Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

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Author: search.filters.author.Shekhawat, Virendra Singh

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    HMR-ODTA: online diverse task allocation for a team of heterogeneous mobile robots
    (2025-05) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, Sudeept
    Coordinating time-sensitive deliveries in environments like hospitals poses a complex challenge, particularly when managing multiple online pickup and delivery requests within strict time windows using a team of heterogeneous robots. Traditional approaches fail to address dynamic rescheduling or diverse service requirements, typically restricting robots to single-task types. This paper tackles the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where autonomous mobile robots are capable of handling varied service requests. The objective is to minimize late delivery penalties while maximizing task completion rates. To achieve this, we propose a novel framework leveraging a heterogeneous robot team and an efficient dynamic scheduling algorithm that supports dynamic task rescheduling. Users submit requests with specific time constraints, and our decentralized algorithm, Heterogeneous Mobile Robots Online Diverse Task Allocation (HMR-ODTA), optimizes task assignments to ensure timely service while addressing delays or task rejections. Extensive simulations validate the algorithm's effectiveness. For smaller task sets (40-160 tasks), penalties were reduced by nearly 63%, while for larger sets (160-280 tasks), penalties decreased by approximately 50%. These results highlight the algorithm's effectiveness in improving task scheduling and coordination in multi-robot systems, offering a robust solution for enhancing delivery performance in structured, time-critical environments.
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    CF-HMRTA: coalition formation for heterogeneous multi-robot task allocation
    (Springer, 2025-07) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, Sudeept
    This paper introduces a novel approach, Coalition Formation for Heterogeneous Multi-Robot Task Allocation (CF-HMRTA), to address the challenge of multi-robot task allocation. The problem, inherently NP-Hard, is tackled using bipartite graph matching. CF-HMRTA forms heterogeneous robot coalitions with unique service skills to complete tasks collaboratively, using a heuristic algorithm for optimal robot-task pairing while preventing task overlap. Recent research work using bipartite graph matching for multi-robot coalition formation and task allocation often assumes homogeneity across tasks and robots, where any robot can be assigned to any task. In contrast, the solution proposed in this paper explicitly considers the diversity of robots with varying service skills. Additionally, tasks demand different sets of skills, such as sensing, monitoring, and data collection, making certain tasks unsuitable for some robots due to hardware constraints. For instance, tasks requiring aerial footage are assigned to drones, while ground robots handle close-ground monitoring. Furthermore, we incorporate task-specific time constraints into our problem formulation, enhancing its realism. Considerably less research has been conducted on heterogeneous robot teams solving tasks that require multiple service skills and temporal constraints, making our work a significant contribution to the field. The algorithm achieves a worst-case time complexity of , where represents the edges in the bipartite graph, and guarantees perfect matching. Simulation results highlight its scalability, successfully allocating up to 2000 robots to 400 tasks in approximately 11 seconds.
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    Modeling and performance evaluation of OpenFlow switches using a MAP/PH/1/n queueing model
    (Elsevier, 2025-07) Shekhawat, Virendra Singh; Kulshrestha, Rakhee
    Software-Defined Networking (SDN) is a paradigm shift in network architecture. It decouples the control plane from the data plane to enable centralized network management and programmability. While Software Defined Networks (SDNs) offer significant advantages by efficient traffic management, it also introduces complexities that require comprehensive network modeling to predict and optimize network behavior before actual deployment. Queueing models provide a mathematical framework for analyzing and predicting how data packets behave as they traverse network devices. This paper presents a discrete-time MAP/PH/1/n queueing model to assess the performance of SDNs in handling complex and bursty traffic patterns. The model integrates packet processing at different switch components, including the switch buffer, ingress processing unit, and egress processing unit. It utilizes a finite buffer queue model with Markovian Arrival Process (MAP) and Phase-Type (PH) service times to capture data transmission behavior at an OpenFlow switch. The matrix geometric method is employed to calculate steady-state probabilities, which helps in evaluating Quality of Service (QoS) metrics such as average delay, throughput, and blocking probabilities. In addition, the mathematical model formulates performance measures, including probabilities for packet forwarding, packet drop, and packets redirected to the controller. We validated our model’s outcomes by conducting packet-based simulations using Mininet and the Ryu controller. The graphs obtained from both the mathematical model and the packet simulations demonstrate qualitatively similar behavior of the OpenFlow switch across different traffic rates, buffer sizes, and service rates.
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    DTA-HMR-TT: dynamic task allocation for a heterogeneous team of mobile robots with task transfer
    (IEEE, 2024-11) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, Sudeept
    Managing time-sensitive deliveries in settings like hospitals is a challenging task, especially when multiple pickup and delivery requests need to be coordinated efficiently within strict time windows. This paper focuses on the Multi-Pickup and Delivery Problem with Time Windows (MPDPTW), where a fleet of autonomous mobile robots works together to fulfill client requests that involve picking up items from specified origins and delivering them to designated destinations. Our objective is to minimize penalties associated with late deliveries while maximizing the number of successfully completed requests. To address this, we introduce a novel approach using a heterogeneous team of robots equipped with an efficient and cost-effective scheduling algorithm. Users submit requests with specific time constraints, and our proposed decentralized algorithm-Dynamic Task Allocation for Heterogeneous Mobile Robots with Task Transfer (DTA-HMR-TT)–optimizes task sharing between robots, ensuring timely service. The algorithm dynamically adjusts to handle rejected or delayed tasks and manages the complex transfer of tasks between robots to improve delivery efficiency. Extensive simulations have demonstrated that our approach significantly outperforms state-of-the-art methods. For smaller task sets (50 to 150 tasks), penalties were reduced by 27%, while for larger sets (150 to 300 tasks), penalties were lowered by 36%. Our results highlight the effectiveness of DTA-HMR-TT in enhancing task scheduling and coordination in multi-robot systems, offering a promising solution for improving delivery performance in structured environments.
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    A Comprehensive Analysis of Cloud Adoption and Cloud Security Issues
    (IEEE, 2024) Shekhawat, Virendra Singh
    Cloud computing has expanded substantially since 2006. By 2011, cloud computing was the top technical goal for organizations worldwide, and industry studies forecast the market would reach ∃441 billion by 2024. Cloud computing has altered IT delivery and management. IT organizations invest in cloud technologies to enhance IT operations and decrease marketing time. The current cloud service model allows enterprises to test new technology and services, such as IoT and Big Data, with little upfront outlay. Most firms have challenges transferring business services and sensitive data to public cloud infrastructures. Over 100 IT executives, managers, and architects were polled about using Public Cloud services. This poll assesses commercial and technology obstacles and cloud storage and sharing concerns. This study examines the Cloud Adoption Landscape in India, including Trends in Offering and Deployment, Adoption Challenges/Roadblocks, and Expectations for Enhanced Adoption. Cloud Computing, Cloud Services, Adoption Challenges, Trends, Data Security, Data Privacy, Hybrid Cloud, Market forecast.
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    Deep Convolutional Neural Network with a Fuzzy (DCNN-F) technique for energy and time optimized scheduling of cloud computing
    (Springer, 2024-07) Shekhawat, Virendra Singh
    Self-adaptive deep learning techniques provide scalability and flexibility in deploying and administrating deep learning models in the cloud environment. DL is widely used in cloud computing architecture, and these methods seek to optimize performance and resource utilization by automatically adjusting the resources allotted to machine learning tasks in response to workload fluctuations. Adaptive task scheduling algorithms maximise the distribution of DL techniques to available resources based on their features and needs. DL algorithms make intelligent judgements regarding job allocation, guaranteeing effective resource utilization and workload management. They consider variables, including task priority, resource availability, and resource capabilities. This research work deploys the Deep Convolutional Neural Network with a Fuzzy (DCNN-F) technique by differentiating the cloud nodes. The complexity of workflow scheduling in the cloud context is optimized by efficient learning, whereas energy and time consumption are effectively handled. The DCNN-F is trained with the resources in the cloud, and the solution for scheduling issues is rectified by learning data. The network is iteratively refined and optimized based on the feedback mechanism in DCNN-F. By combining the power of DCNN-Fs with efficient resource allocation strategies, research can maximise energy and time scheduling precedence-constrained tasks in cloud computing environments. The simulation outcome of DCNN-F is compared with state-of-art techniques, and DCNN-F outperforms Deep Q-Learning (DQL), Deep Reinforcement Learning based Optimization (DRL-O) and Deep Reinforcement Learning based Scheduling (DRL-S) techniques.
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    Visual assist system with enhanced localization for indoor navigation of visually impaired people
    (IEEE, 2024-10) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
    Large indoor spaces having complex layouts are often difficult to navigate. Indoor spaces in hospitals, universities, shopping complexes, etc., carry multi-modal information through text and symbols. Hence, it is difficult for Visually Impaired people to independently navigate such spaces. Indoor environments are usually GPS-denied; therefore, Bluetooth-based, WiFi-based, or Range-based methods are used for localization. These methods incur high setup costs, lack good accuracy, and sometimes need specialized sensing equipment. We propose a Visual Assist (VA) system for the indoor navigation of BVI individuals using visual fiducial markers for localization. State-of-the-art (SOTA) approaches for localization using visual fiducial markers use fixed cameras having a limited field of view. We employ a Pan-Tilt turret-mounted camera, which provides a 360° field of view for enhanced marker tracking. We, therefore, need fewer markers for mapping and navigation. We further use our localization model for enhancing existing SLAM methods, namely, Hector SLAM, ORBSLAM and UCOSLAM. The efficacy of the proposed system is measured on three metrics, i.e., Root Mean Square Error(RMSE), Average Distance to Nearest Neighbours (ADNN), and Absolute Trajectory Error (ATE). The proposed system offers accurate trajectory tracking upto ±8cm . ADNN and RMSE of Hector SLAM, ORB-SLAM, and UcoSLAM improve by 9.1%, 8.9%, and 7%, respectively while ATE is reduced by 6.7%, 4.5%, and 5.2%.
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    Autonomous Mapping and Navigation using Fiducial Markers and Pan-Tilt Camera for Assisting Indoor Mobility of Blind and Visually Impaired People
    (2023-10) Gautam, Avinash; Shekhawat, Virendra Singh; Mohan, Sudeept
    Large indoor spaces have complex layouts making them difficult to navigate. Indoor spaces in hospitals, universities, shopping complexes, etc., carry multi-modal information in the form of text and symbols. Hence, it is difficult for Blind and Visually Impaired (BVI) people to independently navigate such spaces. Indoor environments are usually GPS-denied; therefore, Bluetooth-based, WiFi-based, or Range-based methods are used for localization. These methods have high setup costs, lesser accuracy, and sometimes need special sensing equipment. We propose a Visual Assist (VA) system for the indoor navigation of BVI individuals using visual Fiducial markers for localization. State-of-the-art (SOTA) approaches for visual localization using Fiducial markers use fixed cameras having a narrow field of view. These approaches stop tracking the markers when they are out of sight. We employ a Pan-Tilt turret-mounted camera which enhances the field of view to 360° for enhanced marker tracking. We, therefore, need fewer markers for mapping and navigation. The efficacy of the proposed VA system is measured on three metrics, i.e., RMSE (Root Mean Square Error), ADNN (Average Distance to Nearest Neighbours), and ATE (Absolute Trajectory Error). Our system outperforms Hector-SLAM, ORB-SLAM3, and UcoSLAM. The proposed system achieves localization accuracy within ±8cm compared to ±12cm and ±10cm for ORB-SLAM3 and UcoSLAM, respectively.
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    D-MRFTE: A Decentralized Relay-Based Approach for Multi-Robot Unknown Area Exploration
    (IEEE, 2023) Gautam, Avinash; Mohan, Sudeept; Shekhawat, Virendra Singh
    In this paper, a decentralized relay-based approach (D-MRFTE) for unknown area exploration using a team of autonomous mobile robots is proposed under communication constraints. Using the relay robots, the multi-robot system forms a high-latency decentralized network with distributed copies of exploration information for which eventual consistency and completeness are ensured through meetups. The meetups act as a safety net and set a bound on latency by ensuring data transfer at periodic intervals whenever the multi-robot network gets fragmented. The information exchange related to the robot’s state and the ongoing exploration is facilitated by the relay robots. The robots use timestamps to assimilate the latest available information by using version vectors. To achieve a consistent state of explorer robots, the relays schedule meetups with other relays they come in contact with, creating a tightly-knit group. Our approach, under two communication models, i.e., Disk-based and Line-of-Sight-based, exhibits superior performance compared with two state-of-the-art algorithms in terms of completion time and distance traveled by the robot team. The simulations are conducted in a Player/Stage simulator with different robot team sizes.
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    WDM Network Topologies-A Probabilistic Model
    (IEEE, 2007) Chaubey, V.K.; Shekhawat, Virendra Singh
    This paper presents a simple generic network model to evaluate the network performance of an optical network. A probabilistic traffic model for a WDM optical network employing a ring and a star topology has been developed to investigate the call connection probability and router performances respectively. The numerical results show a huge data transmission through the router with a least dependence on the routing time up to a significant data rate.