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Self-organising Sensor networks.

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  • Qa75 Electronic Computers. Computer Science
  • Biology
  • Earth Science
  • Engineering
  • Geography
  • Mathematics


C:\WINNT\Profiles\marshaiw\Personal\uk-ubinetIM.prn.pdf Self-organising Sensor networks Ian W Marshall1, Christopher Roadknight2, Ibiso Wokoma1 and Lionel Sacks1 1. EE dept. University college London, 2. BTexact Technologies Adastral Park, mailto: [email protected] Sensor Networks [6, 5] consist of a large number of low-cost low-power devices, each with sufficient hardware to monitor one or more variables and send and receive the readings for these variables to other devices. Wireless sensor networks are becoming a powerful tool for monitoring a range of diverse situations [3, 8]. While the devices themselves are mostly still in the prototype stage [7] the theory surrounding these devices is a fast moving area of research. Ad-Hoc networks are a collection of mobile devices with wireless networking capability that may form a temporary peer to peer, multi-hop network without the aid of any established infrastructure or centralised administration. Sensor networks typically make use of ad-hoc networking, but normally lack the processing power to utilize the full richness of many proposed ad-hoc network protocols. In the DTI funded project SECOAS [14] we are investigating how sensor networks can be used to collect rich datasets in an offshore environment, thereby enabling more accurate models of poorly underst ood interactions between coastal sedimentation processes. This will involve a new way of thinking for coastal oceanographers, marine scientists, managers and engineers. It is vital therefore not to further overload these users with the need to learn how t o configure complex networks. We are therefore aiming to build a self-organising, collegiate system. We believe it is desirable that sensor network devices have as much autonomy as possible. Given the mobility of devices and increased likelihood of failure, devices that can learn, adapt and make sensible decisions for themselves will be far more robust and their resulting measurements should be more reliable.

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