This document provides an agenda for the MOBISYS seminar, listing the speakers and their topics. It includes snippets of conversations between the speakers as they introduce themselves and their topics. The document discusses research related to pervasive computing, wireless networks, and mobile systems.

1. MOBISYS: PERVASIVE COMPUTING
ucl/birkbeck research oct. 14 2008
format: 4 slides/minutes per person
speakers:
• Mohamed Ahmed • Dikaios Papadogkonas
• Ettore Ferranti • Panagiotis Papakos
• Tom Hewer • Elisa Rondini
• Eli Katsiri • Jenson Taylor
• Ilias Leontiadis • Michael Zoumboulakis
• Liam McNamara
• Stephen Hailes
• George Roussos

2. liam mcnamara
[next up: panagiotis]

3. Content sharing in Urban Environments
Liam McNamara <l.mcnamara@cs.ucl.ac.uk>

4. Who to download from?
Short-range wireless networks can have
huge churn.
We need to choose someone with matching
tastes that will be colocated long-
enough to transfer a file.
Thus, we need to predict neighbours
colocation length.
'Bad' sources need to be avoided.

5. What to download from them?
If files can be successfully
transferred, which ones should be
shared first?
Pop
Jazz
Classical
Rock
Daniel
Blues
Country Carol
Alice Rock?
Pop Metal?
Rock Bob Blues?

6. Future Work

File percolation through network.

Improve Symbian sharing application.

Deployment (with your help!)

Thesis :/

7. panagiotis papakos
[next up: jenson]

8. [problem]: Adaptive Service Discovery in Mobile Systems
Web Service discovery in mobile systems using a broker.
Service
Request Query
Mobile Service
Broker
Device Repository
Service Matching
Binding Services
Factors that may affect web service requirements in
Mobile Systems
•Context : Network Coverage, Other Local Devices, User
Preferences etc.
• Resources : Battery Level, Memory Usage, CPU load etc
The broker cannot assess such variables.
Panagiotis Papakos

9. [proposal] : A middleware approach
Problem
The web service requirements (QoS requirements,
functionality etc) of a mobile device may change due to
changes in the context or the resources of the mobile
device.
Proposal
A middleware that :
• Monitors the Resources of the Device
• Monitors the Context of the Device
• Adapts the web service Request appropriately

10. Proposed Architecture
Context and Adapted
Resource data Service Request
Middleware Query
Mobile
Service Request Broker
Device
Matching
Service Binding Services
Use of States to indicate status of the device (Low Power,
Low Connection, Emergency, Meeting etc)
Adapt the Web Service Request according to the State of the
device:
• QoS Requirements
• Functional Requirements
• Trust

11. [future work]
Work in Conjunction with the Dino Project by Arun Mukhija
(SENSORIA).
Future Work:
• Further Development
• Prototype Implementation
• Case Studies
Expand To Include :
• Adapt requirements according to broker feedback
• Interaction between devices using this middleware
• Reconfiguration of the device according to state

12. jenson taylor
[next up: mo]

13. [problem] : How can we make efficient use of social
links for routing messages in delay tolerant networks?

How often (and how long) different individuals meet. (e.g. two friends are likely to
meet at least once a week)

How often (and how long) different individuals spend within the proximity of a certain
geographical location. (e.g. two individuals working in the same building)

What time are individuals likely to be in certain locations. (e.g. people working
together are likely to be in the same location during working hours, and only on
certain days of the week)
[Jenson Taylor]

14. [Snout]

Participatory sensing

Users act as mobile nodes

In theory it seamlessly fits into everyday life
-
Snout platform
-
sensors - co,co2,sound & organic solvents
-
GPS
-
Geocentric visualisation of sensor readings

15. routing in “social delay tolerant networks”

Efficient routing in delay tolerant mobile networks

Network characteristics:

Nodes are at one with users

Nodes don’t have permanent connection to network

No logical addresses

Communication is asynchronous

Packet loss is tolerated

Efficiency definition:

Resource usage, Overhead/replication/

Message delivery speed (time)

Message delivery number of hops

Using influence maps to determine the next hop in the
delivery route

16. mohamed ahmed
[next up: elisa]

17. [Problems]

Tools for supporting the development
of control software [With: ARAGORN,
CHAT]

Control of cross-layer optimisation
in wireless network [With: ARAGORN]

Information and Reputation [With:
Steve]
Mohamed Ahmed

18. [complications or proposals]:Plenty of both!

Components based software and policy
languages for real-time and fast
executing systems

ML and control architectures for
management and adaptation

The limits and bounds of reputation
based analysis

19. [future work]:

Can we make these systems work? How
well? What are the compromises?

Development of systems

Experimentation with learning and
control architectures

20. elisa rondini
[next up: michael]

21. [Problem]: How do bandwidth constraints impact
the distribution of intensive jobs in WSNs?
1. Computationally intensive applications.
Resource-constrained devices.
8. Limited available bandwidth.
Radio communication interferences.
[Elisa Rondini]

22. [Proposal]: Bandwidth-Aware Task Scheduling
(BATS) scheme for Distributed Wireless Ad hoc
Grids (DWAG) in WSNs.
• Distributed Wireless Ad hoc Grids (DWAG)
to implement distributed algorithms in WSNs
formed by resource-constrained devices.
• Bandwidth-Aware Task Scheduling (BATS)
to load share location computing tasks among
sensors by assessing both node computational
capabilities and local network conditions.

23. Homogeneous Tasks: application of DWAG with
BATS for Collaborative Node Localisation (i.e.
CCA-MAP*).
* L. Li and T. Kunz, “Cooperative Node Localization for Tactical Wireless Sensor Networks”, in Proc. of the IEEE/Boeing MILCOM, October
2007.
* L. Li and T. Kunz, “Cooperative Node Localization Using Non-Linear Data Projection”, in ACM Transactions on Sensor Networks, 2008.

24. [Future work]: What happens if we deal with
more heterogeneous tasks?
Need to combine nodes’ computational
capabilities and local network conditions
(external characteristics) with tasks’ CPU
and Bandwidth profile characterizations.

25. michael zoumboulakis
[next up: ettore]

26. Efficient Pattern Detection in WSNs
[text or images here]
Complex Events:

Wildfire detection,

Flash flood prediction, etc.
Michael Zoumboulakis

27. Efficient Pattern Detection

Patterns that are extremely difficult
or impossible to capture using
thresholds.

May not be known in advance.

Symbolic Conversion is a successful
technique for mining time-series.

We have adapted a SC algorithm for
efficient real-time mining in WSNs.

28. Efficient Pattern Detection

Multiple-pattern detection using
Suffix Arrays; Advantage: scalability.

Approximate pattern matching;
Advantage: flexibility.

Unknown pattern matching; Advantage:
efficiency through dynamically
adjusting the sampling frequency.

Light-weight implementation optimised
using integer arithmetic.

29. Current & Future Work
[text or images here]

Design a framework that
caters for Detection of
Spatial Events.

Use a few readings to infer
the nature and
characteristics of the event.

30. ettore ferranti
[next up: dikaios]

31. [Problem]: How To Explore Unknown Areas
[Ettore Ferranti]

32. [Proposal]: Team of Mobile Agents

No prior knowledge of the area’s map. (which can change after a disaster).

Lack of exact knowledge of agents’ positions.

Long-range Agent-to-Agent communication is unreliable.

33. Give Us Algorithms!
(Agent-to-Tag):
Multiple D epth First
S earch
B rick&Mortar
(Tag-to-Tag):
HybridE xploration

34. Now Make It Real!

35. dikaios papadogkonas
[next up: tom]

36. Pervasive Navigation

Pervasive computing systems
record user interactions with
physical and digital resources

Common Tasks
− Usage Analysis
− Prediction
− Pattern Recognition

No unified methodology exists
to deal with common problems

37. Proposal

A probabilistic model for the
representation of user interaction with
the pervasive computing space

Trail based analysis
− Landmarks, significant objects in
space
− Trails, series of landmark
interactions

Use of different metrics
− Time, Space, Orientation

Use for different pervasive systems
− Reality Mining, Dartmouth, Cityware

38. Trail Extraction

39. Prediction

40. tom hewer
[next up: ilias]

41. [large-scale simulation of
vehicular networks]
Current network simulation tools
available require many CPU hours
to run even small simulations on
a single-processor machine
collision avoidance
adaptive vehicle routing
information dissemination
charging/toll systems
intelligent transport systems
Given the applications required
of vehicular network
simulations, the results are
often time-dependent
[tom hewer]

42. [computational expense]
complexity of the network and mobility models
tight-coupling such that models interoperate
the granularity of models for locale can be changed
N-squared and N-log(N) problems as a function of
connectivity
42

43. [challenges of HPC]
how to decompose the simulation?
Domain and task farming algorithms for vehicular simulations
require much boundary communication
Component decomposition allows us to keep this boundary
communication low but still keep the simulation free of
causality/synchronisation problems
Technique:
split the nodes into lists
(per processor available)
create a global simulation
object on each processor and
perform all processing for
each node on its home
processor
then update the global object

44. [future work]
efficiency of decomposition algorithm
hierarchical binning method to split nodes
adaptive binning and live movement
parameter search simulations
explore the parameter space and compare results
node aggregation and processing efficiency
visualisation and processing
live view and steering
validation of scenarios and
applications
reference to live studies
accurate and sound analysis/statistics

45. ilias leontiadis
[next up: eli]

46. Information Dissemination
in Vehicular Networks
• Growing number of vehicles with Navigation Systems
• Add short-range radio (e.g. wifi)
• Applications of vehicular networks
– traffic information dissemination
– safe navigation (warnings)
– urban sensing (monitor traffic, vehicles as sensors)
– parking slots (fine grained information)
– gas stations fuel prices
– advertising
– ...
• Push-based
– e.g., notify me on all traffic
jams on my route
• Pull-based
– e.g., how is the traffic on M11 ?
Ilias Leontiadis, Cecilia Mascolo

47. The role of the Navigation System
– Suggested routes  M o bility pa tterns predic ta ble
– Route/Disseminate information in specific areas
– Suggested routes  I nteres ts (e.g. receive warnings on my
route)
– Map information  L o c a tio n a s c o ntex t (flexible
topics/matching)
– Describe warnings/affected areas flexibly
– Describe interests

48. Push based notifications
(e.g. traffic warnings/accidents)
Navigation system now provides automatic
subscription to events about vehicle’s route
– Use infrastructure (if available)
– Vehicle-to-Vehicle communication when:
• No infrastructure is available
• Local information
• Fine grained information (e.g. parking spots)
– We use the Navigation System to:
• Geographically route information in affected areas.
• Keep disseminating information in affected areas
• Inform only affected vehicles (given by the suggested route)

49. Pull based notifications
(e.g. how is the traffic on M11 )
• Opportunistic routing to forward
requests to the nearest infostations
• Issue: how to deliver the reply back ?
– target is moving
• Solution
– We use the Navigation System to Route the
reply back on the suggested route of the
requested vehicle

50. Future work

Currently Implementing the system
− Testing with small fleet of bicycles and limited
number of vehicles

Working on traffic information dissemination
− Every vehicle collects traffic information and
“Publish” this information to the affected vehicles.
− Vehicles that receive information

estimate traffic conditions on parts of their route

select paths that will minimise TIME

51. eli katsiri
[next up: kones]

52. [secure autonomy in pervasive healthcare]
The Nurse SMC loads a Mission (a set of policies) onto a SMC that belongs
to the patient role. E.g. LoadMission(ReadTemp);
The Mission requires the Patient to: Two authorisation policies are needed:
take a temperature reading
•
3.A nurse SMC is the subject of an
•
send the data to the Nurse authorization policy that grants the nurse
role permission to load missions on
if their temperature is abnormal.
members of the patient role.
Mission ReadTemp{
4.The patient SMC needs to be granted
Patient.Temp.read(): Temp permission to execute any remote
invocations on the nurse interfaces are
if Temp>40C specified as part of the mission.
Nurse.Notify(Temp)
}
[Eli Katsiri]

53. [common representation]
if (type==PKT_CMD)
//remote invocation
{
//Access Control Module
if
(AC.authorise(srcid,oid,cmd)==TRUE)
execute(msg);
}
else {
//event
//Policy Service
call EventInterface( msg);
}
}
Publications BSN2008, invited book chapter JCKSBE08

54. [A ECA policy interpreter]
55
Publications AMUSE, BSN2007

55. [first-order logic model]
1. Model based knowledge representation and
reasoning.
3. First-order logic: expressiveness closer to user
intuition
5. Other security/trust mechanisms that are
applicable to BSN.
Publications JKBSE08, invited book chapter JKBSE08

56. kones saranavamutu
[next up: george]

57. [How trustworthy are my friends?]

Does regularity in social behavior imply trustworthiness?

How does the degree of regularity affect trustworthiness?

How can we detect/evaluate regularity?
8. Summary of the social interaction in two real world datasets.
9. Analysis of complex social-spatial-temporal context
10. Recognising social patterns in daily user activity and relationships
11. Identifying socially significant locations
12. applications which will be of significant value to e-business.
[Kones Saravanamutu]

58. N. Eagle and A. Pentland (2007), quot;Eigenbehaviors: Identifying Structure in Routinequot;, Behavioral Ecology and Sociobiology
[What’s the story with my MIT mates?]
N. Eagle and A. Pentland (2007), quot;Eigenbehaviors: Identifying Structure in Routinequot;,
Behavioral Ecology and Sociobiology

59. [What about my ENRON colleagues?]
P.S. Keila and D.B. Skillicorn(2005),quot;Structure in the Enron Email Datasetquot;,
Retrieved on June,2008. http://research.cs.queensu.ca/~skill/enron.pdf

60. [Next Steps]
[text or images here]
•
Apply image processing techniques such as
Eigenbehaviors and PCA to bitmaps that are
derived from the visualisation.
•
N. H. Minsky. “Regularity-based trust in
Cyberspace”. In proceedings of 1st Int, Conf. on

61. george roussos
[next up: steve]

63. steve hailes
[next up: discussion]

64. projects @ UCL mobisys
Digital economy - privacy
CARDyAL
Utiforo
TOUMAZ Security and Trust
MARS
NIRS
Wireless & sensorIU-ATC CLEF
SystemsCHAT RUNES SEINIT
ARAGORN
Divergent GRID
U2010 Networking and
SESAME Distributed systems
SUAAVE HEN
6WINIT
EIFFEL
64