Content sharing in Urban Environments
Liam McNamara <email@example.com>
Who to download from?
Short-range wireless networks can have
We need to choose someone with matching
tastes that will be colocated long-
enough to transfer a file.
Thus, we need to predict neighbours
'Bad' sources need to be avoided.
What to download from them?
If files can be successfully
transferred, which ones should be
Rock Bob Blues?
File percolation through network.
Improve Symbian sharing application.
Deployment (with your help!)
[problem]: Adaptive Service Discovery in Mobile Systems
Web Service discovery in mobile systems using a broker.
Factors that may affect web service requirements in
•Context : Network Coverage, Other Local Devices, User
• Resources : Battery Level, Memory Usage, CPU load etc
The broker cannot assess such variables.
[proposal] : A middleware approach
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
A middleware that :
• Monitors the Resources of the Device
• Monitors the Context of the Device
• Adapts the web service Request appropriately
Context and Adapted
Resource data Service Request
Service Request Broker
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
• QoS Requirements
• Functional Requirements
Work in Conjunction with the Dino Project by Arun Mukhija
• 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
[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)
Users act as mobile nodes
In theory it seamlessly fits into everyday life
sensors - co,co2,sound & organic solvents
Geocentric visualisation of sensor readings
routing in “social delay tolerant networks”
Efficient routing in delay tolerant mobile networks
Nodes are at one with users
Nodes don’t have permanent connection to network
No logical addresses
Communication is asynchronous
Packet loss is tolerated
Resource usage, Overhead/replication/
Message delivery speed (time)
Message delivery number of hops
Using influence maps to determine the next hop in the
Tools for supporting the development
of control software [With: ARAGORN,
Control of cross-layer optimisation
in wireless network [With: ARAGORN]
Information and Reputation [With:
[complications or proposals]:Plenty of both!
Components based software and policy
languages for real-time and fast
ML and control architectures for
management and adaptation
The limits and bounds of reputation
Can we make these systems work? How
well? What are the compromises?
Development of systems
Experimentation with learning and
[Problem]: How do bandwidth constraints impact
the distribution of intensive jobs in WSNs?
1. Computationally intensive applications.
8. Limited available bandwidth.
Radio communication interferences.
[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.
Homogeneous Tasks: application of DWAG with
BATS for Collaborative Node Localisation (i.e.
* L. Li and T. Kunz, “Cooperative Node Localization for Tactical Wireless Sensor Networks”, in Proc. of the IEEE/Boeing MILCOM, October
* L. Li and T. Kunz, “Cooperative Node Localization Using Non-Linear Data Projection”, in ACM Transactions on Sensor Networks, 2008.
[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.
Efficient Pattern Detection in WSNs
[text or images here]
Flash flood prediction, etc.
Efficient Pattern Detection
Patterns that are extremely difficult
or impossible to capture using
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.
Efficient Pattern Detection
Multiple-pattern detection using
Suffix Arrays; Advantage: scalability.
Approximate pattern matching;
Unknown pattern matching; Advantage:
efficiency through dynamically
adjusting the sampling frequency.
Light-weight implementation optimised
using integer arithmetic.
Current & Future Work
[text or images here]
Design a framework that
caters for Detection of
Use a few readings to infer
the nature and
characteristics of the event.
[Problem]: How To Explore Unknown Areas
[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.
Give Us Algorithms!
Multiple D epth First
Pervasive computing systems
record user interactions with
physical and digital resources
− Usage Analysis
− Pattern Recognition
No unified methodology exists
to deal with common problems
A probabilistic model for the
representation of user interaction with
the pervasive computing space
Trail based analysis
− Landmarks, significant objects in
− Trails, series of landmark
Use of different metrics
− Time, Space, Orientation
Use for different pervasive systems
− Reality Mining, Dartmouth, Cityware
[large-scale simulation of
Current network simulation tools
available require many CPU hours
to run even small simulations on
a single-processor machine
adaptive vehicle routing
intelligent transport systems
Given the applications required
of vehicular network
simulations, the results are
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
[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
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
then update the global object
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
reference to live studies
accurate and sound analysis/statistics
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
– e.g., notify me on all traffic
jams on my route
– e.g., how is the traffic on M11 ?
Ilias Leontiadis, Cecilia Mascolo
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
– Map information L o c a tio n a s c o ntex t (flexible
– Describe warnings/affected areas flexibly
– Describe interests
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)
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
– We use the Navigation System to Route the
reply back on the suggested route of the
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
[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.
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.
//Access Control Module
call EventInterface( msg);
Publications BSN2008, invited book chapter JCKSBE08
[first-order logic model]
1. Model based knowledge representation and
3. First-order logic: expressiveness closer to user
5. Other security/trust mechanisms that are
applicable to BSN.
Publications JKBSE08, invited book chapter JKBSE08
[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.
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
[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
[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