IEEEE CCECE 2014 and Cognitive Agent Simulation

This past week I attended IEEE Canadian Conference on Electrical and Computer Engineering (CCECE2014) in Toronto, Canada. I was there because of two papers I was a co-author on. The work was part of a side project I was involved in on cognitive agent simulation. The idea was inspired by the observation that in the spring time, many newborn animals are struck as they cross the street. Later in the year, it seems that fewer animals meet this fate. So have the animals which survived observed the doomed creates being struck, learned something about the environment, and managed to become more intelligent? We aimed to model this type of environment, and then re-create the most basic intelligence to try to replicate this behaviour with a cognitive agent.

I was responsible for implementing the simulation tool and the naiive learning algorithm which we also presented at the conference. The simulator was created in c/c++ and was designed in such a way that later on the intelligence algorithms could be swapped out, so that we could also experiment with more sophisticated learning algorithms.

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BWCCA 2010 – Adaptive Mixed Bias Resource Allocation for Wireless Mesh Networks

Today I presented a recent paper on “Adaptive Mixed Bias Resource Allocation for Wireless Mesh Networks” at the BWCCA conference in Fukuoka Japan. The paper is authored by myself and Thabo Nkwe from the University of Guelph. The abstract is below:

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)