A Delay-Aware Reliable Event Reporting Framework for Wireless Sensor-Actuator Networks

1. A Delay-Aware Reliable Event Reporting Framework for Wireless Sensor-Actuator Networks Presented by Edith Ngai Supervised by Prof. Michael R. Lyu Term Presentation Spring 2006

2. Outline • Introduction • Related Work • Network Model and Objective • Delay-Aware Reliable Event Reporting Framework • Grid-Based Data Aggregation • Priority-Based Event Reporting • Actuator Allocation • Simulation Results • Conclusion

3. WSAN • Collection of sensors and actuators • Sensors • small and low-cost devices with limited energy, sensing, computation, and transmission capability • passive devices for collecting data only and not interactive to the environments • Actuators • resource-rich devices equipped with more energy, stronger computation power, longer transmission range, and usually mobile • make decisions and perform appropriate actions in response to the sensor measurements

4. WSAN • Sensors and actuators collaborate • sensors perform sensing and report the sensed data to the actuators • actuators then carry out appropriate actions in response • Applications • environmental monitoring • sensing and maintenance in large industrial plants • military surveillance, medical sensing, attack detection, and target tracking, etc.

5. Our Focus • Design of a genericframework for reliable event reporting in WSANs • Reliability in this context is closely relatedto the delay, or the freshness of the events, and theyshould be jointly optimized • Non-uniform importance of the events can be exploredin the optimization • A delay- andimportance-aware reliability index for the WSANs

6. Our Framework • Seamlessly integrates three key modules tomaximize the reliability index: • A multi-level dataaggregation scheme, which is fault-tolerant with errorpronesensors • A priority-based transmission protocol,which accounts for both the importance and delayrequirements of the events • An actuator allocationalgorithm, which smartly distributes the actuators tomatch the demands from the sensors.

7. Related Work • Real-time communication protocol in WSN • SPEED [Hu et. al. 2003] • real-time unicast, real-time area-multicast and real-time area-anycast for WSN • achieved by using a combination of feedback control and non-deterministic QoS-aware geographic forwarding with a bounded hop count

8. Related Work • Real-time communications in WSN • MMSPEED [Felemban et al. 2005] • Multi-Path and Multi-Speed Routing Protocol for probabilistic QoS guarantee in WSN • multiple QoS levels are provided in the timeliness domain by guaranteeing multiple packet delivery speed options • supported by probabilistic multipath forwarding in the reliability domain

9. Related Work • Distributed coordination framework for WSAN [Melodia et al. 2005] • based on an event-driven clustering paradigm • all sensors in the event area forward their readings to the appropriate actuators by the data aggregation trees • provides actuator-actuator coordination to split the event area among different actuators • assumes immobile actuators that can act on a limited area defined by their action range

10. Network Model • Compose of sensors and actuators • Nodes aware of their locations • Divide the network into a number of grids cell for data aggregation • A subset of nodes, referred as reporting nodes, v, send data to the actuators • Anycast routing

11. Objective • Reliability index • Measures the probability that that event data are aggregated and received accurately within pre-defined latency bounds

12. Grid-Based Data Aggregation

13. Priority-Based Event Reporting • We adopt a priority queue in each sensor, which plays two important roles: • prioritized scheduling to speed up important event data transmission • queue utilization as an index for route selection to meet the latency bounds • In our preemptive priority queue, the packets for the event data are placed according to its data importance and served in a first-in-first-out (FIFO) discipline

14. Delay • The delay of sensor node is composed of the processing delay, the queueing delay, the transmission delay, and the propagation delay dtotal = dproc + dq + dtran + dprop • The processing delay and the propagation delay are typically only a few microseconds • Our routing protocol allocates routes according to the data importance • Transmission delay dtran • We borrowed the idea from the SPEED protocol to estimate dtranby acknowledgement • Queueing delay dq

15. Queueing Delay • The queueing delay of the highest priority queue

16. Queueing Delay

17. Next Hop Selection • Consider node i receives new type of event data dataewith data rate • It broadcasts a control message to its immediate neighbors • Every neighbors j replies with the message:

18. Next Hop Selection • Node i requires that the end-to-end delay to actuator is less than the latency bound Be • It first estimates the number of hops h from i to the closest actuator a and the maximum delay from i to j, delayi,j. • dq_maxis the maximum queueing delay allowed, such that the latency bound Be can be met

19. Next Hop Selection • Among the neighbors with dq_max>0, node i starts inspecting the neighbors with λhigh=0 and λlow=0 • means it is not forwarding any event data as all • next hop with λhigh>0 means it is transmitting some data with higher importance • If node i selects the next hop j with λlow>0 , then it may need to preempt some less important data

20. Next Hop Selection • For each neighbor above, i calculates the maximum data rate λi that it can forward the data to while satisfying the latency bound • The inspecting process stops when i finds enough neighbors j to forward the data, such that

21. Data transmission with Latency Constraint • The latency bound Be will be updated before forwarding to next hop Be’= Be – (tdepart – tarrive) – dtran – dprop • A sensor always select a next hop that can satisfy the latency bound • If no route can meet the bound, it informs the previous hop forward the packets via another node. • In case of congestion (e.g. high priority packets flows in and preempts low priority packets), previous hop should also be informed

22. Actuator Allocation • The actuators may record the event frequency and re-arrange their standby positionsperiodically • Let freqgbe the event frequency of the grid cell g • Estimate freqgperiodically as follow: , where freqg-1 isprevious record of the event frequencyin grid g

23. Actuator Allocation

24. Simulations • Simulator: NS-2 • Metrics • On-time Reachability • Average Delay • Overall Reliability • 4 events • 2 with high importance • 2 with low importance • Located in left bottom corner

25. On-Time Reachability

26. Average Delay

27. Overall Reliability

28. With Actuator Allocation

29. With Actuator Allocation

30. Conclusion • We provide a distributed, self-organized, and comprehensive solution for reliable event reporting and actuator coordination in WSAN • We formulate the event reporting problem and define reliability as the percentage of event data that can reach the destination and satisfy certain accuracy and latency constraints • We provide a distributed data aggregation mechanism, which can tolerate sensing failures and reduce network traffic • We propose a reliable priority-based event reporting algorithm with event importance. Sensors can route their data based on the affordable service rate provided by its neighbors • We further improve the efficiency of event reporting and reaction by proposing an actuator allocation algorithm. It estimates the event happening frequency in the network and balances the workload among the actuators by allocating them proper locations • Simulation results are provided to demonstrate the effectiveness of our solutions.

31. Q & A