Difference between revisions of "Generalized Submodular Optimization for Integrated Networked Sensing Systems"
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Faculty: [http://www.cs.wustl.edu/~ychen/ Yixin Chen], [http://www.cs.wustl.edu/~lu/ Chenyang Lu] | Faculty: [http://www.cs.wustl.edu/~ychen/ Yixin Chen], [http://www.cs.wustl.edu/~lu/ Chenyang Lu] | ||
| − | PhD Student: | + | PhD Student: Paras Tiwari |
| − | Alumni: [https://sites.google.com/site/sbhatta/ Sangeeta Bhattacharya], [http://youxu.info/ You Xu] | + | Alumni: [https://sites.google.com/site/sbhatta/ Sangeeta Bhattacharya], [http://www.cse.wustl.edu/~saifullaha/ Abusayeed Saifullah], [http://www.cs.wustl.edu/~wuchengjie/ Chengjie Wu], [http://youxu.info/ You Xu] |
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While wireless sensor networks (WSNs) have traditionally been used as a specialized platform for single applications, recent years have witnessed the emergence of integrated wireless sensor networks as shared infrastructure for multiple applications. The evolution of wireless sensor networks from dedicated platforms to shared infrastructure is driven by a wide range of integrated sensing systems such as urban sensing, building automation, and environmental monitoring. Compared to a separate WSN dedicated to each application, a shared WSN offers more flexibility, adaptivity, and cost- effectiveness through dynamic resource and node allocation. | While wireless sensor networks (WSNs) have traditionally been used as a specialized platform for single applications, recent years have witnessed the emergence of integrated wireless sensor networks as shared infrastructure for multiple applications. The evolution of wireless sensor networks from dedicated platforms to shared infrastructure is driven by a wide range of integrated sensing systems such as urban sensing, building automation, and environmental monitoring. Compared to a separate WSN dedicated to each application, a shared WSN offers more flexibility, adaptivity, and cost- effectiveness through dynamic resource and node allocation. | ||
Due to their resource constraints in bandwidth, memory, and energy, shared sensor networks face a critical need for optimizing the Quality of Monitoring (QoM) through dynamic resource allocation to the contending applications. These emerging QoM optimization problems in shared sensor networks are computationally challenging, as they are discrete and nonlinear. Furthermore, the optimization approach must: 1) deal with multiple resource constraints, 2) scale to large networks, and 3) handle network and environmental dynamics. | Due to their resource constraints in bandwidth, memory, and energy, shared sensor networks face a critical need for optimizing the Quality of Monitoring (QoM) through dynamic resource allocation to the contending applications. These emerging QoM optimization problems in shared sensor networks are computationally challenging, as they are discrete and nonlinear. Furthermore, the optimization approach must: 1) deal with multiple resource constraints, 2) scale to large networks, and 3) handle network and environmental dynamics. | ||
| − | To address these challenges, it is important to exploit the special structure of these optimiza- tion problems for sensing applications. A key observation is that most of the QoM functions of the physical phenomena exhibit diminishing returns when more nodes are allocated to an application, a property known as submodularity. Submodular properties are ubiquitous in distributed sensing applications and have been addressed in the literature. However, most of the existing works that utilize submodularity rely on unique problem structures and restrictive assumptions only | + | To address these challenges, it is important to exploit the special structure of these optimiza- tion problems for sensing applications. A key observation is that most of the QoM functions of the physical phenomena exhibit diminishing returns when more nodes are allocated to an application, a property known as submodularity. Submodular properties are ubiquitous in distributed sensing applications and have been addressed in the literature. However, most of the existing works that utilize submodularity rely on unique problem structures and restrictive assumptions only applicable to specific applications. Moreover, all existing submodular optimization algorithms assume a centralized control, which limits their scalability. To ensure scalability and deal with network dynamics, it is essential to develop distributed and online algorithms for submodular optimization. In this project, both centralized and distributed algorithms are proposed to solve submodular optimization problems in WSNs. We develop novel distributed approaches that exploit submodularity. By incorporating submodularity into a market-based framework, we provide the first approximation bounds for distributed submodular optimization. The centralized and distributed algorithms were presented at [http://www.cse.wustl.edu/%7Elu/papers/mobihoc10.pdf MobiHoc'10] and [http://www.cse.wustl.edu/%7Elu/papers/infocom12.pdf INFOCOM'12], respectively. |
| − | + | == Publications == | |
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| − | - | + | * C. Wu, M. Sha, D. Gunatilaka, A. Saifullah, C. Lu and Y. Chen; Analysis of EDF Scheduling for Wireless Sensor-Actuator Networks, ACM/IEEE International Symposium on Quality of Service (IWQoS'14), May 2014. [http://www.cse.wustl.edu/%7Elu/papers/iwqos14.pdf PDF] |
| − | + | * A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu and Y. Chen, Near Optimal Rate Selection for Wireless Control Systems, ACM Transactions on Embedded Computing Systems, Special Issue on Real-Time and Embedded Technology and Applications, 13(4s), Article 128, April 2014. [http://www.cse.wustl.edu/%7Elu/papers/tecs14-rate-selection.pdf PDF] | |
| − | + | * A. Saifullah, Y. Xu, C. Lu, and Y. Chen; Distributed Channel Allocation Protocols for Wireless Sensor Networks; IEEE Transactions on Parallel and Distributed Systems, 2013. [http://www.cse.wustl.edu/%7Elu/papers/tpds-channel.pdf PDF] | |
| − | + | * A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu and Y. Chen; Near Optimal Rate Selection for Wireless Control Systems, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '12), April 2012. (Best Paper Nominee) [http://www.cse.wustl.edu/%7Elu/papers/rtas12-wireless-control.pdf PDF] | |
| − | + | * C. Wu, Y. Xu, Y. Chen, C. Lu; Submodular Game for Distributed Application Allocation in Shared Sensor Networks, The 31st IEEE International Conference on Computer Communications (INFOCOM '12), March 2012. [http://www.cse.wustl.edu/%7Elu/papers/infocom12.pdf PDF] | |
| − | * C. Wu, Y. Xu, Y. Chen, C. Lu; Submodular Game for Distributed Application Allocation in Shared Sensor Networks, The 31st IEEE International Conference on Computer Communications (INFOCOM '12), March 2012. [http://www.cse.wustl.edu/% | ||
| − | * A. Saifullah | + | * A. Saifullah, Y. Xu, C. Lu and Y. Chen; Priority Assignment for Real-time Flows in WirelessHART Networks, Euromicro Conference on Real-Time Systems (ECRTS '11), July 2011. [http://www.cse.wustl.edu/%7Elu/papers/ecrts11-wirelesshart.pdf PDF] |
| − | * A. Saifullah, Y. Xu, C. Lu | + | * A. Saifullah, Y. Xu, C. Lu and Y. Chen; End-to-End Delay Analysis for Fixed Priority Scheduling in WirelessHART Networks, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '11), April 2011. [http://www.cse.wustl.edu/%7Elu/papers/rtas11-wirelesshart.pdf PDF] |
| − | * A. Saifullah, Y. Xu, C. Lu, and Y. Chen; | + | * A. Saifullah, Y. Xu, C. Lu, and Y. Chen; Real-time Scheduling for WirelessHART Networks; IEEE Real-Time Systems Symposium (RTSS '10), December 2010. [http://www.cse.wustl.edu/%7Elu/papers/rtss10.pdf PDF] |
| − | * A. Saifullah, Y. | + | * Y. Xu, A. Saifullah, Y. Chen, C. Lu and S. Bhattacharya, Near Optimal Multi-Application Allocation in Shared Sensor Networks, ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'10), September 2010. [http://www.cse.wustl.edu/%7Elu/papers/mobihoc10.pdf PDF] |
| − | + | * S. Bhattacharya, A. Saifullah, C. Lu and G.-C. Roman, Multi-Application Deployment in Shared Sensor Networks Based on Quality of Monitoring, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'10), April 2010. [http://www.cse.wustl.edu/%7Elu/papers/rtas10-umade.pdf PDF] | |
== Acknowledgements == | == Acknowledgements == | ||
This work is supported by the NSF under NeTS Grant [http://nsf.gov/awardsearch/showAward.do?AwardNumber=1017701 CNS-1017701]. | This work is supported by the NSF under NeTS Grant [http://nsf.gov/awardsearch/showAward.do?AwardNumber=1017701 CNS-1017701]. | ||
Latest revision as of 01:44, 1 December 2015
Faculty: Yixin Chen, Chenyang Lu
PhD Student: Paras Tiwari
Alumni: Sangeeta Bhattacharya, Abusayeed Saifullah, Chengjie Wu, You Xu
While wireless sensor networks (WSNs) have traditionally been used as a specialized platform for single applications, recent years have witnessed the emergence of integrated wireless sensor networks as shared infrastructure for multiple applications. The evolution of wireless sensor networks from dedicated platforms to shared infrastructure is driven by a wide range of integrated sensing systems such as urban sensing, building automation, and environmental monitoring. Compared to a separate WSN dedicated to each application, a shared WSN offers more flexibility, adaptivity, and cost- effectiveness through dynamic resource and node allocation.
Due to their resource constraints in bandwidth, memory, and energy, shared sensor networks face a critical need for optimizing the Quality of Monitoring (QoM) through dynamic resource allocation to the contending applications. These emerging QoM optimization problems in shared sensor networks are computationally challenging, as they are discrete and nonlinear. Furthermore, the optimization approach must: 1) deal with multiple resource constraints, 2) scale to large networks, and 3) handle network and environmental dynamics.
To address these challenges, it is important to exploit the special structure of these optimiza- tion problems for sensing applications. A key observation is that most of the QoM functions of the physical phenomena exhibit diminishing returns when more nodes are allocated to an application, a property known as submodularity. Submodular properties are ubiquitous in distributed sensing applications and have been addressed in the literature. However, most of the existing works that utilize submodularity rely on unique problem structures and restrictive assumptions only applicable to specific applications. Moreover, all existing submodular optimization algorithms assume a centralized control, which limits their scalability. To ensure scalability and deal with network dynamics, it is essential to develop distributed and online algorithms for submodular optimization. In this project, both centralized and distributed algorithms are proposed to solve submodular optimization problems in WSNs. We develop novel distributed approaches that exploit submodularity. By incorporating submodularity into a market-based framework, we provide the first approximation bounds for distributed submodular optimization. The centralized and distributed algorithms were presented at MobiHoc'10 and INFOCOM'12, respectively.
Publications
- C. Wu, M. Sha, D. Gunatilaka, A. Saifullah, C. Lu and Y. Chen; Analysis of EDF Scheduling for Wireless Sensor-Actuator Networks, ACM/IEEE International Symposium on Quality of Service (IWQoS'14), May 2014. PDF
- A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu and Y. Chen, Near Optimal Rate Selection for Wireless Control Systems, ACM Transactions on Embedded Computing Systems, Special Issue on Real-Time and Embedded Technology and Applications, 13(4s), Article 128, April 2014. PDF
- A. Saifullah, Y. Xu, C. Lu, and Y. Chen; Distributed Channel Allocation Protocols for Wireless Sensor Networks; IEEE Transactions on Parallel and Distributed Systems, 2013. PDF
- A. Saifullah, C. Wu, P. Tiwari, Y. Xu, Y. Fu, C. Lu and Y. Chen; Near Optimal Rate Selection for Wireless Control Systems, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '12), April 2012. (Best Paper Nominee) PDF
- C. Wu, Y. Xu, Y. Chen, C. Lu; Submodular Game for Distributed Application Allocation in Shared Sensor Networks, The 31st IEEE International Conference on Computer Communications (INFOCOM '12), March 2012. PDF
- A. Saifullah, Y. Xu, C. Lu and Y. Chen; Priority Assignment for Real-time Flows in WirelessHART Networks, Euromicro Conference on Real-Time Systems (ECRTS '11), July 2011. PDF
- A. Saifullah, Y. Xu, C. Lu and Y. Chen; End-to-End Delay Analysis for Fixed Priority Scheduling in WirelessHART Networks, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '11), April 2011. PDF
- A. Saifullah, Y. Xu, C. Lu, and Y. Chen; Real-time Scheduling for WirelessHART Networks; IEEE Real-Time Systems Symposium (RTSS '10), December 2010. PDF
- Y. Xu, A. Saifullah, Y. Chen, C. Lu and S. Bhattacharya, Near Optimal Multi-Application Allocation in Shared Sensor Networks, ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'10), September 2010. PDF
- S. Bhattacharya, A. Saifullah, C. Lu and G.-C. Roman, Multi-Application Deployment in Shared Sensor Networks Based on Quality of Monitoring, IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'10), April 2010. PDF
Acknowledgements
This work is supported by the NSF under NeTS Grant CNS-1017701.
