PHD DISSERTATION DEFENSE Receiver-Cost Cognizant Maximal Lifetime Routing in Embedded Networks: Model and Solutions Guofeng Deng Advised by Dr. Sandeep

PHD DISSERTATION DEFENSE

Receiver-Cost Cognizant Maximal Lifetime Routing in Embedded Networks:

Model and Solutions

Guofeng DengAdvised by Dr. Sandeep Gupta

The IMPACT LabIra A. Fulton School of Engineering

Arizona State University, Tempe

Ph.D. Dissertation Defense, Deng 2MPACTIArizona State

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

Ph.D. Dissertation Defense, DengMPACTIArizona State

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, Globecom’06]• 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]


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)


WANET Application: Intelligent Shipping Container (ISC)

• Background: Global nature of today’s economy– 90% of the world’s 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

today’s 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.


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


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.


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:


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

2 3

3 4

2

1

2 3

3

2

1

2 3

3 4

Energy cost for rcv a pkt Total number of packets rcved by 2 or 30 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)


Receiving Energy Cost Characterization

• Media Access Control protocol

1

2

3

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.

1

2

3


Receiving Energy Cost Characterization

• Decoding techniques

†Based on transmitter-receiver power tradeoff [Vasudevan et al. Infocom'06]

Turbo decoder: energy consumed for decoding a signal is inversely proportional to signal strength†, i.e. adaptive

Regular decoder: energy consumed for decoding a signal is independent on signal strength, i.e. constant


Receiving Energy Cost Models

Adaptive Receiving Cost (Turbo decoder)

Constant Receiving Cost (regular decoder)

Overhearing Adaptive Receiving cost model (OAR)

Overhearing Constant Receiving cost model (OCR)

Overhearing Cost (Random access MAC protocols )

Designated Adaptive Receiving cost model (DAR)

Designated Constant Receiving cost model (DCR)

Designated Receiving Cost Only (TDMA based MAC protocols)

Objective: to investigate maximal multicast lifetime problems under each of these receiving energy cost models.


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


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


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 Prim’s 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

u v1/l(u,v)


MaxMLT-DCR: Simulation Results

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.

RX = peak TX for each node u For each node u, select RX randomly between peak TX and 2X peak TX


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


MaxMLT under OCR: NP-hard

• Assumptions– identical battery capacity– all links are associated with

same transmit power

MaxMLT under OCR Set cover

Source node

Forwarding nodes

Destination nodes

• Observations:– Node s will die first– Lifetime of resulting multicast tree is

determined by the number of forwarders


MaxMLT-OCR: Heuristic Solution

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

1

4

on-tree nodenon-on-tree node

transmit costs taken into account

link being considered

1 4

2

3

ZRP

1 4

2

3

DRP

1 4

2

3

CRP1 4

2

3

PRP

link that has been established

PRP: Proximity Receiving Power algorithm

CRP: Cumulative Receiving Power algorithm (extending DRP for comparison)

receiving costs taken into account

Note: Link metric defines how the receiving power is taken into account.

overhearing costs taken into account


MaxMLT-OCR: Simulation Results

Identical battery capacity and peak TX power

RX = peak TX RX = 2X peak TX


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.


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


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.


MaxMLT-DAR: Chain of Transforms cont’d

A WANET and its auxiliary graph. Each node has two transmit levels and two receive levels.


MaxMLT-DAR: Chain of Transforms cont’d

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


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


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


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


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


MaxMLT-MANET: Example

Node c moved to a new location. Assume symmetric link weight and Wmax = 99.


MaxMLT-MANET: Simulation

• Measurement– The quality of a multicast service is denoted by

Q=(Δ,Γ,), where Δ,Γ,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.


MaxMLT-MANET: Simulation Results

50 nodes totally, D is a set of destinations, source generates four 512B packets per second.


MaxMLT-MANET: Simulation Results


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


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.


Thank You!


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


Intelligent Shipping Container cont’d

Internal Wireless Internal Wireless Sensor NetworksSensor Networks

2.4 GHz2.4 GHz

External HostsExternal Hosts

802.11802.11

Cellular Cellular NetworkNetwork

RFID Reader

MICAz mote

ContainersContainers

StargateStargate

USB Memory Card

MICAz mote 2.4 GHz

Stargate Managing Internal network (hardware, power and security); data processing, & routing outgoing packets to external interface.

GPS Receiver 1

51-pin

PCMCIA Compact Flash

USB

Eth

ernet

RS

232

GPRS PCMCIA Modem

802.11 Compact Flash card

MICAz mote

Mobile ComputingComputers 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.

Sensors

MICAz mote

Sensors

MICAz mote

TelosB mote

TelosB mote

ML Cargo Tag

MICAz mote

Arch Rock Arch Rock Edge ServerEdge ServerLinux computer running Linux computer running web services-based web services-based environment with web UI environment with web UI for setup, control, monitor, for setup, control, monitor, & management of diverse & management of diverse wireless sensor networks.wireless sensor networks.

Arch Rock DataLoggerLow-power Embedded Linux computer running local data collection & management of diverse wireless sensor networks

EthernetEthernet

INTER-Container TelosB mote INTER-Container TelosB mote Attached to nearby containers. Attached to nearby containers.

Proximity motes form an ad hoc (multi-hop) Proximity motes form an ad hoc (multi-hop) inter-container network.inter-container network.


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.


ISC: Network Entities

Stargate + MICAz mote + WiFi card + memory card:data collection and processing, database management.

PDA: monitor and manually control Stargate, e.g. start/stop RFID reader

TelosB motes with onboard sensors: environmental sensor

Skyetek M8 RFID reader + Cushcraft antenna + MICAz mote:Read RFID tags and forward the reading via wireless interface

Base station: startup control and monitoring

MICAz motes + MTS310: environmental sensor


ISC: System Tests

• Tested in a standalone container over several months in Chandler, Arizona, US

• Tested in a container yard in a 3×3 stacked container configuration in South Kearny, New Jersey, US

• Tested during a 5-day shipment from Singapore to Kaohsiung, Taiwan


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


Background: Designated Receiving Cost

Adaptive Receiving Cost

Constant Receiving Cost







1

2

3

1

2

3

transmitter

designated receiver

not related to the transmitter

transmit power

receiving power


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 Prim’s algorithm to generate a tree that minimizes the maximum link weight

u

i j

kv Transmission link

Overhearing link


Background: Overhearing Cost









1

2

3

1

2

3

transmitter

designated receiver

not related to the transmitter

transmit power

receiving power


Background: Constant Receiving Cost









1 2

1

2

transmitter

receiver

transmit power

receiving power


Background: Adaptive Receiving Cost









1 2

1

2

transmitter

receiver

transmit power

receiving power


Background: Adaptive Receiving Cost cont’d









1 2

1

2

transmitter

receiver

transmit power

receiving power

Based on transmitter-receiver power tradeoff [Vasudevan et al. Infocom'06]


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


MaxMLT-DCR: Solution Analysis

• Optimal analysis

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

number of nodes


ISC: Hierarchical Network Structure

• Server– At shipper’s 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)

Documents

PHD DISSERTATION DEFENSE Receiver-Cost Cognizant Maximal Lifetime Routing in Embedded Networks: Model and Solutions Guofeng Deng Advised by Dr. Sandeep