Location of Repository

Coordinated intelligent power management and the heterogeneous sensing coverage problem

By Richard Tynan, Conor Muldoon, G. M. P. (Greg M. P.) O'Hare and Michael J. O'Grady


One of the most important factors to be considered when developing an application for a Wireless Sensor Network (WSN) is its power consumption. Intelligent Power Management (IPM) for a WSN is crucial in maximising the operational longevity. An established regime for achieving this is through the opportunistic hibernation of redundant nodes. Redundancy, however, has various definitions within the field of WSNs and indeed multiple protocols, each operating using a different definition, coexist on the same node. In this paper, we advocate the use of a MAS as an appropriate mechanism by which different stake-holders, each desiring to hibernate a node in order to conserve power, can collaborate. The problem of node hibernation for the heterogeneous sensing coverage areas is introduced and the manner by which it can be solved using ADOPT, an algorithm for distributed constraint optimisation, is described. We illustrate that the node hibernation strategy discussed here is more useful than the traditional stack-based approach and motivate our discussion using intelligent power management as an exemplar

Topics: Coverage, Connectivity, Hibernation, Intelligent power management, Distributed constraint optimisation, Intelligent agents (Computer software), Wireless sensor networks--Energy consumption, Constraints (Artificial intelligence), Wireless sensor nodes
Publisher: Oxford University Press
Year: 2011
DOI identifier: 10.1093/comjnl
OAI identifier: oai:researchrepository.ucd.ie:10197/2801
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://hdl.handle.net/10197/28... (external link)
  • http://creativecommons.org/lic... (external link)
  • Suggested articles



    1. (2006). Adopt: Asynchronous distributed constraint optimization with quality guarantees. doi
    2. (2003). An asynchronous complete method for distributed constraint optimization. doi
    3. (2004). Autonomic wireless sensor networks. doi
    4. (1995). BDI Agents: from theory to practice. doi
    5. (2003). Beyond prototyping in the factory of the agents. doi
    6. (2003). Coverage in wireless ad-hoc sensor networks. doi
    7. (1957). Discrete Variable Extremum Problems. doi
    8. (2007). Distributed protocols for ensuring both coverage and connectivity of a wireless sensor network. doi
    9. (2003). Distributed target classification and tracking in sensor networks. doi
    10. (2001). Dynamic power management in wireless sensor networks. doi
    11. (2001). Geography-informed energy conservation for ad hoc routing. MobiCom ’01: doi
    12. (2000). Gpsr: greedy perimeter stateless routing for wireless networks. doi
    13. (2005). Heliomote: enabling long-lived sensor networks through solar energy harvesting. doi
    14. (2007). IDBADOPT: A Depth-First Search DCOP Algorithm. IJCAI Distributed Constraint Reasoning Workshop, doi
    15. (2005). Integrated coverage and connectivity configuration for energy conservation in sensor networks. doi
    16. (2005). Intel mote 2: an advanced platform for demanding sensor network applications. doi
    17. (2005). J-sim: A simulation environment for wireless sensor networks. anss, doi
    18. (2005). Maintaining sensing coverage and connectivity in large sensor networks. doi
    19. (2007). Managing Resources in Constrained Environments with Autonomous Agents. Engineering Societies in the Agents’ World, doi
    20. (2002). Mate : A virtual machine for tiny networked sensors. Architectural Support for Programming Languages and Operating Systems (ASPLOS), doi
    21. (2005). Mobile agent middleware for sensor networks: An application case study. doi
    22. (2008). Multiagent systems: algorithmic, gametheoretic, and logical foundations. doi
    23. (2006). Multiply-constrained distributed constraint optimization. doi
    24. (2001). Multisensor data fusion in distributed sensornetworks using mobile agents. doi
    25. (1991). Object Oriented Modeling and Design. doi
    26. (1994). Object-oriented Analysis and Design, 2nd edition. doi
    27. (1988). Objectoriented programming: An objective sense of style, . doi
    28. (2002). Optimizing sensor networks in the energy-latency-density design space. doi
    29. (2003). Poster abstract: density, accuracy, delay and lifetime tradeoffs in wireless sensor networksa multidimensional design perspective. doi
    30. (2002). Rumor routing algorithm for sensor networks. doi
    31. (2006). Signal based node activation in wireless sensor networks. doi
    32. (1968). Some Vistas of Modern Mathematics. doi
    33. (2001). Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. doi
    34. (2003). System Architecture for Wireless Sensor Networks.
    35. (2003). The coverage problem in a wireless sensor network. WSNA ’03: doi
    36. (2005). The squawk virtual machine: Java on the bare metal. doi
    37. (2007). Towards reflective mobile agents for resource-constrained mobile devices. doi
    38. (2004). Versatile low power media access for wireless sensor networks. doi
    39. (2004). Wireless sensor networks for commercial lighting control: Decision making with multi-agent systems. AAAI Workshop on Sensor Networks,

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.