Ants (2008)

Ants are common throughout the world in many different ecosystems. More than 450 species of ants live in North America. They are also one of the most common pests in human environments and dwellings. In human environments, they build nests in soil, open lawns, under concrete slabs, stones or boards, adjacent to foundation walls, in the walls of houses, in decaying wood, or in cavity spaces associated with debris. Some nests are relatively permanent while others last for a short period of time. Less than 50 species have been known to invade homes across the United States, and only about 10 of these commonly enter homes in Missouri.New 7/88 Revised 11/08/3M

Fast Dual-Radio Cross-Layer Handoffs in Multi-Hop Infrastructure-mode 802.11 Wireless Networks for In-Vehicle Multimedia Infotainment

Minimizing handoff latency and achieving near-zero packet loss is critical for delivering multimedia infotainment applications to fast-moving vehicles that are likely to encounter frequent handoffs. In this paper, we propose a dual-radio cross-layer handoff scheme for infrastructure-mode 802.11 Wireless Networks that achieve this goal. We present performance results of an implementation of our algorithm in a Linux-based On-Board-Unit prototype.Comment: Presented (oral) at IEEE Advanced Networking and Telecommunications, 2008 (ANTS 2008) Conference (http://www.antsconference.org) held at Indian Institute of Technology, Mumbai. Awarded Best Paper (Honorable Mention

Power considerations towards a sustainable pan-european network

Energy savings are observed and quantified in the Pan-European network using transparent optical network technology. The network was dimensioned, using realistic traffic predictions of the optical networking roadmap of the European project BONE

Software defined networking: meeting carrier grade requirements

Software Defined Networking is a networking paradigm which allows network operators to manage networking elements using software running on an external server. This is accomplished by a split in the architecture between the forwarding element and the control element. Two technologies which allow this split for packet networks are ForCES and Openflow. We present energy efficiency and resilience aspects of carrier grade networks which can be met by Openflow. We implement flow restoration and run extensive experiments in an emulated carrier grade network. We show that Openflow can restore traffic quite fast, but its dependency on a centralized controller means that it will be hard to achieve 50 ms restoration in large networks serving many flows. In order to achieve 50 ms recovery, protection will be required in carrier grade networks

Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while ?Ant-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman’s statistical test

Discovering Regression Rules with Ant Colony Optimization

The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible