BITS Faculty Publications

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    A probe-based demand responsive signal control for isolated intersections under mixed traffic conditions
    (Taylor & Francis, 2023-01) Maripini, Himabindu
    The paper presents a model-based demand-responsive traffic control system for mixed traffic conditions using sample travel time data. The model incorporates mixed traffic characteristics such as heterogeneity, limited lane discipline of varied vehicle types, and spatio-temporal traffic dynamics across the width of the road. The methodology includes optimization of intersection performance by accommodating the varying traffic demand through signal timing variables. On validation, the model yielded reliable queue estimates within a close proximity of the actual, ranging from 20 to 40 meters. Upon optimization, the proposed model reduced total intersection delay by 15.42% on an average across 14 cycles, for near-saturated traffic conditions. The optimal green splits are found to be responsive to the varying traffic demand. The proposed system is simple and can be easily implemented in the mixed traffic conditions.
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    Optimizing traffic signals for non-uniform arrivals using sparse probe data
    (IEEE, 2024-09) Maripini, Himabindu
    With the field of traffic signal control progressing towards more adaptive, and computationally efficient models, the integration of sparse travel time data and advanced optimization techniques enhances the intersection performance. This paper presents a novel optimization framework for traffic control using sparse travel time data, with a field penetration rate as low as 10%. The paper focuses on developing mathematical models that can handle the variability in arrival rates both within and across signal cycles. By utilizing delay polygons, we accurately model and minimize the total intersection delay caused by non-uniform vehicle arrivals and signals, leading to optimal signal timing solutions. The model effectively prioritizes queue dissipation for highly saturated phases while minimizing overall intersection delay. The algorithm accommodates variations in phase-wise delays across cycles, indirectly reflecting changes in traffic demand. Additionally, the sample-based design demonstrates performance comparable to volume-based dynamic design in terms of average delay, average speed, and total travel time across cycles. With the innovative use of sample re-identification data obtained through various sensor technologies, the proposed algorithm is capable of delivering optimal control of time varying traffic demand with minimal data input.
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    Optimized simultaneous pressurized fluid extraction and in-cell clean-up, and analysis of polycyclic aromatic hydrocarbons (PAHs), and nitro-, carbonyl-, hydroxy -PAHs in solid particles
    (Elsevier, 2020-08) Goonetilleke, Ashantha
    The development, modification and optimization of analytical methods capable of simultaneous extraction and in-cell clean-up of extracts for subsequent determination of parent PAHs and their associated transformed nitro-PAHs (NPAH), carbonyl-PAHs (CPAH) and hydroxy-PAHs (HO-PAH) products (TPPs) is essential for reducing the time and cost of analysis. The aim of this study was to modify and optimize the pressurized fluid extraction (PFE) technique capable of simultaneous extraction and in-cell clean-up of PAHs and TPPs in urban dust standard reference material and road dust for GC-MS analyses. In this study, multivariate data analysis such as factor analysis (FA), and preference ranking organisation method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA) were used to assess the performance of the methods. As the key outcome of the study, an optimized selective reaction monitoring (SRM) Triple Quadrupole (TQ) electron ionization (EI)-GC/MS for measuring PAHs and TPPs without derivatization of polar HO-PAHs was developed. The limits of detection (LOD) for parent PAHs, CPAHs, NPAHs and HO-PAHs using Shimadzu TQ were 1.0–5.0 pg, 1.0–5.0 pg, 1.0–50.0 pg, and 1.0–25.0 pg, respectively. The PROMETHEE-GAIA analysis of the results showed that a combination of 3% deactivated silica gel and activated alumina (2:1) as in-cell clean-up material, and sequential PFE extraction (200 °C ASE temperature, 9 min preheat time and 3 times extraction cycle) using 100% hexane followed by hexane/DCM (1:1) is the best condition for analytes extraction from road dust. An optimized, fast and reliable GC/MS method operated solely in electron ionization (EI) mode was developed for measuring all analytes. The outcomes of this study will contribute significantly to future research on PAHs and TPPs, thereby promoting a safe and sustainable environment.
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    Efficient routing for QKD network using novel quantum optimization approach
    (IEEE, 2025) Bitragunta, Sainath; Bhatia, Ashuthosh
    With exponential growth and associated milestones set in quantum information and quantum computing (QC) technologies, QC is becoming a threat to existing key encryption strategies that leverage asymmetric cryptographic algorithms like RSA (Rivest, Shamir, Adleman) encryption. Since these algorithms form the backbone of Internet communication, it becomes essential to utilize secure quantum methods for key generation and distribution. The quantum key distribution (QKD) networks have since been extensively researched and implemented with various communication protocols, primarily utilizing the Quantum Entanglement and Quantum Key Correction paradigms. Efficient routing is one of the significant problems in classical and hybrid networks. It is important to propose novel hybrid and efficient routing protocols based on modern optimization approaches to design secure, fidelitous, and efficient quantum information networks. We perform this optimization by generating a cost function to implement quantum optimization algorithms, namely the Quantum Approximate Optimization Algorithm (QAOA). We further draw a comparison with the state-of-the-art graph theory-based optimization techniques. The primary objective of this paper is to fabricate a robust quantum communication network and to subsequently analyze the effectiveness of quantum based techniques to carry out network routing and link optimization, generating scope for the utilization of quantum architecture to enhance security in Q KD networks.
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    Modeling and Applications in Operations Research
    (Taylor & Francis, 2023) Shekhar, Chandra
    The text envisages novel optimization methods that significantly impact real-life problems, starting from inventory control to economic decision-making. It discusses topics such as inventory control, queueing models, timetable scheduling, fuzzy optimization, and the Knapsack problem. The book’s content encompass the following key aspects: Presents a new model based on an unreliable server, wherein the convergence analysis is done using nature-inspired algorithms Discusses the optimization techniques used in transportation problems, timetable problems, and optimal/dynamic pricing in inventory control Highlights single and multi-objective optimization problems using pentagonal fuzzy numbers Illustrates profit maximization inventory model for non-instantaneous deteriorating items with imprecise costs Showcases nature-inspired algorithms such as particle swarm optimization, genetic algorithm, bat algorithm, and cuckoo search algorithm The text covers multi-disciplinary real-time problems such as fuzzy optimization of transportation problems, inventory control with dynamic pricing, timetable problem with ant colony optimization, knapsack problem, queueing modeling using the nature-inspired algorithm, and multi-objective fuzzy linear programming. It showcases a comparative analysis for studying various combinations of system design parameters and default cost elements. It will serve as an ideal reference text for graduate students and academic researchers in the fields of industrial engineering, manufacturing engineering, production engineering, mechanical engineering, and mathematics.
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    Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing
    (IEEE, 2024-04) Chamola, Vinay
    Generative Artificial Intelligence(GAI) models such as ChatGPT , DALL-E , and the recently introduced Gemini have attracted considerable interest in both business and academia because of their capacity to produce material in response to human inputs. Cognitive computing is a broader field of machine learning that encompasses GAI, which particularly emphasizes systems capable of creating content, such as images, text, or sound, while semantic computing acts as a fundamental element of GAI, furnishing the comprehension of context and significance essential for GAI systems to generate content akin to human-like standards. GAI is becoming a game-changing technology for consumer electronics industry with a variety of applications that improve user experiences and product development. GAI can revolutionise architectural visualisation by facilitating quick prototyping and the investigation of cutting-edge design ideas. By creating unique compositions and graphics for a variety of applications, it also empowers media production and music composition. Our research identifies several applications of GAI in the consumer electronics industry. We analyze how GAI is utilized in augmented reality (AR) applications, optimizing user interactions and immersive experiences. Moreover, we explore the integration of GAI in voice assistants and virtual avatars, enhancing images, natural language understanding and delivering more personalized interactions. We present a novel case study on a Generative Artificial Intelligence-based Framework for answering consumer electronics queries. We have developed and presented the system using various GAI-based tools and integrations. The paper also discusses the challenges in implementing GAI in consumer electronics, such as ethical considerations, data privacy, compatibility with existing systems, and the need for continuous updates and improvements.
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    On-Device Generative AI: The Need, Architectures, and Challenges
    (IEEE, 2024-12) Chamola, Vinay
    The area of Generative Artificial Intelligence (GenAI) is rapidly expanding, as seen by the regular release of new models and applications every few months. While these GenAI models have impressive capabilities, their computational intensity has presented issues, especially in applications demanding low latency. Hence, substantial research is being conducted to develop ways to scale down these models so that they may be used for on-device computing on edge devices. Examining successful examples of GenAI models implemented on mobile devices with minimum latency becomes critical in understanding the practical consequences of these breakthroughs. Notable instances, such as the deployment of Diffusion-based GenAI models on flagship smartphones like Samsung S23 Ultra and iPhone 14, demonstrate the possibility and promise of bringing GenAI applications to consumers' fingertips. We further analyze and find out the approaches and strategies that make these on-device deployments successful.
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    Fuzzy Logic and AI-Powered, SDR Relay for Secure and Efficient Cooperative Radio Communication
    (IEEE, 2024) Bitragunta, Sainath; Bhatia, Ashuthosh
    In this article, we develop a novel approach that leverages the capabilities of fuzzy logic and artificial intelligence (AI) to develop an intelligent, efficient cooperative RCN. Software defined radio (SDR) is flexible, scalable, and reconfigurable. Considering heterogeneous radio communication networks (RCNs), conventional relays do not perform well due to their limitations (security vulnerabilities in cooperative Internet-of-Things (IoT), inefficiencies in half-duplex relaying, etc.). We propose an AI-powered, fuzzy logic-based SDR relay to address these issues. These intelligent relays could be useful and outperform conventional relays due to their adaptability and reconfigurabilty, with added intelligence based on AI and fuzzy logic. The proposed next generation SDR relays offer significant advantages over traditional relays and have the potential to revolutionize the field of radio communication. Specifically, we analyze the decimation technique in SDR signal-to-interference plus noise ratio (SINR) resampler, Mamdani fuzzy logic controller, and use a machine learning (ML) model that uses RADIOML data set. Based on the simulation results, we show that applying fuzzy logic with an ML-enabled SDR relay could improve energy efficiency and reliability performance in advanced radio networks.
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    Fair Scheduling of Concurrent Transmissions in Directional Antenna Based WPANs/WLANs
    (IEEE, 2018-07) Rajya Lakshmi, L.
    With their capability to support high data rates, millimeter-Wave (mmWave) communications are evolving as a promising and potential technology to support high data rate applications in short range networks. This paper addresses the problem of fair scheduling in mmWave wireless personal and local area networks (WPANs/WLANs) to support applications with varying quality of service (QoS) requirements. To ensure fairness while exploiting the spatial reuse facilitated by directional antennas, concurrent transmission scheduling in mmWave WPANs/WLANs is formulated as a multi-objective optimization problem. Two heuristic schedulers are developed to obtain a schedule in real-time. These schedulers first satisfy the minimum QoS requirements of as many flows as possible, and then, allocate the remaining bandwidth to various flows while ensuring long-term and short-term fairness among the flows. Results from extensive simulations conducted in a dense mmWave WPAN show that the proposed fair schedulers provide better fairness and throughput, compared to existing methods.
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    Achieving Fairness in IEEE 802.11ah Networks for IoT Applications with Different Requirements
    (IEEE, 2019-07) Rajya Lakshmi, L.
    The IEEE 802.11ah standard can provide cost-effective Internet access to a large number of devices in newly evolving Internet-of-Things (IoT) and machine-to-machine (M2M) networks. To handle high collision probability caused by a large number of devices, it adopts a group-based protocol at the MAC layer and divides nodes (or sensors) into a number of groups. The formed groups may not be uniform in terms of data rate requirements, since each group is a combination of sensors with different traffic characteristics. To achieve fair resource utilization across the groups which in turn maximizes the channel utilization, this paper formulates fair grouping in IEEE 802.11ah networks as an optimization problem, and we develop a heuristic method to solve the problem in real-time. In addition, to ensure fair channel utilization by the nodes in each group, a contention window selection and adjustment method is proposed. Results from extensive simulations conducted in a dense IoT network show that the proposed fairness model achieves a superior performance than the existing methods in terms of throughput, packet delay, energy efficiency, and fairness.