Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks

1. Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks Mihail L. Sichitiu Department of Electrical and Computer Engineering North Carolina State University IEEE INFOCOM 2004

2. Outline • Wireless Sensor Networks • Assumptions • The Steady State Phase • The Setup and Reconfiguration Phase • Simulation Results • Conclusion

3. Wireless Sensor Networks • There are several power saving scheduling algorithms proposed in different layers. • The main power savings in these papers results from eliminating (almost completely) idle listening and collisions in the sensor network.

4. Sources of power wastage in WSNs • Idle listening • Retransmissions resulting from collisions • Control packet overhead • RTS, CTS, ACK… • Unnecessarily high transmission power • Results in higher power consumption and also increase the interference at other nodes. • Sub-optimal utilization of the available resources

5. Assumptions • Periodic monitoring (i.e., one sample taken every T seconds). • Common periodT known in the entire WSN. • Neighboring nodes are synchronized to each other. • Rate of changes in network topology or WSN objective is low compared to the monitoring period. • Each node generates one flow toward the base station.

6. State Diagram for each data flow A different query or query parameters

7. Proposed Scheduling Approach • The setup and reconfiguration phase • Its goal is to set up the schedules that will be used during the steady state phase. • The steady state phase • Similar to the forwarding phase. • It utilizes the schedule established in the setup and reconfiguration phase to forward the data to the base station.

8. The Steady State Phase • 3 actions can be scheduled by each sensor node: • Sample (read sensor) • This sample will be forwarded along the (generally multihop) path to the base station. • Transmit (either own sensor reading or forward) • All nodes on the path of a flow, except for the base station, have a transmit actionassociated with the flow. • Receive (to be further forwarded) • All nodes on the path of a flow, except for the source node, have a receive actionassociated with the flow.

9. The Steady State Phase

10. Example of 10 node sensor and a possible distributed schedule Perfect synchronization Node 1 : take a sample(5ms), send it(5ms), then go to sleep for 990ms. Node 2 : needs to be awake for 10ms to forward the message from node 1 (receive for 5ms, then forward for 5ms), then take its measurement and forward it ( another 10ms), then go to sleep for 980ms.

11. The Steady State Phase • When a transmission fails • If the application tolerates it, we can simply ignore lost packets. • In case the MAC layer supports retransmissions, enough time can be reserved in the schedule for several (but a limited number of) retries. • The schedule can have one (or several) special spare cycle(s) reserved specifically for retransmissions.

12. The Setup and Reconfiguration Phase Proceeds in 2 steps • The Route Select Step • A route to the base station is selected by the routing algorithm. • The Route Setup Step • Schedules are being setup along the chosen route. • If it fails, a new route is selected. • A special route setup (RSETUP) packet will be sent on the selected route from the source of the flow to the base station.

14. The Setup and Reconfiguration Phase • If the packet is postponed for more than a period, a route error (RERR) packet is sent back to the source. • If the route setup (RSETUP) packet arrives at the base station, a route acknowledge (RACK) packet will be sent from the base station to the source on the reverse path.

15. Comments • The route setup phase, while being a typical Layer-3 function, relies heavily on Layer-2 information and properties. • The schedule can take full advantage of the spatial reuse inherent in ad-hoc networks. • In Fig. 3, nodes 1 and 7 are scheduled to send at the same time, as their transmissions do not interfere. • Schedule tables should be able to maintain “per flow” information (two entries for every flow).

16. Custom simulator (existing simulators cannot simulate hundreds of nodes for periods of months and years). Network lifetime = when 50% of the nodes cannot report to the base station (either the batteries are depleted or no available routes) Will compare 3 technologies: Always On (sensors in receive mode when not transmitting). 802.11 power savings mode Proposed scheduling approach. Simulation Results

17. Simulation Setup • Will use a base case and vary one parameter at a time. Base case parameters: Transition from “off” to “on”: 3ms Nodes: 100 Area: 100x100m Current in TX mode: 17mA Transmission Radius: 25m Current in RX mode: 10mA One sample sent every: 60s Current in idle mode: 10µA 802.11 PSM beacon interval: 500ms

18. Simulation Results

19. Dependency of the network lifetimes on the number of nodes for a constant deployment area. Adding new nodes in the same area does not increase the depth of the network, and thus the forwarding overhead remains constant with the number of nodes.

20. Dependency of the network lifetimes on the number of nodes for a constant density. • The nodes close to the base station tend to deplete their batteries first. • The rest of the network cannot bridge the gap around the base station. • Resulting in premature system failure (most nodes still have plentiful energy supplies, but no routes to the base station).

21. Dependency of the network lifetimes on the measurement period of the network. A larger period enables longer sleep times and, correspondingly, increased improvements in the lifespan of the sensor network.

22. Dependency of the network lifetimes on the power consumption in idle mode. (1) The power savings of the method result directly from the difference between the power of the system in idle mode and the power in receive mode. (2) When the gap between idle mode and receive mode closes, the power savings of the proposed approach decrease.

23. Conclusion • Present a new distributed scheduling algorithm for stationary, continuous monitoring wireless sensor networks. • The algorithm requires the collaboration of both the routing with MAC layers.