Browsing by Author "Shekhawat, Virendra Singh"

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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.
Balanced partitioning of workspace for efficient multi-robot coordination
(IEEE, 2017) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
Multi-robot terrain coverage approaches that are based on Voronoi partitioning produce unbalanced partitions of the workspace resulting in uneven distribution of the workload to the individual robots. The proposed approach creates partitions of the workspace such that the regions to be covered by individual robots are maximally balanced. This type of partitioning can be especially useful in tasks like floor cleaning, surveillance etc. The proposed approach is suitable for use in indoor environments like office buildings, hospitals etc. It is assumed that the grid map of the workspace is already known. The workspace is transformed into a topological weighted connected graph. Vertex weight is defined by the size of the area it represents. This graph is then partitioned into sub-graphs that are maximally balanced in terms of vertex weights using genetic algorithm. These sub-graphs thus obtained represent balanced partitions which are assigned to the individual robots for further processing.
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.
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.
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.
Datacenter Workload Classification and Characterization: An Empirical Approach
(IEEE, 2018) Shekhawat, Virendra Singh; Gautam, Avinash
Datacenter traffic has increased significantly due to rising number of web applications on Internet. These applications have diverse Quality of Service (QoS) requirements making datacenter management a complex task. For a datacenter the amount of resources required for a given resource type (computing, memory, network and storage) is termed as workload. In cloud datacenters, workload classification and characterization is used for resource management, application performance management, capacity sizing, and for estimating the future resource demand. An accurate estimation of future resource demand helps in meeting QoS requirements and ensure efficient resource utilization. Thus modeling and characterization of datacenter workloads becomes necessary to meet performance requirements of applications in a cost-efficient manner. In this paper, a methodology to classify datacenter workloads and characterize them based on resource usage is proposed. Two different workloads have been used, one is Google Cluster Trace (GCT) dataset and other is Bit Brains Trace (BBT) dataset. Seven different machine learning algorithms for workload classification have been used. Workload distribution is estimated in a mix of heterogeneous applications for both GCT and BBT. The seven machine learning algorithms have been compared on the basis of their classification accuracy. Finally, an algorithm to estimate the importance of different attributes for classification is proposed in this paper.
De-COP: A Decentralized Community Convergence Approach for Message Forwarding in Pocket Switched Networks
(IEEE, 2022) Shekhawat, Virendra Singh; Gautam, Avinash
Pocket Switched Networks (PSNs) are an evolution of mobile ad-hoc and Delay Tolerant Networks in which there is no assumption made about the existence of a complete path between two nodes wishing to communicate, thus making routing even more challenging. Various routing algorithms have been proposed over the years like Epidemic, Spray and Wait, ProPHET, etc. There is a separate class of routing algorithms that exploit social structuralism to selectively forward messages to the best candidates. One such algorithm is BUBBLE Rap, which takes into account the notion of popularity and communities to make forwarding decisions. However, popularity as a sole deal-breaker is a rigid policy. We propose a decentralized community convergence-based message forwarding approach viz., De-COP that makes use of the familiarity metric which is dictated by the characteristic of a community to make message forwarding decisions. When familiarity is used for forwarding messages in converged communities, the messages are delivered with low latency and high probability. A community is defined as converged when the change in its membership is gradual. The forwarding node also takes into account the butter availability at the target node to reduce delay and message loss probability. The results were obtained using ONE simulator on two popular datasets, i.e., Infocomm06 and Cambridge to demonstrate the efficacy of the proposed De-COP approach in terms of improved delivery probability and message overhead ratio compared to ProPHET and BUBBLE Rap.
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.
Design and characterization of a modified WDM ring network – An analytical approach
(Elsevier, 2012-06) Shekhawat, Virendra Singh; Chaubey, V.K.
The present paper proposes the design of a modified ring network topology with a significant improvement in average delay and blocking probability over the simple ring network. The backbone ring network is modified by connecting the alternate nodes in regions identified statistically as of high traffic density. A mathematical model for the proposed modified network has been developed to investigate the blocking probability and average delay as the data traverses from source to destination. The analysis reports an improvement in the average delay performance with the inception of call priority at the processing node and thus provides a scope to implement the given grade of service.
Design and Development of Performance Enhancement Techniques in Optical WDM Networks
(BITS, Pilani, 2013-10) Shekhawat, Virendra Singh
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.
Efficient content caching for named data network nodes
(ACM Digital Library, 2020-02) Shekhawat, Virendra Singh; Gautam, Avinash
Named Data Networking (NDN) is a promising Content-Centric Network (CCN) architecture that supports data distribution and data sharing by in-network ubiquitous content caching. In NDN, each router has content store to cache data packets passed by and, therefore, frequently requested content by consumers (e.g., end hosts) is cached at multiple routers in the network. Content caching at routers enables data delivery to consumers from a nearest location with minimal latency and thereby enhances overall network performance. Content store at nodes should have sufficient space to hold the current frame of locality of reference for attaining a good hit rate. The content store size requirement for each node is different due to their topological characteristics. Homogeneous caching mechanisms distribute the total cache budget equally among the nodes irrespective of their topological characteristics. In contrast, heterogeneous caching allocates cache to the nodes based on their topological importance. In this paper, a heterogeneous on-path cache budget distribution approach is proposed that distributes cache to the content stores based on reference locality of the nodes. The proposed cache distribution algorithm is evaluated for structured and unstructured network topology using the ndnSIM simulator. The results are compared with the homogeneous cache distribution mechanism and 14% improvement in cache hit rate is achieved.
Efficient Heuristic Algorithms for Routing and Wavelength Assignment in Optical WDM Networks
(SSRN, 2012-11) Shekhawat, Virendra Singh; Chaubey, V.K.
The paper presents two heuristic Routing and Wavelength Assignment (RWA) algorithms named Least Count First (LCF) and Least Recently Used First (LRUF) for light-path routing in optical WDM networks under dynamic traffic conditions to meet wavelength continuity constraint. The performance analysis of these two algorithms has been simulated to compare the findings with the existing Fixed Order (FO) algorithm. The analysis reports a significant improvement for the proposed algorithms over the FO algorithm in terms of connection requests handling capability, as the former needs less wavelength scans to satisfy a connection request.
Experimental Evaluation of Multi-Robot Online Terrain Coverage Approach
(IEEE, 2018) Shekhawat, Virendra Singh; Gautam, Avinash; Mohan, Sudeept
This paper presents a empirical evaluation of some approaches suggested in the literature for solving the online terrain coverage task. Our first contribution is that, we have implemented in simulation four state-of-the-art approaches. The first two approaches are based on structured trajectories and use backtracking mechanism for task allocation. The other two are based on the behavior of ants. Also, we have modified one of the state-of-the-art approaches and improved its performance in terms of computation time. The second contribution is that, we have developed a practical test-bed comprising of multiple differential drive robots that are able to coordinate with each other in a distributed fashion by wirelessly communicating with their team-mates. We have implemented the representative set of approaches on our test-bed. The same test-bed can be leveraged for validating multi-robot coordination approaches for solving other tasks like patrolling, foraging, etc.
A Graph Partitioning Approach for Fast Exploration with Multi-Robot Coordination
(IEEE, 2019) Shekhawat, Virendra Singh; Mohan, Sudeept; Gautam, Avinash
A multi-robot exploration approach is suggested in this paper that works on the premise that the topo-metric map of the indoor environment is known a priori. Genetic Algorithms (GAs) are used for spatial partitioning of the topo-metric graph of the environment. Each spatial partition, which represents the sub-graph, is apportioned to a unique robot by using the Hungarian method for task assignment in conjunction with Bully Algorithm for leader election. In the case of robot(s) failure, graph re-partitioning and single item auctions are used for re-assigning the remaining task(s) of the failed robot(s) to other robots. The proposed approach performs better than a recent state-of-the-art strategy that employs Delaunay triangulation and multi-prim algorithm for multi-robot exploration. Empirical results obtained in simulation by varying the number of robots in two different and complex environments prove the efficacy of the proposed approach.
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.
Latency and Routing Efficiency Based Metric for Performance Comparison of DHT Overlay Networks
(IEEE, 2016) Shekhawat, Virendra Singh
The general method available for comparison between the Distributed Hash Table (DHT) based structured overlay networks are average latency per successful lookup and percentage of successful lookups. For a DHT based lookup service with lookup retries for failed lookups, both average latency per successful lookup as well as percentage of successful lookups are key to evaluate the performance. Although, average latency per successful lookup may be less for a DHT overlay protocol, failure of lookups at high churn rates may degrade the protocol's performance if its routing efficiency is less. On the other hand, just routing efficiency is not sufficient to measure the performance of a DHT since an overlay network having higher routing efficiency may not have a good performance due to high average latency per successful lookup. We have developed a metric for DHT comparison which incorporates both average latency per successful lookup and routing efficiency in scenarios where applications do lookup retries. We have also modeled three different timeout mechanisms while developing the performance metric. The paper also incorporates a model where the probability of failure of a lookup is increasing at every retry of lookups.
A Machine Learning Approach for Traffic Flow Provisioning in Software Defined Networks
(IEEE, 2020) Shekhawat, Virendra Singh
With the recent surge of machine learning and artificial intelligence, many research groups are applying these techniques to control, manage, and operate networks. Soft-ware Defined Networks (SDN) transform the distributed and hardware-centric legacy network into an integrated and dynamic network that provides a comprehensive solution for managing the network efficiently and effectively. The network-wide knowledge provided by SDN can be leveraged for efficient traffic routing in the network. In this work, we explore and illustrate the applicability of machine learning algorithms for selecting the least congested route for routing traffic in a SDN enabled network. The proposed method of route selection provides a list of possible routes based on the network statistics provided by the SDN controller dynamically. The proposed method is implemented and tested in Mininet using Ryu controller.
A message acknowledgment based congestion control (MACC) for delay tolerant networks
(IEEE, 2016) Shekhawat, Virendra Singh
Delay Tolerant Networks (DTNs) are vulnerable to intermittent connectivity due to opportunistic contacts between mobile nodes. Therefore, routing protocols designed for these networks rely on spreading multiple copies of data packets or messages in the network to achieve a better delivery ratio. Although, multiple copies of messages enhance the message delivery probability, but simultaneously these extra copies of messages occupy finite buffer space and scarce wireless bandwidth. Eventually, it results in message drops, delay in delivery and poor delivery ratio. This condition in DTNs is termed as network congestion. Further, this congestion is aggravated by the buffering of the delivered messages. Since destination based explicit acknowledgment packet generation is not feasible for such networks, therefore nodes have to buffer the delivered messages until their lifetime expires. Such messages significantly contribute in the congestion. To address the congestion occurred due to the buffering of delivered messages, we propose a node contact based message acknowledgment algorithm. In this algorithm, whenever nodes come into the contact with each other they exchange information about the delivered messages and update their relative message buffers. The proposed mechanism is routing protocol independent as well as it can be easily integrated with other congestion control mechanisms. Extensive simulations, with different mobility models, routing protocols and congestion control schemes, signify the improvement in message delivery ratio for the proposed mechanism.
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.