Decentralized Scattering of Wake-up Times in Wireless Sensor Networks Amy L. Murphy ITC-IRST, Trento, Italy joint work with Alessandro Giusti, Politecnico di Milano, Italy Gian Pietro Picco, University of Trento, Italy

u A common solution to save energy on sensing devices is to periodically turn them on and off u Energy saved while turned off lead to clear gains in system lifetime u We explore duty cycling at the application level, cycling communication and/or sensing –Spreading out awake times, NOT synchronizing them u Results in lifetime increase, but at a cost… Energy Management with Duty Cycling time s1s1 epoch awake interval wakeup time

Cost of Duty Cycling: response time Random WakeupScattered Wakeup 4 transmissions required 2 transmissions required u Scenario –Distributed nodes –Mobile base station –BS queries nearby nodes –Queries repeat until a node responds –Communication duty cycled: respond only when turned on

u Scenario –Distributed sensing nodes –Sensors detect events within a given radius, only when sensors are active –Duty cycle the sensing capability –Note: long wakeup intervals Cost of Duty Cycling: event coverage A C B Random Wakeup A C B A Scattered Wakeup ACB “Less” likely to detect events “More” likely to detect events

Wake-up Scattering: distributed protocol u All nodes discover –Their wake up time, W C –Wake up time of the node before them, W prev –Wake up time of the node after them, W next u Calculate their new target wake up time –W c ’=(W prev +W next )/2 *  –Move toward this new wakeup time in the next epoch u Key Properties –Process is entirely localized –All nodes execute in parallel –No central coordination A B C WCWC W next W prev WC’WC’

Rapid convergence to good scattering (2-3 rounds) Increasing awake intervals A=0.50 A=0.01 Scattering to reduce response delay Average Response Delay (fraction of E) Scattering Iteration Number init A=0.15 A=0.10 Same response delay No scattering. Long awake time. After scattering. 33% shorter awake time.

Effectiveness of scattering for response delay: Different network densities Average Response Delay (fraction of E) Scattering Iteration Number init Range=100, 8.4 neighbors Range=110, 10.1 neighbors Same response delay No scattering. Large radio range. After scattering. 10% smaller radio range.

Scattering to increase sensing coverage u Goal: increase percentage of events detected by scattering the awake intervals of the sensors themselves u Results are similar to those for response delay –Details in the paper After scattering, same coverage. 20% smaller awake interval.

Visualization Sensing radius Node ONNode OFF Overlapping sensing NodePairwise communication

Scattering & Latency in Tree-based data collection on WSN u Many WSN are used to collect data at a central location by constructing an overlay tree along which data flows u Goal: low latency for data from source to sink u In terms of wake-up times, the parent should wake up after the child to receive its data C X AB

Scattering and Tree Formation u Consider a simple tree, and the wake-up times from the perspective of X –Well scattered, but X needs to send data to C –Would be better if C wakes up immediately after X, not A u Scattering never changes the sequence of wakeup times –We introduce jumping, a simple mechanism that allows reordering in the sequence of wakeup times –After jumping, additional scattering is required u Jumping enabled if the W next is a child –With some probability, select next wakeup time between W next and W nextnext A X B C C X AB

Reducing Latency with Jumping Time to root not significantly affected by scattering alone Reducing gap between wake-ups reduces latency. However…. Initial Scattering Jumping 4 different awake intervals Jumping to place parents after children results in significant improvement Time to Root (lower is better) Waving: reduce gap between wakeup times

Tradeoff between “tree” and “coverage” Reducing space between wake-ups reduces latency. However…. …benefits of scattering reduced. Result: lower event coverage Time to Root (lower is better) Percent Coverage (higher is better)

Visualization: Tree

Discussion u Combining wakeup scattering for coverage and jumping to achieve good, tree-based data collection yields a promising complete solution u Wakeup Scattering is fully decentralized –Wake up times are determined based on local information –Epochs need not be synchronized across nodes u Simple algorithm yields significant results –Response delay: same as random wakeup times with 33% longer awake interval –Event coverage: same as random with 20% longer awake interval –Tree: scattering + jumping improve over random from 25 to 45%

Future Directions u Modify the awake interval to meet the application needs, e.g., increased coverage u Exploit signal strength to approximate distance between sensors –Close sensors should have “more scattered” awake times u Combine jumping to improve solutions for response delay and coverage –Avoid local minima in scattering solution u Consider applying scattering at the MAC layer