1 Distributed Adaptive Sampling, Forwarding, and Routing Algorithms for Wireless Visual Sensor Networks Johnsen Kho, Long Tran-Thanh, Alex Rogers, Nicholas R. Jennings 12 th May 2009 Third International Workshop on Agent Technology for Sensor Networks (ATSN-09)

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 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., th May 2009 ATSN-09

Inter-Related S/F + Routing in WVSNs ATSN 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

 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  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

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 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 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

012...M-1M 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

ATSN-0911 Algorithm with Fixed Routing (Phase II) - cont’d 12 th May 2009 Dynamic programming: M-1M Own data (similar to the previous case) Own data + data from J M-1M M-1M Own data + data from J 1 and J Own data + data from all children M-1M M : the capacity of node i’s parent

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 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 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  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  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

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

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 Empirical Result II 12 th May 2009 ATSN-09

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?)