The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization app...
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ISBN:
(纸本)9781450389242
The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization approach can give more accurate localization performance based on a decentralized and low-complexity processing. However, the presence of Non-Line of Sight links between IoT devices detrimentally impacts the localization accuracy. In this paper, we propose a distributed localization algorithm based on a convex relaxation of the Huber loss function. Moreover, to reduce the algorithm convergence time, an iterative stochastic gradient descent algorithm is proposed. Through numerical simulations, we show that the proposed algorithm when used with optimal relaxation parameters of the Huber loss function achieves very low root mean square error and outperforms existing algorithms in the literature. Finally, we validate our proposed scheme using real experimental data.
Internet-of-Things (IoT) devices are connected to the Internet through a gateway, which can host IoT analytics encapsulated in containers to convert raw sensor data into more condensed processed data. In this paper, w...
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ISBN:
(纸本)9781728182988
Internet-of-Things (IoT) devices are connected to the Internet through a gateway, which can host IoT analytics encapsulated in containers to convert raw sensor data into more condensed processed data. In this paper, we study two research problems to maximize the overall Quality-of-Service (QoS) level of all IoT analytics that run on both data center servers and gateways. The first problem is to select additional IoT analytics to deploy on a gateway to save upload bandwidth due to transmitting raw sensor data. The second problem is to allocate the residue upload bandwidth among all IoT analytics to maximize the overall QoS level. We propose several algorithms to solve these two research problems. We have implemented real testbeds to evaluate our proposed system and algorithms. Our experiment results reveal that the proposed algorithms: (i) capitalize the download bandwidth and storage space of the gateway for saving the upload bandwidth consumption and (ii) achieve high QoS levels without overloading the network and gateway.
Increasingly more surveillance cameras in smart environments stream videos to storage servers for on-demand video analytics queries in the future. Unlike on-demand video services, in which maximizing the user-perceive...
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ISBN:
(纸本)9781728169972
Increasingly more surveillance cameras in smart environments stream videos to storage servers for on-demand video analytics queries in the future. Unlike on-demand video services, in which maximizing the user-perceived video quality is the design objective, the considered storage servers aim to retain as much information as possible while offering enough space for incoming video clips. In this paper, we design, optimize, and implement an analytics-aware storage server on a smart campus testbed at NTHU, Taiwan, which consists of eight smart street lamps equipped with various sensors, network devices, analytics servers, and a storage server. We focus on the design and implementation of the storage server, and consider two key research problems: (i) how to efficiently determine the information amount of individual video clips and (ii) how to intelligently downsample individual video clips. More specifically, the first problem is to sample video frames from the stored video clips to analyze for approximations of the information amount without overloading the storage server. The resulting information amount is fed into the second problem to decide the video downsampling approaches for retaining as much information amount as possible without consuming excessive storage space. We propose two efficient algorithms to solve these two problems and compare their performance with the current practices via real experiments on our smart campus testbed. Our experiment results reveal the practicality and efficiency of our proposed design and algorithms, e.g., compared to the current practices, our storage server: (i) improves the per-request information amount by up to similar to 4 times, (ii) increases the total information amount by at most similar to 20%, (iii) boosts the number of saved video clips by up to similar to 35%, (iv) runs in real-time, and (v) scales well with larger storage space.
Apart from ensuring high recognition accuracy, one of the main challenges associated with mobile iris recognition is reliable Presentation Attack Detection (PAD). This paper proposes a method of detecting presentation...
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ISBN:
(纸本)9783030312541;9783030312534
Apart from ensuring high recognition accuracy, one of the main challenges associated with mobile iris recognition is reliable Presentation Attack Detection (PAD). This paper proposes a method of detecting presentation attacks when the iris image is collected in visible light using mobile devices. We extended the existing database of 909 bonafide iris images acquired with a mobile phone by collecting additional 900 images of irises presented on a color screen. We explore different image channels in both RGB and HSV color spaces, deep learning-based and geometric model-based image segmentation, and use Local Binary Patterns (LBP) along with the selected statistical images features classified by the Support Vector Machine to propose an iris PAD algorithm suitable for mobile iris recognition setups. We found that the red channel in the RGB color space offers the best-quality input samples from the PAD point of view. In subject-disjoint experiments, this method was able to detect 99.78% of screen presentations, and did not reject any live sample.
As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance...
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ISBN:
(纸本)9781728182988
As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on single-task offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.
Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the ...
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ISBN:
(纸本)9783030204518;9783030204501
Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the elderly population, causing a range of health problems including coronary heart disease, depression, anxiety, and loneliness. Early detection, proper diagnosis, and treatments for sleep disorders can be achieved by identifying sleep patterns through long-term sleep monitoring. Although many studies developed sleep monitoring systems by using non-invasive measures such as body temperature, pressure, or body movement signal, research is still limited to detect sleep position changes by using a depth camera. The present study is intended (1) to identify concerns on the existing sleep monitoring system based on the literature review and (2) propose to developing a non-invasive sleep monitoring system using an infrared depth camera. For the literature review, various journal/conference papers have been reviewed to understand the characteristics, tools, and algorithms of the existing sleep monitoring systems. For the system development and validation, we collected data for the sleep positions from two subjects (35 years old man and 84 years old women) during the four-hour sleep. Kinect ii depth sensor was used for data collection. We found that the averaged depth data is useful measure to notify the participants' positional changes during the sleep.
The rapid development of distributed storage technology has attracted growing attention from both industry and academia. Although distributed system has been studied for many years, however, it is widely applied to en...
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ISBN:
(纸本)9783030602390;9783030602383
The rapid development of distributed storage technology has attracted growing attention from both industry and academia. Although distributed system has been studied for many years, however, it is widely applied to engineering practice since the rise of cloud computing recently. Cloud storage constructs a storage resource pool with massive common storage devices through networks, which could be allocated to authorized users on demand. In this paper, we present a distributed business-aware storage execution environment towards large-scale applications. Initially, we present the overall of our proposed storage execution environment and different data delivery models according to corresponding business needs. Furthermore, we design a globally shared and cross-regional deployed metadata service to minimize the access delay of metadata. Finally, the approach presented in this paper has been validated to be effective through a series of evaluation experiments and actual use cases.
Early prediction and management of Diabetic Foot Ulcers (DFUs) is an important health factor of Europe. Recent clinical trials have concluded that NIR sensing captures oxy(deoxy)haemoglobin (HbO2, Hb) and peripheral/ ...
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ISBN:
(纸本)9781450377737
Early prediction and management of Diabetic Foot Ulcers (DFUs) is an important health factor of Europe. Recent clinical trials have concluded that NIR sensing captures oxy(deoxy)haemoglobin (HbO2, Hb) and peripheral/ tissue oxygen saturations (StO2, SpO2), thermal Infrared-IR detects hyperthermia, among Regions of Interest (ROIs) and Mid-IR contains rich information about the proteomics, lipidomics and metabolomics (e.g., glucose). Current medical approaches are i) invasive (e.g., skin lesion biopsy), ii) requires consumables, and iii) being operated by certified physicians. Our research aims at developing a non-invasive, reliable and cost-effective photonics-driven device for DFU monitoring and management which can be applied for wide use. Hyper-spectral image data are exploited for this purpose. Cost-effectiveness is achieved by introducing i) targeted photonics technologies for DFU, ii) implementing advanced signal processing/learning algorithms to increase the discrimination accuracy while maintaining hardware cost-benefit, (iii) developing a user-friendly framework operated by non-certified physicians, and even by patients, and (iv) minimizing operational cost with our non-invasive device.
The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz for quantum control. However, its di...
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The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz for quantum control. However, its direct application to emergent quantum technology encounters additional physical constraints: (i) the states of the quantum system are not observable;(ii) obtaining the derivatives of the objective function can be computationally expensive or even inaccessible in experiments, and (iii) the values of the objective function may be sensitive to various sources of uncertainty, as is the case for noisy intermediate-scale quantum (NISQ) devices. Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control. This is advantageous to help mitigate and monitor the potentially unknown sources of errors in modern quantum simulators. We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems, subject to various sources of noise such as error terms in the Hamiltonian, or quantum uncertainty in the measurement process. We show that, in noisy setups, it is capable of outperforming state-of-the-art existing optimization algorithms.
The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization app...
详细信息
ISBN:
(纸本)9781450389242
The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization approach can give more accurate localization performance based on a decentralized and low-complexity processing. However, the presence of Non-Line of Sight links between IoT devices detrimentally impacts the localization accuracy. In this paper, we propose a distributed localization algorithm based on a convex relaxation of the Huber loss function. Moreover, to reduce the algorithm convergence time, an iterative stochastic gradient descent algorithm is proposed. Through numerical simulations, we show that the proposed algorithm when used with optimal relaxation parameters of the Huber loss function achieves very low root mean square error and outperforms existing algorithms in the literature. Finally, we validate our proposed scheme using real experimental data.
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