Challenges in Wireless and Mobile Network Applications

Jie Wu

Dept. of Computer and Information Sciences

Temple University

Overview

1. Introduction Professional Activities

Current State: Wireless and Mobile

2. Sustainable Networking Green Networking

3. Mobility: Foe or Friend Foe: Recovery and Tolerant Schemes

Friend: Mobility Control

4. A Bigger Picture Applications in Cloud and TeraGrid

5. Future of Networking US GENI, SEES, and IGNITE

China Internet of Things

1. Introduction: Professional Activities

MANETs/Sensor Nets

Edtior: IEEE TMC and TC

General Chair: MASS and DCOSS

Program Chair: INFOCOM and MASS

Panel Chair: INFOCOM and MobiCom

Committee: INFOCOM, MobiHoc, and ICNP

Distributed Systems

Chair: IEEE TC on Distributed Processing (TCDP)‏

Executive Program Vice Chair: ICDCS

Committee: ICDCS, HPCA, and SRDS

Parallel Processing

Former Editor: IEEE TPDS and JPDC

General Chair: IPDPS

Committee: IPDPS and ICPP

Center for Networked Computing

Center for Networked Computing

Networks: wireless and mobile

Jie Wu, Center Director

Computer and Information Sciences, Temple University

Mobile Infostation

Roadway Sensors

Opportunistic

High-Speed Link

(MB/s)

Network security and privacy

Social networks and cloud computing

• Internet (NSF GENI)

• Mesh networks (NSF MRI)

• Sensor networks (NSF NeTS)

• Delay tolerant networks (NSF TC)

• Underwater networks (Navy Yard)

• Vehicular networks (SEPTA Regional Rail)

• Wireless networks (ARO)

• RFID and medical applications (PA)

• Online social networks (Amazon & Microsoft))

High performance computing

• NSF GPU/CPU supercomputer (MRI)

2. Current State: Wireless and Mobile

Current Different types: PDA, BlackBerry,

Laptop

Internet connections: more and more wireless

Node mobility

(Near) future 1 billion vehicles

5 billion RFID

10-15 billion sensor/embedded devices

Future: anytime, anywhere

(a) Edge of the Internet

(b) General way of data transmission

3. Sustainable Networking Green networking

Minimization of the Green-House Gases (GHG)

Opposite: over-provisioning and redundancy

Possible solutions

Selective connectedness

Turn off unused resources at the edge of the networks: connected dominating set (CDS)

Virtualization

Allow more than one service on one piece of hardware

Proportional computing

Consume energy proportional to utilization level: adjustable transmission range through topology control

Green Networking: A Wireless Sample

Direction antenna Energy-efficiency

Interference reduction

7

4. Mobility: Friend or Foe

Node mobility is considered to be undesirable in MANETs using a connection-based model

Recovers from and tolerates “bad” effects caused by mobility

Nodes are assumed to be relatively stable

Foe: Recovery and Tolerant Schemes

Recovery Scheme

If a routing path is disrupted by node mobility, it can be repaired quickly

E.g., route discovery and route repair

Tolerant Scheme

Masks the bad effects caused by node mobility

E.g., transmission buffer zone and view consistency

Mobility as a Serious Threat

Mobility threatens localized protocols that use

local information to achieve certain global objectives

“Bad” decisions occur because of Asynchronous sampling of local information

Delays at various stages of handshake

Mobile node movement

Local Information

1-hop information

2-hop information

3-hop information

k-hop information

Discovered via k (1, 2 or 3) rounds of Hello exchanges

Time-space view

Snapshot: a global state in time-space view

Hello interval time

Applications

• Energy saving:

– Sleep mode

• Connected dominating set

(CDS)

• Wu and Li’s 2-hop

neighborhood solution

– Adjustable transmission

range

• Topology control (TC)

• Li, Hou, Sha’s 1-hop location

solution

0 1 2 3 4 5 6 7 8 9 10

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10

0

1

2

3

4

5

6

7

8

9

10

Virtual backbone (CDS)

Topology control (TC)

Two Technical Issues

Link Availability

How protocols deal with imprecise neighborhood information caused by node mobility and delays

Inconsistent Local Views

How each node collects and uses local information in a consistent way

w x w

y

x w x

y

y

Tolerant Scheme I (link availability)‏

A buffer zone is used in existing protocols without having to redesign them.

Tolerant Scheme II (inconsistent local view)‏

Consistent Local View

Each view keeps a version by using a timestamp

Conservative Local View

Maintaining a window of multiple views

New-view(i)= F(view(i), view(i-1), …view(i-k))‏

where F: {union, max, min, …}

(More information on tolerant schemes: Wu and Dai, IEEE IPDPS 2004,

IEEE INFOCOM 2004, IEEE TMC 2005, IEEE TPDS 2006)‏

Sample I (inconsistent local view)‏

Wu and Li’s marking process (for CDS

construction)‏ Node u is marked if there are two unconnected neighbors

Node u is unmarked if its neighbor set is covered by several connected marked nodes with higher IDs

Friend: Mobility Control

Movement-Assisted Routing Views node movement as a desirable feature

Store

Carry

Forward

Random Movement

Epidemic Routing (Vahdat & Becker 00)

• Nodes store data and exchange them when they

meet

• Data is replicated throughout the network

through a random talk

Controlled Mobility

Message Ferrying (Zhao & Ammar 03)

Special nodes (ferries) have completely predictable

routes through the geographic area

Mobility-Assisted Routing

– Replication

• Single copy vs. multiple copy

• E.g., spray-and-wait and spray-and-focus

– Knowledge

• Global vs. local information

• Deterministic vs. probabilistic information

• E.g., MaxProp

(Predict-and-relay: Quan, Cardei, and Wu,

ACM MobiHoc 2009)

Routing in a Cyclic MobiSpace

– Challenges

• How to perform efficient routing in

probabilistic time-space graphs

– Definition (ti,p)

• p is the contact probability of two nodes in ti .

2008-5-29 ACM MobiHoc 2008

Probabilistic Time-Space Graph

A common motion cycle T (=60)

2008-5-29

Probabilistic state-space graph Remove time dimension

Links are labeled: d / pmax (delay/max transition

probability)

2008-5-29

Iterative Process

Iterative steps

Step t+1 based on step t

Ordering of neighbors

pi ≤ pimax and i pi = 1

vst+1 minp1, p2, p3… {p1(d1+ vs1

t)+

p2(d2+ vs2

t)+ p3(d3+ vs3

t)+…}

2008-5-29

Expected Minimum Delay (EMD)

Using EMD as the delivery probability metrics

Optimal single-copy forwarding: Liu and Wu MobiHoc 2008

Optimal prob. forwarding with hop constraints

Single copy: Liu and Wu MobiHoc 2009

Multiple copy: Liu and Wu MASS 2009

Other Challenges

• Intermittent connectivity

– Node mobility

– Unstable wireless links

– Scheduled on/off sensor nodes

• Mobility

• Connectivity

• Complexity

• Bandwidth/storage

• Latency

• Security

• Multicast/broadcast

(Wu and Wang, MASS 2010)

B

D

Connectivity

• (u,v) - connectivity under time-space view

– All i, (u(i), v(i))

– Exist i, (u(i), v(i))

– All i, j, (u(i), v(j))

– Exist i, j, (u(i), v(j))

View(i) View(i+1) View(j)

View window Time

Space

u v

Evolving Graph and Extensions

Time sequence: t1, t2, ..., tL

Gi = (Vi, Ei): subgraph in time [ti, ti + Δ]

Evolving graph

(V, E), where (u,v) = {i | (u, v) є Ei}

(i is called a label)

Weighted evolving graph

(u, v) = {(i, wi) | (u, v) є Ei}

where weight wi can be

bandwidth, reliability, and latency

Optimization Problems Optimization

Earliest-completion

Fastest

NSF grant: A New Algorithm an Graph Model for Networking in Challenged Environments, 2009

Minimum-hop

Maximum-bandwidth

Maximum-reliability

J. Wu, Graph, Combinatorics and

Computing, 2010

Solution: slicing and virtulation

Complexity

Managing complexity of time-space graphs

Lossless translation method

Time-space to state-space (state explosion issue)

Lossy comprehension method

Removing time using averaging in hierarchical

routing

E.g. contact information compression

(Liu & Wu: Scalable Routing in Delay Tolerant Networks,

ACM MobiHoc 2007)

Opportunities

Increasing system performance

Routing capability

Network capacity

Security

Sensor coverage

Information dissemination (mobile pub/sub)

Reducing uncertainty in reputation systems

(Li and Wu, IEEE INFOCOM 2007)

5. Future of Networking Data Management In-network processing

Tradeoffs among communication, computation, and storage

Theory Rigorous model and scaling

properties

Swarm intelligence

Social Networks Small-world (six degrees of

separation)‏

Scale-free networks (power-law)‏

Multi-disciplinary

Network Science: Hype or Reality?

Moderator at ACM MobiCom’09

Jie Wu (Temple U., USA)

Panelists

Anthony Ephremides (U. of Maryland, College Park, USA)

Chuanxiong Guo (Microsoft Research-Asia, China)

Peter Steenkiste (Carnegie Mellon U., USA)

Taieb Znati (NSF, USA)

A Bigger Picture: Cloud & TeraGrid Cloud computing: software, platform, infrastructure

Wireless and mobile applications in cloud computing

Commercial

cloud services

IBM Blue Cloud Microsoft's

Windows Azure

Amazon Web

Services

Amazon S3

Amazon EC2

......

Google Google AppEngine

Google Calendar

…

TeraGrid User Community

Blue: 10 or more PI’s

Red: 5-9 PI’s

Yellow: 2-4 PI’s

Green: 1 PI

1000 projects, 4000 users

Wireless @Temple University Metropolitan WiMax deployment

Part of GENI project

Joint project with Drexel University and City of Philadelphia

Our goal: Provide wireless coverage for downtown Philadelphia

Proposed WiMAX coverage. Downtown Philly + 60,000 students (Temple and Drexel)

Sensor-based Telemedicine through VPNs

Part of US IGNITE initiatives (NSF and OSTP)

AWS education grant

• Improve healthcare via continuous monitoring at home

• Sensors collect physiological data and relay to smartphone

• Data securely archived in public cloud for easy access

• Data processing and mining using supercomputer facilities

• Tied together using L2VPN to ensure QoS and security

.

Microsoft Hawaii

NSF MRI CPU/GPU Supercomputer

• Provide access to researchers through GENI framework.

Research Projects Tourist applications using city-wide WiMax

WiMax centric bodynet for telemedicine

Content delivery networks using WiMax

WiMax mobile surveillance for law enforcement

WiMax enhanced mobile urban sensing

US NSF: SEES Focus Areas

Research at energy-environment-society nexus

Novel energy production, harvesting, storage, transmission, & distribution

Adoption, socioeconomic, and policy issues related to adaptation & mitigation strategies

Understand/optimize life-cycle energy & carbon footprint of natural, social & built systems

Societal factors: Vulnerability, resilience &

sensitivity to regional environment change

What is Internet-of-Things (IOT)

Internet connects all people, so it is called “the Internet of People”

IOT connects all things, so it is called “the Internet of Things”

IOT is a communication network connecting things which have naming, sensing and processing abilities

感知中国 Sensing China

有名有姓 长有五官 大脑思考 语言交流

Naming: name Sensing: 5 sense organs Processing: brain Communication: language

China is Acting

NSFC will focus on the following IT topics:

• Post Moore era

• Nano Technology

• Robotics

• WSN (Sensing China)

• Beyong 4G

• Data Mining in Complex Networks

• Cloud Computing

• Cyber Space and Security

• Complex Networks and Control

• Social Computing

Questions

Why “Sensing China”

Chronicle Reason -天时 “World of Science”

China missed the chance many times

1760-1800: Industrial revolution in Europe.

1860-1900: Westernization Movement.

Early 20th century: Outside intrusion

Late 20th Century: Inside chaos - Cultural revolution

Geographical Reason - 地利 Other countries

Japan has U-Japan，2004

Korea has U-Korea，2004

US has Smart Planet，2008

China is becoming the 2nd largest economy

China should have its own strategy to continue its economical growth.