Agent-based sensor-mission assignment for tasks sharing assets

1. Agent-based sensor-mission assignment for tasks sharing assets Thao Le Timothy J Norman WambertoVasconcelos www.usukita.org www.csd.abdn.ac.uk/research/ita

2. Structure • Introduction & Motivation • Problem description • MSM & GAP-E • Experimental results • Discussion • Conclusion

3. Introduction & Motivation • WSNs consist of a large number of sensing resources

4. Introduction & Motivation • WSNs consist of a large number of sensing resources • form an ad-hoc network • communicating with each other and with data processing centres using wireless links

5. Introduction & Motivation WSNs are required to support multiple missions • arriving at anytime • decomposing into many tasks

6. Introduction & Motivation WSNs are required to support multiple missions • arriving at anytime • decomposing into many tasks • may occur simultaneously

7. Introduction & Motivation WSNs are highly dynamic in terms of: • configuration: sensors move out of range or be damaged, changing weather conditions may interfere with communication, etc... • the environment: missions and phenomena occur frequently and simultaneously The problem: Sensor-Mission Allocation

8. Introduction & Motivation Motivations: • to be more applicable in realistic environments heterogeneous sensors & tasks

9. Introduction & Motivation Motivations: • to be more applicable in realistic environments: heterogeneous sensors & tasks • to save limited energy of sensor resources in real-world application allowing sensors to be shared between multiple tasks

10. Introduction & Motivation Motivations: • to be more applicable in realistic environments: heterogeneous sensors & tasks • to save limited energy of sensor resources in real-world application allowing sensors to be shared between multiple tasks

11. Introduction & Motivation Motivations: • to be more applicable in realistic environments: heterogeneous sensors & tasks • to save limited energy of sensor resources in real-world application allowing sensors to be shared between multiple tasks

12. Introduction & Motivation Motivations: • to be more applicable in realistic environments: heterogeneous sensors & tasks • to save limited energy of sensor resources in real-world application allowing sensors to be shared between multiple tasks

13. Introduction & Motivation Motivations: • to be more applicable in realistic environments: heterogeneous sensors & tasks • to save limited energy of sensor resources in real-world application allowing sensors to be shared between multiple tasks • to cope with the dynamic nature of WSNs considering the possibility of reassigning sensors

14. The Assignment Problem • In the network we have a set of sensors • Each sensor is defined by its: • type, location and sensing range, • the maximum utility it can provide, and • the cost of using the sensor. • Missions may arrive at anytime and are collections of tasks. • Each task is defined by its: • type, location and operational range, and • demand, budget and profit • Each sensor-task assignment has an associated utility (the utility provided to the task by the sensor).

15. The Assignment Problem • Constraints on possible solutions • All tasks within a mission must be satisfied for the mission to be satisfied • The utility achieved must greater than or equal to the threshold for each task within a mission • The total cost of an assignment must be within budget • The set of sensor types of the sensors assigned to must cover its information requirements • Sensors cannot be assigned to more than one type of task

16. Challenges • A huge and dynamic number of constraints and variables  using SAM to reduce the search space • The constraints form an instance of the Generalised Assignment Problem which is NP-Hard  our idea is to use a multi-round Knapsack-based algorithm since GAP can reduce to the Multiple Knapsack problem • Finding solutions requires soft-real time; sensors are only partially observation about environment; the order of arrival of missions is unknown etc. An agent-based approach is highly suited to the coordination of sensor resources in a decentralised and flexible manner

17. MSM • MSM – Multiple Sensor Mode assignment mechanism • Sensors are represented by agents • Sensor agents are cooperative • Each task is delegated to an agent within the operational range • This agent acts as coordinator (not necessarily involved in the solution)

18. MSM • MSM operates as follows: • Coordinator identifies candidate sensors in operational range and issues cfp • Each sensor makes independent decision whether and what utility to bid • Coordinator attempts to allocate sensors using GAP-E • If allocation fails, coord reports failure; mission fails • Coord informs agents of allocations

19. GAP-E • Each task has a priority ordering over sensor types (info requirements) • Each task has a budget, allocated over sensor types • * Compute “cost matrix” for sensors on basis of bids from sensors and priority over types • Run FPTAS algorithm • If no solution, seek sensor that can be released from prior commitment to another task • If solution found within budget for all types, return • Recompute “cost matrix” and iterate from *

20. Experimental results Hypothesis 1: MSM performs well in comparison to the estimated optimum Mission success rate with 4 sensor types and 4 missions arriving per hour Mission success rate with 8 sensor types and 8 missions arriving per hour

21. Experimental results Hypothesis 2: The computational complexity (running time) of MSM is much less than that of other mechanisms Running time (ms) with 4 sensor types and 4 missions arriving per hour Running time (ms) with 8 sensor types and 8 missions arriving per hour

22. Experimental results Hypothesis 3: The computational complexity of MSM is increased in a steadily fashion with the number of missions (or tasks) Running time (ms) with 4 sensor types and 25 sensors per type

23. Future Work • Sensors are assumed to be static • Tasks are independent • Sensor agents are cooperative (will release a sensor even if utility for its task is lower) • We assume that tasks sharing a sensor require the same information

24. Conclusion A decentralised approach to solving the sensor-mission assignment problem for tasks sharing assets • Generic solution to the resource allocation problem (both sensors and tasks are heterogeneous) • Sensor sharing significantly improves the number of successfully allocated missions • Use of polynomial algorithm within GAP-E increases performance, and hence utility of solution in practical use • Allows sensors to be reassigned to reduce effect of mission arrival time on the solution