Minimum Power Configuration in Wireless Sensor Networks Guoliang Xing*, Chenyang Lu*, Ying Zhang**, Qingfeng Huang**, and Robert Pless* *Washington University in St. Louis **Palo Alto Research Center (PARC) Inc. MobiHoc '05 Chien-Ku Lai

Outline Introduction An Illustrating Example Problem Definition Approximation Algorithms Experimentation Discussion Conclusion

Introduction - Wireless Sensor Network (WSN) WSNs must aggressively conserve energy  to operate for extensive periods Wireless communication often dominates the energy dissipation in a WSN  Topology control  Power-aware routing Sleep management  Turning off redundant nodes  Only keeping a small number of active nodes

Introduction - Minimum Power Configuration (MPC) When network workload is low  The power consumption of a WSN is dominated by the idle state  Scheduling nodes to sleep saves the most power  It is more power-efficient for active nodes to use long communication ranges When network workload is high  Short radio ranges may be preferable

Introduction - Minimum Power Configuration (MPC) MPC provides a unified approach integrating  Topology control  Power-aware routing  Sleep management

An Illustrating Example Two network configurations 1. a communicates with c directly using transmission range |ac| while b remains sleeping 2. a communicates with b using transmission range |ab| and b relays the data from a to c using transmission range |bc| a b c

An Illustrating Example (cont.) a b c R ： a needs to send data to c at the rate B ： The bandwidth of all nodes

An Illustrating Example (cont.) a b c

An Illustrating Example - Mica2 Energy Model 433MHz Mica2 radio ：  The bandwidth ： 38.4Kbps  30 transmission power level  Suppose ： P tx (|ac|) = maximum transmission power (80.1mW) P tx (|ab|) = P tx (|bc|) = 24.6mW P id = 24mW P rx = 24mW P s = 6uW  When R > 16.8Kbps  Relaying through node b is more power efficient

Problem Definition A node can either be active or sleeping This work only considers the total active power consumption in a network The MPC problem ：  Given a network and a set of traffic demands, find a subnet that satisfies the traffic demands with minimum power consumption

Problem Definition (cont.) For the node u on the path f(s i,t j ) The data rate for source s i to sink t j

Problem Definition (cont.) Definition 1 (MPC problem)

Approximation Algorithms 1. Shortest-path Tree Heuristic (STH) 2. Incremental Shortest-path Tree Heuristic (ISTH)

Shortest-path Tree Heuristic (STH)

s1s1 s2s2 t r1 = 0.1 r2 = 0.2

Shortest-path Tree Heuristic (STH) s1s1 s2s2 t r1 = 0.1

Shortest-path Tree Heuristic (STH) s1s1 s2s2 t r1 = 0.1

Shortest-path Tree Heuristic (STH) s1s1 s2s2 t r2 = 0.2

Shortest-path Tree Heuristic (STH) s1s1 s2s2 t r2 = 0.2

Incremental Shortest-path Tree Heuristic (ISTH)

s1s1 s2s2 t r1 = 0.1 r2 = 0.2

Incremental Shortest-path Tree Heuristic (ISTH) s1s1 s2s2 t r1 = 0.1

Incremental Shortest-path Tree Heuristic (ISTH) s1s1 s2s2 t r1 = 0.1

Incremental Shortest-path Tree Heuristic (ISTH) s1s1 s2s2 t r2 = 0.2

Incremental Shortest-path Tree Heuristic (ISTH) s1s1 s2s2 t r2 = 0.2

Experimentation 1. Simulation Environment 2. Simulation Settings 3. Results

Simulation Environment Simulator ： Prowler  The MAC layer in Prowler is similar to the B- MAC protocol in TinyOS

Simulation Settings MPCP is compared with two baseline protocols  Minimum transmission （ MT ） routing  Minimum transmission power （ MTP ） routing The number of nodes ： 100 The region of network ： 150m × 150m  Divided into 10 × 10 grids Simulation time ： 300s  60s for initialization phase

Results - Total network energy cost

Results - Data delivery ratio at the sink

Discussion 1. Limitations of this paper 2. Potential future work

Discussion Every node in the network operates in a constant state  Further energy savings can be achieved by reducing the idle time of active nodes

Discussion This approach mainly focuses on minimizing the total energy consumption  Power-balanced should be achieved

Conclusion This paper  proposes the minimum power configuration approach to minimize the total power consumption of WSNs  formulates the energy conservation problem as a joint optimization problem where the power configuration of a network consists of  a set of active nodes  the transmission ranges of the nodes

Question? Thank you.