Richard Tynan, G.M.P. O’Hare, Michael O’Grady & Conor Muldoon School of Computer Science & Informatics University College Dublin Ireland

 WSN Issues  Intelligent Power Management  Agents for WSNs  Current Approach  MAS Approach  Agent Factory Micro Edition  Resource Bounded Reasoning  Experiments  Future Work  Conclusions

 Connectivity  Latency  Density  Accuracy  Energy Consumption  Is a WSN useful if it lasts 1 day?  How does a WSN intelligently manage it’s limited power reserves?

 Option 1: Reduce number of active components on a node  Option 2: Put the entire node to sleep  All activity ceases  No routing capabilities  No sensing capabilities  Potential blind spot in the sensed area  Possible sub-graph disconnection

 Intelligent Lighting Control  Routing  Data Analysis  Agent Environments:  Agilla, Mate, AFME  Characterised by the one-agent-per-node approach  Weak notion of agency

 Stack-based approach  Messages sent using lower layer  Massages received from lower layer  Mediated hibernation

 A node is critical if  Connectivity OR  Sensing are critical  Decision  persistence/timing  What if each layer can hibernate independently?

 Enforcing homogenous timing policy can be highly inefficient - experiments  Stack-based approach allows passing messages through hibernating layers  Solution: Allow each layer to operate as an autonomous agent.

 Open source minimised footprint BDI agent platform developed for resource constrained devices.  Targets devices, such as mobile phones and Sun SPOT leaf nodes.  Based on Agent Factory, a pre-existing agent platform for desktop environments.  Conforms to the CLDC Java Specification.

 AFME agents follow a sense-deliberate-act cycle.  In the control algorithm, initially perceptors are fired and the belief set is updated. The desires are then identified using resolution-based reasoning. Various intentions are then chosen. Depending on the nature of the intentions, various actuators are fired.  AFME supports the Agent Factory Agent Programming Language and augments it with an infrastructure for resource bounded reasoning.

 Perhaps the most obvious difference between development for a desktop machine and a senor concerns the limited spatiotemporal and energy resources available.  This is coupled by the inherent uncertainty in WSN domains.  What then does it mean to say an agent is rational in circumstances where it does not have the information or resources to determine the course of action that yields maximum utility?

 In this application, we are concerned with altering sleep rates in a prudent manner to improve system performance.  Should a system react quickly with a small amount of data or continue operating as more data is collected.  There is an inherent cost in controlling a system.  The macroscopic principle of uncertainty in control theory.

 The BDI model of agency acknowledges that agents are resource bounded and will be unable to achieve all of their desires even if their desires are consistent.  An agent must fix upon a subset of desires an commit resources to achieving them.  This subset is the agents intentions.  In essence, this is a classic 0-1 knapsack problem.

 5 metre node separation  100m x 100m area with a mobile target  Active nodes sample their sensors every 10 seconds  % received

 At present, the application has been implemented using a stack based approach.  We have conducted experiments that illustrate that when combined hibernation strategies are adopted, it leads to poor application performance.  Implement the agent based solution to the problem using AFME.  Such an approach should improve performance.

 The problem: if we use a longer, homogenous evaluation period the routing component improves.  Need to break the homogenous evaluation frequency while still allowing a node to hibernate.  A MAS resident on a node could provide such flexibility and power management.

 More details may be found at:  AFME may be downloaded from: