Page 34 - 2017 Brochure

earch Laboratories
Network Systems and Services Lab
Our research addresses several aspects of network systems and services,
including designing participatory sensing systems for environmental
monitoring, developing critically needed information and communication
technologies for disaster management, improving user experience for audio
streaming, supporting memory-based computation for large-scale de novo
genome assembly, and developing the key components of next-generation
cellular networks, such as Software-Defined Network (SDN), Network Function
Virtualization (NFV), Internet of Things (IoT), and Massive MIMO.
1. Ling-Jyh Chen
We study the Internet of Things (IoT) and participatory sensing systems, and
have combined the two concepts to build a large scale system, called AirBox,
for fine particulate matter (PM2.5) monitoring. The project engages citizens
to participate in environmental sensing and enables them to make low-cost
PM2.5 sensing devices on their own. It also facilitates PM2.5 monitoring at
a finer spatio-temporal granularity and enriches environmental data analysis
by making all measurement data freely available. As of mid-2017, we have
deployed more than 2,000 devices in over 30 countries, and we have
established collaborations with international and domestic researchers on
several key issues regarding IoT systems and the interdisciplinary topic of
environmental monitoring systems.
Specifically, we have investigated low power wireless area networks
(LPWAN) techniques in the AirBox design, and we have studied IoT security
issues to ensure confidentiality and integrity. Moreover, we have been working
closely with environmental researchers and authorities on sensor calibration
under different application scenarios. Using the AirBox measurement data,
we have developed a set of algorithms to rank the confidence levels of
devices, detect ongoing PM2.5 emissions, and assess device attributes (e.g.,
deployed indoors or nearly-consistent emission sources). Our ongoing work
is to improve spatio-temporal data analysis and identify additional intrinsic
properties of PM2.5 distributions from real time measurement data, and to
conduct short-term PM2.5 concentration forecasting at a finer spatio-temporal
granularity with a good accuracy.
2. Jane W. S. Liu
We continue to collaborate with several research fellows and faculty members
in earth science, urban planning and computer science on information and
communication technologies for disaster management. At the start, our focus
was on a framework for building open and sustainable disaster management
information systems. Its elements include a responsive, trustworthy
emergency-information brokerage service; disaster resilient heterogeneous,
plug-n-play networks and dynamic logical information exchange; models
and tools for fusing data from people and intelligent things; and building
blocks and infrastructure for active use of disaster alerts. Prototypes of these been shown to perform well in mobile environments for which the available
elements have demonstrated their concepts and feasibility. For example, an
information delivery middleware over interwoven heterogeneous networks was
prototyped to demonstrate the concept of open information gateway.
Our current efforts emphasize the generation and use of data. One thrust
is directed toward developing methods and tools for generation and collection
of data needed for disaster risk reduction. Examples include CROSS
(CROwdsouring Support system for disaster Surveillance). A typical disaster
surveillance system must assist in making critically important decisions within
minutes or hours before disasters strike. When it is necessary to crowdsource
human sensor data, the emergency manager needs help in selection of
participants, plan tours for them to cover locations where observational data
are needed, and fuse data from them in real-time with physical sensor data
to improve the quality of coverage. CROSS was built to meet these needs.
We have solved realistic variants of underlying participant selection, tour
planning and symbiotic data fusion problems and produced solutions not
only of practical utility to CROSS, but also of theoretical significance. We
plan to integrate these components into the well-known platform Ushahidi.
CROSS is currently being used by our earth science colleagues to coordinate
trained volunteers after each significant earthquake in Taiwan to collect
data on new geo-hazards in order to assess risks of earthquake-triggered
compound disasters. Another major thrust of our work is at developing
active smart embedded devices, mobile applications and services/systems
in smart homes and buildings, which can automatically process alerts from
authorized senders and building safety systems and take location specific
actions to minimize chance of injury and reduce property damages when
disasters strike. Each device (or application) selects its action (or response
32 研究群 Research Laboratories
instructions) in response to an alert based on the type and parameters of
the alert and attributes of the building, interior layout and nearby objects
around the device (or application). Our work to date has demonstrated that
easy to customize and maintain active smart devices/applications for diverse
purposes can be built on a common architectural framework from reusable
components. Moreover, alert messages that are pushed asynchronously over
the Internet can meet the end-to-end delay requirements of time-critical alerts.
Our experimentation with a prototype active emergency response system in
an office building during a simulated strong earthquake has demonstrated the
effectiveness of such systems.
In order to enable the pervasive use of active devices and applications, we
are developing a building/environment data and Information (BeDI) system
(or BeDIS) as a part of the infrastructure needed to support location-specific,
active emergency preparedness and response within large public buildings.
One component of BeDIS is an indoor positioning system (IPS) that is unique
among existing IPSs. Our IPS can reliably deliver location data with 3-5 m or
5-10 m horizontal accuracy to both smart phones and most legacy Bluetooth
devices. It is scalable during orders of magnitude surges in crowd density
and does not require Internet to function. Further, it degrades gracefully when
parts of it are damaged and is easy to deploy and maintain. The other major
component of BeDIS is BeDI mist, which is a virtual repository of data on the
building, interior layouts and facilities. It is capable of delivering fine-scale,
location-specific decision-support data and emergency response instructions
to hundreds and thousands of active devices and people. Prototypes of these
components are ready for experimental use. We are planning pilot studies to
assess usability and effectiveness of BeDIS and active applications supported
by it in representative large public buildings.
3. Jan-Ming Ho
We are interested in the following problems:
1. Improving user experience with DASH
Dynamic Adaptive Streaming over HTTP (DASH), also known as MPEG-
DASH, is a popular video streaming protocol over HTTP. DASH enables
high quality video streaming using conventional HTTP web servers without
the need of an additional streaming server. A DASH video is divided into
segments of a constant duration, usually a couple of seconds. Each video
segment is then encoded into several small files that may be played back
at a specific bit rate. The client can then use current network conditions to
adaptively select appropriate segment size in real time. It was drafted by
MPEG as an international standard in 2011 and published by ISO/IEC as ISO/
IEC DIS 23009-1.2 in 2012.
DASH is designed to dynamically react to network bandwidth and has
bandwidth is difficult to predict. However, if the available bandwidth changes
are dramatic (for example, passengers on an underground subway or a THSR
train may receive large bandwidth at a train station, and total cessation in
a tunnel), video streaming may be problematic. A trivial solution to improve
the quality of video streaming is to install sufficient number of wireless base
stations along the railway. On the other hand, if the available bandwidth can
be predicted, a smart scheduling algorithm might also improve the user
experience.
In this study, we aim to improve quality of experience for DASH video
streaming. We will crowdsource time series data of the mobile network
bandwidth along several routes of Taipei Metro, also known as Taipei Rapid
Transit System, and develop models. We will further design efficient prediction
and scheduling algorithms to optimize video streaming experience for subway
riders. The crowdsourced data will be used to validate efficacy of the models
and algorithms.
2. Architecture design to support memory-based computation of large-scale
de novo genome assembly using MapReduce framework
In processing NGS reads of large genomes for de novo genome assembly,
using the MapReduce computing framework, we must generate suffices for
each read in order to perform sorting. The number of tuples, generated to
hold suffices, ranges from 100 to 400 and equals the length of the read. In
other words, for a 100GB NGS data file, the total size of tuple data is 10TB. To
process this amount of data in memory, we may either allocate a huge number
of computing nodes (on the order of 1000) or we may separate storage nodes
from the computing nodes. In the latter case, we may reduce the total number
of nodes, both for storage and computing, by an order of magnitude. Notably,
in some environments, the number of available computing nodes may be