A lightweight dynamic optimization methodology and application metrics estimation model for wireless sensor networks☆
Section snippets
Introduction and motivation
Advancements in semiconductor technology, as predicted by Moore's law, have enabled high transistor density in a small chip area resulting in the miniaturization of embedded systems (e.g., sensor nodes). Wireless sensor networks (WSNs) are envisioned as ubiquitous computing systems, which are proliferating in many application domains (e.g., defense, health care, surveillance systems) each with varying application requirements that can be defined by high-level application metrics (e.g.,
Related work
There exists much research in the area of dynamic optimizations [9], [10], [11], [31], but most previous work targets the processor or memory (cache) in computer systems. There exists little previous work on WSN dynamic optimization, which presents more challenges given a unique design space, design constraints, platform particulars, and external influences from the WSN's operating environment.
In the area of dynamic profiling and optimization, Sridharan et al. [26] dynamically profiled a WSN's
Dynamic optimization methodology
In this section, we give an overview of our dynamic optimization methodology along with the state space and objective function formulation for the methodology.
Algorithms for dynamic optimization methodology
In this section, we describe our dynamic optimization methodology's three steps (Fig. 1) and associated algorithms.
Application metrics estimation model
This section presents our application metrics estimation model, which is leveraged by our dynamic optimization methodology. This estimation model estimates high-level application metrics (lifetime, throughput, reliability) from low-level tunable parameters and sensor node hardware internals. The use of hardware internals is appropriate for application metrics modeling as similar approaches have been used in literature especially for lifetime estimation [24], [12], [13]. Based on tunable
Experimental results
In this section, we describe the experimental setup and results for three application domains: security/defense, health care, and ambient conditions monitoring. The results include the percentage improvements attained by our initial tunable parameter settings (one-shot operating state) over other alternative initial value settings, and a comparison of our greedy algorithm (which leverages intelligent initial parameter settings, exploration order, and parameter arrangement) for design space
Conclusions and future work
In this paper, we proposed a lightweight dynamic optimization methodology for WSNs, which provided a high-quality solution in one-shot using an intelligent initial tunable parameter value settings for highly constrained applications. We also proposed an online greedy optimization algorithm that leveraged intelligent design space exploration techniques to iteratively improve on the one-shot solution for less constrained applications. Results showed that our one-shot solution is near-optimal and
Acknowledgements
This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) (CNS-0834080). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSERC and the NSF.
Arslan Munir received his B.S. in electrical engineering from the University of Engineering and Technology (UET), Lahore, Pakistan, in 2004, and his M.A.Sc. degree in electrical and computer engineering (ECE) from the University of British Columbia (UBC), Vancouver, Canada, in 2007. He received his Ph.D. degree in ECE from the University of Florida (UF), Gainesville, Florida, USA, in 2012. He is currently a postdoctoral research associate in the ECE department at Rice University, Houston, TX,
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Arslan Munir received his B.S. in electrical engineering from the University of Engineering and Technology (UET), Lahore, Pakistan, in 2004, and his M.A.Sc. degree in electrical and computer engineering (ECE) from the University of British Columbia (UBC), Vancouver, Canada, in 2007. He received his Ph.D. degree in ECE from the University of Florida (UF), Gainesville, Florida, USA, in 2012. He is currently a postdoctoral research associate in the ECE department at Rice University, Houston, TX, USA. From 2007 to 2008, he worked as a software development engineer at Mentor Graphics in the Embedded Systems Division. He was the recipient of many academic awards including the gold medals for the best performance in Electrical Engineering, academic Roll of Honor, and doctoral fellowship from Natural Sciences and Engineering Research Council of Canada (NSERC). He received a Best Paper award at the IARIA International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) in 2010. His current research interests include embedded systems, cyber-physical/transporation systems, low-power design, computer architecture, multi-core platforms, parallel computing, dynamic optimizations, fault-tolerance, and computer networks.
Ann Gordon-Ross received her B.S. and Ph.D. degrees in computer science and engineering from the University of California, Riverside (USA) in 2000 and 2007, respectively. She is currently an associate professor of electrical and computer engineering at the University of Florida (USA) and is a member of the NSF Center for High Performance Reconfigurable Computing (CHREC) at the University of Florida. She is also the faculty advisor for the Women in Electrical and Computer Engineering (WECE) and the Phi Sigma Rho National Society for Women in Engineering and Engineering Technology. She received her CAREER award from the National Science Foundation in 2010 and Best Paper awards at the Great Lakes Symposium on VLSI (GLSVLSI) in 2010 and the IARIA International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) in 2010. Her research interests include embedded systems, computer architecture, low-power design, reconfigurable computing, dynamic optimizations, hardware design, real-time systems, and multi-core platforms.
Susan Lysecky received both her M.S. and Ph.D. degrees in computer science from the University of California, Riverside in 2003 and 2006, respectively. She is currently an assistant professor in the Department of Electrical and Computer Engineering at the University of Arizona. She coordinates research efforts for the Ubiquitous and Embedded Computing lab, and her current research interests include embedded system design, with emphasis on self-configuring architectures, human-computer interaction, and facilitating the design and use of complex sensor-based system by non-engineers. She is a member of IEEE and ACM.
Roman Lysecky is an associate professor of electrical and computer engineering at the University of Arizona. He received his B.S., M.S., and Ph.D. in computer science from the University of California, Riverside in 1999, 2000, and 2005, respectively. His primary research interests focus on embedded systems design, with emphasis on dynamic adaptability, hardware/software partitioning, field-programmable gates arrays (FPGAs), and low-power methodologies. He has coauthored two textbooks on hardware description languages and holds one US patent. He received a CAREER award from the National Science Foundation in 2009, Best Paper Awards from the International Conference on Hardware-Software Codesign and System Synthesis (CODES+ISSS) and the Design Automation and Test in Europe Conference (DATE), and an Outstanding Ph.D. Dissertation Award from the European Design and Automation Association (EDAA) in 2006.
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Also with the NSF Center for High-Performance Reconfigurable Computing (CHREC) at the University of Florida, Gainesville, FL 32611, USA.