Salman Taherian, University of Cambridge
Jean Bacon, University of Cambridge
Abstract:
With the increased realisation of the benefits of studying environmental data, sensor networks are rapidly scaling in size, heterogeneity of data, and applications. In this paper, we present a State-based Publish/Subscribe (SPS) framework for sensor systems with many distributed and independent application clients. SPS provides a state-based information deduction model that is suited to many classes of sensor network applications. State Maintenance Components (SMCs) are introduced that are simple in operation, flexible in placement, and decomposable for distributed processing. Publish/Subscribe communication forms the core messaging component of the framework. SPS uses the decoupling feature of Pub/Sub and extends this across the SMCs to support a more flexible and dynamic system structure. Our evaluation, using real sensor data, shows that SPS is expressive in capturing conditions, and scalable in performance.
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Jean Bacon, University of Cambridge
Abstract:
With the increased realisation of the benefits of studying environmental data, sensor networks are rapidly scaling in size, heterogeneity of data, and applications. In this paper, we present a State-based Publish/Subscribe (SPS) framework for sensor systems with many distributed and independent application clients. SPS provides a state-based information deduction model that is suited to many classes of sensor network applications. State Maintenance Components (SMCs) are introduced that are simple in operation, flexible in placement, and decomposable for distributed processing. Publish/Subscribe communication forms the core messaging component of the framework. SPS uses the decoupling feature of Pub/Sub and extends this across the SMCs to support a more flexible and dynamic system structure. Our evaluation, using real sensor data, shows that SPS is expressive in capturing conditions, and scalable in performance.
ACM Copyright Notice Copyright © by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org.
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