1. 1 Distributed Adaptive Sampling, Forwarding, and Routing Algorithms for Wireless Visual Sensor Networks Johnsen Kho, Long Tran-Thanh, Alex Rogers, Nicholas R. Jennings {jk05r,ltt08r,acr,nrj}@ecs.soton.ac.uk 12 th May 2009 Third International Workshop on Agent Technology for Sensor Networks (ATSN-09)

2. 2  Background: Wireless Visual Sensor Network (WVSN). Research Challenge & Aims.  Inter-Related Adaptive S/F and Routing : Problem Description. Information Metric. The Mechanism:  Algorithm with Fixed Routing.  Algorithm with Flexible Routing.  Empirical Evaluations.  Conclusions & Future Work. Outline 12 th May 2009 ATSN-09

3. 3 Wireless Visual Sensor Network  WVSN characteristics: Array of smart camera devices, Basic processing and compression of (usually large) visual data Decentralised control regime A base-station (BS) to fuse and analyse collected data.  WVSNs are increasingly being deployed for: Object tracking, Unattended area surveillance, Other security related applications. Both pictures are taken from Kleihorst et al., 2006 12 th May 2009 ATSN-09

4. Inter-Related S/F + Routing in WVSNs ATSN-094 12 th May 2009  Constraints : Heavy energy constraints on the nodes Sampling is relatively expensive (due to large size of data packets) Forward own data vs. relay data from the others  Sampling, Forwarding (and Routing) share the same energy budget Their performance in efficient data collection are inter-related Efficient coordination is needed  Related research works: USAC: Utility-based Sensing and Communication Protocol (Padhy et al. 2006)  Goal of deployment: efficient information collection  Problem: these algorithms are not efficient for maximising information collection in WVSNs due to the myopic decisions of the agents during operation

5.  Research Challenge: Efficient energy-aware coordination between sampling and routing actions in WVSNs  Minimise energy waste on taking useless actions  Non-myopic decisions are needed ATSN-095 Research Challenges and Aims 12 th May 2009  Research Aims: Information metric to measure the usefulness of data Efficient S/F + routing mechanisms Small control messages in the coordination phase Energy-awareness

6. 6  Background: Wireless Visual Sensor Network (WVSN). Research Challenge & Aims.  Inter-Related Adaptive S/F and Routing: Problem Description. Information Metric. The Mechanism:  Algorithm with Fixed Routing.  Algorithm with Flexible Routing.  Empirical Evaluations.  Conclusions & Future Works. Outline 12 th May 2009 ATSN-09

7. The goal is to maximise the total information value delivered to BS in each round 7 Model Description 12 th May 2009 ATSN-09  Set of heterogeneous cooperative nodes I={1,…..,n}.  Each node i ∈ I : m i different sampling (or frame) rates Each data packet p has the information value of B i energy budget to:  Sampling data  Forwarding data  The BS collects data from the nodes periodically rounds  Each node’s memory is flushed and reinitialised after each round Not delivered data is useless for the application  The nodes can choose one sampling rate for each round Assumptions:  Nodes are rechargeable B i can be entirely used in each round

8. 8 Information Metric  Several techniques for valuing information: Kalman Filter [Guestrin et al. 2005; Rogers et al. 2006]. Simple Linear Regression [Padhy et al. 2006]. GP Regression Technique [Mackay 1998; Seeger 2004, Stranders et al. 2008, Kho et al. 2009]. 12 th May 2009 ATSN-09  In our model, we use a generic information valuation function non-decreasing function

9. 9 The Algorithms (Overview)  Algorithm with fixed routing: routing tree is already established by a routing protocol (e.g. AODV) calculate optimal sampling rates 12 th May 2009 ATSN-09  Phase I: each parent node broadcasts its capacity to child nodes PPhase II: each node transmits maximal possible contributions to its parent PPhase III: parents allocate packet forwarding capacities to its children  Algorithm with flexible routing: optimal routing + optimal sampling are to be determined Algorithm with fixed routing

10. 012...M-1M 01220...27 012...20 ATSN-0910 Algorithm with Fixed Routing (Phase II) 12 th May 2009  Each node i maintains an array of 3-tuples : : number of packets node i sends to its parent : maximal information value node i can contribute with n packets : sampling rate at node i in this case (node i’s own contribution)  If i is a leaf node:  Only its own data is considered  Filling is straightforward  If i is not a leaf node:  Its child nodes:  Wait until all has arrived  Maintain a table as follows

11. ATSN-0911 Algorithm with Fixed Routing (Phase II) - cont’d 12 th May 2009 Dynamic programming: 012...M-1M Own data (similar to the previous case) Own data + data from J 1 012...M-1M 01220...27 012...20 012...M-1M 01622...58 012...31 0 12 20... 27 27 0 16... 75 75 Own data + data from J 1 and J 2 0 18 30... 100 120 Own data + data from all children 0............... 0 30 40... 100 125 28 012...M-1M 03040...100125 012...1214 M : the capacity of node i’s parent

12. 12 Algorithm with Fixed Routing (Phase III)  Efficient:  it satisfies the data flow conservation of the network  no energy is wasted by transmitting data that later will not be delivered to BS 12 th May 2009 ATSN-09  When control messages reach the leaf nodes, that node start to transmit data  The BS maintains its own T table  Easily detects the contributions of its children

13. 13 Algorithm with Flexible Routing (Overview)  The data readings from different nodes could be sent through different routes if there are more than one option to choose from.  Minor restrictions: Nodes always forward their data toward the BS; that is, they will not forward data to a node that is further from the BS (in terms of hop count) than themselves. Sampled data from a same node must be sent in bundle 12 th May 2009 ATSN-09

14. 14 Algorithm with Flexible Routing 12 th May 2009 ATSN-09 : set of parents of node i : set of descendants of node i bundles to send combinations node i has to send -s for all of the combinations curse of dimensionality!!!

15. 15  Background: Wireless Visual Sensor Network (WVSN). Research Challenge & Aims.  Inter-Related Adaptive S/F and Routing: Problem Description. Information Metric. The Mechanism:  Algorithm with Fixed Routing.  Algorithm with Flexible Routing.  Empirical Evaluations.  Conclusions & Future Work. Outline 12 th May 2009 ATSN-09

16. 16  Algorithm with flexible routing: deliver more information, but has greater computational and communicational cost  Algorithm with fixed routing can be applied on an efficiently chosen spanning tree Fast, but sub-optimal result A trade-off between the loss in information and the saving in resources Empirical Evaluation 12 th May 2009 ATSN-09  Benchmark Algorithm: The Uniform Non-Adaptive S/F and Routing each sensor divides its energy budget equally  Linear information valuation function

17. Empirical Evaluation (cont’d) ATSN-0917 12 th May 2009 Algorithm with flexible routing is used here Random tree is generated, algorithm with fixed routing is used on this tree

18. 18 Empirical Result I  Algorithm with flexible routing produces optimal performance  Uniform non-adaptive algorithm has the worst performance  By choosing a spanning tree efficiently, fixed routing can achieve near-optimal performance 12 th May 2009 ATSN-09

19. 19 Empirical Result II 12 th May 2009 ATSN-09

20. 20 Conclusion & Future Work  Two novel and optimal decentralised algorithm: Algorithm with fixed routing: calculates the optimal sampling actions Algorithm flexible routing : optimal in both sampling and routing  Algorithm with flexible routing is optimal, but has higher communication and computational cost  Algorithm with fixed routing can achieve near-optimal result on an efficiently chosen spanning tree  Future work: Develop an efficient way to choose the best spanning tree (e.g. using learning approach) Relax the assumptions (topology hierarchy, flexible sampling rate) Take more rounds into account (long-term data collection) 12 th May 2009 ATSN-09 Thank you (Any Questions?)