Agent-based sensor-mission assignment for tasks sharing

assetsThao Le

Timothy J NormanWambertoVasconcelos

www.usukita.orgwww.csd.abdn.ac.uk/research/ita

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 environmentsheterogeneous 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 applicationallowing 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 applicationallowing 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 applicationallowing 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 applicationallowing 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 applicationallowing sensors to be shared between multiple tasks

to cope with the dynamic nature of WSNsconsidering 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 solutionsAll tasks within a mission must be satisfied for the

mission to be satisfiedThe utility achieved must greater than or equal to

the threshold for each task within a missionThe total cost of an assignment must be within

budgetThe 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 4missions arriving per hour

Mission success rate with 8 sensor types and 8missions 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 missionsarriving 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 sensorsper 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