PHD DISSERTATION DEFENSE ReceiverCost Cognizant Maximal Lifetime Routing in Embedded Networks: Model

1
PHD DISSERTATION DEFENSEReceiver-Cost Cognizant
Maximal Lifetime Routing in Embedded Networks
Model and Solutions

• Guofeng Deng
• Advised by Dr. Sandeep Gupta
• The IMPACT Lab
• Ira A. Fulton School of Engineering
• Arizona State University, Tempe

2
Outline

• Motivation
• Intrinsic energy constrains in wireless ad hoc
  networks (WANET)
• Existing research assumed that power for transmit
  dominates, and ignored power for receiving
• For low power wireless devices, which are used
  widely nowadays, however, receiving power is not
  negligible.
• Background
• WANET overview and the intelligent shipping
  container project
• Receiving energy cost models
• Key Results
• Receiver-cost cognizant maximal lifetime routing
• Maximal lifetime routing in mobile networks
• Conclusion Future Work

3
Highlights

• What it is all about
• investigating the impact of receiving energy
  costs by revisiting the maximal multicast
  lifetime routing problem, which is trivial if
  receiving packets is free
• Key contributions
• Receiving costs change the problem dramatically
• NP-hard if receiving power is adaptive to
  received signal strength Deng, Gupta,
  Varsamopoulos, IEEE Comm. Letters 2008
• NP-hard if nodes consume energy for overhearing
  as well Deng Gupta, ICDCN'06
• Handling receiving costs properly improve
  multicast lifetime compared with disregarding
  them
• By 15 with no overhearing costs Deng Gupta,
  Globecom06
• By 60 with overhearing costs Deng Gupta,
  ICDCN'06
• First distributed algorithm to adapt to node
  mobility for maximal multicast lifetime Deng,
  Mukherjee Gupta, in preparation

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Wireless Ad Hoc Network Overview

• WANET
• Distributed networks nodes talk to others in the
  proximity directly
• Wireless devices low power, small form factor
  devices with short comm. range
• Multicast medium one transmission can reach
  multi-entities in proximity
• Features
• Flexible robust no dependency on fixed
  infrastructure
• Scalable can accommodate large number of
  entities
• Low cost, low maintenance, mobile,
• Applications
• Surveillance and rescue environment monitoring,
  Body Area Network (BAN),
• Challenges
• Limited resources (e.g. energy, bandwidth)

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WANET Application Intelligent Shipping
Container (ISC)

• Background Global nature of todays economy
• 90 of the worlds trade is transported in cargo
  containers
• 10 million cargo containers enter U.S. ports each
  year
• Motivations
• Homeland security only 5 can be inspected
  because of todays limited time and money
• Commercial values lack of end-to-end visibility
  for supply chain and chain of custody
• Goal
• Architecture design that meets various
  requirements
• Verification of currently available technologies
• A joint effort between Intel Inc.
• the Impact Lab.

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ISC Hierarchical Container Network

• Internal container network (within each
  container)
• Wireless Sensor Network (WSN) collecting
  environmental parameters
• Radio Frequency Identification (RFID) automatic
  and unique identification, multi-level tracking
  (e.g. products, packages, pallets, containers,
  etc)
• Gateway point of access from outside container,
  on scene data processing and storage
• External container network
• WANET interact with neighboring containers

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ISC Severe Energy Constraints

• MicaZ mote with MTS310 Sensor board
• Broadcasts a packet every 10 sec with its voltage
  level
• Uses the power saving mode (switching off radio
  and sensor board after readings)
• 2 new AA batteries
• The base station (4 meters away) collects packets
• The mote lasts about 46 days

• Had to use a car battery to power Stargate
  (gateway) and RFID reader for a 5-day shipment.

• Government regulation requires lifetime at least
  1 year.

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Motivation Receiving Power is Not Negligible

• Most existing research aimed to conserve energy
  for transmitting packets and neglected energy
  consumed for receiving packets
• Receiving power is not negligible in low power
  devices
• Chipcon CC2420 (single-chip 2.4 GHz IEEE 802.15.4
  compliant RF transceiver) data sheet

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Motivation Receiving Power Affects Route
Optimality

• Example scenario to transmit same packets from 1
  to 2 and 3, i.e. multicast
• Goal to transmit as many packets as possible
  before any node exhausts its battery
• Assumption identical nodes, B is battery
  capacity, a link is associated with energy
  consumed for transmitting a packet over the link.

1
1
4
3
3
2
2
3
3
2
Energy cost for rcv a pkt Total
number of packets rcved by 2 or 3
0 B/3
(node 1 dies first) B/4 (node 1 dies first)
4 B/6
(node 2 dies first) B/4 (all nodes die at same
time)
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Receiving Energy Cost Characterization

• Media Access Control protocol

TDMA based a node may switch off the transceiver
based on some schedule, avoid overhearing
irrelevant packets. But it will consume energy
for receiving packets designated to it.
Random access a node may overhear transmissions
in the proximity and consume energy for
demodulating signals not interested in.
2
2
1
1
3
3
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Receiving Energy Cost Characterization

• Decoding techniques

Regular decoder energy consumed for decoding a
signal is independent on signal strength, i.e.
constant
Turbo decoder energy consumed for decoding a
signal is inversely proportional to signal
strength, i.e. adaptive
Based on transmitter-receiver power tradeoff
Vasudevan et al. Infocom'06
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Receiving Energy Cost Models
Objective to investigate maximal multicast
lifetime problems under each of these receiving
energy cost models.
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Maximal Multicast Lifetime Routing in WANET

• Multicast traffic
• A single source generates multicast packets
• A set of nodes in the network need to receive the
  packets
• Metrics
• The duration until the first node in the network
  to fail due to exhausted battery
• State of the art
• Solvable in polynomial time when the receiving
  energy cost is 0

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Related work

• Energy efficient multicast routing
• Reduce overall energy consumption for multicast
  traffic
• Take advantage of multicast media
• Maximal lifetime multicast routing
• Extend the duration until the occurrence of some
  application dependent critical events
• Balance energy consumption among nodes
• Static vs. dynamic approaches
• Overhearing energy costs
• Studied for data-gathering routing
• Adaptive receiving costs
• Studied for data-gathering routing

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MaxMLT under DCR

• Problem
• Maximizing multicast lifetime under the DCR
  model, i.e. designated constant receiving energy
  costs
• Designated Receiving Power algorithm (DRP)
• In the directed network graph, there is a link
  (u,v) if u can be received by v when peak
  transmit power is used.
• Convert the network graph to so called INverse
  longevity Graph (ING)
• Run Prims algorithm on the ING to generate a
  multicast tree
• Result
• Optimal solution of time complexity O(n2 log n),
  where n is the number of nodes

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MaxMLT-DCR Simulation Results
RX peak TX for each node u
For each node u, select RX randomly between peak
TX and 2X peak TX
ZRP Zero Receiving Power
Network size (density) number of nodes in the
network
All the nodes are destinations and have identical
battery capacity and peak TX power.
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MaxMLT under OCR

• Problem
• Maximizing multicast lifetime under OCR, i.e.
  overhearing constant receiving energy cost
• Challenges
• NP-hardness reduce set cover to MaxMLT under OCR

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MaxMLT under OCR NP-hard
Source node
Forwarding nodes
Destination nodes
Set cover
MaxMLT under OCR

• Observations
• Node s will die first
• Lifetime of resulting multicast tree is
  determined by the number of forwarders

• Assumptions
• identical battery capacity
• all links are associated with same transmit power

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MaxMLT-OCR Heuristic Solution
PRP Proximity Receiving Power algorithm

• Link weight computation with various metrics
  adding link (2,4) to the existing tree.

on-tree node
1
Note Link metric defines how the receiving power
is taken into account.
non-on-tree node
4
link being considered
link that has been established
transmit costs taken into account
receiving costs taken into account
CRP Cumulative Receiving Power algorithm
(extending DRP for comparison)
overhearing costs taken into account
PRP
ZRP
DRP
CRP
4
1
4
4
4
1
1
1
2
2
2
2
3
3
3
3
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MaxMLT-OCR Simulation Results?
RX peak TX
RX 2X peak TX
Identical battery capacity and peak TX power
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MaxMLT-OCR A Mcast Tree Snapshot

• The source node is surrounded by a hexagram and
  the rest are destinations
• Solid lines constitute mcast trees
• A circle represents the transmit range of the
  node in the centre
• The diameter of a solid grey dot represents the
  magnitude of overhearing costs

• Observation PRP tends to increase transmit power
  and reduce num of transmitters to decrease
  overhearing costs.

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MaxMLT under DAR

• Problem
• Maximizing the multicast lifetime under the DAR
  model, i.e. designated adaptive receiving costs.
• Assuming discrete levels of transmitting and
  receiving power
• State of the art
• A binary search optimal solution to a weaker
  problem, in which the multicast tree structure is
  given
• Challenges
• NP-hardness

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MaxMLT-DAR Chain of Transforms

• A chain of reduction from X3C (exact cover by
  3-sets) to MaxMLT under DAR

Is there any m-arbor in k-subgraph?
Decision problem of MAL to seek a m-arbor whose
lifetime is no less than some positive bound
Maximal m-arbor lifetime m-arbor is a tree
defined in an auxiliary graph any m-arbor can be
mapped to a mcast tree in the original graph with
same lifetime and vice versa
Special case of MaxMLT nodes can adjust transmit
and receive power only in discrete levels. We
also assume identical battery capacity.
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MaxMLT-DAR Chain of Transforms contd
A WANET and its auxiliary graph. Each node has
two transmit levels and two receive levels.
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MaxMLT-DAR Chain of Transforms contd
Reducing X3C to AIK in a k-subgraph. The transmit
(receive) vertices in each bipartite are sorted
in ascending order using represented
transmit/receive power levels.
A flat through path go through each bipartite
once and only one
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MaxMLT in Mobile Ad Hoc Networks

• Problem
• Maximizing multicast lifetime when nodes are
  mobile
• Challenges
• Dynamic networks node mobility and residual
  energy changes
• No distributed solutions exist
• Solution
• MSL (Multicast Service Lifetime)
• Multicast lifetime definition suitable for mobile
  networks
• DAMIL (Distributed and Adaptive Multicast
  Lifetime algorithm)
• Propose a new metric to decentralize routing
  decisions
• Distributed and adaptive solution that adopts the
  new metric

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MaxMLT-MANET MSL

• Quality of multicast service
• num of packets received (vs. time)
• by some or all destinations (address fairness
  among destinations)
• in some period of time (adapt to dynamic networks
  in timely manner)
• MSL is a measurement of quality multicast service
  received

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MaxMLT-MANET ?Max-Lifetime Tree

• A new metric that leads to distributed algorithm
• Link weight
• Node weight
• s,i is a path from s to i (s-path) and Wmax is
  a large value
• Optimality a multicast tree in which each node
  maximizes its weight is a maximum-lifetime
  multicast tree

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MaxMLT-MANET DAMIL

• Data structure a status table contains an entry
  for each neighboring node and itself
• wi node weight
• pi parent id
• hi hop-count (distance from the source)
• fi forwarding control boolean (FCB, whether to
  forward packets to some children)
• Periodic beacons (Control info is carried in
  periodic beacons)
• (wi, pi, hi, fi )
• Activities
• Each node repeatedly seeks the s-path that
  maximizes its weight
• Upon receiving a beacon
• An entry is created if the sender is a new
  neighbour
• Build or refine s-paths for gain in node-weight
• Updates the FCB accordingly

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MaxMLT-MANET Example
Node c moved to a new location. Assume symmetric
link weight and Wmax 99.
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MaxMLT-MANET Simulation

• Measurement
• The quality of a multicast service is denoted by
  Q(?,G,?), where ?,G,and ? represent window size,
  destination threshold and data threshold
  respectively.
• the MSL is defined as the period of time until
  the service quality drops below Q
• Comparison algorithms
• WMST updates a maximum lifetime tree
  periodically outperforms most lifetime
  maximizing protocols in static networks
• SS-SPST-E a distributed energy minimizing
  multicast protocol designed to overcome the
  impact of node mobility.

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MaxMLT-MANET Simulation Results
50 nodes totally, D is a set of destinations,
source generates four 512B packets per second.
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MaxMLT-MANET Simulation Results
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Conclusion Future Works

• Conclusion
• Showed receiving costs change the maximal
  multicast lifetime problem dramatically
• Showed handling receiving costs properly improves
  multicast lifetime significantly compared with
  disregarding them
• Proposed first distributed algorithm to maximize
  multicast lifetime in mobile ad hoc networks
• Future works
• Scavenging energy management

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Publication

• G. Deng, S. K. S. Gupta, and G. Varsamopoulos,
  Maximizing Multicast Lifetime with
  Transmitter-Receiver Power Tradeoff is NP-Hard,
  IEEE Communications Letters, Vol. 12, No. 9,
  September 2008
• G. Deng and S. K. S. Gupta, On Maximizing Network
  Lifetime of Broadcast in WANETs under an
  Overhearing Cost Model, ICDCN 2006, LNCS 4308
• G. Deng and S. K. S. Gupta, Maximizing Broadcast
  Tree Lifetime in Wireless Ad Hoc Networks, IEEE
  GLOBECOM'06, San Francisco, CA
• Deng, G. and Gupta, S. K. S. (2005). Maximizing
  multicast lifetime in wireless ad hoc networks.
  In L. T. Yang M. Guo (Eds.), High-Performance
  Computing Paradigm and infrastructure (pp.
  643-660). Hoboken, NJ John Wiley Sons.
• S. J. Kim, G. Deng, S. K. S. Gupta and M.
  Murphy-Hoye, Enhancing Cargo Container Security
  during Transportation A Mesh Networking Based
  Approach, IEEE HST, Waltham, MA, USA, April 2008.
  
• S. J. Kim, G. Deng, S. K. S. Gupta and Mary
  Murphy-Hoye, Intelligent Networked Containers for
  Enhancing Global Supply Chain Security and
  Enabling New Commercial Value, COMSWARE,
  Bangalore, India, 2008.
• G. Deng, T. Mukherjee, and S. K. S. Gupta, DAMIL
  A Distributed and Adaptive Algorithms to Extend
  Multicast Service Lifetime in MANETs, in
  preparation for IEEE Communications Letters.

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Thank You!
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Motivation Survive Energy Constrains

• Replenish battery
• Battery replacing expensive and impractical for
  large scale networks, such as sensor networks
• Explore ambient energy source limited capability
• Consume energy intelligently
• Energy efficiency reduce total amount of energy
  consumed
• Lifetime enlarge network life span as a whole

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Intelligent Shipping Container contd
INTER-Container TelosB mote Attached to nearby
containers. Proximity motes form an ad hoc
(multi-hop) inter-container network.
GPS Receiver 1
Containers
MICAz mote
External Hosts
Stargate
Internal Wireless Sensor Networks
USB Memory Card
MICAz mote 2.4 GHz
2.4 GHz
51-pin
USB
Stargate Managing Internal network (hardware,
power and security) data processing, routing
outgoing packets to external interface.
Ethernet
Mobile Computing Computers at point of work
(Handhelds) at the Data Center. Held by custom
officers and load/unload workers. Querying
current and historical data and DB downloading
from the logging systems.
Enterprise Servers Computers at the Data
Center. Collecting real-time data from
containers, managing DB responding to critical
events reported by containers.
802.11
RS232
PCMCIA
Compact Flash
GPRS PCMCIA Modem
802.11 Compact Flash card
Cellular Network
Arch Rock Edge Server Linux computer running web
services-based environment with web UI for setup,
control, monitor, management of diverse
wireless sensor networks.
Arch Rock DataLogger Low-power Embedded Linux
computer running local data collection
management of diverse wireless sensor networks
Ethernet
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ISC Hierarchical Container Network

• External Container Network
• A container forms and participates in networks
  with their neighbors dynamically.

• Internal Container Network
• The network inside a container is isolated from
  the dynamic changes outside a container.

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ISC Network Entities
PDA monitor and manually control Stargate, e.g.
start/stop RFID reader
Skyetek M8 RFID reader Cushcraft antenna
MICAz mote Read RFID tags and forward the
reading via wireless interface
Stargate MICAz mote WiFi card memory
card data collection and processing, database
management.
MICAz motes MTS310 environmental sensor
TelosB motes with onboard sensors environmental
sensor
Base station startup control and monitoring
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ISC System Tests

• Tested in a standalone container over several
  months in Chandler, Arizona, US
• Tested in a container yard in a 33 stacked
  container configuration in South Kearny, New
  Jersey, US
• Tested during a 5-day shipment from Singapore to
  Kaohsiung, Taiwan

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Background Generic Receiving Power Form

• receiving energy of node v
• transmit energy of node u
• base receiving energy per bit of v
• monotonic non-increasing adaptive receiving
  energy function ranging from 0 to 1
• transmission rate of i (bps)
• min transmit power of node i to reach node v
• distance between nodes u and v
• fading exponent
• integer parameters that can be either 0 or 1

For example, under OCR, if for any
i and j, then
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Background Designated Receiving Cost
transmitter
1
designated receiver
2
2
not related to the transmitter
1
3
transmit power
3
receiving power
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MaxMLT-OCR PRP A Heuristic Solution

• Take into account the effects of overhearing
  explicitly on both transmitter and receiver
• The weight of link (i,j) inverse link longevity
  -- incorporates the overhearing cost of i caused
  by v it also takes into account the overhearing
  costs of v and u due to adding link (i,j) .
• Run Prims algorithm to generate a tree that
  minimizes the maximum link weight

v
Transmission link Overhearing link
u
k
i
j
45
Background Overhearing Cost
transmitter
1
designated receiver
2
2
not related to the transmitter
1
3
transmit power
3
receiving power
46
Background Constant Receiving Cost
transmitter
1
receiver
2
2
1
transmit power
receiving power
47
Background Adaptive Receiving Cost
transmitter
1
receiver
2
2
1
transmit power
receiving power
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Background Adaptive Receiving Cost contd
Based on transmitter-receiver power tradeoff
Vasudevan et al. Infocom'06
transmitter
1
receiver
2
2
1
transmit power
receiving power
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MaxMLT-OCR? Feasible Metrics

• Proposed metric Proximity Receiving Power (PRP)
  
• Take into account transmit power cumulated
  receiving power of the transmitter, receiving
  power of the receiver, transmit power cumulated
  receiving power of all the affected neighbors
• Possible metrics
• Zero Receiving Power (ZRP)
• transmission power only, i.e., assume 0
  reception cost
• Designated Receiving Power (DRP)
• transmitter's transmit power, receiver's
  receiving power
• Cumulative Reception Power (CRP)
• transmit power cumulated receiving power of
  the transmitter, receiving power of the receiver

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MaxMLT-DCR Solution Analysis

• Optimal analysis
• Result
• Optimal solution of time complexity O(n2 log n),
  where n is the number of nodes

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ISC Hierarchical Network Structure

• Server
• At shippers control center
• Communication with gateways via the External
  Container Network
• External Container Network
• To support the communication between gateways and
  interface between the server and a gateway
• Internal Container Network
• To support the communication between devices
  within a container (e.g. a gateway, a RFID
  reader, and sensors)