ARKANSAS Arkadelphia Blytheville Fayetteville/Springdale/Rogers Forrest City Fort Smith metro Hot Springs Jonesboro Little Rock/North Little Pine Bluff Russellville Searcy Texarkana CALIFORNIA Bakersfield Chico El Centro Fresno Hanford/Corcoran metro Los Angeles/Anaheim/Long Beach metro Madera Merced Modesto Napa Oxnard/Thousand Oaks/ Ventura metro Red Bluff Redding Riverside/San Bernardino/ Ontario metro Sacramento/Roseville/Arden/ Arcade metro Salinas San Diego/Carlsbad metro San Francisco/Oakland/ Hayward metro San Luis Obispo-Paso RoblesArroyo Grande San Jose/Sunnyvale/Santa Clara metro Santa Cruz/Watsonville metro Santa Maria/Santa Barbara metro Santa Rosa Stockton/Lodi metro Vallejo/Fairfield metro Visalia/Porterville metro Yuba City COLORADO Boulder Colorado Springs Denver/Aurora/Lakewood metro Fort Collins Fort Morgan Grand Junction Greeley Montrose Pueblo

LTE-Advanced 網路之資源管理

Ultra-dense small cell deployment in future 5G networks is a promising solution to the ever increasing demand of capacity and coverage. However, this deployment can lead to severe interference and high number of handovers, which in turn cause increased signaling overhead. In order to ensure service continuity for mobile users, minimize the number of unnecessary handovers and reduce the signaling overhead in heterogeneous networks, it is important to model adequately the handover decision problem. In this paper, we model the handover decision based on the multiple attribute decision making method, namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The base stations are considered as alternatives, and the handover metrics are considered as attributes to selecting the proper base station for handover. In this paper, we propose two modified TOPSIS methods for the purpose of handover management in the heterogeneous network. The first method incorporates the entropy weighting technique for handover metrics weighting. The second proposed method uses a standard deviation weighting technique to score the importance of each handover metric. Simulation results reveal that the proposed methods outperformed the existing methods by reducing the number of frequent handovers and radio link failures, in addition to enhancing the achieved mean user throughput.

Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks

The increasing demand for mobile communication calls for improvements to network operating services in terms of capacity, coverage, and Quality of Services (QoS). Ensuring QoS is one of the challenges faced by wireless network operators, which include the provision of high mobility speeds, thus the implementation of a seamless and fast handover between network cells is a prominent issue that must be addressed, especially when fulfilling QoS prerequisites. Long Term Evolution (LTE)/LTE-Advance has met these demands of QoS through the use of a new Radio Access Network and distribution of Radio Resource Management including the handover decision technique to evolve NodeB instead of relying on centralized control. In this paper, we review the control plane structure of LTE/LTE-A and present a comprehensive discussion of handover procedures such as the phases, techniques, requirements, features, and challenges involved. According to the overview of the handover decision phase, we surveyed and classified the present handover decision algorithms for a LTE-A system-based technology in regard to the primary handover decision technique. For each class, we describe in detail the fundamental operations and decision parameters using representative algorithms. A summary of input parameters, techniques, and performance evaluation of the handover decision algorithms concludes this work.

Handover in LTE-advanced wireless networks: state of art and survey of decision algorithm

A review on classifying abnormal behavior in crowd scene

/pdf/where-is-the-artificial-intelligence-applied-in-dentistry-39eekn76.pdf

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Although streaming video at a higher bitrate (resolution) can lead to better Quality of Experience (QoE), a larger amount of data will have to be downloaded and processed on smartphones and thus consuming more energy. On a moving bus where the wireless signal is weak, more energy will have to be spent on maintaining high bitrate video streaming than at a static environment such as at home or a cafe where the wireless signal is strong. On the other hand, the user perceived QoE does not increase too much by watching high bitrate videos in a vibrating environment (i.e., a moving vehicle), because the perception of video quality is affected by the environment such as the vibration or shaking on a moving bus. To address this problem, we propose to save energy by considering the context (environment) of video streaming. To model the impact of context, we exploit the embedded sensors (e.g., accelerometer) in smartphones to record the vibration level during video streaming. Based on quality assessment experiments, we collect traces and model the impacts of video bitrate and vibration level on QoE, and model the impacts of video bitrate and signal strength on power consumption. Based on the QoE model and the power model, we formulate the energy-aware and context-aware video streaming problem as an optimization problem. We present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires perfect knowledge of future tasks, we further propose an online bitrate selection algorithm. Through real measurements and trace-driven simulations, we demonstrate that the proposed algorithm can significantly outperform existing approaches when considering both energy and QoE.

Energy-Aware and Context-Aware Video Streaming on Smartphones

Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network

Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).

ApneaDetector: Detecting Sleep Apnea with Smartwatches

High quality video streaming for mobile devices implies high energy consumption due to the transmitted data and the variation of wireless signals. As an example, transmissions in mobile scenarios (e.g., inside a moving bus) consumes more energy for devices than when accessing from a static environment (e.g., at home). The QoE for the user does not substantially increase when watching high bitrate videos in a vibrating environment (i.e., a moving vehicle), as the context, in this case vehicle’s vibration, affects the perceived QoE. To address this problem, we propose to save energy by considering the context (environment) of video streaming. To model the impact of context, we exploit the embedded accelerometer in smartphones to record the vibration level during video streaming. Based on quality assessment experiments, we collect traces and model the impact of video bitrate and vibration level on QoE, and model the impact of video bitrate and signal strength on power consumption. Based on the QoE model and the power model, we formulate the context-aware and energy-aware video streaming problem as an optimization problem. We present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires perfect knowledge of future tasks, we propose an online bitrate selection algorithm. To further improve the performance of the online algorithm, we propose a crowdsourcing based bitrate selection algorithm. Through real measurements and trace-driven simulations, we demonstrate that the proposed algorithms can significantly outperform existing approaches when considering both energy and QoE.

Context-Aware and Energy-Aware Video Streaming on Smartphones

Handover is one of the key operations in the mobility management of long-term evolution (LTE)-based systems. Hard handover decided by handover margin and time to trigger (TTT) has been adopted in third Generation Partnership Project (3GPP) LTE with the purpose of reducing the complexity of network architecture. Various handover algorithms, however, have been proposed for 3GPP LTE to maximize the system goodput and minimize packet delay. In this paper, a new handover approach enhancing the existing handover schemes is proposed. It is mainly based on the two notions of handover management: lazy handover for avoiding ping-pong effect and early handover for handling real-time services. Lazy handover is supported by disallowing handover before the TTT window expires, while early handover is supported even before the window expires if the rate change in signal power is very large. The performance of the proposed scheme is evaluated and compared with two well-known handover algorithms based on goodput per cell, average packet delay, number of handovers per second, and signal-to-interference-plus-noise ratio. The simulation with LTE-Sim reveals that the proposed scheme significantly enhances the goodput while reducing packet delay and unnecessary handover.

/pdf/dihat-differential-integrator-handover-algorithm-with-ttt-1oh7xz0ude.pdf

Xianda Chen

Papers

Energy-Aware and Context-Aware Video Streaming on Smartphones

Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network

ApneaDetector: Detecting Sleep Apnea with Smartwatches

Context-Aware and Energy-Aware Video Streaming on Smartphones

DIHAT: Differential Integrator Handover Algorithm with TTT window for LTE-based systems