Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Wireless integrated network sensors
Communications of the ACM
Habitat monitoring: application driver for wireless communications technology
SIGCOMM LA '01 Workshop on Data communication in Latin America and the Caribbean
JouleTrack: a web based tool for software energy profiling
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ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Computation offloading to save energy on handheld devices: a partition scheme
CASES '01 Proceedings of the 2001 international conference on Compilers, architecture, and synthesis for embedded systems
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
The nesC language: A holistic approach to networked embedded systems
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
A Distributed Computation Platform for Wireless Embedded Sensing
ICCD '02 Proceedings of the 2002 IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD'02)
Computation hierarchy for in-network processing
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Fine-grained network time synchronization using reference broadcasts
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Preprocessing in a tiered sensor network for habitat monitoring
EURASIP Journal on Applied Signal Processing
SenQ: a scalable simulation and emulation environment for sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
A multi-tier, multimodal wireless sensor network for environmental monitoring
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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In this paper we explore the network level architecture of distributed sensor systems that perform in-network processing. We propose a system with heterogeneous nodes that organizes into a hierarchical structure dictated by the computational capabilities. The presence of high-performance nodes amongst a sea of resource-constrained nodes exposes new tradeoffs for the efficient implementation of network-wide applications. Our experiments show that even for a low relative density of resource-constrained nodes to high-performance nodes there are certain gains in performance for a heterogeneous and hierarchical network over a homogeneous one. The introduction of hierarchy enables partitioning of the application into sub-tasks that can be mapped onto the heterogeneous nodes in the network in multiple ways. We analyze the tradeoffs between the execution time of the application, accuracy of the output produced and the overall energy consumption of the network for the different mapping of the sub-tasks onto the heterogeneous nodes. We evaluate the performance and energy consumption of a typical sensor network application of target tracking via beamforming and line of bearing (LOB) calculations on the different nodes. Our experiments also include the study of the overall performance and energy consumption of the LOB calculation using two different types of resource constrained sensor nodes (MICA and MICA2 nodes) and show how these metrics are affected by changes in the node architecture and operation. Our results indicate that when using MICA motes as resource-constrained nodes, 85% of the time on average the hierarchical network outperforms a homogeneous network for approximately the same energy budget. When using MICA2 motes as resource-constrained nodes, 54% of the time the hierarchical network performs better than a homogeneous network with approximately the same energy budget.