Load Balancing in Periodic Wireless Sensor Networks for Lifetime - PowerPoint PPT Presentation

Load Balancing in Periodic Wireless Sensor Networks for Lifetime Maximisation Anthony Kleerekoper 2 nd year PhD Multi-Service Networks 2011 Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem ● Uniform distribution of motes ● Regular, periodic reporting eg. Habitat monitoring ● Many-to-one traffic flow ● Multi-hop communication 2 Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem ● Uniform distribution of motes ● Regular, periodic reporting eg. Habitat monitoring ● Many-to-one traffic flow ● Mutli-hop communication 3 Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem ● Non-uniform distribution of work ● Central motes die first 4 Combining the strengths of UMIST and The Victoria University of Manchester

The Energy Hole Problem ● Energy hole appears ● No packets get to sink ● Uniform distribution of location and non-uniform distribution of work 5 Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions Avoidance ● Non-uniform distribution ● Power control ● Mobile sink ● Clustering 6 Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions Avoidance ● Non-uniform distribution ● Power control ● Mobile sink ● Clustering 7 Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions Avoidance ● Non-uniform distribution ● Power control ● Mobile sink ● Clustering 8 Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions Avoidance ● Non-uniform distribution ● Power control ● Mobile sink ● Clustering 9 Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions Mitigation ● Focus on same level balance ● Dynamically switch parents ● Create top load-balanced tree 10 Combining the strengths of UMIST and The Victoria University of Manchester

Existing Solutions Mitigation ● Focus on same level balance ● Dynamically switch parents ● Create top load-balanced tree 11 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Proposal DEgree COnstrained Routing ● Construct degree-constrained minimum spanning tree ● Distributed ● Static routes ● Balanced ● No need for location information ● Designed for periodic applications Trade-off connectivity and latency for extra lifetime 12 Combining the strengths of UMIST and The Victoria University of Manchester

Assumptions ● Uniform distribution of motes in a circular network ● Single, central sink ● Every mote produces 1 new packet per “round” ● Perfect MAC – no collisions, no interference ● All motes transmit the same distance 13 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Preliminaries Average number of children per parent: Level Avg Children 1 3 2 1.66667 3 1.4 4 1.286 Ratio of motes in level n to motes in level 1: Level Ratio 1 1 2 3 3 5 4 7 14 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Theory I ● Limit the number of children per parent during tree construction ● All motes have same number of children = balance ● Average number of children per parent usually not a whole number ● Round down to nearest whole number i.e. 1 for most levels Very few motes connected to tree 15 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Theory II ● Level 1 motes can have 3 children each ● Find levels when ratio to level 1 motes is ● Have 3 children per parent in those levels In practice delay by one level because of imperfect uniformity 16 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Algorithm Phase One ● Start with sink ● Leaf motes broadcast “advert” (incl hop count and subtree number) ● Unconnected motes gather all adverts ● Send offer to “best” parent ● Parents gather all offers respond to “best” child ● Rejected motes reevaluate and send new offers ● Wait until all child motes have finished ● Parent signal children to start next round 17 Combining the strengths of UMIST and The Victoria University of Manchester

Example Subtree After Phase One 18 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Algorithm Phase Two ● Basic distributed minimum spanning tree algorithm ● Motes may only become children of parents in the same original subtree 19 Combining the strengths of UMIST and The Victoria University of Manchester

Example Subtree After Phase Two 20 Combining the strengths of UMIST and The Victoria University of Manchester

DECOR Choices Best Parent ● Maintain network topological shape ● Choose most distant parent ● Use RSSI to indicate distance Best Child ● Maintain network topological shape ● Not deny children only option ● Choose child with fewest parent options ● Distance as tie-breaker 21 Combining the strengths of UMIST and The Victoria University of Manchester

Simulation Set-up ● Radius of network defined in terms of transmission range ● Constant density (10 motes per unit area) ● Sink is unconstrained ● Fixed initial energy values (50J) ● Fixed packet size (50 bytes) ● Average results from 200 runs ● Compare basic minimum spanning tree, dynamic scheme and DECOR 22 Combining the strengths of UMIST and The Victoria University of Manchester

Time to First Mote Death Normalised Time to First Node Death 3.5 3 2.5 Normalised Time 2 Basic Dynamic 1.5 DECOR 1 0.5 0 5 10 15 20 Radius 23 Combining the strengths of UMIST and The Victoria University of Manchester

Balance Balance 1 0.9 0.8 0.7 Balance Ratio 0.6 Basic 0.5 Dynamic 0.4 DECOR 0.3 0.2 0.1 0 5 10 15 20 Radius 24 Combining the strengths of UMIST and The Victoria University of Manchester

Connectivity Percentage of Motes Connected to Sink 100 99 98 97 96 Percentage Basic 95 Dynamic 94 DECOR 93 92 91 90 5 10 15 20 Radius 25 Combining the strengths of UMIST and The Victoria University of Manchester

Average Latency Normalised Average Mote Latency 1.15 Normalised Average Mote Level 1.1 1.05 Basic Dynamic 1 DECOR 0.95 0.9 5 10 15 20 Radius 26 Combining the strengths of UMIST and The Victoria University of Manchester

Worst Case Latency Normalised Worst Case Latency 1.6 Normalised Maximum Mote Level 1.4 1.2 1 Basic 0.8 Dynamic DECOR 0.6 0.4 0.2 0 5 10 15 20 Radius 27 Combining the strengths of UMIST and The Victoria University of Manchester

Discussion ● DECOR provides a large increase in time to first mote death ● Trade-off for lower connectivity and higher latency ● Improvement by much larger factor than trade-offs ● Implicit use of global information 28 Combining the strengths of UMIST and The Victoria University of Manchester

Further Work Investigate the effects of: ● Imperfect uniform distribution ● Non-central sink ● In-network aggregation ● Mobility ● Density ● Shadowing / Random events 29 Combining the strengths of UMIST and The Victoria University of Manchester

Conclusion ● Energy hole problem has many existing solutions ● DECOR tailored for periodic applications ● Introduces new trade-offs ● Large increase in lifetime for small loss of connectivity and latency 30 Combining the strengths of UMIST and The Victoria University of Manchester

Thanks for Listening Any Questions? 31 Combining the strengths of UMIST and The Victoria University of Manchester