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Multi-Agents Supporting Reflection in a Middleware for Mission-Driven Heterogeneous Sensor Networks
Edison Pignaton de FreitasMarco Aurélio Wehrmeister
Armando Morado Ferreira
Carlos Eduardo Pereira
Tony Larsson
3rd ATSN @ 8th AAMAS – May 2009
Outline
• Context• Proposed Approach • Mission-driven Approach
• Mission Description Language• Mission Parameterization
• Middleware Overview• Planning-agent Model• In-network Reasoning• Conclusion and Future Work
Context
• Emerging Sophisticated Sensor Networks Applications• Heterogeneous sensors, great number of nodes, the
need for distributed decisions, dynamic scenarios …
• Dynamic scenarios• Environment conditions, topology changes, …
• System Life Time• Requirements may change
Context
• Motivation Application Scenarios
Surveillance and Patrolling
Rescue Assistance and Disaster Recovering
Context
• Problems to address:• Establishment of a network mission (and its partitioning)• Efficient use of different types of sensors in the network• Data aggregation/fusion • Management of nodes, groups, clusters and the whole
network • Task (Re)Allocation
• Dynamically changes (reachability, capabilities, remaining resources, etc)
Our Focus
This presentation
Proposed Approach• System Overview
• High-level Mission Description Language• Middleware providing interoperability• Agent-based support:
• Mission dissemination and network reasoning
Mission-driven Approach
• Mission Description Language (MDL): high-level mission statements • Text-based, maps and other representations… • Simple Example:
IF DETECT <DECREASE_OF <temperature>> WITH GRANULARITY<3> MONITOR <FOG>WITH ACQUISITION <period = yy>
• MDL Translation:
Mission-driven Approach
Mission is carried by mobile agents…
• Mission Parameterization: Formal representation
QFMMSNSMGM ,,,tempi < tempi-1 – 3
tempSensor <?s?>humSensor <?s?>
Qf(mi,s) = q
Mission-driven Approach
SM = set of node-missions mi = node-mission (set of measurements SME + a set of constraints SMC)SN = set of sensorsMM = mission-mappingQF = quality function
m1 =fog(t,h), per = yy
m2 =
SN =
MM = mm(mi) = s
QF =
SM = SME = temp, humSMC = per, threshold
Middleware Overview
• Service division• Types of agents
• Planning-: missions management
• Mission-: mission carrier
• Service-: service provider
• Adaptation • Reflection• Mission execution
Planning-agent Model
• BDI model• Beliefs: background info, node status, and
environment status• Ex: the current temperature, previous temperature, QF
value for a given node-mission
• Desires: goals related to the node-missions assumed
• Ex: trigger the fog sampling when tempi < tempi-1 -3
Planning-agent Model• BDI model
• Intentions: what have to be done to accomplish with the above goals (SME), respecting the constraints (SMC)
• Ex: measure temperature and compare with the previous value, according the threshold, respecting the sampling rate…
• Plan: sequence of actions to accomplish the goals according its intentions
• Ex: 1) Acquire temperature sample from the device; 2) Store sample, 3) Compare current sample with stored value; 4) if rule, send message to HumSensor …
Planning-agent Model
• Architectural Structure
In-network Reasoning
• Autonomous negotiation mechanism to distribute the node-missions among nodes – Mission Setup
• Evaluation mechanism to assess the efficiency of the mission accomplishment and changes that must take place – Mission Adaptation
In-network Reasoning
• Mission Setup• Local decision about node-mission distribution• Context-awareness• 4-step simple mechanism
• 1st: mi’s SME and SMC analysis (partial belief)
• 2nd: nodes candidacy • 3rd: best effort candidacy• 4th: others candidacy analysis using QF (common belief)
In-network Reasoning
• Mission Adaptation• Node conditions and environmental changes
awareness (updates in nodes’ beliefs)• Two cases considered:
• Node failure: as soon as perceived by the neighbor nodes
• Node is not able to continue the node-mission or another can perform it better: QF based decision
In-network Reasoning
• Considerations about Complexity• Mechanisms are customized to fit the resource
budget of the different types of nodes• Simpler nodes have simpler QF• The internal parts of the planning-agent
architecture are also customized for each kind of node, considering more or less parameters according the nodes’ capabilities and resources
Future Work
• Current in fact… • Simulations using ShoX
• Java based• Network concepts (OSI, signal propagation, interfs… )• Mobility models• Energy Cons/Prod models
Future Work
• On going • Simulations using ShoX
• Evaluation of the cost of the proposed approach• Introduction of the agents’ model in the tool framework
• Future• Interface with the Mission Specification Console
and ShoX• Complete simulation from the MDL to the runtime
agents’ (re)negotiation
Conclusion
• Presentation of a methodology to address heterogeneous sensor networks from high-level directions to autonomous nodes decisions
• A high-level language translated in system parameters
• Middleware and agents to support the proposed mission-driven approach
• A BDI-based agent model to provide the required reasoning features
Thanks for your attention!
Questions ? Suggestions?
Supervisors: [email protected] [email protected]
PhD Student: [email protected]
Backup slides
• Heterogeneity• Mission Dissemination• In-network reasoning
HeterogeneityComputerPlatform
Mobility
Sensor Capabilities
WSN
HPS
HPS: High-performance SensorsMANET: Mobile Adhoc NetworkVANET: Vehicle Adhoc NetworkWSN: Wireless Sensor Network
MANET HeterogeneityCube
2D
3D
StaticLow High
High
VANET
Mission Dissemination
• Mobile agents: Mission-agents
After the mission translationMobile-agents disseminate itin the network
Area described in the mission
MissionAgentsPlanning Agent
Mission Dissemination
• Mobile agents: Mission-agents
One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (1)
Mission Dissemination
• Mobile agents: Mission-agents
One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (2)
move
move
Mission Dissemination
• Mobile agents: Mission-agents
One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (3)
move
clone
Mission Dissemination
• Mobile agents: Mission-agents
One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (3a)
clone
Now the mission is disseminated!
In-Network Reasoning
• Multi-agents reasoning: mission setup
According to mission requirements and nodes capabilities, the node-missions are divided among the nodes needed to accomplish the mission.
Negotiation
NegotiationN
egotiation
In-Network Reasoning
After the negotiation nodes have divided the job that has to be done to accomplish the mission and nodes that are not needed to be employed in the mission, dealocate the respective mission-agent.
Dealocate
WorkDivided!
• Multi-agents reasoning: mission setup
• Multi-agent reasoning: adaptations
Changes in the environment require re-negotiation to decide which node will execute each task:- Node’s capabilities- Actual state- Task requirements
Renegotiation
In-Network Reasoning
In-Network Reasoning
• The negotiation is carried out by means of a light-weight protocol in order to not overload the network with control messages (4-step protocol presented)
• Decision making is based on a quality function, that measures how good a node can perform a given mission (or node-mission). In fact it measures the utility in use a node, or a set of them, in order to perform the tasks needed to accomplish a given mission (or node-mission). Ex.:
)))(),(()),(),(),((),((max)( ,,max tMtESEvtptMtfCtUtU jiii
jii
Skji
TSKi
TSK jj Utility function for the UAVs based on: the employability of the sensor device, the proximity of the node to the phenomenon, environmental influences (e.g. weather conditions) and remaining resources.
Adaptation
• Mobile-agents: Service-agents
Mobile-agents providing Services at different places:- Move/Clone
X
Adaptation
• Mobile-agents: Service-agents
Mobile-agents providing Services at different places:- Move/Clone
Clone
Move