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Edison Pignaton de Freitas Marco Aurélio Wehrmeister Armando Morado Ferreira

Multi-Agents Supporting Reflection in a Middleware for Mission-Driven Heterogeneous Sensor Networks. Edison Pignaton de Freitas Marco Aurélio Wehrmeister Armando Morado Ferreira Carlos Eduardo Pereira Tony Larsson. 3 rd ATSN @ 8 th AAMAS – May 2009. Outline. Context Proposed Approach

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Edison Pignaton de Freitas Marco Aurélio Wehrmeister Armando Morado Ferreira

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  1. Multi-Agents Supporting Reflection in a Middleware for Mission-Driven Heterogeneous Sensor Networks Edison Pignaton de Freitas Marco Aurélio Wehrmeister Armando Morado Ferreira Carlos Eduardo Pereira Tony Larsson 3rd ATSN @ 8th AAMAS – May 2009

  2. Outline • Context • Proposed Approach • Mission-driven Approach • Mission Description Language • Mission Parameterization • Middleware Overview • Planning-agent Model • In-network Reasoning • Conclusion and Future Work

  3. 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

  4. Context • Motivation Application Scenarios Rescue Assistance and Disaster Recovering Surveillance and Patrolling

  5. Context Our Focus • Problems to address: • Establishment of a network mission (and its partitioning) • Efficient use of differenttypes ofsensors 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) This presentation

  6. Proposed Approach • System Overview • High-level Mission Description Language • Middleware providing interoperability • Agent-based support: • Mission dissemination and network reasoning

  7. 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>

  8. Mission-driven Approach • MDL Translation: Mission is carried by mobile agents…

  9. Mission-driven Approach • Mission Parameterization: Formal representation m1 = tempi < tempi-1 – 3 m2 = fog(t,h), per = yy SM = SM = set of node-missions mi = node-mission (set of measurements SME + a set of constraints SMC) SN = set of sensors MM = mission-mapping QF = quality function SME = temp, hum SMC = per, threshold tempSensor <?s?> humSensor <?s?> SN = MM = mm(mi) = s QF = Qf(mi,s) = q

  10. Middleware Overview • Service division • Types of agents • Planning-: missions management • Mission-: mission carrier • Service-: service provider • Adaptation • Reflection • Mission execution

  11. 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

  12. 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 …

  13. Planning-agent Model • Architectural Structure

  14. 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

  15. 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)

  16. 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

  17. In-network Reasoning • Considerations about Complexity • Mechanisms are customized to fit the resource budget of the different types of nodes • Simplernodes have simplerQF • 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

  18. Future Work • Current in fact… • Simulations using ShoX • Java based • Network concepts (OSI, signal propagation, interfs… ) • Mobility models • Energy Cons/Prod models

  19. 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

  20. 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

  21. Thanks for your attention! Questions ? Suggestions? Supervisors: tony.larsson@hh.se cpereira@ece.ufrgs.br PhD Student: edison.pignaton@hh.se

  22. Backup slides • Heterogeneity • Mission Dissemination • In-network reasoning

  23. Heterogeneity Computer Platform High HPS VANET Heterogeneity Cube MANET WSN High Low Sensor Capabilities Static 2D HPS: High-performance Sensors MANET: Mobile Adhoc Network VANET: Vehicle Adhoc Network WSN: Wireless Sensor Network 3D Mobility

  24. Mission Dissemination Area described in the mission • Mobile agents: Mission-agents Mission Agents Planning Agent After the mission translation Mobile-agents disseminate it in the network

  25. 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)

  26. Mission Dissemination • Mobile agents: Mission-agents move move One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (2)

  27. Mission Dissemination • Mobile agents: Mission-agents clone move One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (3)

  28. Mission Dissemination • Mobile agents: Mission-agents clone One time in the network, they follow the path to the area defined in the mission directions, by moving and/or cloning. (3a)  Now the mission is disseminated!

  29. In-Network Reasoning • Multi-agents reasoning: mission setup Negotiation Negotiation Negotiation According to mission requirements and nodes capabilities, the node-missions are divided among the nodes needed to accomplish the mission.

  30. In-Network Reasoning • Multi-agents reasoning: mission setup Dealocate 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. Work Divided!

  31. In-Network Reasoning Renegotiation • 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

  32. 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.: 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.

  33. Adaptation • Mobile-agents: Service-agents X • Mobile-agents providing • Services at different places: • Move/Clone

  34. Adaptation • Mobile-agents: Service-agents Clone • Mobile-agents providing • Services at different places: • Move/Clone Move

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