Theses

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Browsing Theses by Supervisor "Badarla, Venkata Ramana"

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    Improving Security in Wireless Sensor Networks through Bio-Inspired Approaches
    (Indian Institute of Technology Jodhpur, 2015-10) Badarla, Venkata Ramana
    A Wireless Sensor Network (WSN) is a network made up of a set of sensor nodes and gateway nodes where sensor nodes monitor physical parameters such as temperature, moisture etc. and send it to gateway nodes. Security in WSN plays an important aspect since it provides sustainability to the network. Security threats can be introduced in WSN through various means, where the ongoing data transmissions can be tampered with or the nodes can be altered to behave in an unpredictable manner. The proposed research work uses biological inspirations and takes aspects from machine learning and social psychology for improving security in WSNs. The proposed work is a two-step model, where the detection of fraudulent nodes is the first step and reducing the effect of these nodes using bio-inspirations is the second step. For detection of fraudulent nodes, two approaches based on machine learning and social psychology has been proposed. The machine learning model is based on techniques like k-means and Support Vector Machine. Sociopsychological model is another model which is less complex to deal with fraudulent node detection. It enhances the performance by taking concepts from social psychology. After the detection of the fraudulent nodes, an immune model is instantiated. Biological immune systems have intelligent capabilities of detecting foreign bodies which attack our body. Moreover they have inherent insightful capabilities to remember the same foreign body when it hits the body again. In this work, a similar healing procedure is instigated in WSN to quickly recover from the attack. The proposed work has been implemented in LabVIEW platform and extensive simulations were carried out to study its performance for the metrics such as detection and recovery time, reliability, robustness, scalability, complexity etc. Further, it is experimentally evaluated on hardware test bed of size 16 nodes to obtain results that demonstrate the accuracy and robustness of the proposed model.
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    A Multi Objective Beacon Placement Strategy for 3D Point Cloud Representation of Indoor Environments
    (Indian Institute of Technology Jodhpur, 2021-01) Badarla, Venkata Ramana
    Estimating the position of a target device using Wireless Sensor Networks (WSN) has gained significant usage in smart indoor applications. This is typically accomplished by a to-andfro communication between the device and wireless beacons which are essentially sensor nodes with known spatial locations. For indoor environments, the research over localization has evolved using the signal’s propagation time, phase and strength in combination with popular short range communication standards such as WiFi, Bluetooth, ZigBee etc. One of the promising domains in this direction remains with identifying the locations of beacons that provide optimum coverage and accuracy of localization while keeping the involved cost constrained. Finding an optimal configuration of sensors that maximizes localization accuracy and coverage, conforming to given placement constraints for indoor environments, is frequently termed as Beacon Placement Problem (BPP) or Sensor Placement Optimization (SPO). Solving a BPP is typically approached by linear programming or meta-heuristics optimization methods and proved to be NP-Hard with reference to the classical art gallery problem. Hardship in BPP’s tractability is primarily attributed to the size of its search domain comprising of Candidate Device Locations (CDL) and correspondingly visible Candidate Beacon Locations (CBL) within the Region of Interest (RoI). The complexity of BPP’s implementation gets severe with the transition from two dimensional (2D) to three dimensional (3D) localization perspective due to the significant increase in coordinate counts within the RoI. Also, due to the presence of static and dynamic obstacle elements in the RoI of practical indoor spaces along with application dependent constraints, benchmarking of BPP’s complexity and optimization methods stands infeasible. Available BPP approaches usually consider a 2D coordinate grid or floor planned designs for the RoI and perform Single Objective Optimization (SOO) for objectives such as coverage maximization, beacon count minimization and localization accuracy maximization. The research still lacks an exhaustive approach that can guide a system designer to choose a suitable beacon configuration in the presence of multiple practical objectives and algorithmic constraints. This thesis proposes a methodology that works by considering an indoor RoI comprising of 3D point clouds of CDL, CBL and Obstacle coordinates. We developed a novel point-to-point LoS detection algorithm that discovers CDL-CBL connectivity as an adjacency matrix. Using this adjacency information along with minimum connectivity and precision constraints, the BPP is approached by Multi Objective Optimization (MOO) for coverage-accuracy assessment. The proposed method ology is an optimization tool-chain that explores a set of Pareto optimal beacon configurations using the Non-dominated Sorting Genetic Algorithm (NSGA)-II. The optimizer generates beacon configurations prepared to tolerate 10, 20 and 30% of observation noise while providing GDoP values within 1, 2 and 5 as per the requirement. The derived non-dominant solutions are then analysed by simulations for their performance for accuracy and coverage over different indoor designs. The results suggest that configurations with over 80% of coverage and < 1 unit of accuracy are achievable for all the designs generated above using NSGA-II. Finally, a performance based ranking system is presented to recommend users a single optimal solution.
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    Parametric network models, network reconstruction and diffusion protocols for networks
    (Indian Institute of Technology Jodhpur, 2018-03) Adhikari, Bibhas; Mazumdar, Mainak; Badarla, Venkata Ramana
    In this thesis we propose parametric network models for generation of complex networks that can inherit statistical properties of real networks. The models are based on different growth processes that are observed in different social contexts, for example, preferential attachment, random attachment with local growth. The chemical process, known as nucleation is investigated as a network formation process and thus a network model is proposed inspired by nucleation. Further, the parametric model approach for generation of networks is extended and employed in to solving the problem of structural reconstruction of real scale-free networks. In this attempt, a 2-parameter network generation model, called Network-Reconstruction-Model (NRM) is developed. A reconstruction technique is introduced to reconstruct a given real scale-free network by finding optimal values of the model parameters, utilizing the power-law exponent of the degree distribution of the real network, such that the corresponding model network inherit multiple structural properties of the real network. The performance of all the models in order to inherit properties of real networks is tested with different examples of real networks. The efficiency of NRM and the proposed reconstruction technique in order to solve the structural reconstruction problem are compared with some existing network models. Preferential attachment is one of the well known procedures that has been considered in literature to explain the existence of power-law in the degree distribution of real networks. However, often a diffusion process on a network influences the structural organization of the network and vice versa. Thus a natural question is: How do the structural dynamics and diffusion dynamics interact each other so that power-law in degree distribution arise or sustain in a network? This is thoroughly investigated in the thesis by introducing continuous and discontinuous truncated biased random walks on networks, where the diffusion process is considered as a random walk dynamics on the network. These proposed random walk dynamics could justify preferential growth of networks. Moreover, a diffusion protocol is proposed that can help detecting structural irregularities in static and dynamic networks, for example, the phenomena of link failure. Finally, a framework is proposed to identify existence of links in a network by investigating datasets of Susceptible-Infected-Susceptible (SIS) diffusion dynamics on networks.
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    Reconfigurable Architecture for Cross Layer Design Optimization and its Applications
    (Indian Institute of Technology Jodhpur, 2016-02) Yadav, Sandeep; Badarla, Venkata Ramana
    Sharing of physical (PHY) and medium access control (MAC) layer knowledge with the higher layers in wireless networks provides efficient methods of allocating network resources and applications over the Internet. Cross Layer Design (CLD) refers to protocol design done by actively exploiting the dependence between protocol layers to obtain performance gains. This thesis presents a new cross layer protocol which uses the knowledge of radio's front end impairments to build reconfigurable MAC and PHY layers and demonstrates its performance via implementation on an experimentation system. One needs to be aware that any proposed cross layer design optimizations must be carefully evaluated on experimentation systems, which are optimized to meet the requirements of the particular layer being modified. A novel contribution of the thesis is the presentation of a framework for objectively evaluating various experimentation systems. The proposed framework, which contains metrics such as cost, latency and throughput, will help designers make informed trade-off decisions between various requirements and develop systems most optimized for CLD. The thesis evaluates various platforms using the proposed framework and shows that, while these platforms have significantly accelerated the pace of PHY layer research, they are not perfectly suited for MAC layer research. To overcome this gap, the thesis presents a new hardware architecture which is specifically optimized for MAC layer requirements, such as latency, processing speed, and cost. The thesis demonstrates how availability of commercial technology and careful trade-off is making it feasible to design such a system.
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    Utilizing Topology Structures for Delay Sensitive Traffic in Data Center Network
    (Indian Institute of Technology Jodhpur, 2018-07) Badarla, Venkata Ramana
    Nowadays data centers are attracting tremendous attention of researchers because they can accommodate a variety of applications. Data center network is an essential component to allow distributed applications to run efficiently and predictably. Performance of data centers require promising conduct round the clock. Incompetency in providing high performance may lead to dissatisfaction in customers. Customers are very sensitive to delay, and long delay implies throw away customers. Latency in data centers affects adversely on business revenue. However, reducing latency for services, which require gigantic database consultation and heavy computation, is a difficult job. Data center topologies are well structured, and this topology information of data center can be utilized for diminishing latency. This thesis is about TAP, a topology aware version of an existing flow scheduler (pFabric) and JFEPM, which takes advantage of higher capacity links of topology structures and use jumbo frames on them. Recent Data center network topology puts forward multiple paths between the end hosts to provide high bisection bandwidth. Load-balancing proposals mainly deal with how to distribute traffic among multiple paths. In this thesis, FlowFurl, a distributed, end host driven, flow-level load-balancing technique will be discussed. This technique re-routes a flow not only based on the congestion at switch, but also takes care of the status of the links along the path to maximize the benefit of flow-level re-routing.