A greedy ant colony forwarding algorithm for Named Data Networking

The Named Data Networking (NDN) is a newly proposed Internet architecture based on Content-Centric Networking, which transforms data, instead of hosts, into a first-class entity. However, one of the major challenges is supporting intelligent forwarding of Interests over multiple paths while allowing an unbounded name space. To address this challenge, this paper proposes a Greedy Ant Colony Forwarding (GACF) algorithm which uses the ISP-based aggregation to reduce the content naming space. There are two kinds of ants in GACF. One is Hello Ant which is used to discover the all possible paths and optimize them; the other is Normal Ant which is used to get data and reinforce the optimization of the paths simultaneously. The GACF algorithm is a Quality of Service aware forwarding algorithm. It adaptively reduces the impacts incited by the dynamic complex network

An Improved Mathematical Formulation For the Carbon Capture and Storage (CCS) Problem

abstract: Carbon Capture and Storage (CCS) is a climate stabilization strategy that prevents CO2 emissions from entering the atmosphere. Despite its benefits, impactful CCS projects require large investments in infrastructure, which could deter governments from implementing this strategy. In this sense, the development of innovative tools to support large-scale cost-efficient CCS deployment decisions is critical for climate change mitigation. This thesis proposes an improved mathematical formulation for the scalable infrastructure model for CCS (SimCCS), whose main objective is to design a minimum-cost pipe network to capture, transport, and store a target amount of CO2. Model decisions include source, reservoir, and pipe selection, as well as CO2 amounts to capture, store, and transport. By studying the SimCCS optimal solution and the subjacent network topology, new valid inequalities (VI) are proposed to strengthen the existing mathematical formulation. These constraints seek to improve the quality of the linear relaxation solutions in the branch and bound algorithm used to solve SimCCS. Each VI is explained with its intuitive description, mathematical structure and examples of resulting improvements. Further, all VIs are validated by assessing the impact of their elimination from the new formulation. The validated new formulation solves the 72-nodes Alberta problem up to 7 times faster than the original model. The upgraded model reduces the computation time required to solve SimCCS in 72% of randomly generated test instances, solving SimCCS up to 200 times faster. These formulations can be tested and then applied to enhance variants of the SimCCS and general fixed-charge network flow problems. Finally, an experience from testing a Benders decomposition approach for SimCCS is discussed and future scope of probable efficient solution-methods is outlined.Dissertation/ThesisMasters Thesis Industrial Engineering 201