Topics in Routing and Network Coding for Wireless Networks

This dissertation presents topics in routing and network coding for wireless networks. We present a multipurpose multipath routing mechanism. We propose an efficient packet encoding algorithm that can easily integrate a routing scheme with network coding. We also discuss max-min fair rate allocation and scheduling algorithms for the flows in a wireless network that utilizes coding. We propose Polar Coordinate Routing (PCR) to create multiple paths between a source and a destination in wireless networks. Our scheme creates paths that are circular segments of different radii connecting source-destination pairs. We propose a non-euclidean distance metric that allows messages to travel along these paths. Using PCR it is possible to maintain a known separation among the paths, which reduces the interference between the nodes belonging to two separate routes. Our extensive simulations show that while PCR achieves a known separation between the routes, it does so with a small increase in overall hop count. Moreover, we demonstrate that the variances of average separation and hop count are lower for the paths created using PCR compared to the existing schemes, indicating a more reliable system. Existing multipath routing schemes in wireless networks do not perform well in the areas with obstacles or low node density. To overcome adverse areas in a network, we integrate PCR with simple robotic routing, which lets a message circumnavigate an obstacle and follow the multipath trajectory to the destination as soon as the obstacle is passed. Next we propose an efficient packet encoding algorithm to integrate a routing scheme with network coding. Note that this packet encoding algorithm is not dependent on PCR. In fact it can be coupled with any routing scheme in order to leverage the benefits offered by both an advanced routing scheme and an enhanced packet encoding algorithm. Our algorithm, based on bipartite graphs, lets a node exhaustively search its queue to identify the maximum set of packets that can be combined in a single transmission. We extend this algorithm to consider multiple next hop neighbors for a packet while searching for an optimal packet combination, which improves the likelihood of combining more packets in a single transmission. Finally, we propose an algorithm to assign max-min fair rates to the flows in a wireless network that utilizes coding. We demonstrate that when a network uses coding, a direct application of conventional progressive filling algorithm to achieve max-min fairness may yield incorrect or suboptimal results. To emulate progressive filling correctly for a wireless networks with coding, we couple a conflict graph based framework with a linear program. Our model helps us directly select a bottleneck flow at each iteration of the algorithm, eliminating the need of gradually increasing the rates of the flows until a bottleneck is found. We demonstrate the caveats in selecting the bottleneck flows and setting up transmission scheduling constraints in order to avoid suboptimal results. We first propose a centralized fair rate allocation algorithm assuming the global knowledge of the network. We also present a novel yet simple distributed algorithm that achieves the same results as the centralized algorithm. We also present centralized as well as distributed scheduling algorithms that help flows achieve their fair rates. We run our rate allocation algorithm on various topologies. We use various fairness metrics to show that our rate allocation algorithm outperforms existing algorithms (based on network utility maximization) in terms of fairness

A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Genetic Programming to Optimise 3D Trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTrajectory optimisation is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on non-intersection with any obstacles as well as predefined performance metrics. In the context of UAVs, the goal is to minimise the cost of the route, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques including evolutionary computation have been applied to trajectory optimisation with various degrees of success. This thesis explores the use of genetic programming (GP) to optimise trajectories in 3D space, by encoding 3D geographic trajectories as syntax trees representing a curve. A comprehensive review of the relevant literature is presented, covering the theory and techniques of GP, as well as the principles and challenges of 3D trajectory optimisation. The main contribution of this work is the development and implementation of a novel GP algorithm using function trees to encode 3D geographical trajectories. The trajectories are validated and evaluated using a realworld dataset and multiple objectives. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness. Finally, insights and recommendations for future research in this area are provided, highlighting the potential for GP to be applied to other complex optimisation problems in engineering and science

Multimodal Route Planning Algorithm for Encouraging the Usage of Different Means of Public Transportation

Jätkuv linnastumine ja linnade kasv muudab ka linnasisese teekonna planeerimise aina keerulisemaks.. Tihti pole võimalik reisida ühest punktist teise, kasutades ainult üht transpordiliiki. Veelgi enam, juhul, kui kasutajal on spetsiifilisi eelistusi, nagu soov võtta ühistransporti kaasa ratastool, lapsevanker või jalgratas, kindlat tüüpi ühistranspordivahendi kasutamine (näiteks ratastoolisõbralik buss) on tarvilik. Sellest olenemata kalduvad olemasolevad teekonnaplaneerimise mootorid suurel määral eelistama esimest suvalist tüüpi ühistranspordi reisi, kui see vastab aegruumilistele nõudmistele, selle asemel, et kinni pidada kasutaja poolt valitud ühistranspordi liikidest. Käesoleva lõputöö eesmärk on pakkuda välja alternatiivne meetod multimodaalseks teekonnaplaneerimiseks, mis kasutaks ainult neid ühistranspordiliike, mis on kasutaja poolt lubatud. Selles lõputöös on alternatiivne kiireima multimodaalse teekonna leidmise meetod, mis kasutab ühistransporti, on arendatud. See on võimeline pakkuma konkurentsivõimelisi alternatiive olemasolevate teekonnaleidmise otsingumootorite poolt pakutud lahendustele, samal ajal kasutades vaid neid ühistranspordiliike, mis on kasutaja poolt lubatud.The ongoing urbanization and the growth of the cities is leading to the increase of complexity of the route planning in urban areas. Often it is not possible or feasible to travel from one location to another using only one mode of transportation. Moreover, in case of specific preferences like taking a wheelchair, baby carriage or a bicycle in the mean of public transport, a specific type of mean of transport (e.g. wheelchair-accessible bus) is needed. However, the existing routing engines tend to heavily prefer the first public transport trip of any mean of public transport that meets the spatiotemporal conditions instead of sticking to user’s selected modes.The aim of this thesis is to propose an alternative method for multimodal route planning, using only the modes and means of public transport that are allowed by the user. In this thesis work an alternative method for multimodal fastest pathfinding with use of public transportation is developed. It is able to propose competitive alternatives to the results of the existing routing engines at the same time using only the modes and means of public transport that are allowed by the user

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Hybrid metaheuristics for solving multi-depot pickup and delivery problems

In today's logistics businesses, increasing petrol prices, fierce competition, dynamic business environments and volume volatility put pressure on logistics service providers (LSPs) or third party logistics providers (3PLs) to be efficient, differentiated, adaptive, and horizontally collaborative in order to survive and remain competitive. In this climate, efficient computerised-decision support tools play an essential role. Especially, for freight transportation, e efficiently solving a Pickup and Delivery Problem (PDP) and its variants by an optimisation engine is the core capability required in making operational planning and decisions. For PDPs, it is required to determine minimum-cost routes to serve a number of requests, each associated with paired pickup and delivery points. A robust solution method for solving PDPs is crucial to the success of implementing decision support tools, which are integrated with Geographic Information System (GIS) and Fleet Telematics so that the flexibility, agility, visibility and transparency are fulfilled. If these tools are effectively implemented, competitive advantage can be gained in the area of cost leadership and service differentiation. In this research, variants of PDPs, which multiple depots or providers are considered, are investigated. These are so called Multi-depot Pickup and Delivery Problems (MDPDPs). To increase geographical coverage, continue growth and encourage horizontal collaboration, efficiently solving the MDPDPs is vital to operational planning and its total costs. This research deals with designing optimisation algorithms for solving a variety of real-world applications. Mixed Integer Linear Programming (MILP) formulations of the MDPDPs are presented. Due to being NP-hard, the computational time for solving by exact methods becomes prohibitive. Several metaheuristics and hybrid metaheuristics are investigated in this thesis. The extensive computational experiments are carried out to demonstrate their speed, preciseness and robustness.Open Acces

Quantum annealing for vehicle routing and scheduling problems

Metaheuristic approaches to solving combinatorial optimization problems have many attractions. They sidestep the issue of combinatorial explosion; they return good results; they are often conceptually simple and straight forward to implement. There are also shortcomings. Optimal solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of experimentation; and conceptual differences between metaheuristics make absolute comparisons of performance difficult. There is also the difficulty of configuration of the algorithm - the process of identifying precise values for the parameters which control the optimization process. Quantum annealing is a metaheuristic which is the quantum counterpart of the well known classical Simulated Annealing algorithm for combinatorial optimization problems. This research investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult problem of practical significance within industries such as logistics and workforce scheduling. The work devises spin encoding schemes for routing and scheduling problem domains, enabling an effective quantum annealing algorithm which locates new solutions to widely used benchmarks. The performance of the metaheuristic is further improved by the development of an enhanced tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further enhanced by taking advantage of multiprocessor environments, using threading techniques to parallelize the optimization workload. The work also shows quantum annealing applied successfully in an industrial setting to generate solutions to complex scheduling problems, results which created extra savings over an incumbent optimization technique. Components of the intellectual property rendered in this latter effort went on to secure a patent-protected status