jennifer c. hou department of computer science university of illinois at urbana-champaign

Jennifer C. HouJennifer C. HouDepartment of Computer ScienceDepartment of Computer Science

University of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-ChampaignApril 24, 2023April 24, 2023

DAWN: Dynamic Ad-hoc Wireless NetworksProgress Report Presentation

Energy Efficient Network TrackPower, CS Threshold, and Rate Control

DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGNJennifer Hou Jennifer Hou

PHY/MAC Control KnobsTo mitigate interference and maximize the network capacity, there are several control knobs:

Transmit power power/topology controlCarrier sense threshold trade-off between spatial reuse and interference levelSpatial diversity scheduling consecutive transmission for interference-free connectionsChannel diversity use of non-overlapping channels


Power Control

Definition: Each node adjusts its transmission power so as to maintain network connectivity using the minimum possible power.

Objectives: Maintaining network connectivity Reducing energy consumption Mitigating MAC interference Achieving network capacity and spatial reuse


Preliminary Work on Power Control

Local minimum spanning tree (LMST) [INFOCOM’03, IEEE TWC, IEEE TPDS]Localized algorithm Relies only on local informationPreserves connectivity.Ensures bi-directional links.Handles node heterogeneity (i.e., nodes have different maximal transmit powers)Bounds the degree of any node by 6.


Xi : location of node iRi : transmission range of node iLink (ji) exists if Rj | Xi – Xj |:Transmission power of node i

Total power:k-connectivity: requires to remove at least k nodes to disconnect the networkCritical total power Wc: minimum total power W for maintaining k-connectivity

i iW R

i ii iW W R

1

1

Ri

RjXj

Xi

Preliminary Work on Total PowerRequired for K-Connectivity

Poisson point process with density n in a unit-

area square


Question To AskIn what order does the critical total power Wc increase/decrease as the node density increases?All nodes choose common power

[Gupta & Kumar 98] studied the critical transmission range rn for 1-connectivity

[Wan & Yi 04] for k-connectivityAll nodes choose different power

[Blough 02] critical total power for 1-connectivity

Based on the total weight of minimum spanning tree

Our study: critical total power or k-connectivity

log , where ,as nn n

nr nn


Major ResultsMain theory: [Infocom 2005, ACM/IEEE ToN 2006]

The critical total power for maintaining k-connectivity

is with probability approaching 1

Comparison with common powerThe critical total power for k-connectivity with common power is

Allowing power control at each node reduces the total power by a factor of

1 / 2( / 2 )( )( 1)!

k nk

1 / 2 / 2( (log ) )n n

/ 2((log ) )n


Rescaling to Expanded Networks

Expanded networks Node density fixed

Side length L

Expected number of nodes n= L2

Allowing power controlAverage power is bounded

Using common power The common power grows as

(1)

/ 2((log ) )n

1

1ijd

L

L 'ijd

ijd

'ij ijd L d


Cross Layer Aspects of Power Control

Physical Layer

MAC Layer

Network Layer

Power C

ontrol Incorporating Physical Layer Characteristics

Cross Layer Design

Effect of MAC-Layer Interference

Dynamic Topology Control w.r.t.

Network TrafficNetwork Capacity

Network Lifetime

Critical Power

Analysis

Physical Layer

Incorporating Physical Layer Characteristics


When Power Control Meets SINR

All the topology control algorithms in literature defined the neighbor relation based on the protocol model

A link exists between nodes i and j if dij <= dmax.

TC: L(n) T(n)The protocol model ignores the effect of SINR.

What is more appropriate to define a link is the use of physical model

A link exists between i and j if

TC: L(n) x C(n) T(n) Set of node locationsSet of configurations (, max/min transmit power)

Topology that defines the neighbor relations


When Power Control Meets SINR

All existing topology control algorithms fail (i.e., cannot maintain network connectivity) under the physical model.We are re-investigating topology control under the physical model.


Controlling Carrier Sense Threshold

• The contending area can also be adapted through tuning the carrier-sensing threshold

AB C D

distance

Sign

al S

tren

gth

CS Threshold

E F


A large CS threshold leads tosmaller contending areaLess contending nodes within the contending areaMore concurrent transmissionHigher interference

Transmission rate depends on Signal-to-Interference-Noise Ratio

Controlling Carrier Sense Threshold

AB C D

distance

Sign

al S

tren

gth

CS Threshold

E F


Tradeoff AnalysisSpatial reuse can be achieved at the cost of higher interference level and lower transmission rate

High rate links Low rate links

What is the optimal CS Threshold? How does it relate to the transmit power?


Network CapacityNetwork capacity as a function of transmit power and carrier sense threshold [ACM Mobicom 2006]


Power Control vs. Data Rate

RX1r2SINR :d1

RX2r1SINR :d2

Power: PCS Threshold:

Tx1

Tx2Rx

1

Rx2

d1r2

r1

d2D1 D

2

SINR requirements


DR[2]thrSINR

DR[3]thrSINR

DR[1]thrSINR


Power Control vs Data Rate


'Power: P (>P)CS Threshold:

'

RX1P r2SINR : *P d1

Tx1

Tx2Rx

1

Rx2

d1r2

r1

d2D1

D2

RX2 '

P r1SINR : *P d2

SINR requirements

DR[2]thrSINR

DR[3]thrSINR

DR[1]thrSINR


Power and Rate ControlPRC algorithm:

A localized algorithm that enables each transmitter to adapt to the interference level that it perceives and determines its transmit power.The transmit power is so determined that the transmitter can sustain the highest possible data rate, while keeping the adverse interference effect on the other neighboring concurrent transmissions minimal.


Simulation SetupModified ns-2 Ver. 2.28

The interference perceived at a receiver is the collective aggregate interference from all the concurrent transmissionsEach node uses physical carrier sense to determine if the medium is freeIEEE 802.11a radios supporting 8 discrete data rate (6 ~ 54 Mbps)

Random topology3, 10, 20, 30, and 50 transmitter-receiver pairs are randomly generated in a 300m X 300m area, and represent sparsely, moderately, and densely populated networks, respectively,.

Algorithms used for evaluationsStaticGreedy Power Control (GPC)Power and Rate Control (PRC)


Simulation Results

Performance gain mainly because ofHigher concurrent transmissions

Simulation Track1. Expediting Wireless Simulation2. Incorporating Model Checking into Simulation


Expediting Wireless Simulation

Our profiling work indicates more than 50% of the execution time is spent on event scheduling and channel related activity handling.Can we expedite simulation by reducing the number of unnecessary events while not impairing the accuracy. Proportion of the execution time that is

spent on event enqueueing in a 100-node ad hoc network over a 1000mX1000m field. There are 40 CBR connections carrying a total of 120 packets/sec. Traffic (pkt. Size = 512B)

[1] Chunyu Hu and Jennifer C. Hou, ``A reactive channel model for expediting wireless network simulation,'' ACM SIGMETRICS, Banff, Alberta, Canada, June 2005


Reactive Channel Model

R

L

The channel only notifies nodes in the following sets of the signal-arrival event

Nodes in range RNodes in (range L but not R) that are registered

When does a node register?

Whenever it needs to monitor the channel status, e.g., when it would like to gain access to the channel or when it is in the process of receiving a signal


A Case Study: IEEE 802.11 MAC

State To register?

transmitting * ☒receiving ☑

idlebacking-off ☑

deferring ☑

-- ☒sleep ☒

turn-off ☒

*: assuming half-duplex radio is used


RCM Performance (Execution Time) RCM


RCM Performance(Memory Consumption)


Virtual Wireless Ad-Hoc Network (J-Sim)Champaign-Urbana Wireless

Community Network(Currently 40 wireless nodesin downtown Urbana; expectedto extend to 100 nodes providingfull coverage of Champaign and Urbana).

Integration of Real/Virtual Integration of Real/Virtual WorldsWorlds

• Channel behavior modeling• Physical capacity analysis• Incentive-based resource management• Multi-radio, multi-path routing• Cross-layer optimization


Marriage of Modeling Checking and SimulationMarriage of Modeling Checking and Simulation

s0

s1

s2

s3

s4

s5

s6

s7

X

J-Sim

[1] Ahmed Sobeih, Mahesh Viswanathan and Jennifer C. Hou, “Check and Simulate: A Case for Incorporating Model Checking in Network Simulation,” Proceedings of the ACM-IEEE International Conference on Formal Methods and Models for Codesign (ACM-IEEE MEMOCODE 2005), San Diego, CA, June 2005.

s0

s1

s2

s3

s4

s5

s6

s7

X

J-Sim w/ MC

• Stateful on-the-fly explicit-state model checking into J-Sim• Explore the state space of a network protocol up to a (configurable) maximum depth of transitions• No changes to the core design and implementation of J-Sim


An Overview of Our WorkAn Overview of Our Work• Build the model checker as a component in J-Sim

P1 P2 Pn

Model Checker

J-Sim

Error Trace / No Error

Initial State

Current State

Next State

Component

Port

Communication via ports


Evaluation and ResultsEvaluation and Results

• Two case studies: AODV and Directed Diffusion• Representative routing and data dissemination protocols• Reasonably complex network protocols

• 1200 – 1400 LOC (excluding the J-Sim library)• Safety property:

• The loop-free property of routing/data dissemination paths

[2] Ahmed Sobeih, Mahesh Viswanathan, Darko Marinov and Jennifer C. Hou, “Finding Bugs in Network Protocols Using Simulation Code and Protocol-Specific Heuristics,” Proceedings of the International Conference on Formal Engineering Methods (ICFEM 2005), Springer-Verlag LNCS 3785, Manchester, United Kingdom, November 2005.

• Summary of our discoveries: • A previously unknown bug in the J-Sim implementation of AODV• A previously unknown deficiency in directed diffusion


Guiding Model Checking with Network PropertiesWe have developed search heuristics that exploit properties inherent to the network protocol and the safety property being checked and better guide the model checker to discover counter examples. An interesting and important research question is how to determine a suitable BeFS strategy for a specific network protocol.

jennifer c. hou department of computer science university of illinois at urbana-champaign

Documents

power controlaverage

minimum total power

boundedusing common

minimum possible power

networkcritical total

total powerrequired

node density increases

handles node heterogeneity