parameters in advance to alleviate the orphan problem. They can automatically suggest proper parameters for different network topologies and thus help the original ZigBee address assignment mechanism to effectively reduce orphans.

Power saving for mobile stations (MSs) is one of the most critical issues in IEEE 802.16j relay networks. To reduce power consumption of MSs, IEEE 802.16j borrows the sleep mode of 802.16e, but new parameters are introduced. Up to now, no previous work has addressed the sleep scheduling problem in IEEE 802.16j networks. Therefore, this paper proposes an energy-efficient, standard-compliant sleep scheduling scheme which involves relay stations (RSs) and realizes spatial reuse on RS transmissions to minimize energy consumption of MSs while guaranteeing their QoS. The main idea of the proposed scheme is to interleave the sleep patterns of MSs and exploit spatial reuse on MS-RS transmissions to reduce resource consumption while enlarging the available frame space. Comprehensive simulation has been conducted to verify the effeteness of our scheduling scheme. It shows that our scheme can serve more requests of MSs while increasing their sleep ratios.

The 3GPP LTE-A (Long Term Evolution-Advanced) is the most promising technology for next-generation wireless communications. It provides high transmission rate up to 1 Gbps and supports plentiful multimedia services, especially for those bandwidth required multicast type of services, such as IPTV and Voice/Video-over-IP services. However, when users activate more services at their user equipments (UEs), more energy is consumed. To save UEs' energy, the LTE-A standard defines the Discontinuous Reception Mechanism (DRX) to allow UEs turning off their radio interfaces and going to sleep to save energy when no data needs to be received. But, how to optimize DRX configurations for UEs is still left as an open issue. In this paper, we address the DRX optimization problem for multicast services, which asks how to guarantee the quality of service (QoS) of the multicast streams while minimize UEs' wake-up time. We propose an energy-efficient scheme to tackle this problem. The scheme tries to arrange the best multicast data reception orders to reduces UEs' wake-up periods while consider the resource collision avoidance. Simulation results show that the performance of the proposed scheme is effective even if the network is under saturated condition.

Recent developments in mobile techniques have enabled a great variety of Location Based Services (LBS). A high positioning accuracy is a fundamental requirement for precision LBS applications, e.g., precise LBS marketing in shopping malls or indoor emergency evacuation services with mobile devices. However, most offerable commercial positioning systems, such as GPS/GNSS and RF-based systems, can not provide positioning accuracy within one meter. In this work, a new positioning approach is proposed for mobile devices, Called Photo Positioning, it can provide a high accuracy positioning service. The “positioning” here means to find the location where a photo was taken by investigating the geometric relations between the images of points of interest (POI) in the photo and their location in the real world based on the principle of photo imaging. To implement a photo positioning system, three major components are needed, including a POI database, a method to recognize and locate POIs in a photo and an algorithm to calculate the position where the photo was taken from the POI information. A positioning algorithm based on the geometric similarity in photo imaging is presented in this work, and a prototype system is developed for Android smartphone platforms. Our experimental results shows that the average positioning error of the proposed photo positioning approach can be as low as 74.34 cm.

Traffic congestion in modern cities seriously affects our living quality and environments. Inefficient traffic management leads to fuel wastage in volume of billion gallons per year. In this paper, we propose a dynamic traffic control framework using vehicular communications and fine-grained information, such as turning intentions and lane positions of vehicles, to maximize traffic flows and provide fairness among traffic flows. With vehicular communications, the traffic controller at an intersection can collect all fine-grained information before vehicles pass the intersection. Our proposed signal scheduling algorithm considers the flows at all lanes, allocates more durations of green signs to those flows with higher passing rates, and also gives turns to those with lower passing rates for fairness provision. Simulation results show that the proposed framework outperforms existing works by significantly increasing the number of vehicles passing an intersection while keeping average waiting time low for vehicles on non-arterial roads. In addition, we discuss our implementation of an Zigbee-based prototype and experiences.

The articles in this special section focus on new applications that are supported by cloud computing.

ABSTRACT Constrained by the physical environments, thelong‐thintopology has recently been promoted for many practical deployments of wireless sensor networks (WSNs). In general, a long‐thin topology is composed of a number of long branches of sensor nodes, where along a branch each sensor node has only one potential parent node toward the sink node. Although data aggregation may alleviate excessive packet contention, the maximum payload size of a packet and the dynamically changing traffic loads may severely affect the amount of sensor readings that may be collected along a long branch of sensor nodes. In addition, many practical applications of long‐thin WSNs demand the exact sensor readings at each location along the deployment areas for monitoring and analysis purposes, so sensor readings may not be aggregated when they are collected. This paper proposes a lightweight, self‐adaptive scheme that designates multiple collection nodes, termedlock gates, along a long‐thin network to collect sensor readings sent from their respective upstream sensor nodes. The self‐adaptive lock gate designation scheme balances between the responsiveness and the congestion of data collection while mitigating the funneling effect. The scheme also dynamically adapts the designation of lock gates to accommodate the time‐varying sensor reading generation rates of different sensor nodes. A testbed of 100 Jennic sensor nodes is developed to demonstrate the effectiveness of the proposed lock gate designation scheme. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT This paper considers a spatially separated wireless sensor network, which consists of a number of isolated subnetworks that could be far away from each other in distance. We address the issue of using mobile mules to collect data from these sensor nodes. In such an environment, both data‐collection latency and network lifetime are critical issues. We model this problem as a bi‐objective problem, called energy‐constrained mule traveling salesman problem (EM‐TSP), which aims at minimizing the traversal paths of mobile mules such that at least one node in each subnetwork is visited by a mule and the maximum energy consumption among all sensor nodes does not exceed a pre‐defined threshold. Interestingly, the traversal problem turns out to be a generalization of the classical traveling salesman problem (TSP), an NP‐complete problem. With some geometrical properties of the network, we propose some efficient heuristics for EM‐TSP. We then extend our heuristics to multiple mobile mules. Extensive simulation results have been conducted, which show that our proposed solutions usually give much better solutions than most TSP‐like approximations. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Recently, digital video broadcasting‐handheld (DVB‐H) and IP datacast over DVB‐H (DVB‐IPDC) have been developed to support broadcasting services. DVB‐H is designed to support digital video broadcast for handheld devices, whereas DVB‐IPDC can integrate with an IP‐relay network to complement the data loss problem in DVB‐H. Assuming that WiMAX networks are adopted to support DVB‐IPDC, this paper points out two critical problems: group packet loss (GPL) and broadcast data handover (BDH). GPL occurs when there is a burst of retransmission requests for the same pieces of data with high spatial or temporal correlation. BDH happens when some devices that made the above requests handover to new serving cells. To solve these problems, we propose lazy wait and group acknowledgement schemes to alleviate duplicate requests by exploiting their spatial and temporal correlations. This not only reduces the requests submitted by neighboring devices in both space and time domains but also avoids handovering devices from sending duplicate requests in new cells. Through mathematical analysis, we show how to adaptively adjust the timers of lazy wait and group acknowledgement based on channel quality. Simulation results prove that our schemes can efficiently reduce retransmission requests and retransmission packets, thus alleviating congestion in the IP‐relay network. Copyright © 2011 John Wiley & Sons, Ltd.

In cloud computing environments, users can rent virtual machines (VMs) from cloud providers to execute their programs or provide network services. While using this kind of cloud services, one of the biggest problems for the users is to determine the proper number of VMs to complete the jobs considering both budget and time. In this paper, we propose a resource prediction framework (RPF), which can help users choose the minimum number of virtual machines to complete their jobs within a user specified time constraint. In order to verify the feasibility of RPF, we have done three case studies, namely parallel frequent pattern growth (FP-Growth), parallel K-means, and Particle Swarm Optimization (PSO). FP-growth, K-means and PSO are data intensive algorithms. These algorithms are typically executed repeatedly with different execution parameters to find the optimal results. When evaluating RPF by these algorithms in cloud environments, we have to modify them to parallel versions. The evaluation results indicate that RPF can successfully obtain the minimum number of VMs with acceptable errors. According to our case studies, the proposed RPF can be adopted by data intensive jobs by providing flexibility to both end users and cloud system providers.

This paper considers an ad hoc network, where a set of energy-rich mobile nodes, termed mobile relays, are used to facilitate relaying packets so as to mitigate the energy consumption of static nodes. Existing work has focused on link-level relaying behaviors. In this work, we show how to achieve route-level relaying by overhearing route control and data packets. This allows mobile relays to redirect the traffic of static nodes and thus reduce their energy consumption. As the relocation of mobile relays needs to dynamically adjust the current traffic condition, a dynamic relocation scheme of mobile relays needs to be designed. In this paper, we refer to the mobile relay deployment (MRD) problem and design a distributed protocol for mobile relays. In our protocol, mobile relays do not necessarily participate in the routing discovery process, so static nodes can quickly switch back to their original routing paths without reconstruction efforts once mobile relays leave their communication ranges. Simulations by QualNet are presented to evaluate the performance of our protocol as compared to link-level relaying.

Smart phones with touch screens have become very popular and have changed our behaviors of using handsets. However, using touch screens is not safe for mobile phone users especially when they are driving cars. Thus, many applications using smart phones cannot be initiated because users who are driving the cars cannot easily touch the small icons on the screens of smart phones. To overcome this issue, we propose a proximity sensors based "no-touch" mechanism for smart phones by applying proximity sensors to initiate mobile applications without the need of touching the screen. We will discuss how to implement the proximity-sensors driven "no-touch" mechanism in Android platform and investigate its performance issues regarding detection accuracy and power consumption. Speech-oriented applications using the proposed "no-touch" mechanism on Android is also demonstrated in this paper.

Due to hardware design or cost consideration, sensors may possess sector-like sensing coverage. Furthermore, by stepper motors, sensors can rotate to cover the objects around them. This type of sensors are called rotatable and directional (R&D) sensors. Through rotation, R&D sensors provide temporal coverage to objects by "periodically” detecting their existence. In the paper, we first develop an event-driven surveillance system by R&D sensors, where objects are monitored by the sensors equipped with infrared detectors and cameras. When an object is taken away, the sensor monitoring the object reports a warning message along with detailed snapshots from the surroundings. Then, motivated by the system, we formulate an R&D sensor deployment problem, which tries to deploy the minimum number of R&D sensors to cover a given set of objects such that each object is covered by 0 < \delta \le 1 ratio of time in every frame. We show this problem to be NP-hard and propose two efficient heuristics. The maximum covering deployment (MCD) heuristic iteratively deploys a sensor to cover more objects, and performs well when objects congregate together. The disk-overlapping deployment (DOD) heuristic deploys sensors to cover the joint sectors of overlapped disks, so it works better when objects are arbitrarily placed in the sensing field. The paper contributes in defining a new temporal coverage model by R&D sensors, developing a surveillance application for this model, and proposing efficient heuristics to reduce the deployment cost.

Abstract Wireless sensor networks (WSNs) offer a convenient way to monitor physical environments. In the past, WSNs are all considered static to continuously collect information from the environment. Today, by introducing intentional mobility to WSNs, we can further improve the network capability on many aspects, such as automatic node deployment, flexible topology adjustment, and rapid event reaction. In this paper, we survey recent progress in mobile WSNs and compare works in this field in terms of their models and mobility management methodologies. The discussion includes three aspects. Firstly, we discuss mobility management of mobile sensors for the purposes of forming a better WSN, enhancing network coverage and connectivity, and relocating some sensors. Secondly, we introduce path‐planning methods for data ferries to relay data between isolated sensors and to extend a WSN's lifetime. Finally, we review some existing platforms and discuss several interesting applications of mobile WSNs. Copyright © 2010 John Wiley & Sons, Ltd.

Among all the wireless localization techniques, the Wi-Fi pattern-matching scheme is one of the most widely used approaches, which estimates the user's location by comparing his/her device's received signal strength (RSS) against a pre-trained radio map on the fly. The pattern-matching solutions have an inherent drawback: the expensive calibration operation of war-driving. In order to reduce the calibration operation cost of war-driving, many solutions based on the community approach have been proposed [1]-[3], which collect the Wi-Fi training data from users' contributions. However, most works only consider the Wi-Fi patterns. For the heterogeneous training data, such as a radio map combined signals from both Wi-Fi and cellular networks, a method shows how to collect and exploit the data through an ongoing way is still missing. In this paper, our objective is to grow a heterogeneous radio map from barren to beautiful over a large area, such as a regional area or a national area. Based on the concept of community approach, we propose a geography-based method to combine the cellular and the Wi-Fi radio maps. We believe that our framework can provide a valuable solution for pattern-matching localization which shows how to effectively build a radio map and quickly estimate the user's location with acceptable distance errors.

We are interested in tracking human postures by deploying accelerometers on a human body. One fundamental issue in such scenarios is how to calculate the gravity, no matter when human body parts are moving or not. Assuming multiple accelerometers being deployed on a rigid part of a human body, a recent work proposes a data fusion method to estimate the gravity on that rigid part. However, how to find the optimal deployment of sensors that minimizes the estimation error of the gravity is still an open problem. In this paper, we formulate the deployment optimization problem. Since the problem is hard, we conduct experiments to observe what the optimal deployments should be.

Inertial sensors for pedestrian dead-reckoning (PDR) have been attracting considerable attention recently. Since accelerometers are prone to the accumulation of errors, a “Zero Velocity Update” (Z-UPT) technique [1], [2] was proposed as a means to calibrate the velocity of pedestrians. However, these inertial sensors must be mounted on the bottom of the foot, resulting in excessive vibration and errors when measuring speed or orientation. This paper proposes a self-calibrating PDR solution using two inertial sensors in conjunction with a novel concept called “Walking Velocity Update” (W-UPT). One inertial sensor is mounted on the lower leg to identify a point suitable for calibrating the walking velocity of the user (when its pitch value becomes zero), while another sensor is mounted on the upper body to track the velocity and orientation. We have developed a working prototype and tested the proposed system using actual data.

The Eco-Sign system is a traffic light control system for minimizing greenhouse gases emitted by idling vehicles at intersections. Eco-Sign provides the following features: (i) it can notify vehicles to turn on/off their engines based on expected waiting time for green lights at intersections, (ii) it can dynamically adjust traffic light timing to minimize the number of vehicles stopping at an intersection based on vehicle arrival and departure rates, and (iii) it is a fully distributed system in the sense that each intersection can learn its local traffic condition and optimize its traffic sign setting to prevent congestions and thus traffic jams. Eco-Sign thus demonstrates a new traffic light control system for environmental protection.

While sensor-enabled devices have greatly enriched human interactions in our daily life, discovering the essential knowledge behind sensing data is a critical issue to connect the cyber world and the physical world. This motivates us to design an innovative sensor-aided social network system, termed cyber-physical handshake. It allows two users to naturally exchange personal information with each other after detecting and authenticating the handshaking patterns between them. This work describes our design of detection and authentication mechanisms to achieve this purpose and our prototype system to facilitate handshake social behavior.

In large-scale fingerprinting localization systems, fine-grained location estimation and quick location determination are conflicting concerns. To achieve finer grained localization, we have to collect signal patterns at a larger number of training locations. However, this will incur higher computation cost during the pattern-matching process. In this paper, we propose a novel discriminant minimization search (DMS)-based localization methodology. Continuous and differentiable discriminant functions are designed to extract the spatial correlation of signal patterns at training locations. The advantages of the DMS-based methodology are threefold. First, with through slope of discriminant functions, the exhaustive pattern-matching process can be replaced by an optimization search process, which could be done by a few quick jumps. Second, the continuity of the discriminant functions helps predict signal patterns at untrained locations so as to achieve finer grained localization. Third, the large amount of training data can be compressed into some functions that can be represented by a few parameters. Therefore, the storage space required for localization can be significantly reduced. To realize this methodology, two algorithms, namely, Newton-PL and Newton-INT, are designed based on the concept of gradient descent search. Simulation and experiment studies show that our algorithms do provide finer grained localization and incur less computation cost.

In IEEE 802.16, power management at the Mobile Subscriber Station (MSS) side is always an important issue. The standard defines three types of power saving classes (PSCs). A PSC can bind one or multiple traffic flows. However, given multiple flows in an MSS, the standard does not define how to form PSCs, how to organize the cooperation of multiple PSCs to obtain better energy efficiency, and how to guarantee QoS of these flows. Given a set of flows and their QoS parameters, the objective of this paper is to define multiple PSCs and their listen-and-sleep-related parameters and packet-scheduling policy such that the unavailability intervals of the MSS can be maximized and the QoS of each flow can be guaranteed. To achieve this, we propose a novel fold-and-demultiplex method for an IEEE 802.16 network with PSCs of types I and II together with an earliest-next-bandwidth-first packet scheduler. Given a set of traffic flows in an MSS, the fold-and-demultiplex method first gives each flow a tentative PSC satisfying its bandwidth requirement. Then we fold them together into one long series so as to calculate the total bandwidth requirement. Finally, we demultiplex the series into multiple PSCs, each supporting one or multiple flows. It ends up with high energy efficiency of MSSs while meets flows' bandwidth requirements. Furthermore, our packet scheduler ensures that real-time flows' delay constraints can be met. To the best of our knowledge, this is the first result offering bounded packet delays under MSS's sleep-and-listen behaviors.

We consider a body-area sensor network (BSN) consisting of multiple small, wearable sensor nodes deployed on a human body to track body motions. Concerning that human bodies are relatively small and wireless packets are subject to more serious contention and collision, this paper addresses the data compression problem in a BSN. We observe that, when body parts move, although sensor nodes in vicinity may compete strongly with each other, the transmitted data usually exist some levels of redundancy and even strong temporal and spatial correlations. Unlike traditional data compression approaches for large-scale and multihop sensor networks, our scheme is specifically designed for BSNs, where nodes are likely fully connected and overhearing among sensor nodes is possible. In our scheme, an offline phase is conducted in advance to learn the temporal and spatial correlations of sensing data. Then, a partial ordering of sensor nodes is determined to represent their transmission priorities so as to facilitate data compression during the online phase. We present algorithms to determine such partial ordering and discuss the design of the underlying MAC protocol to support our compression model. An experimental case study in Pilates exercises for patient rehabilitation is reported. The results show that our schemes reduce more than 70 percent of overall transmitted data compared with previous approaches.

Power management is one of the most important issues in IEEE 802.16e wireless networks. In the standard, it defines three types of power saving classes (PSCs) for flows with different QoS characteristics. It allows a mobile device to turn off its wireless radio when all its PSCs are in sleep states. In this paper, we consider the scheduling of power saving classes of type II in an IEEE 802.16e network with a BS and multiple MSSs (mobile subscriber stations). Previous work proposes to enforce all MSSs to have the same sleep cycle, thus leading to higher energy cost for those MSSs with less strict delay bounds. We observe that if the sleep cycles of MSSs can be assigned according to their delay bounds, MSSs can significantly reduce their duty cycles. We propose an efficient tank-filling algorithm, which is standard-compliant and can allocate resources to MSSs according to their QoS characteristics with the least number of active frames. Simulation results verify that our algorithm incurs less power consumption and leads to higher bandwidth utilization than the previous schemes.

This paper considers storage management in an isolated WSN, under the constraint that the storage space per node is limited. We formulate the memory spaces of these sensor nodes as a distributed storage system. Assuming that there is a sink in the WSN that will be visited by mobile mules intentionally (e.g., pre-arranged buses) or occasionally (e.g., non-pre-arranged taxis), we address three issues: (1) how to buffer sensory data to reduce data loss due to shortage of storage spaces, (2) if dropping of data is inevitable, how to avoid higher priority data from being dropped, and (3) how to keep higher priority data closer to the sink, such that the mobile mules can download more important data first when the downloading time is limited. We propose a Distributed Storage Management Strategy (DSMS) based on a novel shuffling mechanism similar to heap sort. It allows nodes to exchange sensory data with neighbors based on only local information. To the best of our knowledge, this is the first work addressing distributed and prioritized storing strategies for isolated WSNs.

In this paper, we propose a Vehicular Surveillance and Sensing System (VS3), which targets at car security and tracking applications. VS3 can be triggered by events detected inside or outside a car, such as abnormal air quality, potential burglary, and identification of some target vehicles (such as stolen cars). Via a 3G module, a user can interact with VS3 via multimedia communications. For security applications, we show how VS3 detects an abnormal CO2 level or potential car burglary, notifies the vehicle owner, and then interacts with the owner. For tracking applications, we show how VS3 identifies potential stolen vehicles, transmits reports to the police department, and get neighboring cars involved to cooperatively track suspicious vehicles. This paper demonstrates our current prototype.

While social networks on Web platforms have attracted a lot of interests, including more natural and physical inputs into such systems to enhance human interaction is desirable. Using body-area sensor networks (BSNs) to capture human motions opens up an opportunity toward this goal. These motivate us to design a novel virtual-physical social network platform1 with typical social network functions and capable of receiving various inputs from remote BSNs. Through our platform, users can share conventional messages and images as well as sensory data in several interesting ways. We demonstrate a Tai-Chi exercise social network and some testing results.

By introducing the relay capability, the IEEE 802.16j standard is developed to improve the WiMAX performance. Under the transparent mode, existing studies aim at improving network throughput by increasing the transmission rates of mobile stations (MSs). However, we show that using higher rates will let MSs consume more energy. In the paper, we define an energy-conserved uplink resource allocation (EURA) problem in 802.16j networks under the transparent mode, which asks how to arrange the uplink resource to 1) satisfy MSs' requests and 2) minimize their energy consumption. Objective 1 is necessary while objective 2 should be achieved when objective 1 is met. The above bi-objective problem is especially important when the network is non-saturated. The EURA problem is NP-hard and we propose a heuristic with two key designs. First, we exploit relay stations to allow more concurrent uplink transmissions to fully use the frame space. Second, we reduce MSs' transmission powers by adjusting their rates and paths. Simulation results show that our heuristic can save up to 80% of MSs' energy as compared with existing work.

One important goal of surveillance systems is to collect information about the behavior and position of interested targets in the sensing environment. Traditional video surveillance systems usually cannot provide complete information of interested targets since they are limited by single and fixed monitoring directions. Recently, surveillance systems combining wireless sensor networks with video cameras have become more and more popular. In this demonstration, we show a multi-view surveillance system consisting of several rotatable video cameras and objects. By implementing the proposed visual sensor selection algorithm, our system can provide more meaningful information from multiple rotatable cameras.

This work addresses the mini-slot scheduling problem in IEEE 802.16d wireless mesh networks (WMNs). A practical mini-slot scheduling needs to take into account following issues: the transmission overhead, the scheduling complexity, and the signaling overhead to notify the scheduling results to subscriber stations. We focus in a grid-based WMN, which is the most recommended topology due to its high capacity and connectivity. In this paper, we propose scheduling schemes featured by low complexity and low signaling overhead. The proposed schemes help find periodical and regular schedules, which can balance between transmission overhead and pipeline efficiency. They can achieve near-optimal transmission latencies. Simulation results show that our schemes outperform other schemes, especially when the network size is larger.

Vehicular Ad-hoc NETworks (VANETs) adopting Dedicated Short-Range Communications (DSRCs) have emerged as a preferred choice of network design for the Intelligent Transportation System (ITS). A possible application of the ITS is to disseminate emergency messages by multihop broadcast. Due to the high density and high mobility of vehicles, it is difficult to design an efficient broadcast protocol for VANETs in urban areas. In this work, we propose a broadcast protocol, named \emph{Streetcast}, to provide efficient broadcast service. Street maps are used to assist the selection of relay nodes, and multicast RTS (Request-To-Send) is adopted to protect wireless communications for providing high reliability. In addition, an adaptive beacon control heuristic is proposed to reduce beacon overheads. At last, we evaluate our broadcast protocol in a real roadmap scenario with real traffic flows. The simulation results show that the proposed broadcast protocol has a superior performance in terms of packet delivery ratio and the number of collisions under various traffic load patterns.

An important issue of wireless sensor networks is object tracking, where the key steps include event detection, target classification, location estimation and location management. The main theme of this paper is location management. Because imprecision is an inherent property in object-tracking sensor networks, this paper focuses on the scenarios where users can tolerate a certain degree of imprecision in their query results. We intend to develop a location management scheme that can achieve two goals. First, multiple precision levels are provided. Second, the query cost is proportional to the precision level. To achieve these two goals, we propose a tree-based imprecision-tolerant location management scheme that includes three major components: (1) update and query mechanisms that can support imprecision-tolerant queries, (2) the approach to taking the statistics of imprecision-tolerant queries and (3) a tree construction algorithm that can reduce the query cost and minimize the increment of update cost. Performance evaluations are conducted through simulations to verify the proposed scheme.

You-Chiun Wang, Tomoaki Ohtsuki, Athanasios (Thanos) Vasilakos, Ashutosh Sabharwal, Yuh-Shyan Chen, Yu-Chee Tseng; Algorithms, Protocols and Future Applica

We consider a hybrid wireless sensor network with static and mobile nodes. Static sensors monitor the environment and report events occurring in the sensing field. Mobile sensors are then dispatched to visit these event locations to conduct more advanced analysis. A big challenge is how to schedule these mobile sensors' traveling paths in an energy-balanced way so that their overall lifetime is maximized. We formulate this problem as a multiround sensor dispatch problem and show it to be NP-complete. Then, we propose a centralized and a distributed heuristics to schedule mobile sensors' traveling paths. Our heuristics allow arbitrary numbers of mobile sensors and event locations in each round and have an energy-balanced concept in mind. The centralized heuristic tries to minimize mobile sensors' moving energy while keeping their energy consumption balanced. The distributed heuristic utilizes a grid structure for event locations to bid for mobile sensors. Through simulations, we show the effectiveness of our schemes. This paper contributes in defining a more general multiround sensor dispatch problem and proposing energy-efficient solutions to it.

The DVB-H standard has been defined to provide digital video broadcast to mobile handheld devices. However, without a request channel, a handheld device may encounter data loss and thus it requires an additional recovery architecture to relieve this problem. In this paper, we follow the DVB-IPDC standard and propose combining a DVB-H system with an IP- relay wireless network. A handheld device losing DVB-H data can thus request the relay network for retransmission. Under this architecture, we identify two critical issues, group packet loss (GPL) and broadcast data handover (BDH). The GPL problem occurs when there are bursty requests for retransmissions of the same data with high spatial and temporal locality, causing network congestion. The BDH problem occurs when these requesting devices move out of their current serving cells. In this paper, we propose a bulk request recovery (BRR) scheme to solve these problems by exploiting spatial and temporal locality of recovery requests. Our scheme can efficiently reduce duplicate requests and schedule retransmissions of lost packets, so both GPL and BDH problems are relieved. Simulations results are also presented to verify the effectiveness of our result.

The Vehicular Surveillance and Sensing System (VS3) is a 3G-based mobile device for car security applications. On the car side, it consists of a CO2sensor, a camera module, a 3G module, and a microprocessor. On the user side, only a 3G mobile phone is needed. VS3provides the following features: (i) it can be triggered by events detected on car, (ii) events can be abnormal air quality or potential burglary, and (iii) it supports text or multimedia interaction with users. Application scenarios include detecting an abnormal CO2level or potential car burglary, which triggers VS3to transmit SMS, MMS, or interactive video call to the vehicle owner, who can then monitor the car situation in return. VS3thus demonstrates a new car security and burglarproof prototype.

Following the trend of miniature intelligent sensing, wearing small, integrated wireless sensor nodes, such as one with accelerometers and compasses, to capture human body motions may have many applications in medical care and computer animation. In this paper, we demonstrate the use of intelligent sensors to capture human motions for home rehabilitation. We design a game to help a patient to conduct his/her rehabilitation program. For each exercise, the patient is instructed to wear sensors on specified movable body parts. The system will then estimate the quality of the movements and give scores as if it is advised by a therapist. In this way, patients will no longer feel painful and boring as that in traditional rehabilitation, which is typically done in hospitals.

While wireless sensor networks (WSNs) are typically targeted at large-scale deployment, due to many practical or inevitable reasons, a WSN may not always remain connected. In this paper, we consider the possibility that a WSN may be spatially separated into multiple subnetworks. Data gathering, which is a fundamental mission of WSN, thus may rely on a mobile mule (ldquomulerdquo for short) to conduct data gathering by visiting each subnetwork. This leads to the problem of minimizing the path length traversed by the mobile mule. We show that minimizing the path length, which may reflect the data gathering latency and the energy consumption of the mule is a generalization of the traveling salesman problem and is NP-complete. Some heuristics based on geometrical properties of node deployment are proposed. Our simulation results show that these heuristics perform very close to optimal solutions in most practical cases.

The iLamp system is a sensor-enhanced desk lamp with surface-tracking capability based on received light intensity. It consists of two components: lamp and bookmark. The bookmark is a ZigBee-enabled sensor node that can report its sensed light intensity to the lamp with a user-friendly interface and two-way communication capability. The lamp can use its LEDs to locate user's reading surface to which the bookmark is attached, move toward the surface, and further tune its luminous intensity to meet user's preference. We develop the geometrical model for surface tracking. iLamp demonstrates a new centimeter-level location-tracking system using light intensity alone without other extra media or devices.

WiFi networks have been deployed in many regions such as buildings and campuses to provide wireless Internet access. However, to support ubiquitous wireless service, one possibility is to integrate these narrow-range WiFi networks with a wide-range network such as WiMAX. Under this WiMAX-WiFi integrated network, how to conduct energy-efficient handovers is a critical issue. In this paper, we propose a handover scheme with geographic mobility awareness (HGMA) by considering the past handover patterns of mobile devices. HGMA can conserve the energy of handovering devices from three aspects. First, it prevents mobile devices from triggering unnecessary handovers by measuring their received signal strength and moving speeds. Second, it includes a handover candidate selection (HCS) method for mobile devices to intelligently select a subset ofWiFi access points or WiMAX relay stations to be scanned. Therefore, mobile devices can reduce their network scanning and thus save their energy. Third, HGMA prefers mobile devices staying in their original WiMAX or WiFi networks. This can prevent devices from consuming too much energy on interface switching. Simulation results show that HGMA can reduce about 69% and 30% of energy consumption on network scanning and interface switching, respectively, and with 16% to 64% more probabilities for mobile devices staying in WiFi networks.

In many WSN (wireless sensor network) applications, such as [1], [2], [3], the targets are to provide long-term monitoring of environments. In such applications, energy is a primary concern because sensor nodes have to regularly report data to the sink and need to continuously work for a very long time so that users may periodically request a rough overview of the monitored environment. On the other hand, users may occasionally query more in-depth data of certain areas to analyze abnormal events. These requirements motivate us to propose a multiresolution compression and query (MRCQ) framework to support in-network data compression and data storage in WSNs from both space and time domains. Our MRCQ framework can organize sensor nodes hierarchically and establish multiresolution summaries of sensing data inside the network, through spatial and temporal compressions. In the space domain, only lower resolution summaries are sent to the sink; the other higher resolution summaries are stored in the network and can be obtained via queries. In the time domain, historical data stored in sensor nodes exhibit a finer resolution for more recent data, and a coarser resolution for older data. Our methods consider the hardware limitations of sensor nodes. So, the result is expected to save sensors' energy significantly, and thus, can support long-term monitoring WSN applications. A prototyping system is developed to verify its feasibility. Simulation results also show the efficiency of MRCQ compared to existing work.

Localization is a critical issue in wireless sensor networks. In most localization systems, beacons are being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always reliable. In this work, we define a new beacon movement detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem concerns how to automatically monitor such situations and identify such unreliable beacons based on the mutual observations among beacons only. Existence of such unreliable beacons may affect the localization accuracy. After identifying such beacons, we can remove them from the localization engine. Four BMD schemes are proposed to solve the BMD problem. Then, we evaluate how these solutions can improve the accuracy of localization systems in case there are unnoticed movements of some beacons. Simulation results show that our solutions can capture most of the unnoticed beacon movement events and thus can significantly alleviate the degradation of such events.

ZigBee is a communication standard which is considered to be suitable for wireless sensor networks. In ZigBee, a device (with a permanent 64-bit MAC address) is said to join a network if it can successfully obtain a 16-bit network address from a parent device. Parent devices calculate addresses for their child devices by a distributed address assignment scheme. This assignment is easy to implement, but it restricts the number of children of a device and the depth of the network. We observe that the ZigBee address assignment policy is too conservative, thus usually making the utilization of the address pool poor. Those devices that cannot receive network addresses will be isolated from the network and become orphan nodes. In this paper, we show that the orphan problem can be divided into two subproblems: the bounded-degree-and-depth tree formation (BDDTF) problem and the end-device maximum matching (EDMM) problem. We then propose algorithms to relieve the orphan problem. Our simulation results show that the proposed schemes can effectively reduce the number of orphan devices compared to the ZigBee strategy.

This paper proposes a new application framework for group tour guiding services based on RFIDs and wireless sensor networks. We consider a sensing field mixed with multiple independent tourist groups, each with a leader and several members. Members of a group will follow the moving path of their leader, but may occasionally roam around randomly on their own interests. Sensor nodes have to track leaders' locations and maintain guiding paths from members to leaders. A member may inquire where his/her leader is, and a leader may "recall" his/her members. We propose a feasible solution to such an application by using existing technologies and off-the-shelf components. A group guiding protocol is presented. The design enables reliable group guiding at low cost and low traffic load. Our prototyping system is reported and system performance is discussed.

Wireless sensor networks (WSNs) have been extensively researched recently. This paper makes two contributions to this field. First, we promote a new concept of long-thin (LT) topology for WSNs, where a network may have a number of linear paths of nodes as backbones connecting to each other. These backbones are to extend the network to the intended coverage areas. At the first glance, a LT WSN only seems to be a special case of numerous WSN topologies. However, we observe, from real deployment experiments, that such a topology is quite general in many applications and deployments. The second contribution is that we show that the address assignment and thus the tree routing scheme defined in the original ZigBee specification may work poorly, if not fail, in a LT topology. We thus propose simple, yet efficient, address assignment and routing schemes for a LT WSN. Simulation results and prototyping experiences are also reported.

Wireless sensor networks provide a convenient manner to monitor the physical environments. How to extend the network lifetime by reducing the amount of message transmissions is a critical issue. In this paper, we propose a multiresolution compression and storage (MCS) framework to compress and preserve sensing data in a wireless sensor network. Our MCS framework adopts spatial and temporal compression schemes to reduce the amount of message transmissions, so the network lifetime can be prolonged and the network congestion can be alleviated. In addition, we also develop a storage mechanism to maintain sensing data in sensor nodes, so that users can query more detailed data when necessary. Our proposed methods consider the hardware limitations of sensor nodes. We also implement a prototyping system on the MICAz Mote platform.

Signal strength fluctuation is one of the major problems in a fingerprint-based localization system. To alleviate this problem, we propose a scrambling method to exploit temporal diversity and spatial dependency of collected signal samples. We present methods on how these properties can be applied to enhance the positioning accuracy of several existing schemes. Simulation studies and experimental results show that the scrambling method can greatly improve positioning accuracy, especially when the tracked object has some degree of mobility.

Sensor deployment is a critical issue because it affects the cost and detection capability of a wireless sensor network. In this work, we consider two related deployment problems: sensor placement and sensor dispatch. The former asks how to place the least number of sensors in a field to achieve sensing coverage and network connectivity, and the latter asks how to determine from a set of mobile sensors a subset of sensors to be moved to an area of interest with certain objective functions such that the coverage and connectivity properties are satisfied. This work is targeted toward planned deployment. Our solution to the placement problem allows an arbitrary-shaped sensing field possibly with arbitrary-shaped obstacles and an arbitrary relationship between the communication distance and sensing distance of sensors and, thus, significantly relaxes the limitations of existing results. Our solutions to the dispatch problem include a centralized one and a distributed one. The centralized one is based on adopting the former placement results and converting the problem to the maximum-weight maximum-matching problem with the objective of minimizing the total energy consumption to move sensors or maximizing the average remaining energy of sensors after movement. Designed in a similar way, the distributed one allows sensors to determine their moving directions in an autonomous manner.

One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.

In this paper, a hybrid sensor network consisting of static and mobile sensors is considered, where static sensors are used to detect events, and mobile sensors can move to event locations to conduct more advanced analysis. By exploring the load balance concept, we propose a CentralSD algorithm to efficiently dispatch mobile sensors. Our algorithm is general in that the numbers of mobile sensors and events can be arbitrary. When mobile sensors are more than event locations, we transform the dispatch problem to a maximum-matching problem in a weighted bipartite graph. When there are fewer mobile sensors than event locations, we propose an efficient clustering scheme to group event locations so that the maximum-matching approach can still be applied. To reduce message cost, we also develop a distributed GridSD algorithm. Simulation results are presented to verify the effectiveness of the proposed algorithms.

In recent years, the wireless mesh network (WMN) attracts the interest of many people as a new broadband Internet access technology. However, increasing throughput is still an open and challenging research issue. One potential solution is to enable transceivers to utilize multiple channels dynamically. However, most of existing works do not consider the routing issue, and trivially use some popular single-path routing protocols like AODV and DSR. In this paper, we exploit the benefit of multi-path routing in multi-channel WMNs from the aspect of end-to-end throughput. Between medium access control and network layers, we propose a novel protocol named Joint Multi-channel and Multi-path control (JMM) which combines multi-channel link layer with multi-path routing. Dividing time into slots, JMM coordinates channel usage among slots and schedules traffic flows on dual paths. Our scheme efficiently and intelligently decomposes contending traffics over different channels, different time, and different paths, and hence leads to significant throughput improvement. To the best of our knowledge, this is the first work discussing the joint design of multi-channel control and multi-path routing for WMNs.

This paper proposes a new application framework for group tour guiding services based on RFIDs and wireless sensor network. We consider a sensing field mixed with multiple independent tourist groups, each with a leader and several members. Members of a group will follow the moving path of their leader, but may occasionally roam around randomly based on their interest. Sensor nodes have to track leaders' locations and maintain following paths from members to leaders. A member may ask where his/her leader is, and a leader may "recall" his/her members. We propose a feasible solution to such an application by using existing technologies. A group guiding protocol is presented. The design enables reliable group guiding at low cost and low traffic load.

This paper presents a middleware architecture to support location-based services (LBSs) based on heterogeneous localization systems. A GEOlNFO gateway is implemented to hide the discrepancy of different localization systems from application developers. Hence, application developers can deploy their LBSs easily without considering the underlying localization techniques. To demonstrate the detailed process and advantages of the proposed architecture, we present a location-based service, called GeoAds, which is a streaming music service with geographical advertisement. This novel integration is expected to attract more mobile users' attention.

In location-based services, the response time of location determination is critical, especially for realtime applications. This is especially true for pattern-matching localization methods, which rely on comparing an object's current signal strength pattern against a pre-established location database of signal strength patterns collected in the training phase. In this work, we propose some cluster-enhanced techniques to speed up the positioning process while avoiding the possible positioning errors caused by this accelerated mechanism. Through grouping training locations with similar signal strength patterns together and characterizing them by a single feature vector, we show how to reduce the associated comparison cost so as to accelerate the pattern-matching process. To deal with signal fluctuations, several clustering strategies allowing overlaps are proposed. Extensive simulation studies are conducted. Experimental results show that compared to the pattern-matching systems without clustering techniques, a reduction of more than 90% in computation cost can be obtained in average without degrading the positioning accuracy.

ZigBee is a standard which is considered to be suitable for wireless sensor networks. In ZigBee, a device is said to join a network if it can obtain a network address from a parent device. Devices calculate addresses for their child devices by a distributed address assignment scheme. This assignment is easy to implement, but it restricts the number of children of a device and the depth of the network. We observe that if one uses the random formation policy specified in ZigBee, the utilization of the address pool will be very low. Those devices that can not receive network addresses will be isolated from the network and become orphan nodes. In this paper, we model the orphan problem by two subproblems: the bounded-degree-and-depth tree formation (BDDTF) problem and the end-device maximum matching (EDMM) problem. We then present solutions to these problems. The results can be applied to network formation in ZigBee networks.

The IEEE 802.16 standard for wireless metropolitan area networks (WMAN) has been created to meet the need of wide-range broadband wireless access at low cost. The objective of this paper is to study how to exploit spectral reuse in an IEEE 802.16 mesh network through timeslot allocation, bandwidth adaptation, hierarchical scheduling, and routing. To the best of our knowledge, this is the first work which formally quantifies spectral reuse in IEEE 802.16 mesh networks and which exploits spectral efficiency under an integrated framework. Simulation results show that the proposed spectral reuse scheduling and load-aware routing significantly enhance the network throughput performance in IEEE 802.16 mesh networks.

Localization is a critical issue in wireless sensor networks. In most localization schemes, there are beacons being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always static. In this work, we define a new Beacon Movement Detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem is concerned about how to automatically monitor such situations and identify these beacons. Removal of such beacons in the positioning engine may improve the localization accuracy. Two schemes are proposed to solve the BMD problem. Finally, we evaluate how these solutions can improve the accuracy of localization schemes in case that there are unnoticed movement of some beacons. Simulation results show that our solutions alleviate 53% the decrease of positioning accuracy caused by the exceptional beacon movement.

Wireless sensor networks have attracted a lot of attention recently. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with neighboring nodes through wireless links. For a sensor network to operate successfully, sensors must maintain both sensing coverage and network connectivity. This issue has been studied in wang et al. [2003] and Zhang and Hou [2004a], both of which reach a similar conclusion that coverage can imply connectivity as long as sensors' communication ranges are no less than twice their sensing ranges. In this article, without relying on this strong assumption, we investigate the issue from a different angle and develop several necessary and sufficient conditions for ensuring coverage and connectivity of a sensor network. Hence, the results significantly generalize the results in Wang et al. [2003] and Zhang and Hou [2004a]. This work is also a significant extension of our earlier work [Huang and Tseng 2003; Huang et al. 2004], which addresses how to determine the level of coverage of a given sensor network but does not consider the network connectivity issue. Our work is the first work allowing an arbitrary relationship between sensing ranges and communication distances of sensor nodes. We develop decentralized solutions for determining, or even adjusting, the levels of coverage and connectivity of a given network. Adjusting levels of coverage and connectivity is necessary when sensors are overly deployed, and we approach this problem by putting sensors to sleep mode and tuning their transmission powers. This results in prolonged network lifetime.

This paper presents a design and implementation of a location-aware service system, which combines a radio-frequency-based positioning engine and a multimedia human-machine interface (HMI). In this system, a number of Zigbee sensors are deployed for the localization purpose. Users' locations can be estimated according to their received signal strength samples from the Zigbee sensors. In order to improve the positioning efficiency, a hierarchical positioning architecture is proposed. The multimedia HMI is to help users to access the provided services. A speech recognition system is designed as the input of the system and a visualization system has been developed as the output interface of the location-aware system. Moreover, the visualization service is integrated with the open service access (OSA) platform via mobile network systems. The whole system can be directed to future homecare applications.

A team of researchers including computer scientists, electrical and control engineers, architects, industrial designers, human factor engineers, and cognitive scientists in the National Chiao Tung University (NCTU), Taiwan, along with their overseas collaborators launched the project Kannon, a multi-disciplinary effort to develop Adaptive Assistive Technologies that can be deployed incrementally into existing private/public spaces and collaborate opportunistically to offer monitoring, assisting, communicating and rejuvenating services to healthy elders. The team combined the state-of-art information, communication and robotic know-how with the activity oriented method for product design and the modular functional approach in modern architecture in order to devise a holistic support for successful aging. This paper presents the philosophy, approach and first fruits of this project.

Handoff is a critical issue in IEEE 802.11-based wireless networks. In this paper we propose a fast and seamless handoff solution for IEEE 802.11 wireless LAN with IAPP. It is based on a concept of neighbor graph, which describes the nearby access points (APs) that a mobile host (MH) may find. Then we further derive selective scanning with unicast in power-save mode, pre-registration of IAPP, and frame forwarding-and-buffering mechanisms. Selective scanning allows a MH to only try potential handoff targets. Pre-registration allows early transfer of a MH's security context from its old AP to new AP. The forwarding-and-buffering mechanism is to solve the packet loss problem during handoff. Our performance evaluation shows that the proposed solution can result in 90% reduction in the handoff latency from standard handoff procedure

The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions.

In this article, we propose several decentralized protocols that schedule sensors' active and sleeping periods to prolong the network lifetime while maintain the sensing field sufficiently covered. The proposed protocols are based on a model similar to that of Yan et al. [2003], but improve its results in several senses. First, our approach can significantly reduce the computational complexity incurred, and at the same time achieve better accuracy in determining the coverage of the sensing area. Second, we extend the result such that it can support multilayer coverage of the sensing field. Third, we further enhance it [Yan et al. 2003] by proposing several optimization mechanisms to balance or reduce sensors' energy expenditure.

The deployment of sensor networks have attracted a lot of attention recently. In essence this issue is concerned with how well a sensing field is monitored by sensors to achieve a particular coverage. In this paper, we propose a signal-strength-based approach to evaluate how well a sensing field is covered/monitored. We first formulate object tracking by a single sensor as a Gaussian-error model. Then we establish an error model on location estimation given that the location of an object is known. This leads to a model to evaluate a sensor network with given locations of sensors. We then apply the result to several applications, such as adding more sensor nodes for error reduction and scheduling power modes (awake or sleep) of sensors, and demonstrate our simulation results.

Existing power-saving (PS) protocols for mobile ad hoc networks (MANETs) can be categorized into synchronous and asynchronous ones. The synchronous PS protocol cannot be applied to a multi-hop MANET, because it can cause three problems: clock synchronization; neighbor discovery; network partitioning. Although asynchronous solutions are attractive, the cost is high compared to synchronous protocols. Hosts in asynchronous protocols need to keep awake for a longer time, so as to discover asynchronous PS neighbors. Besides, a broadcast message has to be sent multiple times if a sending host's neighbors wake up asynchronously. To conquer the deficiency of asynchronous PS protocols, we propose several cluster-based semi-asynchronous PS protocols for multi-hop MANETs. The basic idea is to cluster neighboring hosts such that synchronous PS protocols can be adopted within individual clusters, conserving a lot of energy. Two asynchronous schemes are provided in the inter-cluster level, including SNR-probability-based and location-based schemes. Simulation results show that the proposed semi-asynchronous approaches outperform the asynchronous PS protocols when applied to a multi-hop MANET.

Wireless sensor networks (WSN) provide an inexpensive and convenient way to monitor physical environments. Integrating the context-aware capability of WSN into surveillance systems is an attractive direction. We thus propose an integrated mobile srveillance and wireless sensor (iMouse) system, which consists of a large number of inexpensive static sensors and a small number of more expensive mobile sensors. The former is to monitor the environment, while the latter can move to certain locations and takes more advanced actions. The iMouse system is a mobile, context-aware surveillance system. We demonstrate our current prototyping for home security applications. Besides, we analyze its event detection delay under an any-sensor-detection model.

As one of the killer applications, instant messaging has become a simple yet efficient tool for peer-to-peer communications in data networks. In telephone networks, short message services are gaining more popularity as well. However, this kind of services typically operates in their own respective networks and is triggered by fairly simple events (such as pushing a "send" button or prescheduling the transmission at later time). A promising direction is to trigger instant messages by environmental information from the physical world. In light of this, this paper proposes to establish an event-driven messaging service over a cellular-and-sensor-integrated network. We have prototyped a system which adopts Global System for Mobile Communication (GSM) as the cellular network, and Bluetooth technology as the sensor network. The latter is to realize a Bluetooth surveillance network with location-sensing capabilities to be deployed within an office building area. While using other technologies is possible, GSM and Bluetooth are two dominating technologies in telephone and data networks. So the proposed technology is immediately feasible, given the fact that many handsets are already Bluetooth-enabled. Through this combination, we demonstrate a visitor system (VS) that offers several attractive features/services for visitors arriving at an office. First, messaging services in VS are driven by preconfigured events which can be collected from the Bluetooth surveillance network. Simple events might be a person entering/leaving a space, while complicated events might be a compound logic statement involving multiple users and multiple locations in VS. Second, we believe that the proposed system justifies the potential of cross-network applications and services. Third, the proposed architecture takes a modular approach by dividing the system into several subsystems according to their functionality. Logically dispatching jobs is the key to future extensions and further value-added services. The system architecture and implementation details are reported. Performance analyses are presented to model the detection latency of a Bluetooth sensor network.

Packet scheduling in a WCDMA system poses a new challenge due to its nature of variable bit rates and location-dependent, time-varying channel conditions. In this work, three new downlink scheduling algorithms for a WCDMA base station are proposed to support multimedia transmissions. Using a credit management and a compensation mechanism, our algorithms provide rate guarantee and fair access to mobile terminals. In particular, we propose to allow a user to simultaneously use multiple OVSF codes in a time-sharing manner, which we call a multicode, shared model. Using multiple codes allows us to compensate those users suffering from bad communication quality or even errors. The proposed schemes can tolerate a multistate link condition (compared to the typically assumed two-state, or good-or-bad, link condition) by adjusting the number of OVSF codes and the spreading factor of each code. Simulation results show that the proposed schemes do achieve higher bandwidth utilization while keeping transmission delay low.

In the third-generation partnership project technical specification, orthogonal variable spreading factor (OVSF) codes are used as channelization codes. The use of OVSF codes can provide variable data rates to flexibly support applications with different bandwidth requirements. Most works in the literature assume that only one single OVSF code is used to support one connection. This may sometimes waste the scarce wireless bandwidth since the allocated bandwidth will increase exponentially as the spreading factor decreases, i.e., a user may be "overserved." In this paper, we consider the possibility of using multiple OVSF codes to support a connection. We show how using multiple codes can reduce the internal and external fragmentations of an OVSF code tree. The tradeoff between bandwidth utilization and hardware complexity of a multicode system is analyzed. The result shows that using two or three codes will be quite cost effective. Several multicode assignment and reassignment strategies, namely, random, leftmost, crowded-first-space, and crowded-first-code, are proposed based on such an environment. Our simulation results show significant increase in code tree utilization and significant reduction in code blocking probability by using the crowded-first-space and crowded-first-code schemes.

One important application of wireless sensor networks is the tracking of moving objects. The recent progress has made it possible for tiny sensors to have more computing power and storage space. Therefore, a sensor network can be considered as a distributed database, on which one can conduct in-network data processing. This paper considers in-network moving object tracking in a sensor network. This typically consists of two operations: location update and query. We propose a message-pruning tree structure that is an extension of the earlier work (H.T. Kung and D. Vlah, March 2003), which assumes the existence of a logical structure to connect sensors in the network. We formulate this problem as an optimization problem. The formulation allows us to take into account the physical structure of the sensor network, thus leading to more efficient solutions than in the previous paper of H.T. Kung and D. Vlah (March 2003) in terms of communication costs. We evaluate updating and querying costs through simulations.

The mobile ad hoc networks (MANETs) have received a lot of attention recently. While many routing protocols have been proposed for MANETs based on different criteria, few have considered the impact of multi-rate communication capability that is supported by many current WLAN products. Given a routing path, this paper provides an analytic tool to evaluate the expected throughput of the route, assuming that hosts move following the discrete-time, random-walk model. The derived result can be added as another metric for route selection. Simulation results are also presented.

One of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least /spl alpha/ sensors, where /spl alpha/ is a given parameter and the sensing regions of the sensors are modeled by balls (not necessarily of the same radius). This problem in a 2D space is solved in (C. F. Huang et al, ACM Int'l W'kshop on Wireless Sensor Networks and App., p.115-12 1, 2003) with an efficient polynomial-time algorithm (in terms of the number of sensors). In this paper, we show that tackling this problem in a 3D space is still feasible within polynomial time. The proposed solution can be easily translated into an efficient polynomial-time distributed protocol.

Due to its flexibility, mobile ad hoc network (MANET) has attracted a lot of attention recently. Most existing works, however, limit a MANET as a stand-alone network. In this paper, we propose a two-tier MANET by extending the connectivity of the MANET to the Internet. Some hosts in the MANET are equipped with cellular interfaces and are called gateways, which can provide Internet connections. Such extension would greatly improve the connectivity of MANET. To maintain seamless connectivity, we modify the mobile IP to make traversing private networks (i.e., NAT) of cellular networks possible. We also propose a load-balancing routing protocol to utilize the cellular interfaces of gateways, which are likely to be bottlenecks to the Internet. A prototype is implemented and performance results obtained from the testbed is reported.

Bluetooth has a master–slave configuration called a piconet. Unspecified in the Bluetooth standard, the link polling policy adopted by a master may significantly influence the bandwidth utilization of a piconet. Several works have been dedicated to this issue. However, none of them addresses the asymmetry of traffics between masters and slaves, and the different data packet types provided by Bluetooth are not fully exploited. In this paper, we propose an efficient pattern matching polling (PMP) policy for data link scheduling that properly resolves these deficiencies. A polling pattern is a sequence of Bluetooth packets of different type combinations (e.g. DH1/DH3/DH5/DM1/DM3/DM5) to be exchanged by a master–slave pair that can properly reflect the traffic ratio (i.e. asymmetry) of the pair. By judiciously selecting a proper polling pattern together with polling times for the link, the precious wireless bandwidth can be better utilized. The ultimate goal is to reduce the unfilled, or even null, payloads in each busy slot. In addition, an overflow mechanism is included to handle unpredictable traffic dynamics. Extensive simulations are presented to justify the capability of PMP in handling regular as well as bursty traffics.

The wireless sensor network is an emerging technology that may greatly aid humans by providing ubiquitous sensing, computing and communication capabilities, through which people can more closely interact with the environment wherever they go. To be context-aware, one of the central issues in sensor networks is location tracking, whose goal is to monitor the roaming path of a moving object. While similar to the location-update problem in PCS networks, this problem is more challenging in two senses: (1) there is neither a central control mechanism nor a backbone network in such an environment and (2) the wireless communication bandwidth is very limited. In this paper, we propose a novel protocol based on the mobile agent paradigm. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object. The agent is mobile since it will choose the sensor closest to the object to stay. The agent may invite some nearby slave sensors to cooperatively position the object and inhibit other irrelevant (i.e. farther) sensors from tracking the object. As a result, the communication and sensing overheads are greatly reduced. Our prototyping of the location-tracking mobile agent based on IEEE 802.11b NICs and our experimental experiences are also reported.

Operating in the unlicensed 2.4-GHz ISM band, a Bluetooth piconet will inevitably encounter the interference problem from other piconets. With a special channel model and packet formats, one research issue is how to predict the packet collision effect in a multipiconet environment. In several earlier works, El-Hoiydi (2001), El-Hoiydi and Decotignie (2001), Lim et al. (2001), this problem is studied, but the results are still very limited in that packets are usually assumed to be uniform in lengths and in that time slots of each piconet are assumed to be fully occupied by packets. These assumptions have been successfully removed in the analytical results proposed in Lin and Tseng (2003). In this paper, we further improve the analytical results in Lin and Tseng (2003) by taking into account the frequency-hopping guard time effect in Bluetooth baseband. The result would offer a way to better estimate the network performance in a multipiconet environment.

In the literature, many broadcasting-based schemes have been proposed to efficiently support near-VOD services. However, none of these schemes allows the server to dynamically and seamlessly change the number of channels allocated to a video. Naively allocating a new set of channels for the transition could increase server's load, waste communication bandwidth, and even drain the channels of the system. In Tseng et al. (2000), it is shown how to enhance the Fast Broadcasting (FB) scheme for seamless channel transition. The problem remains open whether other broadcasting-based schemes can sustain seamless channel transition. In this paper, we show how to enhance the Staircase Broadcasting (SB) scheme so that a server can seamlessly increase or decrease the channels allocated to a video. The SB scheme has been proved to require significantly less buffering space than FB, while sustaining the same startup latency as FB. Detailed performance comparisons are presented to demonstrate the advantages of the proposed scheme.

Abstract One critical issue in multihop ad‐hoc networks is the medium access control (MAC). The IEEE 802.11 MAC protocol is originally designed for fully connected, one‐hop ad‐hoc networks but not for multihop ad‐hoc networks. In addition to the well known hidden‐terminal problem, we found that IEEE 802.11 also suffers from an erroneous reservation problem which occurs when RTS‐CTS exchange fails but the channel is incorrectly reserved. In this paper, we propose a jamming‐based MAC (JMAC) protocol that is not only free from both the hidden‐terminal and the erroneous reservation problems but also allows more concurrent transmission/receipt activities for stations within each other's transmission range. The idea behind the JMAC is to separate source stations' traffic from destination stations' traffic into different channels (i.e. dividing the shared medium into two channels), and explicitly signal the channel status by jamming the channels. Simulation results show that although the channel division incurs some cost, the advantages of being free from the erroneous reservation and the hidden‐terminal problems, and the benefits of more concurrent transmissions will compensate the cost and provide higher channel utilization when data frame size is median or large. Copyright © 2004 John Wiley & Sons, Ltd.

We investigate the power mode management problem for an IEEE 802.11-based mobile ad hoc network (MANET) that allows mobile hosts to tune to the power-saving (PS) mode. We adopt an asynchronous approach proposed in [Y. C. Tseng et al., (2002)] and correlate this problem to the quorum system concept. We identify a rotation closure property for quorum systems. It is shown that any quorum system that satisfies this property can be translated to an asynchronous power-saving protocol for MANETs. We derive a lower bound for quorum sizes for any quorum system that satisfies the rotation closure property. We identify a group of quorum systems that are optimal or near optimal in terms of quorum sizes, which can be translated to efficient asynchronous power-saving protocols. We also propose a new e-torus quorum system, which can be translated to an adaptive protocol that allows designers to trade hosts' neighbor sensibility for power efficiency.

Packet scheduling in a WCDMA system poses a new challenge due to its nature of variable bit rates and location-dependent, time-varying channel conditions. In this work, three new downlink scheduling algorithms for a WCDMA base station are proposed to support multimedia transmissions. Using a credit management and a compensation mechanism, our algorithms provide rate guarantee and fair access to mobile terminals. In particular, we propose to allow a user to simultaneously use multiple OVSF codes in a time-sharing manner, which we call a multicode, shared model. Using multiple codes allows us to compensate those users suffering from bad communication quality or even errors. The proposed schemes can tolerate a multi-state link condition (compared to the typically assumed twostate, or good-or-bad, link condition) by adjusting the number of OVSF codes

In the 3GPP technical specification, OVSF codes are used as channelization codes. The use of OVSF codes can provide variable data rates to flexibly support applications with different bandwidth requirements. Most works in the literature assume that only one signal OVSF code is used to support one connection. This may sometimes waste the scarce wireless bandwidth since the allocated bandwidth will increase exponentially as the spreading factor decreases, i.e., a user may be "over-served". In this paper, we consider the possibility of using multiple OVSF codes to support a connection. We show how using multiple codes can reduce internal fragmentation and external fragmentation of a OVSF code tree. The tradeoff between bandwidth utilization and hardware complexity of a multi-code system is analyzed. The result shows that using 2 or 3 codes will be quite cost-effective. Several multi-code assignment and reassignment strategies, namely random, left-most, crowded-first-space, and crowded-first-code, are proposed based on such environment. Our simulation results show significant increase in code tree utilization and significant reduction in code blocking probability by using the crowded-first-space and crowded-first-code schemes.

WIRELESS communications have made spectacular progress in recent years. In particular, the merge of various wireless communication systems and the Internet has become more critical so that users can enjoy all kinds of services in the Internet while owning mobility. For example, modern wireless networks can support Internet applications for web accessing and multimedia services. Many issues need to be addressed for such combinations. Host mobility adds a new dimension to many research issues, such as network protocols, resource managements, Web access, and multimedia transmission. For different applications, diverse traffic types should be supported. Lightweight mobile computing devices, such as PDAs and Bluetooth, will be attached to the Internet. Various wireless communication devices, such as Bluetooth and wireless LANs, will be hooked up to the Internet. All these issues bring new challenges. This special section is targeted at the related issues in wireless Internet. More than 50 submissions were received, and 10 papers were accepted in this special issue. The first five papers cover the service issues in wireless Internet. The paper by B. Emako, R. Glitho, and S. Pierre discusses the design, implementation, and evaluation issues of wireless Internet telephony. Y. Lee and I.F. Akyildiz present a new scheme to reduce the link and signaling costs in Mobile IP. The paper by P. McKinley, U.I. Padmanabhan, N. Ancha, and S.M. Sadjadi describes a composable proxy infrastructure that enables mobile Internet users to collaborate via heterogeneous devices and network connections. V.A. Chitre and J.N. Daigle present an analytical model to assess GPRS’s capability of supporting IP-based Internet services. Performance optimization of VoIP calls over wireless links using H.323 protocol is proposed by S.K. Das, E. Lee, K. Basu, and S.K. Sen. Thenextsetof threepaperscovers issuesrelatedtowireless LANs and sensor networks. The important coverage issue concerning the deployment of sensors in a wireless ad hoc sensor network is addressed in the first paper by X.-Y. Li, P.-J. Wan, and O. Frieder. A polling mechanism to support QoS in IEEE 802.11 wireless LANs is proposed by S.-C. Lo, G. Lee, and W.-T. Chen. Formation of scatternets for Bluetooth-based wireless networks is discussed in the paper by C. Petrioli, S. Basagni, and I. Chlamtac. The last set of two papers covers management issues in mobile networks. An analysis for movement-based mobility management in mobile networks is discussed in the first paper by Y. Fang, who makes a thorough numerical analysis for finding the optimal mobility management under various network operation scenarios possible. Admission control for schedulingbursty traffic inCDMAsystems isdiscussed in the paper by Y.-K. Kwok and V.K.N. Lau, where six efficient rate allocation schemes are proposed. Finally, wewould like to take the opportunity to thank the contributingauthors, reviewers, theAreaEditor,Dr. S.L.Min, and the Editor-in-Chief, Professor J.-L. Gaudiot. Without their support, this special section would not be possible.

In a multihop mobile ad hoc network, broadcasting is an elementary operation to support many applications. Previously, it is shown that naively broadcasting by flooding may cause serious redundancy, contention, and collision in the network, which we refer to as the broadcast storm problem. Several threshold-based schemes are shown to perform better than flooding in that work. However, how to choose thresholds also poses a dilemma between reachability and efficiency under different host densities. In this paper, we propose several adaptive schemes, which can dynamically adjust thresholds based on local connectivity information. Simulation results show that these adaptive schemes can offer better reachability as well as efficiency as compared to the previous results.

One wireless network architecture that has received a lot of attention recently is the mobile ad hoc network (MANET). It is attractive because the network can be quickly deployed without the infrastructure of base stations. One main feature of MANET is that mobile hosts may communicate with each other through a sequence of wireless links (i.e., in a multihop manner). While many routing protocols have been proposed for MANET by considering criteria such as length, quality, bandwidth, and signal strength [S. Agarwal et al., 2000], [D.B. Johnson et al., 2000], [R. Dube et al., Feb. 1997], [Z. Haas and M. Pearlman, 2000], [M. Jiang et al., 1998], [C. Perkins and P. Bhagwat, Sept. 1994], the issue of route lifetime has not been addressed formally. This paper presents a formal model to predict the lifetime of a routing path based on the random walk model. Route lifetime is derived based on a probabilistic model. Through such investigation, we hope to provide further insight into issues such as route selection, route maintenance, and network scalability related to MANETs.

Abstract The basic networking unit in Bluetooth is piconet, and a larger‐area Bluetooth network can be formed by multiple piconets, called scatternet. However, the structure of scatternets is not defined in the Bluetooth specification and remains as an open issue at the designers' choice. It is desirable to have simple yet efficient scatternet topologies with good supports of routing protocols, considering that Bluetooths are to be used for personal area networks with design goals of simplicity and compactness. In the literature, although many routing protocols have been proposed for mobile ad hoc networks, directly applying them poses a problem due to Bluetooth's special baseband and MAC‐layer features. In this work, we propose an attractive scatternet topology called BlueRing, which connects piconets as a ring interleaved by bridges between piconets, and address its formation, routing, and topology‐maintenance protocols. The BlueRing architecture enjoys the following fine features. First, routing on BlueRing is stateless in the sense that no routing information needs to be kept by any host once the ring is formed. This would be favorable for environments such as Smart Homes where computing capability is limited. Second, the architecture is scalable to median‐size scatternets easily (e.g. around 50 ∼ 70 Bluetooth units). In comparison, most star‐ or treelike scatternet topologies can easily form a communication bottleneck at the root of the tree as the network enlarges. Third, maintaining a BlueRing is an easy job even as some Bluetooth units join or leave the network. To tolerate single‐point failure, we propose a protocol‐level remedy mechanism. To tolerate multipoint failure, we propose a recovery mechanism to reconnect the BlueRing. Graceful failure is tolerable as long as no two or more critical points fail at the same time. As far as we know, the fault‐tolerant issue has not been properly addressed by existing scatternet protocols yet. In addition, we also evaluate the ideal network throughput at different BlueRing sizes and configurations by mathematical analysis. Simulation results are presented, which demonstrate that BlueRing outperforms other scatternet structures with higher network throughput and moderate packet delay. Copyright © 2003 John Wiley & Sons, Ltd.

This paper considers the bandwidth reservation problem in a mobile ad hoc network (MANET) to support QoS (quality-of-service) routing. We approach this problem by assuming a common channel shared by all hosts under a TDMA (time division multiple access) channel model. Existing solutions have addressed this problem by assuming a stronger multi-antenna model, where the bandwidth of a link is independent of the transmitting/receiving activities of its neighboring links, or a less strong CDMA-over-TDMA channel model, where the use of a time slot on a link is only dependent on the status of its one-hop neighboring links. While more practical and less costly, using a TDMA model needs to face the challenge of radio interference problems. In this paper, we propose a new protocol that can reserve routes by addressing both the hidden-terminal and exposed-terminal problems. The protocol can conduct accurate bandwidth calculation while performing route discovery. Simulation results are presented to verify how this new protocol performs.

A mobile ad hoc network (MANET) is one consisting of a set of mobile hosts which can operate independently without infrastructure base stations. Power saving is a critical issue for MANET since most mobile hosts will be operated by battery power. In this paper, we address the power-saving issue for IEEE 802.11-based MANET from several protocol layers, including physical, MAC, and network layers. Our solution is fully power-aware and location-aware in the sense that it exploits location information of mobile hosts to achieve energy conservation on all these protocol layers. In comparison, existing protocols only exploit location information in limited layers (e.g., power control, power mode management, power-aware MAC, and power-aware routing). Similar to cellular networks, our approach is based on partitioning the network area into squares/hexagons, thus leading to a powerful energy and mobility management capability.

Power-saving is a critical issue for almost all kinds of portable devices. In this paper, we consider the design of power-saving protocols for mobile ad hoc networks (MANETs) that allow mobile hosts to switch to a low-power sleep mode. The MANETs being considered in this paper are characterized by unpredictable mobility, multi-hop communication, and no clock synchronization mechanism. In particular, the last characteristic would complicate the problem since a host has to predict when another host will wake up to receive packets. We propose three power management protocols, namely dominating-awake-interval, periodically-fully-awake-interval, and quorum-based protocols, which are directly applicable to IEEE 802.11-based MANETs. As far as we know, the power management problem for multi-hop MANETs has not been seriously addressed in the literature. Existing standards, such as IEEE 802.11, HIPERLAN, and Bluetooth, all assume that the network is fully connected or there is a clock synchronization mechanism. Extensive simulation results are presented to verify the effectiveness of the proposed protocols.

This paper presents a new cross-slot interference-resolving algorithm for supporting unbalanced traffic between downlink and uplink in TDD-CDMA systems. To avoid cross-slot interference between the downlink and uplink, traditional algorithms typically require a global control on the transmission direction in each time slot among all cells. Obviously, this requirement significantly limits one of the key advantages in the TDD system - enabling different rate asymmetry. We observe that the tri-sector cellular architecture possesses additional orthogonality inherently due to the directivity of directional antenna, which can provide an additional degree of freedom in allocating radio resource (i.e., time slots). Thus we propose a new distributed slot allocation algorithm based on the tri-sector cellular architecture. The advantage of the proposed algorithm is to enable a TDD-CDMA system to provide asymmetric services with different rates of asymmetry in all cells, thereby having more flexibility in handling uniform traffic patterns.

In a mobile ad hoc network (MANET), one essential issue is Medium Access Control (MAC), which addresses how to utilize the radio spectrum efficiently and to resolve potential contention and collision among mobile hosts on using the medium. Existing works have been dedicated to using multiple channels and power control to improve the performance of MANET. In this paper, we investigate the possibility of bringing the concepts of power control and multi-channel medium access together in the MAC design problem in a MANET. Existing protocols only address one of these issues independently. The proposed protocol is characterized by the following features: (i) it follows an ‘on-demand’ style to assign channels to mobile hosts, (ii) the number of channels required is independent of the network topology and degree, (iii) it flexibly adapts to host mobility, (iv) no form of clock synchronization is required and (v) power control is used to exploit frequency reuse. Power control may also extend battery life and reduce signal interference, both of which are important in wireless communication. Through simulations, we demonstrate the advantage of our new protocol.

In an earlier paper (Chen et al., 2000), we proposed a steganography scheme for hiding a piece of critical information in a host binary image. That scheme ensures that, in each m /spl times/ n image block of the host image, as many as [log/sub 2/ (mn + 1)] bits can be hidden in the block by changing at most 2 bits in the block. We propose a new scheme that improves (Chen et al., 2000) in its capability to maintain higher quality of the host image after data hiding by sacrificing some data hiding space. The new scheme can still offer a good data hiding ratio. It ensures that, for any bit that is modified in the host image, the bit is adjacent to another bit which has a value equal to the former's new value. Thus, the hiding effect is quite invisible.

This letter presents a novel steganography scheme capable of concealing a piece of critical information in a host message which is a binary image (e.g., a facsimile). A binary matrix and a weight matrix are used as secret keys to protect the hidden information. Given a host image of size m/spl times/n, the proposed scheme can conceal as many as /spl lfloor/log/sub 2/ (mn+1)/spl rfloor/ bits of data in the image by changing, at most, two bits in the host image. This scheme can provide a higher security, embed more information, and maintain a higher quality of the host image than available schemes.

One way to broadcast a popular/hot video is to let multiple users share a few channels. The stress on the scarce channels can be alleviated without sacrificing viewer waiting time. One common approach is to partition the video into fixed-length segments, which are broadcast on several channels periodically. Two representative approaches are the fast broadcasting scheme and the PAGODA scheme, which can broadcast a video using k channels by having new viewers wait no longer than /spl Theta/(D/2/sup k/) and /spl Theta/(D/5/sup k/2/) time, respectively, where D is the length of the video. In this paper, we propose a new scheme, called recursive frequency splitting (RFS), that significantly improves on existing schemes in terms of viewer waiting time. Some lower bounds on the viewers' waiting time are also developed.

The use of OVSF codes in WCDMA systems has offered opportunities to provide variable data rates to flexibly support applications with different bandwidth requirements. Two important issues in such an environment are the code placement problem and code replacement problem. The former may have significant impact on code utilization and, thus, code blocking probability, while the latter may affect the code reassignment cost if dynamic code assignment is to be conducted. The general objective is to make the OVSF code tree as compact as possible so as to support more new calls by incurring less blocking probability and less reassignment costs. Earlier studies about these two problems either do not consider the structure of the OVSF code tree or cannot utilize the OVSF codes efficiently. To reduce the call blocking probability and the code reassignment cost, we propose two simple yet efficient strategies that can be adopted by both code placement and code replacement: leftmost and crowded-first. Numerical analyses on call blocking probability and bandwidth utilization of OVSF code trees when code reassignment is supported are provided. Our simulation results show that the crowded-first strategy can significantly reduce, for example, the code blocking probability by 77 percent and the number of reassignments by 81 percent, as opposed to the random strategy when the system is 80 percent fully loaded and the max SF = 256.

The use of OVSF codes in WCDMA systems has offered opportunities to provide variable data rates to flexibly support applications with different bandwidth requirements. Two rarely addressed issues in such environments are the code placement problem and code replacement problem. The former may have significant impact on code utilization and thus code blocking probability, while the latter may affect the code reassignment cost if dynamic code assignment is to be conducted. The general objective is to make the OVSF code tree as compact as possible in order to support new calls, either with less blocking or with less reassignment cost. This paper is perhaps the first one which addresses these issues in WCDMA. Three simple strategies, which can be adopted by both code placement and code replacement, are proposed: random, leftmost, and crowded-first. Among them the crowded-first strategy looks most promising, which is shown to be able to reduce, for example, the code blocking probability by 77% and the number of reassignments by 81% as opposed to the random strategy when the system is 80% fully loaded and the max SF=256.

The packet radio network (PRN) is an attractive architecture to support mobile and wireless communication. Although the code assignment problem has been studied extensively on PRN, we observe that the power control problem has been ignored by most works, but may have significant impact on performance. By power control, we mean that the transmission ranges of stations are tunable. We show, given a PRN in which each host already received a code, how to adjust the powers of stations to control/improve the topology of the PRN without violating the original code assignment. Several schemes are proposed. Through simulations, we demonstrate that although the code assignment problem is NP-complete and thus computationally very expensive, using our power adjustment schemes can easily improve the network performance by about 20% with polynomial costs.

This paper proposes two efficient single-node broadcasting schemes for a network of workstations (NOW) based on a network-partitioning concept. To broadcast a message, the scheme works in three phases. First, we partition the network into two sub-networks (data-distributed networks, DDN). The broadcast message is evenly divided into two sub-messages, each being sent to one representative node in each subnetwork. Second, each sub-message is distributed in its subnetwork independently. Finally, through a sub-message combination step, each node obtains the whole broadcast message. Two network-partitioning schemes, namely 0-1 partitioning and odd-even partitioning, are proposed. Through simulations on irregular and regular networks, we confirm the average latency of these schemes achieve performance improvement compared with the optimal broadcast scheme.

In a multihop mobile ad hoc network, broadcasting is an elementary operation to support many applications. In (Ni et al., 1999), it is shown that naively broadcasting by flooding may cause serious redundancy, contention, and collision in the network, which we refer to as the broadcast storm problem. Several threshold-based schemes are shown to perform better than flooding in (Ni et al., 1999). However, how to choose thresholds also poses a dilemma between reachability and efficiency under different host densities. We propose several adaptive schemes, which can dynamically adjust thresholds based on local connectivity information. Simulation results show that these adaptive schemes can offer better reachability as well as efficiency as compared to the results in (Ni et al., 1999).

This paper considers the location tracking problem in PCS networks. How a solution to this problem performs in fact highly, depends on the mobility patterns of users. In this paper we propose a new traveling salesman mobility (TSM) model, in the hope of catching the mobility patterns of a large group of users. The TSM model is characterized by features of "stop-or-move", "infrequent transition" "memory of roaming direction", and "oblivious in different moves". Then a location tracking strategy based on this TSM model is developed. The scheme only needs to keep very little information for each user. Analyses and simulations are provided, which show that the strategy is very prospective.

In an earlier paper, we propose a steganography scheme for hiding a piece of critical information in a host binary image. That scheme ensures that in each m/spl times/n image block of the host image, as many as [log/sub 2/(mn+1)] bits can be hidden in the block by changing at most 2 bits in the block. As a sequel of that work, in this paper we propose a revised scheme that can maintain higher quality of the host image by sacrificing some data hiding space. The new scheme can still offer a good data hiding ratio. It ensures that for any bit that is modified in the host image, the bit is adjacent to another bit which has a value equal to the former's new value. Thus, the hiding effect is quite invisible.

This paper considers the multi-node multicast problem in a wormhole-routed 2-D torus/mesh, where an arbitrary number of source nodes each intends to multicast a message to an arbitrary set of destinations. To resolve the contention and the congestion problems, we propose to partition the network into subnetworks to distribute, and thus balance, the traffic load among all network links. Several ways to partition the network are explored. The network-partitioning idea was used in earlier works for single-node broadcast and single-node multicast. This paper contributes in extending its applicability to multi-node multicast and demonstrating its capability to balance load on torus/mesh. Simulation results show significant improvement over existing results for torus and mesh networks.

One way to broadcast a popular video is to use a number of dedicated channels, each responsible for broadcasting some portion of the video periodically in a predefined way. The stress on the channels can be alleviated, and new viewers do not have to wait long to start their playback. Many approaches falling in this category have been proposed. One such scheme that interests us is the fast broadcasting (FB) scheme, which can broadcast a video using k channels by incurring at most O(D/2/sup k/) waiting time on new-coming viewers, where D is the length of the video. We consider a set of videos, each being broadcast by the FB scheme. Since the demand levels on these videos may change with time, it is sometimes inevitable to change the numbers of channels assigned to some videos. We propose a novel seamless channel transition enhancement on top of the FB scheme to dynamically change the number of channels assigned to a video on-the-fly. Clients currently viewing this video will not experience any disruption because of the transition. A channel allocation scheme is also proposed based on the arrival rates of videos to minimize the average waiting experienced by all viewers. From the system manager's point of view, the enhancement will make the FB scheme more attractive.

This paper studies a fundamental problem, the termination detection problem, in distributed systems. Under a wireless network environment, we show how to handle the host mobility and disconnection problems. In particular, when some distributed processes are temporarily disconnected, we show how to capture a weakly terminated state where silence has been reached only by those currently connected processes. A user may desire to know such a state to tell whether the mobile distributed system is still running or is silent because some processes are disconnected. Our protocol tries to exploit the network hierarchy by combining two existing protocols together. It employs the weight-throwing scheme on the wired network side, and the diffusion-based scheme on each wireless cell. Such a hybrid protocol can better pave the gaps of computation and communication capability between static and mobile hosts, thus more scalable to larger distributed systems. Analysis and simulation results are also presented.

This paper considers the multi-node multicast problem in a multi-dimensional wormhole-routed torus/mesh, where there are an arbitrary number of source nodes each intending to multicast a message to an arbitrary set of destinations. This problem requires a large amount of bandwidth, and thus typically incurs heavy contention and congestion. Evenly balancing the traffic load around the network is a critical issue to achieve good performance. We show how to use a network-partitioning approach to achieve this goal. Simulation results show significant improvement over existing results in 3D tori and meshes. This work is an extension of our earlier work (2000) from 2D tori/meshes to higher dimensional ones.

One way to broadcast a popular video is to let multiple users share fewer channels. The stress on channel demand can be alleviated without sacrificing viewers' waiting time. One such scheme that interests us is the fast broadcasting (FB) scheme (Juhn et al., 1997, 1998), which can broadcast a popular video using k channels without keeping newcoming viewers waiting for more than O(D/2/sup k/) time, where D is the length of the video. In this paper, we propose two enhancements to the FB scheme. First, since the level of demand on a video may change by time, we show how to dynamically change the number of channels assigned to the video and seamlessly perform this transition. Clients currently viewing this video will not experience any disruption during the transition. Second, given a set of channels and a set of popular videos, we propose a scheme to assign these channels to the videos such that the average viewers' waiting time is minimal. From the system manager's point of view, these enhancements will make the FB scheme more attractive.

This paper considers the multi-node multicast problem in a wormhole-routed 20 torus/mesh, where an arbitrary number of source nodes each intending to multicast a message to an arbitrary, set of destinations. To resolve the contention and the congestion problems, we propose to partition the network into subnetworks to distribute, and thus balance, the traffic load among all network links. Several ways to partition the network are explored. Simulation results show significant improvement over existing results for torus and mesh networks.

In this paper we propose a new steganography scheme for hiding a piece of critical information in a host binary image (such as facsimiles). A secret key and a weight matrix are used to protect the hidden data. Given an image block of size m/spl times/n, our scheme can hide as many as [log/sub 2/(mn+1)] bits of data in the image by changing at most 2 bits in the image. This scheme, as compared to an existing scheme by M. Y. Wu et al. 1998), can provide higher security, embed more data, and maintain higher quality of the host image.

In mobile ad hoc networks (MANETs), one essential issue is how to increase channel utilization while avoiding the hidden-terminal and the exposed-terminal problems. Several MAC protocols, such as RTS/CTS-based and busy-tone-based schemes, have been proposed to alleviate these problems. In this paper, we explore the possibility of combining the concept of power control with the RTS/CTS-based and busy-tone-based protocols to further increase channel utilization. A sender will use an appropriate power level to transmit its packets so as to increase the possibility of channel reuse. The possibility of using discrete, instead of continuous, power levels is also discussed. Through analyses and simulations, we demonstrate the advantage of our new MAC protocol. This, together with the extra benefits such as saving battery energy and reducing cochannel interference, does show a promising direction to enhance the performance of MANETs.

The one-to-all broadcast is the most primary collective communication pattern in a multicomputer network. We consider this problem in a wormhole-routed torus which uses the all-port and dimension-ordered routing model. We derive our routing algorithms based on the concept of "span of vector spaces" in linear algebra. For instance, in a 3D torus, the nodes receiving the broadcast message will be "spanned" from the source node to a line of nodes, to a plane of nodes, and then to a cube of nodes. Our results require at most 2(k-1) steps more than the optimal number of steps for any square k-D torus. Existing results, as compared to ours, can only be applied to tori of very restricted dimensions or sizes and either rely on an undesirable non-dimension-ordered routing or require more numbers of steps.

In a mobile ad-hoc networks (MANETs), one essential issue is how to increase channel utilization while avoiding the hidden-terminal and the exposed-terminal problems. Several MAC protocols, such as RTS (request to send)/CTS (clear to send) based and busy tone-based schemes, have been proposed to alleviate these problems. In this paper, we explore the possibility of combining the concept of power control with the RTS/CTS-based and busy tone-based protocols to further increase channel utilization. A sender will use an appropriate power level to transmit its packets so as to increase the possibility of channel reuse. The possibility of using discrete, instead of continuous, power levels is also discussed. Through analyses and simulations, we demonstrate the advantage of our new MAC protocol. This, together with the extra benefits such as saving battery energy and reducing co-channel interference, does show a promising direction to enhance the performance of MANETs.

Broadcasting is a common operation in a network to resolve many issues.In a mobile ad hoc network (MANET) in particular, due to host mobility, such operations are expected to be executed more frequently (such as finding a route to a particular host, paging a particular host, and sending an alarm signal).Because radio signals are likely to overlap with others in a geographical area, a straightforward broadcasting by flooding is usually very costly and will result in serious redundancy, contention, and collision, to which we refer as the broadcast storm problem.In this paper, we identify this problem by showing how serious it is through analyses and simulations.We propose several schemes to reduce redundant rebroadcasts and differentiate timing of rebroadcasts to alleviate this problem.Simulation results are presented, which show different levels of improvement over the basic flooding approach.

Trees are a common structure to represent the inter-task communication pattern of a parallel algorithm. In this paper, we consider the embedding a complete binary tree in a star graph with the objective of minimizing congestion and dilation. We develop two embeddings: i) a congestion-free, dilation-2, load-1 embedding of a level- p binary tree, and ii) a congestion-free, dilation-2, load-2 k embedding of a level-(p+k) binary tree, into an n-dimensional star graph, where p = \\Sigma n i=2 blog ic = \\Omega\\Gamma n log n) and k is any positive integer. The first result offers a tree of size comparable or superior to existing results, but with less congestion and dilation. The second result provides more flexibility in the embeddable tree sizes compared to existing results. Keywords: Graph embedding, interconnection network, complete binary tree, star graph, parallel processing. This research was supported in part by the National Science Council, R.O.C., under grant numbers NS...

In this paper, we propose new routing schemes to perform all-to-all personalized communication (or known as complete exchange) in wormhole-routed, one-port tori. On tori of equal size along each dimension, our algorithms use both asymptotically optimal startup and transmission time. The results are characterized by several interesting features: (1) the use of gather-scatter tree to achieve optimality in startup time, (2) enforcement of shortest paths in routing messages to achieve optimality in transmission time, (3) application of network-partitioning techniques to reduce the constant associated with the transmission time, and (4) the dimension-by-dimension and gather-scatter-tree approach to make possible applying the results to nonsquare, any-size tori. In the literature, some algorithms are optimal in only one of startup and transmission costs, while some, although asymptotically optimal in both costs, will incur much larger constants associated with the costs. Numerical analysis and experiment both show that significant improvement can be obtained by our scheme on total communication latency over existing results.

Recently, wormhole routers with multidestination capability have been proposed to support fast multicast in a multicomputer network. To avoid communication deadlock, existing results have proposed to construct a Hamilton path, Euler path, trip, or their variants in the network, perhaps with some degree of support of virtual channels. In this paper, we identify that a network which is itself Eulerian or is Eulerian after some links are removed, can enjoy the multidestination capability without support of virtual channels. From this definition, we then develop several techniques to achieve fault-tolerant multicast in a torus/mesh of any dimension with regular fault patterns (such as single node, block, L-shape, T-shape, +-shape, U-shape, and H-shape) and even irregular fault patterns. The result improves over existing results on the requirement of support of virtual channels and fault-tolerant capability. Simulation results on tori are presented.

In this paper, a network-partitioning approach for one-to-all broadcasting on wormhole-routed networks is proposed. To broadcast a message, the scheme works in three phases. First, a number of data-distributing networks (DDNs), which can work independently, are constructed. Then the message is evenly divided into submessages, each being sent to a representative node in one DDN. Second, the submessages are broadcast on the DDNs concurrently. Finally, a number of data-collecting networks (DCNs), which can work independently too, are constructed. Then, concurrently on each DCN, the submessages are collected and combined into the original message. Our approach, especially designed for wormhole-routed networks, is conceptually similar but fundamentally very different from the traditional approach of using multiple edge-disjoint spanning trees in parallel for broadcasting in store-and-forward networks. One interesting issue is on the definition of independent DDNs and DCNs, in the sense of wormhole routing. We show how to apply this approach to tori, meshes, and hypercubes. Thorough analyses and comparisons based on different system parameters and configurations are conducted. The results do confirm the advantage of our scheme, under various system parameters and conditions, over other existing broadcasting algorithms.

Incorporating mobile components into a distributed system has posed new challenges to the design of distributed computation. This paper studies a fundamental problem in distributed computing, the termination detection problem, in a mobile environment. Two types of termination detection protocols already exist: the diffusion-based schemes and the weight-throwing schemes, that are designed for traditional static distributed systems. We propose a hybrid scheme by combining these two protocols together. The scheme can better exploit the communication hierarchy (in terms of wired and wireless bandwidths) and can pave the gaps of computation and communication capability between static and mobile hosts, thus more scalable to larger distributed systems. Simulation results are presented, which show the advantage of the hybrid scheme over existing schemes.

Recently, wormhole routers with multi-destination capability have been proposed to support fast multicast in a multi-computer network. In this paper, we develop a new multicasting model for such networks based on the concept of Euler path/circuit in graph theory. The model can support multiple concurrent multicasts freely from deadlock and can be applied to any network which is Eulerian or is Eulerian after some links being removed. No virtual channels are needed. In particular, we demonstrate the potential of this model by showing its fault-tolerant capability in supporting multicasting in the currently popular torus/mesh topology of any dimension with regular fault patterns (such as single node, block, L-shape, +-shape, U-shape, and H-shape) and even irregular fault patterns. The result has improved over existing fault-tolerant routing algorithms for meshes/tori in at least one of the following aspects: the number of faults tolerable, the shape of fault patterns, the number of deactivated healthy nodes, the requirement of support of virtual channels, and the range of network topology acceptable.

The one-to-all broadcast is the most primary collective communication pattern in a multicomputer network. We consider this problem in a wormhole-routed torus which uses all-port and dimension-ordered routing model. We derive our routing algorithms based on the concept of "span of vector spaces" in linear algebra. For instance, in a 3-D torus, the nodes receiving the broadcast message will be "spanned" from the source node to a line of nodes, to a plane of nodes, and then to a cube of nodes. Our results require at most 2(k-1) steps more than the optimal number of steps for any square k-D torus. Existing results, as compared to ours, can only be applied to tori of very restricted dimensions or sizes, and either rely on an undesirable non-dimension-ordered routing or require more numbers of steps.

The one-to-all broadcast is the most primary operation in a multicomputer network. In this paper, we consider this problem in a wormhole-routed 2D torus network which provides the dimension-ordered routing and all-port communication capability. With the distance-insensitive characteristic of wormhole routing, we model the broadcast as a sequence of communication phases. We propose a novel scheme to solve this problem. The scheme requires at most two communication phases more than the optimal number of phases when the torus is square, or at most four or five communication phases more than optimum when the torus is nonsquare. The best known results, as compared to ours, can only be applied to square tori of very restricted network sizes and either rely on an undesirable non-dimension-ordered routing or require more numbers of phases.

In a multicomputer network, sending a packet typically incurs two costs: start-up time and transmission time. This work is motivated by the observation that most broadcast algorithms in the literature for the star graph networks only try to minimize one of the costs. Thus, many algorithms, though claimed to be optimal, are only so when one of the costs is negligible. In this paper, we try to optimize both costs simultaneously for four types of broadcast problems: one-to-all or all-to-all broadcasting in an n-star network with either one-port or all-port communication capability. As opposed to earlier solutions, the main technique used in this paper is to construct from a source node multiple spanning trees, along each of which one partition of the broadcast message is transmitted.

The star graph interconnection network has been recognized as an attractive alternative to the hypercube network. Previously, the star graph has been shown to contain a Hamiltonian cycle. In this paper, we consider an injured star graph with some faulty links and nodes. We show that even with f/sub e//spl les/n-3 faulty links, a Hamiltonian cycle still can be found in an n-star, and that with f/sub v//spl les/n-3 faulty nodes, a ring containing at most 4f/sub v/ nodes less than that in a Hamiltonian cycle can be found (i.e. the ring contains at least n!-4f/sub v/ nodes). In general, in an n-star with f/sub e/ faulty links and f/sub v/ faulty nodes, where f/sub e/+f/sub v//spl les/n-3, our embedding is able to establish a ring containing at least n!-4f/sub v/ nodes.

All-to-all personalized communication, or complete exchange, is at the heart of numerous applications in parallel computing. Several complete exchange algorithms have been proposed in the literature for wormhole meshes. However, these algorithms, when applied to tori, cannot take advantage of wrap-around interconnections to implement complete exchange with reduced latency. In this paper, a new diagonal-propagation approach is proposed to develop a set of complete exchange algorithms for 2D and 3D tori. This approach exploits the symmetric interconnections of tori and allows to develop a communication schedule consisting of several contention-free phases. These algorithms are indirect in nature and they use message combining to reduce the number of phases (message start-ups). It is shown that these algorithms effectively use the bisection bandwidth of a torus which is twice that for an equal sized mesh, to achieve complete exchange in time which is almost half of the best known complete exchange time on an equal sized mesh. The effectiveness of these algorithms is verified through simulation studies for varying system and technological parameters. It is also demonstrated that synchronous implementations of these algorithms (by introducing barriers between phases) lead to reduced latency for complete exchange with large messages, while the asynchronous ones are better for smaller messages.

In this paper we show that given a binary n-cube with f/sub e//spl les/n-4 faulty edges and f/sub v//spl les/n-1 faulty vertices such that f/sub e/+f/sub v//spl les/n-1, a ring of length at least 2/sup n/-2f/sub v/ can be obtained. On the contrary, existing results can tolerate only faulty edges or only faulty vertices.

Efficiently solving the personalized broadcast problem in an interconnection network typically relies on finding an appropriate spanning tree in the network. In this paper, we show how to construct in a complete star graph an asymptotically balanced spanning tree, and in an incomplete star graph a near-balanced spanning tree. In both cases, the tree is shown to have the minimum height. In the literature, this problem has only been considered for the complete star graph, and the constructed tree is about 4/3 times taller than the one proposed in this paper.

All-to-all personalized communication, or complete exchange, is at the heart of numerous applications in parallel computing. It is one of the most dense communication patterns. In this paper, we consider this problem in a torus of any dimension with the wormhole-routing capability. We propose complete exchange algorithms that use optimal numbers of phases (if each side of the tori is a multiple of eight) or asymptotically optimal numbers of phases (otherwise). Interestingly, in order to achieve this, we only make weak assumptions-that a node is capable of sending and receiving at most one message at a time, and the network is capable of supporting the dimension-ordered (or e-cube) minimum routing.

This paper focuses on efficient multicasting in wormhole-routed networks. A trip-based model is proposed to support adaptive, distributed, and deadlock-free multiple multicast on any network with arbitrary topology using at most two virtual channels per physical channel. This model significantly generalizes the path-based model proposed earlier which works only for Hamiltonian networks and cannot be applicable to networks with arbitrary topology resulted due to system faults. Fundamentals of the trip-based model, including the necessary and sufficient condition to be deadlock-free, and the use of appropriate number of virtual channels to avoid deadlock are investigated. The potential of this model is illustrated by applying it to hypercubes with faulty nodes. Simulation results indicate that the proposed model can implement multiple multicast on faulty hypercubes with negligible performance degradation.

A wide range of graphs with regular structures are shown to be embeddable in an injured hypercube with faulty links. These include rings, linear paths, binomial trees, binary trees, meshes, tori, and many others. Unlike many existing algorithms which are capable of embedding only one type of graphs, our algorithm embeds the above graphs in a unified way, all centered around a notion called edge matrix. In many cases, the degree of fault tolerance offered by the algorithm is optimal or near-optimal.

We consider the problem of embedding a ring in a hypercube that contains possible faulty nodes. Existing algorithms allow the number of faulty nodes to be at most 2n-\Theta(\sqrt {nlogn}), where n is the dimension of the hypercube. We propose an embedding scheme that can tolerate up to \Theta(2^{n/2}) faulty nodes, largely increasing the number of tolerable faulty nodes in a ring embedding.

The authors present an efficient routing algorithm for realizing any permutation in LIN (linear-permutation-class) on single-stage shuffle-exchange networks with k*k switching elements, where k=p is a prime number. For any positive integer number n there are N=k/sup n/ processors connected by the network. The proposed algorithm can realize LIN in 2n-1 passes; it can be implemented by using Nn processors in O(n) time. It can also be extended to the shuffle-exchange networks with (p/sup t/*p/sup t/) switching elements, where t is a positive integer number. In addition, the routing of any arbitrary permutations on the networks with any integer k>2 is discussed. Further, by using the techniques developed here, the authors present an optimal O(log n) parallel algorithm for solving a set of linear equations with a nonsingular coefficient matrix when the arithmetic is over the finite field GF(p/sup t/).>