New sensors. More intelligent apps. Mobile-connected smart objects. Wearables. LTE. Augmented reality. Multi-platform development tools. Precision indoor location sensing. Ultra HD. Flexible screens. The list of anticipated future mobile technologies goes on and on. On April 23, NYC Media Lab and Razorfish presented an evening of demos and discussion on Mobile Futures to learn what’s on the verge of commercialization, what’s still in the lab, and what advances will change the nature of media and communications in the future. Read our takeaways at https://medium.com/@nycmedialab/524d50740b79.

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

11. gil@ee.columbia.edu
wimnet.ee.columbia.edu
enhants.ee.columbia.edu
Ques6ons?