Department of Computer Science and Engineering

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4-4, 1-4: A Novel architecture for data center networks and its performance study
(2016) Kumar, Ashk A.R.
The advancements in the Internet technologies, changed the service delivery model from in house delivery to delivery through Cloud. This cost effective service delivery created a greater demand for cloud and thus the cloud infrastructure. The major cloud service providers such as Google, IBM, Microsoft use data centers as central resource for their cloud computing. Therefore, data center is defined as a central repository of computing, storage and networking for storing and processing large data and information that can be accessed globally. With data center hosting millions of servers, it faces challenges such as reliability, availability, maintainability and safety. One of the major challenges in the design of data center is to combine very large number of servers into a single network fabric called data center network (DCN) and design protocols that addresses the growing needs of data centers. The major advantages in the design of protocols for DCNs are due the proprietorship of data centers. Since, data centers are private, they can have more controlled structure for the DCNs and often does not face interoperability problem. This leads to many designs proposed for DCNs addressing various aspects of data centers. In our first contribution, we propose a new architecture for DCN called 4-4, 1-4, based on IP address hierarchy to overcome the short comings in the earlier location based routing. In our second contribution we studied performance of 4-4, 1-4 for energy efficiency. In third contribution we proposed packet scheduler for meeting the dealines for the flows. In fourth and last contribution we proposed a redesign of modular data center networks for efficiency.
Adaptive Resource Allocation for Faster Formation of 6TiSCH IoT Network
(2022) Kalita, Alakesh
The IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) network is standardized to meet high reliability, end-to-end latency, and network lifetime requirements of various IoT applications. The formation of 6TiSCH network should happen before establishing end-to-end data communication. So, the 6TiSCH Working Group published 6TiSCH Minimal Configuration (6TiSCH-MC) standard for 6TiSCH network formation. Basically, 6TiSCH network formation is started by the Join Registrar/Coordinator (JRC), and all the new nodes (aka pledges) join one-by-one in the multi-hop 6TiSCH networks. Faster formation of 6TiSCH network is challenging because of the inherent channel hopping feature of TSCH as the pledges do not know in which channel and at what time control packets are transmitted by the already joined nodes and/or the JRC. Apart from this, the resource allocated by 6TiSCH-MC standard is static in nature, and it did not provide any mechanism to handle congestion in shared cell and to regulate the transmission rates of different control packets used to form the network. Therefore, we set the objective of this thesis is to augment the 6TiSCH-MC standard by updated mechanisms for achieving faster formation of 6TiSCH IoT network. It is observed that congestion in shared cell becomes an inevitable problem when the number of joined nodes increases, and it degrades the performance of 6TiSCH network formation. Therefore, to reduce the congestion in shared cell, we proposed two schemes, namely, channel condition based dynamic beacon interval (C2DBI) and game theory based congestion control (GTCC). We further observe that due to fixed priority assignment to the control packets and insufficient transmission of routing control packet, formation of 6TiSCH network gets delayed. Therefore, for sufficient and efficient transmission of routing control packet, we propose another two schemes, namely, opportunistic transmission of control packets (OTCP) and adaptive control packet broadcasting (ACB). Further, we leverage all the available channels at a time in order to increase the number of shared cells per slotframe for quicker transmission of control packets. For this, we propose another two schemes i.e., autonomous allocation and scheduling of minimal cell (TACTILE) and time-variant RGB (TRGB) model. Finally, it is worthwhile to mention that all the proposed schemes are evaluated by Markov Chain based theoretical analysis, simulation, and testbed experiments. As a whole, this dissertation improves the performance of 6TiSCH network during its formation period in terms of joining time and energy consumption of the IoT nodes while maintaining stable network
Adaptivity and Interface Design: A Human-Computer Interaction Study in E-Learning Applications
(2013) Deshpande, Yogesh D
Computer based teaching-learning or e-learning provides more flexible methods of interactions with learning contents compared to the traditional classroom set-up. It motivates learners towards self learning and evaluation in an open virtual environment. However, usefulness of e-learning depends upon learner beliefs and the degree of adjustments or adaptations shown by him in his learning behavior. The learning goal and the learning interface have a decisive role in influencing learner adaptations. Various researchers have addressed issues in learner adaptations to the (a) cognitive levels of learning goals and the (b) interaction environment. However they have been addressed separately. Also an efficient methodology of quantifying learner adaptations and learner ability of familiarizing with learning interfaces was lacking. Both these shortcomings have been addressed in this thesis by providing a methodology of measuring adaptations. In this thesis an adaptation score that quantifies adaptations and an adaptivity score that quantifies ability of adapting have been proposed. The thesis attempts to explain the combined impact of learning task complexity and user interface design on learner adaptations in beliefs, interactions and performance which was not done before. Quantitative data of e-learning interactions involving basic three cognitive levels of learning complexity viz. knowledge, comprehension and application and two types of navigation designs viz. hierarchical horizontal menu and non-hierarchical split menu was analyzed. The empirical data suggest the fact that learning task complexity (cognitive level) affects adaptations in interactions between similar tasks (task adaptation) on same interface. Since these task adaptations did not vary across user interfaces, they were found to be task-dependant. As a result, the cognitive load of learning could be judged by the task adaptation score and utilized to adapt pedagogic strategy or learning content. Results of our study reveal that belief in self e-learning skills (self efficacy) affected adaptations in learning behavior and learning performance. On the other hand, adaptivity to navigation design of user interface was found to be interface-dependant and, interestingly, also influenced learning performance. The beliefs were found to mediate the adaptivity scores. Based on the results of the experiments, the thesis provides recommendations on utilization of these metrics in personalization of e-learning on the bases of the adaptations. The study reveals research on the phenomenon of interactions between human and computer using a multidisciplinary view of Human Computer Interaction (HCI) combining computer science, behavioral science and education.
Algorithms for Facility Location Problems in Geometric Settings
(2023) Mishra, Pawan Kumar
Facility Location Problems (FLPs) have been the subject of extensive research because of its diverse range of applications in VLSI, networks, clustering, and other areas. The covering problem and the dispersion problem are two popular FLPs. Covering problem refers to selecting a subset of covering objects from a given set of objects such that the union of the selected objects contains all the elements. On the other hand, the dispersion problem refers to selecting a subset of a given set of objects such that the closeness between the objects in the selected set is undesirable. In this thesis, we investigated capacitated version of the covering problem and the dispersion problem and many of its variants. We established a necessary and sufficient condition through which we can ensure whether the given instance of the capacitated covering problem is feasible or not. Further, we prove that the problem is NP-complete. Finally, we proposed a local search algorithm for the capacitated covering problem, and proved that the proposed algorithm is a PTAS. For the dispersion problem, we introduce the concept of dispersion partial sum, which generalizes the notion of dispersion. Based on the dispersion partial sum, we defined variants of the dispersion problem, namely the 1-dispersion problem, the 2-dispersion problem, and the c-dispersion problem. We studied the following dispersion problems in Euclidean space: the 2-dispersion problem in both R2 and R1, and the 1-dispersion problem in R1. We presented a (2√3 + ε)-factor approximation result for the 2-dispersion problem in R2. We also developed a common framework for the dispersion problem in Euclidean space, which produces a 2√3-factor approximation result and an optimal result for the 2-dispersion problem in R2 and R1, respectively. We studied the c-dispersion problem in a metric space. We proposed a greedy algorithm for the c-dispersion problem, which produces a 2c-factor approximation result. We also proved that the c-dispersion problem in a metric space parameterized by solution size is W[1]-hard. Finally, we considered a variant of the 1-dispersion problem, where a set of locations is the vertices of a convex polygon. This variant of the 1-dispersion problem is referred to as the convex 1-dispersion problem. We proposed an O(n3)-time algorithm that returns an optimal result where the objective is to select k(= 4) vertices for the convex 1-dispersion problem. We also proposed a √3 (≈ 1.733)-factor approximation algorithm for the convex 1-dispersion problem for any value of k.
Algorithms for some Steiner tree problems on Graphs
(2020) Saikia, Parikshit
In this research work we study the Steiner tree (ST) problem in the distributed setting. Given a connected undirected graph with non-negative edge weights and a subset of terminal nodes, the goal of the ST problem is to find a minimum-cost tree spanning the terminals. The first contribution is a deterministic distributed algorithm for the ST problem (DST algorithm) in the CONGEST model which guarantees an approximation factor of 2(1 − 1/ℓ), where ℓ is the number of leaf nodes in the optimal ST. It has a round complexity of O(S + √nlog*(n)) and a message complexity of O(Sm + n3/2) for a graph of n nodes and m edges, where S is the shortest path diameter of the graph. The DST algorithm improves the round complexity of the best distributed ST algorithm known so far, which is Õ(S + √(min{St,n}), where t is the number of terminal nodes. We modify the DST algorithm and show that a 2(1 – 1/ℓ)-approximate ST can be deterministically computed using Õ(S+ √n) rounds and Õ(mS) messages in the CONGEST model. The CONGESTED CLIQUE model (CCM) is a special case of the CONGEST model in distributed computing. In this model we propose two deterministic distributed algorithms for the ST problem: STCCM-A and STCCM-B. The first one computes an ST using Õ(n1/3) rounds and Õ(n7/3) messages. The second one computes an ST using O(S + loglog(n)) rounds and O(Sm+n2) messages. Both the algorithms achieve an approximation ratio of 2(1 − 1/ℓ). To the best of our knowledge, this is the first work to study the ST problem in the CCM till date. We also study a generalized version of the ST problem called prize-collecting Steiner tree (PCST). Problems such as MST, ST, Steiner forest, etc. which are related to the PCST, have been widely studied in the distributed setting. However, PCST has seen very little progress in the distributed setting (the only attempt seems to be a manuscript due to Rossetti, an M.Sc. thesis, University of Iceland, Reykjavik, 2015). We present two deterministic distributed algorithms for the PCST problem in the CONGEST model: D-PCST and modified D-PCST. Both the algorithms are based on the primal-dual technique, preserve the dual constraints in a distributed manner, and achieve an approximation factor of (2 – 1/(n-1)). The D-PCST algorithm computes a PCST using O(n2) rounds and O(mn) messages. The modified one computes a PCST using O(Dn) rounds and O(mn) messages, where D is the unweighted diameter of the graph. Both the algorithms require O(Δlog(n)) bits of memory in each node, where Δ is the maximum degree of a node in the graph.
(An) Acoustic Study of Tone Contrasts in Manipuri Language
(2024) Devi, Thiyam Susma
Speech is a natural and intuitive mode of human communication, underscoring the essence of interpersonal interaction. Automatic Speech Recognition (ASR) is a pivotal innovation in digital technology, empowering devices to comprehend and process spoken language seamlessly. ASR’s applications span various domains, including dictation software, voice-activated assistants and automated call centers, thus revolutionizing how we engage with technology. Its significance extends further to the development of assistive devices for individuals with disabilities and the preservation of endangered languages, wherein ASR catalyzes documentation and linguistic conservation. Manipuri is a low-resource Tibeto-Burman tonal language primarily spoken in the northeastern state of Manipur, India. Tone identification is crucial to speech comprehension for tonal languages, where tone defines the word’s meaning. ASR for those languages can perform better by including tonal information from a powerful tone detection system. Despite extensive research on tonal languages such as Mandarin, Thai, Cantonese and Vietnamese, there is a significant gap in exploring Manipuri’s tonal features. This thesis presents the development of a meticulously crafted speech corpus called ManiTo, explicitly designed to analyze the tones of Manipuri. Comprising 17,837 labeled audio samples from twenty native speakers, ManiTo facilitates a nuanced examination of Manipuri’s tonal contrasts. Initial investigations reveal the presence of two distinct tones: Falling and Level. A comprehensive acoustic feature analysis was conducted to differentiate between the two tones to deepen our understanding. Two sets of features, focusing on pitch contours, jitter and shimmer measurements, were explored to delineate Manipuri’s tonal nuances. Various classification algorithms were employed to validate the selected feature sets, including Support Vector Machine, Long Short-Term Memory, Random Forest and k-Nearest Neighbors. Results demonstrate that the second feature set consistently outperformed the first, especially when utilizing the Random Forest classifier. These findings provide crucial insights for advancing speech recognition technology in low-resource tonal languages like Manipuri. This thesis contributes to the broader understanding of tonal languages through the development of ManiTo and the insights gained from acoustic feature analysis. It sets the stage for future research to enhance speech recognition technologies in linguistically diverse and underrepresented languages.
(An) Online Semi Automated Part of Speech Tagging Technique Applied To Assamese
(2013) Dutta, Pallav Kumar
Developing annotated tagged corpora for a language with limited electronic resources can be very demanding. Although Assamese is a language spoken by about 15 million people in the Indian state of Assam as a first language, the development of electronic resources for the language has been lagging behind other Indian languages. Also, there has not been much work done in POS tagging for Assamese. In order to fill this gap, we have designed a POS Tagger for Assamese. Our approach is to use a combination of methods to try and get good results. Further, we amortise the manual intervention over the period of tagging rather than doing all manual work at the beginning. This allows us to quickly start the tagging system. But it also means that what we have is a semi-automatic tagger and not an automatic tagger. Our method requires only native speakers intervention in stages other than the beginning making the system amenable to some form of with a few experts for moderation. This will enable our system to create very large tagged corpora in the language. We first create a knowledge base using a number of methods. This knowledge base is then used to automatically tag sentences in the language. This tagging uses a combination of stemming, application of a few grammatical rules, and a bigram tagger. The tagged sentences are then shown to non-expert native speakers for verification and correction. Before starting the actual tagging process, the knowledge base was tuned by examining the results on small data sets using experts instead of native speakers. The design of a user friendly interface plays an important role in reducing the time taken by native speakers in their examination.
Anomaly Detection in Oil Well Drilling Operation Using Artificial Intelligence Based Approaches
(2021) Tripathi, Achyut Mani
Artificial intelligence (AI) based approaches and in particular machine learning techniques have been extensively applied in domains such as health care, computer vision, and network security to build complex and accurate models that can produce more efficient solutions. Oil and gas sector is an area that generates massive and high volume data during the extraction of oil and gases. Stuck pipe, borehole instability, washout, and kick are among the more recurrent problems that occur during drilling operation and cause enormous financial loss to the oil and gas industries. In the ongoing situation, these problems are solved by first principle models and also appeal highly experienced drillers who can prevent such unwanted situations. Machine learning and AI techniques have shown tremendous performance to solve various re- search problems that involve massive real-time data, but their capabilities have not been explored entirely in the domain of oil industries. Still, there is a requirement of data-driven models that can solve the oil well drilling complications. The oil well drilling process needs a mechanical framework, also known as a rig. The rig contains different functional units having multiple sensors that provide the measurements of different hydraulic and mechanical parameters further helpful to monitor the oil well drilling process. The data measured by the rig sensors are stored in a database known as supervisory control and data acquisition (SCADA) system. The data stored in the SCADA system is multivariate time series data. The multivariate time series data stored in the SCADA system can be utilized to develop various machine learning models that can accurately provide the ongoing insight of the oil well drilling process. These data-driven supervisory models can be used for identifying oil well drilling complications. This research work primarily aims at developing AI based models that can be used to realize systems capable of automatically detecting anomalies during oil well drilling operations. The focus is on stuck pipe anomalies that care recurrent during the drilling operation. The above mentioned aim is attained through the following three contributions: The first contribution is development of a hierarchical classifier that identifies oil well drilling activities from the real-time oil well drilling data and also provides a detailed report that shows the percentage of time the drilling activity is performed in one complete cycle of the oil well drilling. The second contribution describes a novel probabilistic model that combines Dynamic Naive Bayesian Classifier and Fuzzy AdaBoost to identify the anomalies that lead to stuck pipe complication during the oil well drilling process. \The last contribution explains novel Contextual Dynamic Bayesian Network that detects contextual anomalies that occur during the oil well drilling process. All the developed models have been tested using real data from various wells located in Assam. The activity detection module has also been validated by deploying it at the well sites and the results are satisfactory.
An Architectural Framework for Seamless Hand-off between UMTS and WLAN Network
(2014) Barooah, Maushumi
In recent years, Cellular wireless technologies like GPRS, UMTS, CDMA and Wireless Local Area Network (WLAN) technologies like IEEE 802.11 have seen a quantum leap in their growth. Cellular technologies can provide data services over a wide area, but with lower data rates. WLAN technologies offer higher data rates, but over smaller areas, popularly known as Spots The demand for an ubiquitous data service can be fulfilled, if it is possible for the end-user to seamlessly roam between these heterogeneous technologies. In this thesis, a novel architectural framework is proposed consisting of an intra-ISP network called Switching Network which is fused between UMTS and WLAN networks as well as data (Internet) services for providing seamless mobility without affecting user activities. The ISN uses MPLS and Multiprotocol-BGP to switch the data traffic between UMTS to IEEE 802.11 networks, as per the movements of the user. The ISN is integrated with the UMTS network at the GGSN-3G and at the Access Point for IEEE 802.11 network respectively. The Mobile Node considered, is a high end device (e.g. PDA or Smart Phone) which is equipped with two interfaces, one for UMTS and the other for WiFi and can use both the interfaces simultaneously. The simulation result shows the improved performance of the ISN based framework over existing schemes. Most of the traffic in today networks use the Transmission Control Protocol (TCP) as the transport layer protocol for reliable end-to-end packet delivery. However, TCP considers packet loss to be the result of network congestion which makes it unsuitable for mobile wireless communication, where sporadic and temporary packet losses are usually due to fading, shadowing, hand-off and other radio effects. During the vertical handoff between different wireless technologies, the problem of end-to-end connection and reliability management for TCP becomes more severe. This thesis also evaluates the performance of TCP over the proposed ISN based framework. The improved TCP scheme uses a cross layer interaction between the network and the transport layer to estimate TCP retransmit timeout and congestion window during handover. Simulation results establishes effectiveness of the proposed scheme. Ensuring Quality of Service(QoS) for the mobile users during vertical handover between IEEE 802.11 and UMTS is another key requirement for seamless mobility and transfer of existing connections from one network to another. The QoS assurance criteria for existing connections can be affected by fluctuations of data rates when a user moves from the high speed WLAN network to the low speed UMTS network, even in the presence of another WLAN network in its vicinity. This can happen if the alternate WLAN network is highly loaded. Therefore handover from a high speed network to a low speed network should be avoided, whenever possible. The final contribution of this thesis proposes a QoS based handover procedure that prioritizes the existing connection over the new connections, so that rate fluctuations due to handover can be avoided if there exist another WLAN network in the range of the mobile user. Whenever the possibility of handover is detected, a pre-handover bandwidth reservation technique is used to reserve bandwidth at the alternate WLAN networks to avoid QoS degradation. The proposed scheme is implemented in Qualnet network simulator and...
Asymmetric Region Local Binary Patterns for face Image Analysis
(2014) Naika C. L., Shrinivasa
This Thesis explores feature extraction techniques based on local binary Patterns(LBP) for automatic face Image Analysis.
Automatic Language Identification in Online Multilingual Conversations
(2021) Sarma, Neelakshi
With the abundance of multilingual content on the Web, Automatic Language Identification (ALI) is an important pre-requisite for different Natural Language Processing applications. While ALI of well-edited text over a fairly distinct collection of languages may be regarded as a trivial problem, ALI in social media text is considered to be a non-trivial task due to the presence of slang words, misspellings, creative spellings, and special elements such as hashtags, user mentions, etc. Additionally, in a multilingual environment, phenomena such as code-mixing and lexical borrowing make the problem even more challenging. Further, the use of the same script to write content in different languages whether due to transliteration or due to shared script between languages imposes additional challenges to language identification. Also, many existing studies in ALI are not suitable for low resource languages due to either of the two reasons. First, the languages may actually lack the resources required like dictionaries, annotated corpus, clean monolingual corpus, etc. Second, the languages may consist of the basic resources in the native scripts, but due to the use of transliterated text, the available resources are rendered useless. Considering the challenges involved, this thesis work aims to address the problem of automatic language identification of code-mixed social media text in transliterated form in a highly multilingual environment. The objective is to use minimal resources so that the proposed techniques can be easily extended to newer languages with fewer resources. Although the language identification techniques explored in this study are generic in nature and not specific to any languages, to conduct various experimental investigations, this study generates three manually annotated and three automatically annotated language identification datasets. The datasets are generated by collecting code-mixed user-comments from a highly multilingual social media environment. Altogether, the datasets are composed of six languages - Assamese, Bengali, Hindi, English, Karbi and Boro. Apart from dataset generation, this thesis work makes four important contributions. First, it studies the language characteristics of user conversations in a highly multilingual environment. Interesting observations with regards to language usages and factors influencing language choices in a multilingual environment are obtained from this study. Second, a technique for sentence-level language identification is proposed taking advantage of the social and conversational features in user conversations. The proposed technique outperforms the baseline set-ups and enhances language identification performance in a code-mixed noisy environment. Third, a word-level language identification framework is proposed that makes use of sentence-level language annotations instead of traditionally used word-level language annotations. The proposed method focuses on learning word-level representations by exploiting sentence-level structural properties to build suitable word-level language classifiers. The proposed technique substantially reduces the manual annotation effort required while yielding encouraging performance. Fourth, a word-level language identification technique is proposed that makes use of a dynamic switching mechanism to enhance word-level language identification performance in a highly multilingual environment. The proposed switching mechanism attempts to make the correct choice between two different classification outcomes when one of the outcomes is incorrect. The proposed framework yields better performance than the constituent classifiers trained over a set of non-complementary features. The proposed set-up also outperforms the baseline set-ups using mini-mum annotated resources and no external resources thus making it suitable for low resource languages. The various automatic language techniques proposed in this study make use of minimal resources. Information obtained from the same set of sentence-level annotated data is used to train both sentence-level as well as wordlevel classification models. As such, the proposed techniques are also deemed suitable for automatic language identification of low resource languages. The proposed techniques are also able to enhance language identification performance in a code-mixed noisy environment.
Automatic speaker recognition using low resources: Experiments in feature reduction and learning
(2018) Kumar, Mohit
The main objective of this thesis is to explore experiments about reduction of computations involved in the Automatic Speaker Recognition (ASR) task and about generating representations for speakerrelated information from speech data automatically. ASR systems heavily depend on the features used for representation of speech information. Over the years, there has been a continuous effort to generate features that can represent speech as best as possible. This has led to the use of larger feature sets in speech and speaker recognition systems. However, with the increasing size of the feature set, it may not necessarily be true that all features are equally important for speech representation. We investigate the relevance of individual features in one of popular feature sets, MFCCs.
Brownout-based Power Allocation Strategies in Microgrids
(2023) Raj, Basina Deepak
"Future Smart Grids are expected to transform into hierarchically connected networks of miniaturized power systems called Microgrids, which are usually aimed to cater to a small geographical region. Microgrids have been envisaged to integrate heterogeneous distributed energy resources (DERs) including renewables (like photovoltaics, wind turbines and hydro–electricity), fossil fuel based micro–generators and batteries, along with occasional transfers of power from/to the main grid. Microgrid operations management is an important and yet a challenging problem. This is especially because, microgrids must be effectively tuned to maximize renewable energy penetration which are often volatile/intermittent, while still being able to schedule power generation, distribution and consumption in a fashion that ensures an economic and reliable operation. In order to achieve an effective balance between demand and supply, in the face of possible intermittent power shortage scenarios, Demand Response (DR) management strategies for microgrids must include mechanisms that allow adaptive urgency–prioritized control over electricity consumption. Today, with the advancements in smart metering as well as information and networking technologies, implementation of such adaptive power management schemes which require precise knowledge and finer controllability over consumer loads on the demand side, have become practical. The thesis of this dissertation is as follows: In the presence of intermittent power deficits, efficient policies for microgrid sizing, equitable power distribution, appliance scheduling, real–time power balancing, etc., can be designed by using brownout–oriented approaches which allow selective provisioning of electricity to essential appliances while curtailing supply to less important ones, in times of power shortages."
Capacity Enhancement, QoS and Rate Adaptation in IEEE 802.11s: A Performance Improvement Perspective
(2014) Chakraborty, Sandip
Current deployment of wireless community and municipal area networks provide ubiquitous connectivity to end users through wireless mesh backbone, that aims at replacing wired infrastructure through wireless multi-hop connectivity. IEEE 802.11s standard is published recently to support the mesh connectivity over well-deployed IEEE 802.11 architecture based on Wireless Fidelity (WiFi) access network. This thesis explores a number of research directions to optimize the mesh peering, channel access, scheduling and mesh path selection protocols for IEEE 802.11s mesh network. The standard provides three major protocols to support mesh functionality - Mesh Peer Management Protocol (MPM) to establish mesh connectivity and for topology management, Mesh Coordinated Channel Access (MCCA) for channel access and scheduling, and Hybrid Wireless Mesh Protocol (HWMP) to support mesh path establishment based on link layer characteristics. The objective of this thesis is to augment the existing protocols for better connectivity and e cient usage of the resources. In a mesh network, the e ciency of the backbone network can be improved through directional communication by exploring spatial reuse capability. However, uses of directional antennas impose several new research challenges that are explored in this thesis. The rst contribution of this thesis enhances the functionality of the mesh channel access and path selection protocols to support directional communication over an IEEE 802.11s mesh backbone. Though MCCA provides reservation based channel access, the standard does not implement any speci c mechanism for multi-class tra c services to improve the Quality of Service (QoS) for the end-users. The next contribution in this direction is to provide QoS support and service di erentiation for MCCA based channel access mechanism over the multi-interface communication paradigm. Modern wireless hardwares are capable of providing multiple data rate supports depending on wireless channel dynamics. As a consequence, the MPM protocol has been augmented to support multi-rate adaptation over IEEE 802.11s protocol elements.
Computational Modeling of Free-viewing Attention on Multimodal Webpages - A Machine Learning Approach
(2020) Sandeep, Vidyapu
With the progressive expansion of competitive e-commerce and Web resources, attention modeling is essential for Web authors, information creators, advertisers, and Web-designers to understand and predict the user attention on webpages. State-of-the-art models often overlook the design-oriented visual features of constituent web elements, including text and images. The bottleneck was to incorporate the elements' heterogeneous features into the model as texts are represented using features such as `text-size' and `text-color' whereas images are represented using `brightness', `intensity' and `color histograms'. This thesis work is predominantly centered around overcoming the heterogeneity bottleneck to predict the user's free-viewing attention on multi-modal webpages, precisely consisting of text and image modalities. Owing to the prominence of position, primarily, the position-based free-viewing attention allocation is investigated and computationally modeled, separately for text and image elements. The analyses revealed: (i) the elements positioned in the Right and Bottom regions of a webpage are not always ignored; (ii) Space-related (columngap, line-height, padding) and font Size-related (font-size, font-weight) intrinsic text features, and Mid-level Color Histogram intrinsic image features are informative, while position and size are informative for both the types; (iii) the informative visual features predict the ordinal visual attention on an element with 90% average accuracy and 70% micro-F1 score; (iv) For the prominent images, the visual features also help in predicting the weighted-voting-based, kernel-based, and multiple-levels of user attention. Leveraging the prominence of web elements’ visual features, Canonical Correlation Analysis (CCA) based computational approach is proposed to unify both the modalities and to predict the user attention at the granularity of web elements as well as webpages. The results reveal: (i) text and images are unifiable if the interface idiosyncrasies alone or along with user idiosyncrasies are constrained; (ii) The font-families of text are as influential and comparable to image color histogram visual features in achieving the unification. The achieved unification also outperforms the random baseline in predicting the user attention on individual web elements as well as overall webpages. This thesis work finds applications in user attention prediction, web-designing, and user-oriented webpage rendering.
Consistent Online Backup in Transactional File Systems
(2012) Deka, Lipika
A consistent backup, preserving data integrity across les in a le system, is of utmost importance for the purpose of correctness and minimizing system downtime during the pro- cess of data recovery. With the present day demand for continuous access to data, backup has to be taken of an active le system, putting the consistency of the backup copy at risk. We propose a scheme referred to as mutual serializability to take a consistent backup of an active le system assuming that the le system supports transactions. The scheme extends the set of con icting operations to include read-read con icts, and it is shown that if the backup transaction is mutually serializable with every other transaction individually, a consistent backup copy is obtained. The user transactions continue to serialize within themselves using some standard concurrency control protocol such as Strict 2PL. Starting with considering only reads an writes, we extend the scheme to include le operations such as directory operations, le descriptor operations and operations such as append, truncate, rename, etc., as well as operations that insert and delete les. We put our scheme into a for- mal framework to prove its correctness, and the formalization as well as the correctness proof is independent of the concurrency control protocol used to serialize the user transactions. The formally proven results are then realized by a practical implementation and evalua- tion of the proposed scheme. In the practical implementation, applications run as a sequence of transactions and under normal circumstances when the backup program is not active, they simply use any standard concurrency control technique such as locking or timestamp based protocols (Strict 2PL in the current implementation) to ensure consistent operations. Now, once the backup program is activated, all other transactions are made aware of it by some triggering mechanism and they now need to serialize themselves with respect to the backup transaction also. If at any moment a con ict arises while establishing the pairwise mutu- ally serializable relationship, the con icting user transaction is either aborted or paused to resolve the con ict. We ensure that the backup transaction completes without ever having to rollback by always ensuring that it reads only from committed transactions and never choosing it as the victim for resolving a con ict. To be able to simulate the proposed technique, we designed and implemented a user space transactional le system prototype that exposes ACID semantics to all applications. We simulated the algorithms devised to realize the proposed technique and ran experiments to help tune the algorithms. The system was simulated through workloads exhibiting a wide range of access patterns and experiments were conducted on each workload in two scenarios, one with the mutual serializability protocol enabled (thus capturing a consistent online backup) and one without (thus capturing an online inconsistent backup) and comparing the results obtained from the two scenarios to calculate the overhead incurred while capturing a consistent backup. The performance evaluation shows that for workloads resembling most present day real workloads exhibiting low inter-transactional sharing and actively accessing only a small percentage of the entire le system space, has very little overheads (2.5% in terms of transactions con icting wit.
Content and Coherence Based Strategies for Optimizing Refreshes in Volatile Last Level Caches
(2022) Manohar, Sheel Sindhu
With each process generation, Moore’s law offers us an exponential growth in the transistor budget on the chip. Technically, these extra transistors were used to improve processor architecture speed by adding more complicated and simple pipelines and better arithmetic and floating-point units. The futher advances included multi-core systems which demanded larger on-chip caches to support the data demands. Larger last-level caches are deployed across the chip to meet the increasing need for higher cache capacity due to included CMPs in processing cores. LLCs play an important function in the cache hierarchy by giving necessary data to hungry CMPs. SRAMs are not scalable and require advancements in power, performance, and scalability. In order to deploy massive LLCs, researchers are focusing on the construction of caches using alternative technologies that have advantages over traditional SRAM. High scalability, lower leakage power, and higher capacity in the same area footprint as SRAM are among the benefits of these technologies. However, we must investigate the best of these alternatives because they are not without flaws.
Context Aware Handover for WiFi and Its Extension to WiMAX
(2014) Sarma, Abhijit
IEEE 802.11 or Fidelity has become a popular wireless technology to offer high speed Internet access at public places called the as well as to support ubiquitous Internet connectivity through institute wide wireless local area networks (WLANs). However, existing researches has shown that due to wide-spread deployments of WiFi based network connectivity zones, more numbers of wireless access points (APs) are deployed than requirements, however, users tend to concentrate at few areas making traffic load imbalance across the network. The design philosophy of IEEE 802.11 connection establishment and handover from one AP to another is based on signal strength which is biased towards the distance between the AP and the client nodes. Severe performance and quality of service (QoS) degradation and capacity underutilization are observed due to this imbalance traffic distribution, which is the main concern of research in this thesis. The first contribution of the thesis explores the inherent problems of IEEE 802.11 handover management policies, and proposes a context-aware handover mechanism to balance traffic load across the network. The proposed mechanism works in coordination of information exchange between the AP and the wireless client that experiences performance degradation due to traffic overload at its present point of attachment. This coordination helps the wireless client to perform a horizontal handover to another AP in the vicinity, that significantly improves the network capacity. The performance of the proposed context aware handover mechanism is analyzed using theoretical analysis as well as from practical testbed results. The second contribution of the thesis extends the context aware handover to incorporate multiple traffic classes, where different traffic classes require different amount of bandwidth to sustain for acceptable quality of experience (QoE) to the end users. Consequently, a class aware load balancing is designed to reserve traffic resources a prior when an impending handover is observed.
Cost-effective Video streaming for Internet of Connected Vehicles using Heterogeneous Networks
(2023) Chowdhury, Debanjan Roy
"Internet of Connected Vehicles (IoCV) comprises smart vehicles which communicate among themselves and are connected to the Internet through static infrastructure nodes. Infrastructure nodes may use heterogeneous network technologies like cellular networks, Wifi networks, or Dedicated Short Range Communication (DSRC) networks. Among these networks, cellular networks have limited resources and impose access costs. Therefore, reducing the number of simultaneous cellular connections in an IoCV is a requirement. Smart vehicles of IoCV need persistent Internet connections for various safety messages and infotainment services. Among the infotainment services, video type infotainment services are prevalent. As the major portion of the traffic carried by the Internet core is of video type, reducing video traffic is the need of the hour. To meet the high-quality and low-latency demands for video services, content originators use the services of Content Distribution Networks (CDN). While providing video infotainment services over IoCVs, the objectives of CDN providers are to reduce the traffic volume of the Internet core, reduce service costs, and increase service profitability. To reduce the traffic volume, CDN providers deploy replica servers to serve the demands locally. However, if several vehicles demand the same video content simultaneously, like in the case of a live video streaming, a CDN replica server may get overwhelmed by the number of concurrent and redundant flow requests. As the content demand is homogeneous, the number of one-to-one flows to the CDN replica server can be reduced by bringing the content further closer to an IoCV using edge servers. Using infrastructure nodes as edges incurs deployment costs or carrier partnership costs, whereas using vehicles as edges needs Vehicle-to-Vehicle (V2V) collaborations. To reduce the service cost, the CDN provider needs to minimize the usage of simultaneous cellular connections and maximize V2V collaborations while ensuring service quality and client satisfaction. To generate additional revenues, CDN providers offer multi-tier video services where higher-tier clients pay more for enhanced video quality. However, the dynamic connectivity among vehicles and the intermittent availability of different networks (Wifi, cellular, DSRC) make the above-mentioned tasks extremely challenging. Accordingly, the objective of this dissertation is to find cost-effective solutions for CDN providers to run video infotainment services over IoCVs. This dissertation has four contributions toward the objective. The first contribution is focused on devising a centralized solution for reducing Internet bandwidth usage and the number of simultaneous cellular connections by minimizing the number of edge vehicles. The second contribution has proposed a distributed version of the first contribution, which helps CDN providers to reduce capital expenditure by avoiding setting up expensive servers of high-computing facilities. In the third contribution, a solution is provided for efficient Vehicle-to-Infrastructure (V2I) mode selection to increase CDN providers' profit in heterogeneous network scenarios. The fourth contribution of this dissertation devises an edge selection solution for CDN providers to provide multi-tier streaming services. The experiment results show that in comparison to existing solutions, the proposed solutions are the most cost-effective for CDN providers."
Data Pruning Based Outlier Detection
(2015) Pamula, Rajendra
Due to the advancement of the data storage and processing capabilities of computers, most of the real life applications are shifted to digital domains and many of them are data intensive. In general, most of the applications deal with similar type of data items, but due to variety of reasons some data points are present in the data set which are deviating from the normal behaviors of common data points. Such type of data points are referred as outliers and in general the number of outliers in a data set is less in number. Identifying the outliers from a reasonably big data set is a challenging task. Several methods have been proposed in the literature to identify the outliers, but most of the methods are computation intensive. Due to the diverse nature of data sets, a particular outlier detection method may not be effective for all types of data set. The main focus of this work is to develop algorithms for outlier detection with an emphasis to reduce the number of computations. The number of computations can be reduced if the data set is reduced by removing some data points which are obviously not outliers. The number of computations again depends on the number of attributes of data points. While detecting outliers it may be possible to work with less number of attributes by considering only one attributes from a set of similar or correlated attributes. The objective of this work is to reduce the number of computations while detecting outliers and study the suitability of the method for a particular class of data set. Our methods are based on the clustering techniques and divide the whole data set into several clusters at the beginning. Depending on the nature of the clusters we propose methods to reduce the size of the data sets, and then apply outliers detection method to find the outliers. We propose three methods based on the characteristics of the clusters to identify the clusters that may not contain outliers and such clusters are pruned from the data set. We also propose a method to identify the inlier points from each cluster and prune those points from clusters. We use the principle of data summarization and propose a method that involves both cluster pruning and point pruning. For high dimensional data set, we propose a method that involves attributes pruning to reduce the number of computations while detecting outliers. Once we perform the pruning step, a reduced data set is resulted and then outlier detection techniques are used to detect the outliers. For each method we demonstrate the effectiveness of our proposed methods by performing experiments.

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