TY - GEN
T1 - Generating an efficient sensor network program by partial deduction
AU - Li, Li
AU - Taylor, Kerry
PY - 2010
Y1 - 2010
N2 - Partial deduction is an optimisation technique developed by the logic programming community. We propose the use of Partial deduction in the domain of wireless sensor network programming where programs are written for small computational platforms and energy is typically scarce. We show how, together with a declarative programming language which has been shown to be suitable for several demanding sensor network applications, it can address key issues such as rewriting a query using views and reducing redundancy of rewritings as long as some computation and abstraction can be performed at compile-time, which obviously leads to the improvement of energy efficiency at run-time. We argue that energy efficiency can be achieved with: (1) minimised sensor network programming workload by forcing the folding of goals into the view partially; (2) reduced redundant computation with fewer computation steps at network nodes by forcing the unfolding of simple goals; (3) reduced inter-node message transmission by more specific addressing of messages to nodes; and (4) reduced memory requirements by specialising network-wide programs to smaller programs for specific nodes. A partial deduction system is developed and an extended example is provided to demonstrate the potential performance improvement of the technique.
AB - Partial deduction is an optimisation technique developed by the logic programming community. We propose the use of Partial deduction in the domain of wireless sensor network programming where programs are written for small computational platforms and energy is typically scarce. We show how, together with a declarative programming language which has been shown to be suitable for several demanding sensor network applications, it can address key issues such as rewriting a query using views and reducing redundancy of rewritings as long as some computation and abstraction can be performed at compile-time, which obviously leads to the improvement of energy efficiency at run-time. We argue that energy efficiency can be achieved with: (1) minimised sensor network programming workload by forcing the folding of goals into the view partially; (2) reduced redundant computation with fewer computation steps at network nodes by forcing the unfolding of simple goals; (3) reduced inter-node message transmission by more specific addressing of messages to nodes; and (4) reduced memory requirements by specialising network-wide programs to smaller programs for specific nodes. A partial deduction system is developed and an extended example is provided to demonstrate the potential performance improvement of the technique.
UR - http://www.scopus.com/inward/record.url?scp=78049271872&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15246-7_15
DO - 10.1007/978-3-642-15246-7_15
M3 - Conference contribution
SN - 3642152457
SN - 9783642152450
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 145
BT - PRICAI 2010
T2 - 11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
Y2 - 30 August 2010 through 2 September 2010
ER -