Abstract:
In wireless networks, conditions may change rapidly and unpredictably. Often wireless networks are not designed to adapt to these changing conditions and perform poorly when they become congested. The multi-hop broadcast nature of wireless mesh networks amplifies the problem of poor wireless performance. Mixed bias scheduling has previously been applied successfully to wireless mesh networks however, it still suffers from similar problems when conditions change rapidly. In this work we propose an adaptive mixed bias (AMB) algorithm which uses a tabu search approach to change based on delay and dropped packets in the network. The proposed scheduling approach consists of three important algorithms, namely, the tabu search algorithm, move generation, and utility function. The adaptive mixed bias approach is compared against IEEE 802.11 and the non-adaptive mixed bias approach. The performance is evaluated using the packet delivery ratio and average end-to-end delay metrics.

Here are the slides from the talk: BWCCA-NGWMN2010-final (pdf)
and here is the link to the pdf from the conference: Adaptive Mixed Bias Resource Allocation for Wireless Mesh Networks (pfd)

While I don’t have a video for you to watch the entire presentation, I do have some pictures, and I’ll post the paper and slides I presented in case any one is interested. If you have any questions feel free to post on here or email me directly and I’ll be happy to answer any of them.

J.B. Ernst and M.K. Denko, “Cross-Layer Mixed Bias Scheduling for Wireless Mesh Networks,” in Proc. IEEE International Conference on Communications (ICC), Cape Town, South Africa, 2010.

Abstract—In this paper we propose a mixed bias approach which makes use of cross layer optimization. The cross-layer parameters are based on conditions in the network from multiple layers and are used to determine resource and time allocation for nodes in the network. Unlike existing proposals, we propose to bias against several parameters such as link quality and queue size in addition to node distance. We also propose a combined mixed bias approach which takes into account multiple parameters together. The scheme is evaluated using simulation experiments. The performance results are reported in this paper.

Download the paper (pdf)
Download the presentation slides (pdf)