Integration of Sensor Networks with Mobile Devices

Integration of Sensor Networks with Mobile Devices

Computer Science Department

University College London

Vladimir Dyo

November 22, 2005

Outline

Introduction Problem and Motivation Isolating Problems Expected Contribution Initial Research Results Conclusion

Data Dissemination in SensorNets

The Concept Sensors, Sinks

Tasks: Dissemination, Aggregation, Streaming Limitation: Energy, Low Reliability

Current Research

Infrastructure Routing

GPSR

Clustering LEECH

Data Dissemination and Aggregation protocols Directed Diffusion, TAG, Synopsys Diffusion

Middleware Milan, TinyLime, Slime, EnviroTrack

Problem - I

Existing solutions work with static sinks:Very strong assumptionNot Realistic Not Energy Efficient No Coverage

Examples: TinyDB, Cougar

Problem - II: Example

Static collection:• Energy

consumption• Congestion• Reliability• Not practical

Suggested Approach: Mobile Collection

Solution – Mobile Collection

Technical Advantages Coverage, energy, disconnected operations, flexibility

Application Potential Environmental monitoring, industrial and pervasive

applications

Not Well Researched

•M. Batalin, “Coverage, Exploration and Deployment by a Mobile Robot and Communication Network”

•A. Chakrabarty, “Exploiting Predictable Observer Mobility For Power Efficient Sensor Network Configuration”

• “S. Bhattacharya, Energy-conserving Data Placement and Asynchronous Multicast in WSN”

Isolating Problems

Protocol Support Mobile Data Collection. Routing Data towards a

mobile collector Spatial Queries and Distributed Index

select locations where (temp > 30) OR (humidity > 0.4) within 30km from (X,Y)

Middleware Defining Primitives Implementing Protocols

Expected Contribution

Protocols Data Collection Protocol for Mobile Collectors Distributed Index for Spatial Queries in Sensor

Networks Middleware for integration of sensor networks

with mobile devices Architecture Primitives Implementation

Evaluation

Data Collection Protocol: Performance analysis in ns-2 simulator Critical Metric – total communication overhead Real traces from other projects Implementation in TSky motes

Middleware Implementation Real Deployment

Climate and wildlife monitoring (in collaboration with Oxford zoologists)

Micro-climate monitoring (UCL campus)

Initial Results

Adaptive Distributed Indexing for Spatial Queries in Sensor Networks.

V. Dyo and C. Mascolo. In IEEE Proceedings of 8th International Workshop on Mobility in Databases and Distributed Systems (co-located with DEXA05). August 2005, Copenhagen, Denmark. IEEE Computer Society Press

Proactive + Reactive Mode

Proactive = 3600Proactive = 1

Reactive = 1200Reactive = 1200

600 query/hourTotal: 3601Total:

2400

Total: 1201

Future work on Index

Data Resolution High update rates High Power

Consumption Low response

Low latency High Power Consumption Trade-off between:

Data resolution/response latency Total energy consumption

Conclusion

Interesting problem Isolating issues Initial results

Questions

Vladimir [email protected]