Agent-based sensor-mission assignment for tasks sharing assets Thao Le Timothy J Norman WambertoVasconcelos

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

Introduction & Motivation  WSNs consist of a large number of sensing resources

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

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

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

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

Introduction & Motivation Motivations:  to be more applicable in realistic environments heterogeneous sensors & tasks

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

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

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

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

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

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).

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

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

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)

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

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 *

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

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

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

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

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