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PI: Anita Raja
Collaborators: Dr. Linda Xie, ECE, UNCC; Professor Ivan Howitt, UNCC.
RA: Shanjun Cheng
Sponsor: NSF
This research investigates cooperative resource management in WLAN (wireless local area networks) /WPAN (wireless personal area networks) interference environments. The objective of this research is to manage shared system resources fairly among multiple WLANs to optimize the overall performance. Expected results from the project will have a significant impact on next generation WLAN network management based on employing algorithms of agent interaction and coordination to facilitate resource management, predictive models for parameter estimation, and dynamic load balancing algorithms.
A multi-agent system-based approach is used to achieve information sharing and decision distribution among multiple WLANs in a distributed manner.
Figure 1: WLAN as a Multiagent system [Xie07b] Figure 2: Block diagram of physical envoronment prediction and agent operations [Xie07b]
This research maps the WLAN resource management problem as a distributed constraint optimization problem (DCOP) as shown in Figure 2. It studies the effectiveness of DCOP algorithms to find the optimal resource assignment through communications between distributed agents.
A simple WLAN scenario(M:Mobile Station, A: Access Point
We extend Petcu's DPOP algorithm, a distributed constraint optimization algorithm to solve the mapped WLAN resource allocation problem. DPOP is a Utility Propagation algorithm based on dynamic programming , it requires a linear number of messages.
Figure 3: 8-APs WLAN Scenario [Cheng09] Figure 4: Pseudo-tree for the WLAN scenario in Figure 3 [Cheng09]
Figure 5: Perturbation at AP1[Cheng09] Figure 6: Repaired Pseudo-tree where edge AP1-AP4 is removed [Cheng09]
We developed a multi-agent approach for decentralized load balancing in WLANs. This approach uses DLB-SDPOP [Cheng09], a constraint optimization algorithm to determine the optimal allocation of MSs under each AP. DLB-SDPOP dynamically repairs the affected nodes in the original pseudo-tree retaining the topology and states of unaffected nodes when inconsistency is detected.
Figure 7: Simulation Scenario [Cheng09]
Figure 8: DLB-SDPOP Compared with other DCOP algorithms [Cheng10]
Co-PIs: Anita Raja (MAS focus), Professor Linda Xie (ECE), Professor Ivan Howitt (ECE)
RAs: Shanjun Cheng, James Rozi
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Distributed Artificial Intelligence Research Lab
9201 University City Blvd.
Charlotte, NC 28223
Dr. Anita Raja
Office: Woodward 310D
Phone: 704‑687‑8651
Fax: 704‑687‑4893