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- All Subjects: Computer Science
- Genre: Doctoral Dissertation
- Resource Type: Text
- Status: Published
Description
The primary function of the medium access control (MAC) protocol is managing access to a shared communication channel. From the viewpoint of transmitters, the MAC protocol determines each transmitter's persistence, the fraction of time it is permitted to spend transmitting. Schedule-based schemes implement stable persistences, achieving low variation in delay and throughput, and sometimes bounding maximum delay. However, they adapt slowly, if at all, to changes in the network. Contention-based schemes are agile, adapting quickly to changes in perceived contention, but suffer from short-term unfairness, large variations in packet delay, and poor performance at high load. The perfect MAC protocol, it seems, embodies the strengths of both contention- and schedule-based approaches while avoiding their weaknesses. This thesis culminates in the design of a Variable-Weight and Adaptive Topology Transparent (VWATT) MAC protocol. The design of VWATT first required answers for two questions: (1) If a node is equipped with schedules of different weights, which weight should it employ? (2) How is the node to compute the desired weight in a network lacking centralized control? The first question is answered by the Topology- and Load-Aware (TLA) allocation which defines target persistences that conform to both network topology and traffic load. Simulations show the TLA allocation to outperform IEEE 802.11, improving on the expectation and variation of delay, throughput, and drop rate. The second question is answered in the design of an Adaptive Topology- and Load-Aware Scheduled (ATLAS) MAC that computes the TLA allocation in a decentralized and adaptive manner. Simulation results show that ATLAS converges quickly on the TLA allocation, supporting highly dynamic networks. With these questions answered, a construction based on transversal designs is given for a variable-weight topology transparent schedule that allows nodes to dynamically and independently select weights to accommodate local topology and traffic load. The schedule maintains a guarantee on maximum delay when the maximum neighbourhood size is not too large. The schedule is integrated with the distributed computation of ATLAS to create VWATT. Simulations indicate that VWATT offers the stable performance characteristics of a scheduled MAC while adapting quickly to changes in topology and traffic load.
ContributorsLutz, Jonathan (Author) / Colbourn, Charles J (Thesis advisor) / Syrotiuk, Violet R. (Thesis advisor) / Konjevod, Goran (Committee member) / Lloyd, Errol L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation studies routing in small-world networks such as grids plus long-range edges and real networks. Kleinberg showed that geography-based greedy routing in a grid-based network takes an expected number of steps polylogarithmic in the network size, thus justifying empirical efficiency observed beginning with Milgram. A counterpart for the grid-based model is provided; it creates all edges deterministically and shows an asymptotically matching upper bound on the route length. The main goal is to improve greedy routing through a decentralized machine learning process. Two considered methods are based on weighted majority and an algorithm of de Farias and Megiddo, both learning from feedback using ensembles of experts. Tests are run on both artificial and real networks, with decentralized spectral graph embedding supplying geometric information for real networks where it is not intrinsically available. An important measure analyzed in this work is overpayment, the difference between the cost of the method and that of the shortest path. Adaptive routing overtakes greedy after about a hundred or fewer searches per node, consistently across different network sizes and types. Learning stabilizes, typically at overpayment of a third to a half of that by greedy. The problem is made more difficult by eliminating the knowledge of neighbors' locations or by introducing uncooperative nodes. Even under these conditions, the learned routes are usually better than the greedy routes. The second part of the dissertation is related to the community structure of unannotated networks. A modularity-based algorithm of Newman is extended to work with overlapping communities (including considerably overlapping communities), where each node locally makes decisions to which potential communities it belongs. To measure quality of a cover of overlapping communities, a notion of a node contribution to modularity is introduced, and subsequently the notion of modularity is extended from partitions to covers. The final part considers a problem of network anonymization, mostly by the means of edge deletion. The point of interest is utility preservation. It is shown that a concentration on the preservation of routing abilities might damage the preservation of community structure, and vice versa.
ContributorsBakun, Oleg (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andrea (Thesis advisor) / Syrotiuk, Violet R. (Committee member) / Czygrinow, Andrzej (Committee member) / Arizona State University (Publisher)
Created2011
Description
Mobile ad hoc networks (MANETs) have attracted attention for mission critical applications. This dissertation investigates techniques of statistical monitoring and control for overhead reduction in a proactive MANET routing protocol. Proactive protocols transmit overhead periodically. Instead, we propose that the local conditions of a node should determine this transmission decision. While the goal is to minimize overhead, a balance in the amount of overhead transmitted and the performance achieved is required. Statistical monitoring consists of techniques to determine if a characteristic has shifted away from an in-control state. A basic tool for monitoring is a control chart, a time-oriented representation of the characteristic. When a sample deviates outside control limits, a significant change has occurred and corrective actions are required to return to the in-control state. We investigate the use of statistical monitoring of local conditions in the Optimized Link State Routing (OLSR) protocol. Three versions are developed. In A-OLSR, each node uses a Shewhart chart to monitor betweenness of its two-hop neighbourhood. Betweenness is a social network metric that measures a node's influence; betweenness is larger when a node has more influence. Changes in topology are associated with changes in betweenness. We incorporate additional local node conditions including speed, density, packet arrival rate, and number of flows it forwards in A+-OLSR. Response Surface Methodology (RSM) is used to optimize timer values. As well, the Shewhart chart is replaced by an Exponentially Weighted Moving Average (EWMA) chart, which is more sensitive to small changes in the characteristic. It is known that control charts do not work as well in the presence of correlation. Hence, in A*-OLSR the autocorrelation in the time series is removed and an Auto-Regressive Integrated Moving Average (ARIMA) model found; this removes the dependence on node speed. A*-OLSR also extends monitoring to two characteristics concurrently using multivariate cumulative sum (MCUSUM) charts. The protocols are evaluated in simulation, and compared to OLSR and its variants. The techniques for statistical monitoring and control are general and have great potential to be applied to the adaptive control of many network protocols.
ContributorsShaukat, Kahkashan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Colbourn, Charles J (Committee member) / Montgomery, Douglas C. (Committee member) / Sarjoughian, Hessam S. (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
Many applications require efficient data routing and dissemination in Delay Tolerant Networks (DTNs) in order to maximize the throughput of data in the network, such as providing healthcare to remote communities, and spreading related information in Mobile Social Networks (MSNs). In this thesis, the feasibility of using boats in the Amazon Delta Riverine region as data mule nodes is investigated and a robust data routing algorithm based on a fountain code approach is designed to ensure fast and timely data delivery considering unpredictable boat delays, break-downs, and high transmission failures. Then, the scenario of providing healthcare in Amazon Delta Region is extended to a general All-or-Nothing (Splittable) Multicommodity Flow (ANF) problem and a polynomial time constant approximation algorithm is designed for the maximum throughput routing problem based on a randomized rounding scheme with applications to DTNs. In an MSN, message content is closely related to users’ preferences, and can be used to significantly impact the performance of data dissemination. An interest- and content-based algorithm is developed where the contents of the messages, along with the network structural information are taken into consideration when making message relay decisions in order to maximize data throughput in an MSN. Extensive experiments show the effectiveness of the above proposed data dissemination algorithm by comparing it with state-of-the-art techniques.
ContributorsLiu, Mengxue (Author) / Richa, Andréa W. (Thesis advisor) / Johnson, Thienne (Committee member) / Syrotiuk, Violet R. (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation studies three classes of combinatorial arrays with practical applications in testing, measurement, and security. Covering arrays are widely studied in software and hardware testing to indicate the presence of faulty interactions. Locating arrays extend covering arrays to achieve identification of the interactions causing a fault by requiring additional conditions on how interactions are covered in rows. This dissertation introduces a new class, the anonymizing arrays, to guarantee a degree of anonymity by bounding the probability a particular row is identified by the interaction presented. Similarities among these arrays lead to common algorithmic techniques for their construction which this dissertation explores. Differences arising from their application domains lead to the unique features of each class, requiring tailoring the techniques to the specifics of each problem.
One contribution of this work is a conditional expectation algorithm to build covering arrays via an intermediate combinatorial object. Conditional expectation efficiently finds intermediate-sized arrays that are particularly useful as ingredients for additional recursive algorithms. A cut-and-paste method creates large arrays from small ingredients. Performing transformations on the copies makes further improvements by reducing redundancy in the composed arrays and leads to fewer rows.
This work contains the first algorithm for constructing locating arrays for general values of $d$ and $t$. A randomized computational search algorithmic framework verifies if a candidate array is $(\bar{d},t)$-locating by partitioning the search space and performs random resampling if a candidate fails. Algorithmic parameters determine which columns to resample and when to add additional rows to the candidate array. Additionally, analysis is conducted on the performance of the algorithmic parameters to provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both.
This work proposes anonymizing arrays as a class related to covering arrays with a higher coverage requirement and constraints. The algorithms for covering and locating arrays are tailored to anonymizing array construction. An additional property, homogeneity, is introduced to meet the needs of attribute-based authorization. Two metrics, local and global homogeneity, are designed to compare anonymizing arrays with the same parameters. Finally, a post-optimization approach reduces the homogeneity of an anonymizing array.
One contribution of this work is a conditional expectation algorithm to build covering arrays via an intermediate combinatorial object. Conditional expectation efficiently finds intermediate-sized arrays that are particularly useful as ingredients for additional recursive algorithms. A cut-and-paste method creates large arrays from small ingredients. Performing transformations on the copies makes further improvements by reducing redundancy in the composed arrays and leads to fewer rows.
This work contains the first algorithm for constructing locating arrays for general values of $d$ and $t$. A randomized computational search algorithmic framework verifies if a candidate array is $(\bar{d},t)$-locating by partitioning the search space and performs random resampling if a candidate fails. Algorithmic parameters determine which columns to resample and when to add additional rows to the candidate array. Additionally, analysis is conducted on the performance of the algorithmic parameters to provide guidance on how to tune parameters to prioritize speed, accuracy, or a combination of both.
This work proposes anonymizing arrays as a class related to covering arrays with a higher coverage requirement and constraints. The algorithms for covering and locating arrays are tailored to anonymizing array construction. An additional property, homogeneity, is introduced to meet the needs of attribute-based authorization. Two metrics, local and global homogeneity, are designed to compare anonymizing arrays with the same parameters. Finally, a post-optimization approach reduces the homogeneity of an anonymizing array.
ContributorsLanus, Erin (Author) / Colbourn, Charles J (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Montgomery, Douglas C. (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2019
Description
Modern software and hardware systems are composed of a large number of components. Often different components of a system interact with each other in unforeseen and undesired ways to cause failures. Covering arrays are a useful mathematical tool for testing all possible t-way interactions among the components of a system.
The two major issues concerning covering arrays are explicit construction of a covering array, and exact or approximate determination of the covering array number---the minimum size of a covering array. Although these problems have been investigated extensively for the last couple of decades, in this thesis we present significant improvements on both of these questions using tools from the probabilistic method and randomized algorithms.
First, a series of improvements is developed on the previously known upper bounds on covering array numbers. An estimate for the discrete Stein-Lovász-Johnson bound is derived and the Stein- Lovász -Johnson bound is improved upon using an alteration strategy. Then group actions on the set of symbols are explored to establish two asymptotic upper bounds on covering array numbers that are tighter than any of the presently known bounds.
Second, an algorithmic paradigm, called the two-stage framework, is introduced for covering array construction. A number of concrete algorithms from this framework are analyzed, and it is shown that they outperform current methods in the range of parameter values that are of practical relevance. In some cases, a reduction in the number of tests by more than 50% is achieved.
Third, the Lovász local lemma is applied on covering perfect hash families to obtain an upper bound on covering array numbers that is tightest of all known bounds. This bound leads to a Moser-Tardos type algorithm that employs linear algebraic computation over finite fields to construct covering arrays. In some cases, this algorithm outperforms currently used methods by more than an 80% margin.
Finally, partial covering arrays are introduced to investigate a few practically relevant relaxations of the covering requirement. Using probabilistic methods, bounds are obtained on partial covering arrays that are significantly smaller than for covering arrays. Also, randomized algorithms are provided that construct such arrays in expected polynomial time.
The two major issues concerning covering arrays are explicit construction of a covering array, and exact or approximate determination of the covering array number---the minimum size of a covering array. Although these problems have been investigated extensively for the last couple of decades, in this thesis we present significant improvements on both of these questions using tools from the probabilistic method and randomized algorithms.
First, a series of improvements is developed on the previously known upper bounds on covering array numbers. An estimate for the discrete Stein-Lovász-Johnson bound is derived and the Stein- Lovász -Johnson bound is improved upon using an alteration strategy. Then group actions on the set of symbols are explored to establish two asymptotic upper bounds on covering array numbers that are tighter than any of the presently known bounds.
Second, an algorithmic paradigm, called the two-stage framework, is introduced for covering array construction. A number of concrete algorithms from this framework are analyzed, and it is shown that they outperform current methods in the range of parameter values that are of practical relevance. In some cases, a reduction in the number of tests by more than 50% is achieved.
Third, the Lovász local lemma is applied on covering perfect hash families to obtain an upper bound on covering array numbers that is tightest of all known bounds. This bound leads to a Moser-Tardos type algorithm that employs linear algebraic computation over finite fields to construct covering arrays. In some cases, this algorithm outperforms currently used methods by more than an 80% margin.
Finally, partial covering arrays are introduced to investigate a few practically relevant relaxations of the covering requirement. Using probabilistic methods, bounds are obtained on partial covering arrays that are significantly smaller than for covering arrays. Also, randomized algorithms are provided that construct such arrays in expected polynomial time.
ContributorsSarakāra, Kauśika (Author) / Colbourn, Charles J. (Thesis advisor) / Czygrinow, Andrzej (Committee member) / Richa, Andréa W. (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Today, many wireless networks are single-channel systems. However, as the interest in wireless services increases, the contention by nodes to occupy the medium is more intense and interference worsens. One direction with the potential to increase system throughput is multi-channel systems. Multi-channel systems have been shown to reduce collisions and increase concurrency thus producing better bandwidth usage. However, the well-known hidden- and exposed-terminal problems inherited from single-channel systems remain, and a new channel selection problem is introduced. In this dissertation, Multi-channel medium access control (MAC) protocols are proposed for mobile ad hoc networks (MANETs) for nodes equipped with a single half-duplex transceiver, using more sophisticated physical layer technologies. These include code division multiple access (CDMA), orthogonal frequency division multiple access (OFDMA), and diversity. CDMA increases channel reuse, while OFDMA enables communication by multiple users in parallel. There is a challenge to using each technology in MANETs, where there is no fixed infrastructure or centralized control. CDMA suffers from the near-far problem, while OFDMA requires channel synchronization to decode the signal. As a result CDMA and OFDMA are not yet widely used. Cooperative (diversity) mechanisms provide vital information to facilitate communication set-up between source-destination node pairs and help overcome limitations of physical layer technologies in MANETs. In this dissertation, the Cooperative CDMA-based Multi-channel MAC (CCM-MAC) protocol uses CDMA to enable concurrent transmissions on each channel. The Power-controlled CDMA-based Multi-channel MAC (PCC-MAC) protocol uses transmission power control at each node and mitigates collisions of control packets on the control channel by using different sizes of the spreading factor to have different processing gains for the control signals. The Cooperative Dual-access Multi-channel MAC (CDM-MAC) protocol combines the use of OFDMA and CDMA and minimizes channel interference by a resolvable balanced incomplete block design (BIBD). In each protocol, cooperating nodes help reduce the incidence of the multi-channel hidden- and exposed-terminal and help address the near-far problem of CDMA by supplying information. Simulation results show that each of the proposed protocols achieve significantly better system performance when compared to IEEE 802.11, other multi-channel protocols, and another protocol CDMA-based.
ContributorsMoon, Yuhan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Huang, Dijiang (Committee member) / Reisslein, Martin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2010
Description
Arc Routing Problems (ARPs) are a type of routing problem that finds routes of minimum total cost covering the edges or arcs in a graph representing street or road networks. They find application in many essential services such as residential waste collection, winter gritting, and others. Being NP-hard, solutions are usually found using heuristic methods. This dissertation contributes to heuristics for ARP, with a focus on the Capacitated Arc Routing Problem (CARP) with additional constraints. In operations such as residential waste collection, vehicle breakdown disruptions occur frequently. A new variant Capacitated Arc Re-routing Problem for Vehicle Break-down (CARP-VB) is introduced to address the need to re-route using only remaining vehicles to avoid missing services. A new heuristic Probe is developed to solve CARP-VB. Experiments on benchmark instances show that Probe is better in reducing the makespan and hence effective in reducing delays and avoiding missing services.
In addition to total cost, operators are also interested in solutions that are attractive, that is, routes that are contiguous, compact, and non-overlapping to manage the work. Operators may not adopt a solution that is not attractive even if it is optimum. They are also interested in solutions that are balanced in workload to meet equity requirements. A new multi-objective memetic algorithm, MA-ABC is developed, that optimizes three objectives: Attractiveness, makespan, and total cost. On testing with benchmark instances, MA-ABC was found to be effective in providing attractive and balanced route solutions without affecting the total cost.
Changes in the problem specification such as demand and topology occurs frequently in business operations. Machine learning be applied to learn the distribution behind these changes and generate solutions quickly at time of inference. Splice is a machine learning framework for CARP that generates closer to optimum solutions quickly using a graph neural network and deep Q-learning. Splice can solve several variants of node and arc routing problems using the same architecture without any modification. Splice was trained and tested using randomly generated instances. Splice generated solutions faster that are also better in comparison to popular metaheuristics.
ContributorsRamamoorthy, Muhilan (Author) / Syrotiuk, Violet R. (Thesis advisor) / Forrest, Stephanie (Committee member) / Mirchandani, Pitu (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2022
Description
Complex systems are pervasive in science and engineering. Some examples include complex engineered networks such as the internet, the power grid, and transportation networks. The complexity of such systems arises not just from their size, but also from their structure, operation (including control and management), evolution over time, and that people are involved in their design and operation. Our understanding of such systems is limited because their behaviour cannot be characterized using traditional techniques of modelling and analysis.
As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant on a response of a system. However, traditional approaches for screening are ineffective for complex systems because of the size of the experimental design. Consequently, the factors considered are often restricted, but this automatically restricts the interactions that may be identified as well. Alternatively, the designs are restricted to only identify main effects, but this then fails to consider any possible interactions of the factors.
To address this problem, a specific combinatorial design termed a locating array is proposed as a screening design for complex systems. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs.
As a proof-of-concept, a locating array is applied to screen for main effects and low-order interactions on the response of average transport control protocol (TCP) throughput in a simulation model of a mobile ad hoc network (MANET). A MANET is a collection of mobile wireless nodes that self-organize without the aid of any centralized control or fixed infrastructure. The full-factorial design for the MANET considered is infeasible (with over 10^{43} design points) yet a locating array has only 421 design points.
In conjunction with the locating array, a ``heavy hitters'' algorithm is developed to identify the influential main effects and two-way interactions, correcting for the non-normal distribution of the average throughput, and uneven coverage of terms in the locating array. The significance of the identified main effects and interactions is validated independently using the statistical software JMP.
The statistical characteristics used to evaluate traditional screening designs are also applied to locating arrays.
These include the matrix of covariance, fraction of design space, and aliasing, among others. The results lend additional support to the use of locating arrays as screening designs.
The use of locating arrays as screening designs for complex engineered systems is promising as they yield useful models. This facilitates quantitative evaluation of architectures and protocols and contributes to our understanding of complex engineered networks.
As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant on a response of a system. However, traditional approaches for screening are ineffective for complex systems because of the size of the experimental design. Consequently, the factors considered are often restricted, but this automatically restricts the interactions that may be identified as well. Alternatively, the designs are restricted to only identify main effects, but this then fails to consider any possible interactions of the factors.
To address this problem, a specific combinatorial design termed a locating array is proposed as a screening design for complex systems. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs.
As a proof-of-concept, a locating array is applied to screen for main effects and low-order interactions on the response of average transport control protocol (TCP) throughput in a simulation model of a mobile ad hoc network (MANET). A MANET is a collection of mobile wireless nodes that self-organize without the aid of any centralized control or fixed infrastructure. The full-factorial design for the MANET considered is infeasible (with over 10^{43} design points) yet a locating array has only 421 design points.
In conjunction with the locating array, a ``heavy hitters'' algorithm is developed to identify the influential main effects and two-way interactions, correcting for the non-normal distribution of the average throughput, and uneven coverage of terms in the locating array. The significance of the identified main effects and interactions is validated independently using the statistical software JMP.
The statistical characteristics used to evaluate traditional screening designs are also applied to locating arrays.
These include the matrix of covariance, fraction of design space, and aliasing, among others. The results lend additional support to the use of locating arrays as screening designs.
The use of locating arrays as screening designs for complex engineered systems is promising as they yield useful models. This facilitates quantitative evaluation of architectures and protocols and contributes to our understanding of complex engineered networks.
ContributorsAldaco-Gastelum, Abraham Netzahualcoyotl (Author) / Syrotiuk, Violet R. (Thesis advisor) / Colbourn, Charles J. (Committee member) / Sen, Arunabha (Committee member) / Montgomery, Douglas C. (Committee member) / Arizona State University (Publisher)
Created2015