Hardware/software partitioning for multi-function systems
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Software Synthesis from Dataflow Graphs
Software Synthesis from Dataflow Graphs
Data Gathering Algorithms in Sensor Networks Using Energy Metrics
IEEE Transactions on Parallel and Distributed Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Computation hierarchy for in-network processing
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Scheduling dynamic dataflow graphs with bounded memory using the token flow model
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Parameterized dataflow modeling for DSP systems
IEEE Transactions on Signal Processing
Energy-driven distribution of signal processing applications across wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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In a sensor network, as we increase the number of nodes, the requirements on network lifetime, and the volume of data traffic across the network, it is often efficient to move towards hierarchical network architectures (e.g., see [5]). In such hierarchical networks, sensor nodes are clustered into groups, and their roles are divided into master and slave nodes for more efficient structuring of network traffic. The opera tional complexity of each sensor node and the amount of data to be transmitted across sensor nodes strongly influence the energy consump tion of the nodes, which ultimately determines the network lifetime. This paper provides a new way of reducing data traffic across nodes by determining and exploiting the lowest data token delivery points within an application graph that is distributed across a network. The technique divides an application graph into two sub-graphs and then distributes each divided subgraph over a master node and its associated slave nodes. The buffer costs of the graph edges over the cutting line corre sponds to the amount of data to be transmitted between nodes after allo cating the two partial subgraphs such that one subgraph executes on a master node, and the other subgraph is distributed across the associated slave nodes. Since the energy consumption on each node is dominated by the transceiver, the reduced data traffic allows for reducing the turn-on time of the transceivers, and thereby leads to high energy savings. This technique also distributes the workload of sensor nodes in a sys tematic manner. The more balanced workload also contributes to effi cient battery usage, and also improves the latency for processing the data frames captured by the sensor nodes.