Faculty: Chenyang Lu, Robert Pless
Students: Octav Chipara, Guoliang Xing
Energy is a paramount concern in wireless sensor networks that must achieve long lifetimes (from several months to several years) while operating on limited battery energy. A promising approach to reduce energy consumption of sensor networks is to dynamically control the duty cycles of sensors. In such a approach, a small subset of active sensors remain awake all the time to maintain continuous services of the network while all other sensors are scheduled to sleep or enter a power-saving mode to save energy. The energy management problem is especially challenging in many mission-critical sensor networks that must maintain certain performance constraints throughout their operational lifetime. Specifically, wireless sensor networks must satisfy both sensing and communication constraints simultaneously.
Sensing constraints: Since the primary purpose of sensor networks is to monitor the environment, a sensor network must maintain sufficient sensing coverage over the region of interest even when it operates in an energy conservation mode. The requirement of sensing coverage are tightly coupled with distributed sensing applications. 1) A application may require a certain degree of coverage, i.e., every point of some region must be sensed by a certain number of sensors. Different applications require different degrees of sensing coverage. 2) In more realistic sensing models, the sensing coverage can be expressed by the event detection probability and the false alarm rate. For example, a distributed monitoring application may require that the minimal event detection probability over a geographic region to be above certain threshold while the the maximal system false alarm rate below another threshold.
Communication constraints: Meanwhile, both data fusion among multiple sensors and data services for end-users may have quality of service requirements on the communication network. The minimum constraint is that the subset composed of active nodes must guarantee connectivity whenever they need to communicate. Furthermore, end-to-end communication delay are often important to mission-critical applications. For example, a firefighter fighting a wild fire may request periodic updates of a temperature map around his location to maintain awareness of the fire condition. Late data updates may endanger the fire fighter.
Publications
G. Xing, C. Lu, R. Pless and Q. Huang, Impact of Sensing Coverage on Greedy Geographic Routing Algorithms, IEEE Transactions on Parallel and Distributed Systems, Special Issue on Localized Communication and Topology Protocols for Ad Hoc Networks, 17(4), April 2006. Note: Extended version of the MobiHoc'04 paper. new!
G. Xing, X. Wang, Y. Zhang, C. Lu, R. Pless and C. Gill, Integrated Coverage and Connectivity Configuration for Energy Conservation in Sensor Networks, ACM Transactions on Sensor Networks, 1(1): 36-72, August 2005. Note: Extended version of the SenSys'03 paper on CCP. new!
O. Chipara, C. Lu and G.-C. Roman, Efficient Power Management based on Application Timing Semantics for Wireless Sensor Networks, International Conference on Distributed Computing Systems (ICDCS'05), June 2005. Note: One of 5 papers nominated for Best Paper Award (543 papers submitted).
G. Xing, C. Lu, Y. Zhang, Q. Huang and R. Pless, Minimum Power Configuration in Wireless Sensor Networks, ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'05), May 2005.
G. Xing, C. Lu, R. Pless and Q. Huang, On Greedy Geographic Routing Algorithms in Sensing-Covered Networks, ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'04), May 2004.
G. Xing, C. Lu, R. Pless and J.A. O'Sullivan, Co-Grid: An Efficient Coverage Maintenance Protocol for Distributed Sensor Networks, International Symposium on Information Processing in Sensor Networks (IPSN'04), April 2004.
X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless and C.D. Gill, Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks, First ACM Conference on Embedded Networked Sensor Systems (SenSys'03), November 2003.
Download
We implemented CCP in ns2 network simulator based on the SPAN protocol from MIT. The ns2 code of CCP can be downloaded at here. The code is close to (but not exactly is) the snapshot of our SenSys paper. The ns-2 code of SPAN can be downloaded at this website.
Last updated on 07/01/2006