Spectrum-aware Energy Efficient Multi-hop
Device-to-Device IoT Communication
Priyanka Samanta, Amina Bashir, Mainak Chatterjee,
Saptarshi Debroy
Presented By: Priyanka Samanta
Department of Computer Science, The Graduate Center, CUNY
www.gc.cuny.edu
Outline
Introduction
System Model
Analysis
• IoT and it’s challenges
• Concept of DSA
• Routing challenges in DSA networks
• Inter & Intra domain routing
• Mathematical model
• Simulation results
• Integration plans with GENI
www.gc.cuny.edu
1
IoT and it’s challenges
• IoT: Network of physical objects
embedded with electronics, software,
sensors and network connectivity to
collect and exchange data.
• IoT devices are expected to be
connected wirelessly, so unprecedented
need for higher capacity wireless
networks.
• Current wireless networks operate on
the Industrial, Scientific and Medical
(ISM) ; Limited availability
• Remedy:
(DSA)
www.gc.cuny.edu
Dynamic
Spectrum
Access
2
Dynamic Spectrum Access
Spectrum utilization can be significantly improved by allowing
“unlicensed” users to borrow idle spectrum from “spectrum
licensees”
– A phenomenon known as Dynamic Spectrum Access (DSA)
– Spectrum Licensees →Primary
– Unlicensed User →IoT devices
www.gc.cuny.edu
3
Traditional Static spectrum access
Regulatory bodies allocate spectrum licenses to wireless service providers
(WSPs) where chunks of spectrum are allocated for specific services under
restrictive licenses.
– Example: Federal Communications Commission (FCC) in USA.
These allocations are now usually done via auctions.
– Since 07/1994, FCC conducted 87 auctions raising $60 billion .
– In 2010, only 3G and 4G auctions earned the Government of India
more than $20 billion.
Licenses are granted on a long term basis to WSPs.
– Licenses specify the range of frequencies to be used in particular
geographic areas.
– Restrictions are imposed on the technologies to be used and the
services to be provided.
www.gc.cuny.edu
4
US spectrum access scenario
www.gc.cuny.edu
5
NYC occupancy summary
White Space – Unused spectrum bands
www.gc.cuny.edu
6
Outline
Introduction
System Model
Analysis
• IoT and it’s challenges
• Concept of DSA
• Routing challenges in DSA networks
• Inter & Intra domain routing
• Mathematical model
• Simulation results
• Integration plans with GENI
www.gc.cuny.edu
7
DSA challenges for multi-hop IoT
communication
•
The need for spatio-temporal spectrumawareness in terms of finding unused or
underused channels at different locations
along an end-to- end route
•
Protecting licensed primary transmission on
channels when and where they arrive from
harmful interference caused by secondary
IoT communication
•
Ensuring
power
controlled
IoT
communication to preserve strict energy
preservation requirements of the IoT
devices.
www.gc.cuny.edu
8
DSA challenges for multi-hop IoT
communication
www.gc.cuny.edu
•
Heterogeneous IoT devices coexisting with
primary licensed users within DSA
environment.
•
Two
concurrent
IoT
multi-hop
communications between D1 to D6 and D3 to
D5 where different channels (i.e., Ch1 and
Ch2) are used at different hops based on the
channel spatial and temporal availability.
•
For route D1 to D6, sub- optimal transmission
power selection on Ch1 at D1 yields harmful
interference to primary receiver PRX;
although an alternate route (D1 → D7 → D8
→ D9 → D6) was available that would have
ensured protection of PRX and energy
preservation through selection of low
transmission power.
.
9
System Model
Sensors and domains
• Intra-domain routing
• Inter-domain routing
Types of nodes:
• Uncovered devices
• Non-edge devices
• Edge devices
Route discovery
• Route request
• Route reply
Route maintenance
www.gc.cuny.edu
10
Intra domain routing and flow chart
𝑛
Sensor calculates optimal power 𝑃𝑜𝑝𝑡
for all
channels n.
𝑛
𝑛
𝑃𝑜𝑝𝑡
= min{𝑃ℎ𝑤 , 𝑃𝑢𝑏
}
Creates graph and assigns weight per desired
maximizing performance metric.
Applies any shortest path algorithm viz.
Dijkstra’s.
Sensor instructs the source and intermediate
IoT about the next hop ID, channel ID and
transmit power value.
For inter-domain routing, destination is all
the edge-nodes associated with the sensor.
www.gc.cuny.edu
11
Inter Domain routing and selective
flooding
Sourc
e
Destinati
on
www.gc.cuny.edu
12
Reachability among IoT devices
𝑹𝟐
𝒇𝟏
𝒇𝟐
𝒇𝟑
www.gc.cuny.edu
13
Calculating optimum power for Primary
protection
Determine the safe zone around the source for all
channels n from the Radio envionoment Map.
Determine safe zone radius 𝑑𝑖𝑛 for channel n.
𝑛
Estimate 𝑃𝑢𝑏
from PU interference threshold Τ.
𝑛
𝑃𝑢𝑏
=
Τ
η𝑛 ×10( 10) ×16π2 (𝑑𝑖𝑛 )γ
(γ−2)
λ2 𝑑0
η𝑛 : Noise on channel n from the map
λ : Wavelength of light
𝑑0 : Antenna far field
γ : Average path loss factor
𝑛
Transmitting in 𝑃𝑢𝑏
guarantees no interference to primary receivers.
www.gc.cuny.edu
14
Outline
Introduction
System Model
Analysis
• IoT and it’s challenges
• Concept of DSA
• Routing challenges in DSA networks
• Inter & Intra domain routing
• Mathematical model
• Simulation results
• Integration plans with GENI
www.gc.cuny.edu
15
Analysis
Definition I. Edge node probability is defined as the probability of any IoT device to
be an edge node, i.e., be in an overlapping region.
𝑝𝑒𝑑𝑔𝑒 =
𝑁𝑜𝑣𝑒𝑟𝑙𝑎𝑝 ∗ 𝐴𝑜𝑣𝑒𝑟𝑙𝑎𝑝
𝑙2
Definition II (Connectivity Condition). The connectivity condition of any IoT
network is defined as the sufficient condition for the existence of at least one path
from any domain to all other domains in the network.
Definition III (Mapped Graph). The graph representation of a IoT network with
domains as vertices and overlapping regions as edges is called a mapped graph.
www.gc.cuny.edu
16
Connectivity Condition
Connected
Mapped graph
Not
connected
www.gc.cuny.edu
17
Simulation Setup
Simulations are done in C and MATLAB.
Parameters (Unless stated otherwise)
• 100×100 sq. km. area
• Number of primaries = 5-30
• Number of channels = 5-30 with 1 MHz bandwidth each
• Primary distribution mimics real world TV transmitters.
• Primary transmitter power = 50W-1 MW
• Primary detection threshold = -116 dBm
• Number of sensors = 9 oriented in a square grid pattern
• Secondary IoT distribution follows Poisson Point Process.
• Number of IoTs = 50-500
• Secondary IoT transmitter power = 100mW
• Primary tolerable co-channel interference threshold = -80 dBm
• IoT successful communication SNR threshold= -15 to -75 dB
www.gc.cuny.edu
18
Result: Reachability
www.gc.cuny.edu
19
Result: Routing with and without power
control
Without power control
With power control
www.gc.cuny.edu
20
Result: Primary receiver protection
www.gc.cuny.edu
21
GENI integration/implementation plans
Short term: Using existing GENI wireless testbeds
– In the next stage of our evaluation, we plan to use ORBIT wireless
grid testbed through GENI
– ORBIT wireless nodes --> IoT devices and primary receivers
– Program the devices to implement the proposed power control
based route discovery and data transfer
– Issues that are unknown/to address:
• Are these nodes in-built with spectrum sensors?
• How to emulate/simulate spectrum availability at different
devices/nodes?
• Are the nodes equipped to perform channel switching?
www.gc.cuny.edu
22
GENI integration/implementation plans
Long term: Creating a new spectrum aware IoT testbed
– In future, we plan to create an in-lab IoT testbed at CUNY
COSSMO lab
– An experimental indoor radio environment map (REM) with
multiple spectrum sensors is already being built in collaboration
with UCF
– The new IoT testbed will made spectrum aware using the REM
– The entire IoT testbed and REM setup will be integrated with
GENI for the community to use
www.gc.cuny.edu
23
Conclusion
Proposed multi-channel multi-hop routing
• Is targeted for IoT devices with sensing incapable low cost secondary
devices.
• Employs a selective flooding technique for RREQ without unnecessary
flooding of the network
• Guarantees route discovery though edge nodes.
• Guarantees primary receiver protection though power control
• Maximizes route capacity.
www.gc.cuny.edu
24
Questions????
www.gc.cuny.edu