Transcript Adaptive Multi-Layer Mobile Backbone Based Ad Hoc Networking

Professor Izhak Rubin
Unmanned-Vehicle Aided Multi-Tier
Autonomous Intelligent Wireless
Networks:
Mobile Backbone Networks
Professor Izhak Rubin
Electrical Engineering Department
UCLA
August 2005
[email protected]
Professor Izhak Rubin
FORCEnet Architecture using
AINS Technologies
Development of AINS system
architecture for realizing FORCEnet
using intelligent autonomous
collaborating agents embedded in
entities that perform communications
networking, sensing, maneuvering and
striking functions.
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AINS Innovative Networking Technologies
enable a Network-Centric C4ISR
Operation
Professor Izhak Rubin
Development of survivable and autonomously
adaptable mobile communications network
systems that support high quality transport of
critical messaging flows and real-time streams
in an adverse environment to enable network
centric combat operations and warfare.
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Professor Izhak Rubin
Our Approach
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Breakthrough methods to guide intelligent
platforms to rapidly mitigate network system
gaps, substantially re-constitute degraded
configurations and enhance performance, at
the right place at the right time.
Such methods include the autonomous layout
and control of unmanned networked platform
formations and UAV swarms in a multi-tier
hierarchical mobile backbone networked
infrastructure, and the formation of internetsin-the-sky.
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Professor Izhak Rubin
Our Innovative Networking
Technologies: I
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UV aided Mobile Backbone Networks (MBNs):
Multi-tier adaptive autonomous networking
Robust survivable QoS Routing for mobile ad hoc
wireless networks employing multi tier UV
swarms
Architecture, infrastructure and approaches for
the configuration of UAV platforms and swarms to
jointly best support
 Communications networking
 Sensing tasks
 Area search and surveillance
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Professor Izhak Rubin
Our Innovative Networking
Technologies: II
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Power-control spatial-reuse Medium
Access Control (MAC) protocols and
algorithms
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Integrated MAC scheduling, power control
and routing leading to significant
enhancements in the throughput efficiency
of shared radio channels
Integrated System Management (ISM)
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New paradigm in the design of system
management architecture that combines
monitoring, control and resource allocations
for C4ISR systems
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Professor Izhak Rubin
Robust Wireless Networking –
Architecture and topology Synthesis
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Synthesis of a multi-tier (land, air and
sea based) mobile backbone network
(MBN)
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New distributed algorithms to configure the
multi tier backbone network
Dynamical adaptivity to failures, application
mixes and capacity requirements
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Hierarchical
Configuration
of
Professor Izhak Rubin
UV-aided Mobile Backbone
Network (UV-MBN)
ANet 1
Backbone Node
Gateway
ANet 2
ANet 3
ASPN 1
ASPN 2
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Professor Izhak Rubin
AINS based UV-aided
Dynamically Reconfigurable
Network
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mbns.exe
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Fig. 4. Sample of Flow
Blocking rates for flows
of different classes using
the IRI QoS based
admission control
mechanism
UV aided Mobile Backbone Network Protocol
(MBNP)
Quality of Service (QoS) UV-aided operation
MBN based On Demand Routing with Flow Control (MBNR-FC)
Swarm Networking
Professor Izhak Rubin
Illustration of our heterogeneous Mobile Backbone Network (MBN)
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Professor Izhak Rubin
UV aided Autonomous
Mobile Backbone Network
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Professor Izhak Rubin
Backbone Construction
(a)
(b)
(c)
(d)
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The MBN Topology Synthesis
Algorithm (TSA)
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Neighbor Discovery
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Every node exchange “Hello Message”
periodically. – Short timer
Every node updates its neighbor list (BCN: 1,3)
periodically. – Long timer
Hello
Hello
Each node learns its 1-hop neighbor
information and 2-hop BN neighbor
information.
Association Algorithm
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Every node that is in a BCN state or
RN state attempts to associate with a
BN with highest Weight.
The Weight of a node can be based
on its ID, degree, congestion level,
and a nodal/link stability measure.
If no acceptable neighboring BN is
detected, try BCNs; If no BCN either,
try RNs
(BCN: 3,6)
Hello
(BCN: 4,5,7)
Hello
(BCN: 1,2,5,7)
Hello
Hello
(BCN: 3,4,6) Hello
(BCN: 2,3)
(BCN: 6,7)
Hello Message: ID, Weight,
BN Neighbor List
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The MBN Topology Synthesis
Algorithm (TSA)
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BCN to BN Conversion Algorithm
(BCN: 3,6)
(1) Client coverage:
a BCN that receives an association
(BCN: 1,3)
request from a BCN or RN,
converts itself to a BN.
(2) Connectivity of the BNet:
A BCN node finds that by
BN
converting itself to a BN it will
upgrade the Bnet connectivity.
(BCN: 1,2,5,7)
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(BCN: 4,5,7)
BN
BN to BCN Conversion Algorithm
(1) All of its BN neighbors have at
least one common BN neighbor
whose weight is higher than the
weight of the underlying BN that is
considering to convert.
(2) Each of its BCN clients have at
least one other BN neighbor.
BN
(BCN: 3,4,6)
(BCN: 2,3)
(BCN: 6,7)
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MBN Topology Synthesis Algorithm
Convergence Time
Convergence Time
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TSA
Number of Cycles
20
w/ Rule 1 & 2 bound
Dai & Wu
15
10
5
0
100
200
300
400
500
Number of Nodes
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The MBN topology synthesis algorithm convergence in constant time,
of the order of O(1).
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Total number of backbone nodes
(BNs) in the network
Backbone Network Size
80
Number of BNs
70
60
TSA
Minimum
Dai & Wu
50
40
30
20
10
0
100
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200
300
Number of Nodes
400
500
The backbone network (Bnet) size is independent of the number of
nodes in the network or the nodal density.
The backbone network (Bnet) size is only proportional to the area size.
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Control Message Overhead of TSA
Hello Message Rate (per node)
1.6
1.4
Rate (Kbps)
1.2
TSA
Dai & Wu
1.0
0.8
0.6
0.4
0.2
0.0
100
200
300
400
500
Number of Nodes
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The control message overhead of TSA is independent of
the number of nodes in the network or the nodal density.
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Data Delivery Radio of 25 UDP flows
Data Delivery Ratio
Delievery Ratio (%)
100
80
60
40
AODV
20
w/ Rule 1 & 2
Dai & Wu
0
100
200
300
400
500
Number of Nodes
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Average End-to-end Delay
Performance
Average End-to-End Delay
3.0
AODV
2.5
Delay (s)
2.0
w/ Rule 1 & 2
Dai & Wu
1.5
1.0
0.5
0.0
100
200
300
400
500
Number of Nodes
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Average Data Path Length
Average Path Length
7.0
6.5
Number of Hops
6.0
5.5
AODV
w/ Rule 1 & 2
Dai & Wu
5.0
4.5
4.0
3.5
3.0
2.5
2.0
100
200
300
400
500
Number of Nodes
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Average Path Length
We expect the employment of the MBNR scheme to yield a longer average path length
value than that obtained under AODV (since routes are now established only across the
backbone network). Interestingly, our simulation results indicate that the MBNR
protocol does not always produce longer path lengths.
RREQ packets are transmitted as broadcast packets, when such a packet experiences
collision, no MAC layer retransmission takes place. Consequently, if the network is
already overwhelmed by RREQ storm, it is likely that a route will not be discovered in
time or that a “non-shortest path route” will be selected
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100 nodes
200 nodes
300 nodes
Average Path Length
4.0
4.5
Path Length (hops)
Path Length (hops)
4.5
3.5
3.0
2.5
2.0
1.5
100 nodes
200 nodes
300 nodes
Average Path Length
4.0
3.5
3.0
2.5
2.0
1.5
1.0
1.0
0
3
6
9
12
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BN Neighbor Limit
(a) Stationary network
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21
0
3
6
9
12
15
18
21
BN Neighbor Limit
(b) Mobile network
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QoS based Robust Scalable
Routing (MBNR)
Professor Izhak Rubin
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MBN based Robust Routing protocols (MBNR)
On-demand routing mechanism that uses selective control
packet forwarding (across the MBN) to discover routes
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Proactive routing for route establishment in smaller subnets and certain Access Nets
Unique MBN based Flow and Congestion control mechanism
(MBNR-FC protocol) to preserve the quality of service (QoS) of
established flows and to ensure that, under overloading
conditions, only high priority flows are supported at desired QoS
Unique cross physical, MAC and network layer algorithms and
protocols to ensure that the realistic nature of the wireless radio
environment is dynamically incorporated into communications
resource allocations and routing operations.
Effective use of UGV and UAV swarms to establish backbone
routes and to distribute control packets
Hybrid backbone and non-backbone routing and
flow/congestion control to efficiently utilize resources in areas
that are not covered or are away from the mobile backbone
and its UGV and UAV agents
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Professor Izhak Rubin
MBN Routing with Flow Control (MBNR-FC):
Delay Jitter Performance Comparison among Different
Protocols
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Network Performance: packet delay and delay jitter
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Delay jitter vs. Traffic loading
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The delay jitter is reduced as traffic loading rate is increased (when the network is not
saturated). Explanation: route discovery produces a larger delay which is different from the
delay experienced when the route is available.
When the network is congested, more route discovery attempts take place.
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Hybrid Routing Strategy
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Capacity utilization of pure MBNR-FC
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When the number of BCNs is not able to form a backbone to cover the
whole network area, backbone-only paths will limit the overall
throughput capacity of the network.
Allowing both backbone routing and non-backbone routing could fully
utilize the network capacity.
Long-distance traffic vs. Short-distance traffic
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Short-distance traffic obtains shorter path lengths by routing through all
type of nodes, while long-distance traffic does not.
Long-distance traffic obtains routing overhead reduction by routing
through backbone network, while short-distance traffic does not.
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Delay-throughput performance of
MBNR-FC/DA under 2-hop Anets
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The delay-throughput performance with distance thresholds equal to 7 hops and 9hops demonstrate a significant throughput capacity gain compared to that with
distance threshold equal to 0-hops (which is obtained by pure MBNR-FC).
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Under Development: Adaptive Scheme for
Distance threshold Selection
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Adaptive scheme for distance threshold selection
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Execute in a distributed manner.
Adjust the distance threshold according to the current traffic distribution.
Procedures:
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Each BN collects the congestion information of its own Anet: the number of clients that
are not eligible for participating in the route discovery process (i.e.; if they or their
neighbors are congested.)
BNs that are within 2 hops from each other exchange their Anet congestion indices.
The obtained congestion information is used by each BN to compute a distance threshold
dth which it broadcasts to its Anet clients
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Dr. Izhak Rubin
High Capacity QoS MAC
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Power-control spatial-reuse Medium
Access Control (MAC) protocols and
algorithms
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Integrated MAC scheduling, power control
and routing leading to significant
enhancements in the throughput efficiency
of shared radio channels
Provision of quality of service (QoS) by
prioritized scheduling and cross layer
MAC/Networking operations
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Dr. Izhak Rubin
MAC Mechanisms
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Power control spatial reuse (PCSR)
Medium Access Control (MAC) layer
operations
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Scheduling based QoS based MAC
mechanisms (such as: PCSR demand
assigned TDMA / FDMA / CDMA)
Random access based PCSR techniques
providing enhanced performance
Directional and omnidirectional operations
PHY-MIMO driven power control MAC
operations
Autonomous power control MAC
operations using UAV swarms
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Professor Izhak Rubin
6
2
5
7
1
large
increase in
spatial
reuse
factor
BN
1
10mW
9
9
50mW
8
4
10mW
BN
7
10mW
6
3
Power: 1mW
Power: 10mW
Power: 50mW
Power: 100mW
9
Slot 1 Slot 2
BN
3
10mW
4
2
10mW
Power Control
Spatial-Reuse
MAC DA/TDMA
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Slot 3 Slot 4
4
50mW
BN
3
10mW
1
BN
7
10mW
1
9
10mW
4
10mW
2
Slot
5
1
10mW
9
2
2
50mW
9
6 8
7
1mW 100mW
5
50mW
3
Slot
10
Slot 6 Slot 7 Slot 8 Slot 9
3
50mW
5
1
50mW
6
BN
3
10mW
6
9
50mW
BN
7
10mW
9
8
50mW
5
50mW
1
50mW
2
5
50mW
7
4
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Professor Izhak Rubin
Throughput Analysis of our Power Control
Scheduling Algorithm (PCSA) and alternative
scheme (TPA) (for an illustrative network with 10 active nodes)
3.5
PCSA
TPA,D=100m
TPA,D=250m
TPA,D=600m
TPA,D=1000m
Throughput (packets/slot)
3
2.5
2
1.5
1
0.5
0
0
0.02
0.04
0.06
0.08
0.11
0.15
0.19
Packet Generation Rate (packets/slot)
0.23
0.27
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Uniform Traffic
1000*1000m area, 100 nodes, 30 flows,
Fixed Routing
4.5
4
delay (sec)
3.5
3
2.5
2
1.5
1
0.5
0
0
200
400
600
800
1000
Throughput (Kbps)
Regular
DPC
1200
1400
In this experiment, we fix
the routing in advance so we can
focus on understanding purely
the characteristics of the 802.11
MAC.
DPC offers a significantly better
Throughput-delay characteristics
compared to low power
transmissions (blue) and regular
802.11 with no power control
(green).
LOW
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Localized Traffic
400*400m area, 100 nodes, 15 flows,
Fixed Routing
2.5
Benefits of our distributed
power control algorithm are
especially apparent when traffic
patterns are localized.
Delay (sec)
2
1.5
1
0.5
0
0
500
1000
1500
2000
2500
3000
Throughput (Kbps)
Regular
DPC
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Cross Layer Power Control based
Topology Synthesis
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What is the optimal number of APs needed
for best network performance (in terms of
throughput, delay, delay-jitter, packet loss
ratio)?
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APs should not only be deployed to provide
coverage but also to accommodate different
capacity needs of nodes
What is the optimal power to operate at?
When is it useful to employ “Cell Splitting” and get
new APs or “Soft APs” (a laptop configured to
work as an AP) into the network?
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Adaptation of AP / BN selection
to the traffic profile
When using power control
the number of APs deployed
Should depend on the
Traffic characteristics in the
Network.
Throughput vs. Number of AP
25
AP
20
15
When the traffic is mostly
Long distance, it’s better to
Employ a fewer number of
APs, and vice versa.
10
5
0
0.5
0.7
0.9
1.1
1.3
Throughput (Mbps)
Short Range
Long Range
1.5
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On going developments: Simulation Results for
Hybrid TDMA/CSMA
Experiment with three APs, 9
flows, 3 of which are inter-AP
flows.
Case 1: Hybrid scheme
Case 2: Regular 802.11
We can clearly see that the
hybrid scheme delivers
significant throughput and
delay benefits over the
regular, non power controlled
IEEE802.11
Note: inter-AP flows can
traverse paths that are as
long as 3 hops
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Integrated System
Management (ISM)
Professor Izhak Rubin
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New paradigm in the design of system
management architecture that combines
monitoring, control and resource allocations for
C4ISR systems
Hierarchical Integrated System Management and
control architecture using nodal, subnetwork and
system wide monitors and control elements
Monitoring attributes and Management
Information Bases (MIBs) for communications,
sensing, UV, maneuverable and strike segments
ISM algorithms for joint resource, performance,
failure and topology management of MBN based
C4ISR systems using UAV swarms
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Professor Izhak Rubin
Integrated System Management: system configuration
MIB
Integrated System Manager
MIB
ITM1
MIB
Integrated Network
Manager - MBN
MIB
Integrated Network
Manager - Sensor
Integrated Network
Manager - UAV
Sensor Proxy
UAV Proxy
ITM2
Cloudcap
BNs
RNs
UGVs
Nodes
GCS
MIB
MIB
MIB
MIB
MIB
UAV
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Integrated System Management
Professor Izhak Rubin
Illustration of ISM display of status of communications, sensing and
UAV networked systems
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Professor Izhak Rubin
ISM: Topology Display
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Professor Izhak Rubin
ISM: Traffic Graph Display
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On-Going & Planned Research
Works
Professor Izhak Rubin
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Power control spatial reuse MACs
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Topology Synthesis of the Backbone Networks
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Hybrid MAC for meshed architectures
Characterization and tuning of the algorithms;
performance features and comparisons; stability
and efficiency adaptations
MBN based QoS Routing
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Development and analysis of the hybrid MBNRFC/DA scheme
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Professor Izhak Rubin
outstanding research works
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UAV and UGV aided networking
UAV swarms
Cross Layer networking
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Distributed cross-layer PCSR MACs
Integrated power control MACs and MBN based QoS routing
Phy / MAC / Link / Network and topology synthesis cross
layer protocols and algorithms
Performance analyses and simulations under a multitude of
multimedia applications and C4ISR scenarios
Incorporation of QoS oriented network management
schemes
Energy aware MBN based networking
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