Energy-Driven Partitioning of Signal Processing Algorithms in Sensor Networks

  • Dong-Ik Ko
  • Chung-Ching Shen
  • Shuvra S. Bhattacharyya
  • Neil Goldsman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4017)


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.


Sensor Network Sensor Node Network Lifetime Data Frame Master Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-Ik Ko
    • 1
  • Chung-Ching Shen
    • 1
  • Shuvra S. Bhattacharyya
    • 1
  • Neil Goldsman
    • 1
  1. 1.Department of Electrical and Computer Engineering, and Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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