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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Hongkong
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DTSTART:19791021T023000
TZOFFSETFROM:+0900
TZOFFSETTO:+0800
TZNAME:HKT
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BEGIN:VEVENT
DTSTAMP:20190324T134010Z
UID:3F693EF2-88F9-4CF6-A62E-DD651ABAE18A
DTSTART;TZID=Hongkong:20190318T093100
DTEND;TZID=Hongkong:20190319T115900
DESCRIPTION:In mobile access networks\, different types of Internet of Thin
 gs (IoT) devices (e.g.\, sensor nodes and smartphones) will generate vast 
 traffic demands\, thus dramatically increasing the traffic loads of their 
 connected access nodes\, especially in the 5G era. Mobile edge computing e
 nables data collected by IoT devices to be stored in and processed by loca
 l fog nodes as well as allows IoT users to access IoT applications via the
 se nodes at the same time. In this case\, the communications latency criti
 cally affects the response time of IoT user requests. Owing to the dynamic
  distribution of IoT users\, drone base station (DBS)\, which can be flexi
 bly deployed over hotspot areas\, can potentially improve the wireless lat
 ency of IoT users by mitigating the heavy traffic loads of macro BSs. Dron
 e-based communications poses two major challenges: 1) DBS should be deploy
 ed in suitable areas with heavy traffic demands to serve more users\; 2) t
 raffic loads in the network should be allocated among macro BSs and DBSs t
 o avoid instigating traffic congestions. Therefore\, we propose a TrAffic 
 Load baLancing (TALL) scheme in such drone-assisted fog network to minimiz
 e the wireless latency of IoT users. In the scheme\, we divide the problem
  into two sub-problems and design two algorithms to optimize the DBS place
 ment and user association\, respectively. Extensive simulations have been 
 set up to validate the performance of TALL.\n\nCo-sponsored by: City Unive
 rsity of Hong Kong\n\nSpeaker(s): Prof Nirwan Ansari\, \n\nRoom: G6315\, B
 ldg: Yeung Kin Man Academic\,  6/F\, Green Zone\, Hong Kong\, Hong Kong\, 
 Hong Kong
LOCATION:Room: G6315\, Bldg: Yeung Kin Man Academic\,  6/F\, Green Zone\, H
 ong Kong\, Hong Kong\, Hong Kong
ORGANIZER:r.cheung@cityu.edu.hk
SEQUENCE:0
SUMMARY:IEEE Communication Society Distinguished Lecture - Drone-assisted M
 obile Edge Computing
URL;VALUE=URI:https://events.vtools.ieee.org/m/196425
X-ALT-DESC:Description: <br /><p>In mobile access networks\, different type
 s of Internet of Things (IoT) devices (e.g.\, sensor nodes and smartphones
 ) will generate vast traffic demands\, thus dramatically increasing the tr
 affic loads of their connected access nodes\, especially in the 5G era. Mo
 bile edge computing enables data collected by IoT devices to be stored in 
 and processed by local fog nodes as well as allows IoT users to access IoT
  applications via these nodes at the same time. In this case\, the communi
 cations latency critically affects the response time of IoT user requests.
  Owing to the dynamic distribution of IoT users\, drone base station (DBS)
 \, which can be flexibly deployed over hotspot areas\, can potentially imp
 rove the wireless latency of IoT users by mitigating the heavy traffic loa
 ds of macro BSs. Drone-based communications poses two major challenges: 1)
  DBS should be deployed in suitable areas with heavy traffic demands to se
 rve more users\; 2) traffic loads in the network should be allocated among
  macro BSs and DBSs to avoid instigating traffic congestions. Therefore\, 
 we propose a TrAffic Load baLancing (TALL) scheme in such drone-assisted f
 og network to minimize the wireless latency of IoT users. In the scheme\, 
 we divide the problem into two sub-problems and design two algorithms to o
 ptimize the DBS placement and user association\, respectively. Extensive s
 imulations have been set up to validate the performance of TALL.</p>
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