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

 <    R

ANGE    >    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  L

ayer  

LTE  

Wireless  and  Mobile  Networks  -­‐  Future…  

ZigBee  

SHORT

 <    R

ANGE    >    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 40

200

400

I (µ

W/c

m2 )

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)

012345

Relax Walk Fast w. Run Cycle Upst. Downst.

f m (H

z)

(b)

0

500

1000

Relax Walk Fast w. Run Cycle Upst. Downst.

P(µW

)(c)

Figure 5: Characterization of kinetic energyfor 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 SummaryThe dataset we examine [33] contains motion sam-

ples for 7 common human activities – relaxing, walk-ing, fast walking, running, cycling, going upstairs, andgoing downstairs, – performed by over 40 di!erent par-ticipants and recorded from the 3 sensing unit place-ments, shown in Fig. 2(b). For each 20-second motionsample, we use the acceleration, a(t) trace to calculateD, fm, P , and r. To obtain P , we use the exhaus-tive search harvester optimization algorithm, describedin Section 3.4. By determining the best harvester foreach motion, we can o!er important insights into theharvester design.To validate the data from [33], we replicated the mea-

surements with our sensing units. The results of ourmeasurements were consistent with the provided data.We note that the fm values calculated for the di!erentmotions in the dataset are consistent with the physiol-ogy of human motion.The statistics of the calculated D, fm, and P are

summarized in the boxplots in Fig. 5. For each ofthe 7 motions the leftmost (black), middle (red), andrightmost (blue) boxes correspond to shirt pocket, waist

belt, and trouser pocket sensing unit placements, respec-tively. For each motion and sensing unit placement, thenumber of participants that had a(t) samples appearson the top of Fig. 5(a). At each box, the central mark isthe median, the edges are the 25th and 75th percentiles,the “whiskers” extend to cover 2.7! of the data, andthe outliers are plotted individually. In Table 3 we sep-arately summarize the results and the data rates for 4important motions.

5.2 Energy for Different ActivitiesWe discuss below the energy availability and proper-

ties for the di!erent examined motions.

Relaxing: As expected, almost no energy can be har-vested when a person is not moving (P < 5 µW).

Walking and fast walking: Walking is the predom-inant periodic motion in normal human lives and thusparticularly important for motion energy harvesting.For walking, the median P is 155 µW for shirt pocketsensing unit placement, 180 µW for waist belt place-ment, and 202 µW for trouser pocket placement. TheseP values are in agreement with the previous, smaller-scale, studies of motion energy harvesting for humanwalking [13, 31]. In comparison, indoor light energyavailability is on the order of 50–100 µW/cm2. Takingharvester energy conversion e"ciency estimates into ac-count [11, 35], a similarly sized harvester would harvestmore energy from walking than from indoor light. Fastwalking (which was identified as “fast” by the partic-ipants themselves) has higher D and fm than walkingat a normal pace (Fig. 5) and generates up to twice asmuch P .

Running: Running, an intense repetitive activity, isassociated with high D and fm (Fig. 5(a,b)), and henceresults in 612 ! P ! 813 µW.

Cycling: For the examined unit placements, cyclinggenerates relatively little energy – the median P valuesare 41–52 µW, 3.7–3.9 times less than the P for walk-ing. While the high cadence of cycling motion resultsin relatively high fm (Fig. 5(b)), a harvester not on thelegs will be subject to only small displacements, result-ing in small values of D (Fig. 5(a)) and P (Fig. 5(c)).For cycling IoT applications, harvester placements onthe lower legs should be considered.

Walking upstairs and downstairs: Our examina-tion demonstrates that human exertion (perceived ef-fort and energy expenditure) does not necessarily corre-spond to higher motion energy harvesting rates. Whilepeople exert themselves more going upstairs, the P forgoing downstairs is substantially higher than for goingupstairs. Specifically, for the downstairs motion, themedian P is 1.78 times higher than the upstairs mo-tion for shirt unit placement, 2.1 times higher for waistplacement, and 1.65 times higher for trouser placement.

6

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?