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Mobile	
  Futures?	
  
Gil	
  Zussman	
  
Wireless	
  and	
  Mobile	
  Networking	
  Lab	
  
Department	
  of	
  Electrical	
  Engineering	
  	
  
Columbia	
  University	
  
Wireless	
  and	
  Mobile	
  Traffic	
  Sta6s6cs	
  
u  In	
  2012,	
  the	
  number	
  of	
  cellular	
  users	
  
exceeded	
  the	
  number	
  of	
  toothbrush	
  
users	
  
Wireless	
  and	
  Mobile	
  Traffic	
  Sta6s6cs	
  
Mobile	
  data	
  traffic	
  increase	
  (©	
  Cisco)	
  
(81%	
  increase	
  in	
  2013)	
  	
  
u  In	
  2012,	
  the	
  number	
  of	
  cellular	
  users	
  
exceeded	
  the	
  number	
  of	
  toothbrush	
  
users	
  
u  Cellular	
  and	
  Wi-­‐Fi	
  devices	
  –	
  generate	
  
~40%	
  of	
  Internet	
  traffic	
  (Cisco	
  VNI)	
  
Wireless	
  and	
  Mobile	
  Networks	
  
u  Most	
  specificaVons	
  define	
  the	
  Physical	
  	
  
and	
  Medium	
  Access	
  control	
  (MAC)	
  layers	
  
u  Research,	
  development,	
  specificaVons	
  
ZigBee	
  
SHORT	
  <	
  	
  RANGE	
  	
  >	
  	
  LONG	
  
LOW	
  	
  	
  	
  <	
  	
  	
  	
  DATA	
  RATE	
  	
  	
  	
  >	
  	
  	
  	
  HIGH	
  
Body/Personal	
  Area	
  
Networks	
  
Local	
  Area	
  Networks	
  
Bluetooth	
  
Small	
  cells	
  
Wi-­‐Fi	
  a,	
  g,	
  n,	
  ac,	
  …	
  
Cellular	
  Networks	
  LTE	
  
RFID	
  
DAS	
  
ApplicaVon	
  
PHY	
  
MAC	
  
Network	
  
Transport	
  
Cross	
  Layer	
  
LTE	
  
Wireless	
  and	
  Mobile	
  Networks	
  -­‐	
  Future…	
  
ZigBee	
  
SHORT	
  <	
  	
  RANGE	
  	
  >	
  	
  LONG	
  
LOW	
  	
  	
  	
  <	
  	
  	
  	
  DATA	
  RATE	
  	
  	
  	
  >	
  	
  	
  	
  HIGH	
  
Body/Personal	
  Area	
  
Networks	
  
Local	
  Area	
  Networks	
  
Bluetooth	
  
Small	
  cells	
  
Wi-­‐Fi	
  a,	
  g,	
  n,	
  ac,	
  …	
  
Cellular	
  Networks	
  
RFID	
  
DAS	
  
LTE	
  Advanced	
  
Cellular	
  &	
  WLAN	
  –	
  Research	
  Challenges	
  
Self-­‐interference	
  
Cross-­‐interference	
  
Coopera6ve	
  Mul6point	
  (CoMP)	
  /	
  Network	
  MIMO	
  
Full	
  Duplex	
  
HetNets	
  
Cloud-­‐RAN	
  
Cloud-­‐RAN	
  
 
	
  	
  	
  	
  	
  	
  	
  
The	
  Internet	
  of	
  Things	
  (IoT)	
  
u  ConnecVng	
  “Everything”	
  
u  Smart	
  grid/buildings/etc.	
  
u  Tracking,	
  supply	
  chain	
  
u  Healthcare,	
  wearable	
  
u  Cyber-­‐Physical	
  systems,	
  control	
  
u  There	
  are	
  already	
  ~20M	
  wearable	
  
devices	
  and	
  ~300M	
  M2M	
  connecVons	
  	
  
u  Protocols	
  –	
  design	
  &	
  standardizaVon	
  
§  Various	
  applicaVons	
  
u  Security	
  
u  Energy	
  efficiency	
  	
  
u  Previous	
  work	
  –	
  sensor	
  networks,	
  RFIDs	
  
u  Energy	
  harves6ng	
  wireless	
  nodes	
  
§  Due	
  to	
  Moore’s	
  law,	
  Dennard	
  scaling,	
  improved	
  transceivers,	
  and	
  improved	
  
harvesVng	
  efficiency,	
  nodes	
  can	
  self-­‐power	
  
	
  	
  	
  	
  	
  	
  M3	
  	
  	
  	
  	
   	
  	
  Ambient	
  Backscajer	
   	
  	
  	
  EnHANTs	
  	
  
	
  (Michigan)	
   	
  	
  	
  	
  	
  (U.	
  Washington)	
   	
  (Columbia)	
  
	
  
	
  
The	
  Internet	
  of	
  Things	
  –	
  Challenges	
  
Energy	
  HarvesVng	
  AcVve	
  Networked	
  Tags	
  (EnHANTs)	
  –	
  
Lessons	
  Learned	
  
u  Small	
  and	
  flexible	
  
u  Harvest	
  their	
  own	
  energy,	
  form	
  a	
  wireless	
  network,	
  	
  
and	
  exchange	
  basic	
  informaVon	
  (e.g.,	
  IDs)	
  
u  Extensive	
  light	
  and	
  kineVc	
  energy	
  measurement	
  studies	
  
	
  
u  Energy/power	
  budget	
  –	
  1J/day	
  or	
  12	
  μW	
  
u  AA	
  bajery	
  will	
  be	
  depleted	
  aner	
  40	
  years…	
  
1 2 3 4
0
200
400
I(µW/cm2
)
Days
0
5
10
15
Relax Walk Fast w. Run Cycle Upst. Downst.
D(m/s2
)
42 42 42 42 42 42 41 41 42 42 42 42 30 29 30 41 42 42 41 42 42
(a)
0
1
2
3
4
5
Relax Walk Fast w. Run Cycle Upst. Downst.
f
m
(Hz)
(b)
0
500
1000
Relax Walk Fast w. Run Cycle Upst. Downst.
P(µW)
(c)
Figure 5: Characterization of kinetic energy
for common human activities, based on a 40-
participant study: (a) average absolute devia-
tion of acceleration, D, (b) dominant motion fre-
quency, fm, and (c) power harvested by an opti-
mized inertial harvester, P.
ergy availability on the participant’s physical parame-
ters.
5.1 Study Summary
The dataset we examine [33] contains motion sam-
ples for 7 common human activities – relaxing, walk-
ing, fast walking, running, cycling, going upstairs, and
going downstairs, – performed by over 40 different par-
ticipants and recorded from the 3 sensing unit place-
ments, shown in Fig. 2(b). For each 20-second motion
belt, and trouser pocket sens
tively. For each motion and
number of participants tha
on the top of Fig. 5(a). At e
the median, the edges are th
the “whiskers” extend to c
the outliers are plotted indiv
arately summarize the resu
important motions.
5.2 Energy for Differe
We discuss below the ene
ties for the different examin
Relaxing: As expected, alm
vested when a person is not
Walking and fast walkin
inant periodic motion in no
particularly important for
For walking, the median P
sensing unit placement, 18
ment, and 202 µW for trous
P values are in agreement
scale, studies of motion en
walking [13, 31]. In comp
availability is on the order o
harvester energy conversion
count [11,35], a similarly si
more energy from walking t
walking (which was identifi
ipants themselves) has high
at a normal pace (Fig. 5) a
much P.
Running: Running, an in
associated with high D and
results in 612 ≤ P ≤ 813 µW
Cycling: For the examine
generates relatively little en
are 41–52 µW, 3.7–3.9 time
u  Device	
  and	
  testbed	
  development	
  	
  
(with	
  Carloni,	
  Kymissis,	
  Kinget,	
  Rubenstein)	
  
u  With	
  ultra-­‐low-­‐power	
  transceivers	
  
§  Transceiver	
  consumes	
  1nJ/b	
  
§  Energy	
  consumpVon	
  for	
  	
  
transmission	
  ~10	
  Vmes	
  lower	
  than	
  for	
  recepVon	
  
§  Can	
  sustain	
  1-­‐2	
  Kb/s	
  
u  Networking	
  
§  Dynamic	
  energy	
  availability	
  
§  Perpetual	
  operaVon	
  rather	
  than	
  	
  
lifeVme	
  maximizaVon	
  
§  Limited	
  control	
  informaVon	
  and	
  	
  
computaVonal	
  power	
  
IoT	
  Communica6ons	
  and	
  Networking	
  Challenges	
  
gil@ee.columbia.edu	
  
wimnet.ee.columbia.edu	
  
enhants.ee.columbia.edu	
  
Ques6ons?	
  

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Insights on Mobile Futures from Columbia University's Gil Zussman

  • 1. Mobile  Futures?   Gil  Zussman   Wireless  and  Mobile  Networking  Lab   Department  of  Electrical  Engineering     Columbia  University  
  • 2. Wireless  and  Mobile  Traffic  Sta6s6cs   u  In  2012,  the  number  of  cellular  users   exceeded  the  number  of  toothbrush   users  
  • 3. Wireless  and  Mobile  Traffic  Sta6s6cs   Mobile  data  traffic  increase  (©  Cisco)   (81%  increase  in  2013)     u  In  2012,  the  number  of  cellular  users   exceeded  the  number  of  toothbrush   users   u  Cellular  and  Wi-­‐Fi  devices  –  generate   ~40%  of  Internet  traffic  (Cisco  VNI)  
  • 4. Wireless  and  Mobile  Networks   u  Most  specificaVons  define  the  Physical     and  Medium  Access  control  (MAC)  layers   u  Research,  development,  specificaVons   ZigBee   SHORT  <    RANGE    >    LONG   LOW        <        DATA  RATE        >        HIGH   Body/Personal  Area   Networks   Local  Area  Networks   Bluetooth   Small  cells   Wi-­‐Fi  a,  g,  n,  ac,  …   Cellular  Networks  LTE   RFID   DAS   ApplicaVon   PHY   MAC   Network   Transport   Cross  Layer  
  • 5. LTE   Wireless  and  Mobile  Networks  -­‐  Future…   ZigBee   SHORT  <    RANGE    >    LONG   LOW        <        DATA  RATE        >        HIGH   Body/Personal  Area   Networks   Local  Area  Networks   Bluetooth   Small  cells   Wi-­‐Fi  a,  g,  n,  ac,  …   Cellular  Networks   RFID   DAS   LTE  Advanced  
  • 6. Cellular  &  WLAN  –  Research  Challenges   Self-­‐interference   Cross-­‐interference   Coopera6ve  Mul6point  (CoMP)  /  Network  MIMO   Full  Duplex   HetNets   Cloud-­‐RAN   Cloud-­‐RAN  
  • 7.                 The  Internet  of  Things  (IoT)   u  ConnecVng  “Everything”   u  Smart  grid/buildings/etc.   u  Tracking,  supply  chain   u  Healthcare,  wearable   u  Cyber-­‐Physical  systems,  control   u  There  are  already  ~20M  wearable   devices  and  ~300M  M2M  connecVons    
  • 8. u  Protocols  –  design  &  standardizaVon   §  Various  applicaVons   u  Security   u  Energy  efficiency     u  Previous  work  –  sensor  networks,  RFIDs   u  Energy  harves6ng  wireless  nodes   §  Due  to  Moore’s  law,  Dennard  scaling,  improved  transceivers,  and  improved   harvesVng  efficiency,  nodes  can  self-­‐power              M3              Ambient  Backscajer        EnHANTs      (Michigan)            (U.  Washington)    (Columbia)       The  Internet  of  Things  –  Challenges  
  • 9. Energy  HarvesVng  AcVve  Networked  Tags  (EnHANTs)  –   Lessons  Learned   u  Small  and  flexible   u  Harvest  their  own  energy,  form  a  wireless  network,     and  exchange  basic  informaVon  (e.g.,  IDs)   u  Extensive  light  and  kineVc  energy  measurement  studies     u  Energy/power  budget  –  1J/day  or  12  μW   u  AA  bajery  will  be  depleted  aner  40  years…   1 2 3 4 0 200 400 I(µW/cm2 ) Days 0 5 10 15 Relax Walk Fast w. Run Cycle Upst. Downst. D(m/s2 ) 42 42 42 42 42 42 41 41 42 42 42 42 30 29 30 41 42 42 41 42 42 (a) 0 1 2 3 4 5 Relax Walk Fast w. Run Cycle Upst. Downst. f m (Hz) (b) 0 500 1000 Relax Walk Fast w. Run Cycle Upst. Downst. P(µW) (c) Figure 5: Characterization of kinetic energy for common human activities, based on a 40- participant study: (a) average absolute devia- tion of acceleration, D, (b) dominant motion fre- quency, fm, and (c) power harvested by an opti- mized inertial harvester, P. ergy availability on the participant’s physical parame- ters. 5.1 Study Summary The dataset we examine [33] contains motion sam- ples for 7 common human activities – relaxing, walk- ing, fast walking, running, cycling, going upstairs, and going downstairs, – performed by over 40 different par- ticipants and recorded from the 3 sensing unit place- ments, shown in Fig. 2(b). For each 20-second motion belt, and trouser pocket sens tively. For each motion and number of participants tha on the top of Fig. 5(a). At e the median, the edges are th the “whiskers” extend to c the outliers are plotted indiv arately summarize the resu important motions. 5.2 Energy for Differe We discuss below the ene ties for the different examin Relaxing: As expected, alm vested when a person is not Walking and fast walkin inant periodic motion in no particularly important for For walking, the median P sensing unit placement, 18 ment, and 202 µW for trous P values are in agreement scale, studies of motion en walking [13, 31]. In comp availability is on the order o harvester energy conversion count [11,35], a similarly si more energy from walking t walking (which was identifi ipants themselves) has high at a normal pace (Fig. 5) a much P. Running: Running, an in associated with high D and results in 612 ≤ P ≤ 813 µW Cycling: For the examine generates relatively little en are 41–52 µW, 3.7–3.9 time
  • 10. u  Device  and  testbed  development     (with  Carloni,  Kymissis,  Kinget,  Rubenstein)   u  With  ultra-­‐low-­‐power  transceivers   §  Transceiver  consumes  1nJ/b   §  Energy  consumpVon  for     transmission  ~10  Vmes  lower  than  for  recepVon   §  Can  sustain  1-­‐2  Kb/s   u  Networking   §  Dynamic  energy  availability   §  Perpetual  operaVon  rather  than     lifeVme  maximizaVon   §  Limited  control  informaVon  and     computaVonal  power   IoT  Communica6ons  and  Networking  Challenges