Browsing by Author "Chalapathi, G.S.S."

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A Blockchain based Framework for Secure Data Offloading in Tactile Internet Environment
(IEEE, 2020) Chamola, Vinay; Chalapathi, G.S.S.
The rapid increase in the number of mobile devices across the globe has brought a new challenge to the forefront, one of mobile traffic management. The ever-increasing number of mobile devices leads to the generation of a large amount of data and computationally intensive applications, which contributes heavily to cellular network congestion. To solve this issue, we propose a mobile data offloading scheme based on a distributed ledger technology (DLT). Existing mobile data offloading schemes based on DLT employ conventional blockchain to set up a peer-to-peer (P2P) network of mobile users. Although these schemes have gained ground in improving the Quality of Experience (QoE) for end-users, they lack efficiency and scalability. Furthermore, generic blockchain does not provide timestamp ordering of events, which is necessary to ensure the computation of delay-sensitive tasks. To overcome these challenges, we propose the use of a directed acyclic graph (DAG) data structure for mobile data offloading. Finally, to ensure time and cost optimality, a game-theoretic approach has been proposed in this paper.
E-SATS: An Efficient and Simple Time Synchronization Protocol for Cluster- Based Wireless Sensor Networks
(IEEE, 2019) Chamola, Vinay; Chalapathi, G.S.S.
Over the past decade, the Internet of Things (IoT) has attracted enormous interest from the research community and industry. IoT requires a synergy of various technologies, and wireless sensor networks (WSNs) are poised to play a critical role in many IoT applications like weather monitoring, smart-grids, smart-city and so on. The synchronization of local clocks of the WSN nodes is essential in many network functionalities, and thus a time synchronization protocol is required in WSNs. Although several synchronization protocols have been proposed for WSNs, most of them are simulation-based works. They make many assumptions at a high-abstraction level and do not consider the conditions of the line-of-sight (LOS) in the network. These factors significantly affect the performance of these protocols. Thus, conclusive experimental proof of the effectiveness of these protocols for different LOS conditions is required. In this direction, this paper proposes a time synchronization protocol called efficient and simple algorithm for time synchronization (E-SATS) for a cluster-based WSN. In this paper, E-SATS has been tested on a large-sized WSN testbed in different LOS scenarios and compared with the existing state-of-the-art protocols. E-SATS outperformed existing protocols by achieving up to six times better accuracy as compared to existing protocols with significantly lesser computations and energy consumption.
EFTA: An Energy-efficient, Fault-Tolerant, and Area-optimized UAV Placement Scheme for Search Operations
(IEEE, 2022) Chalapathi, G.S.S.
Unmanned aerial vehicle (UAV) networks have widespread applications, ranging from surveillance and disaster management in the military domain to transportation and delivery of goods in the civilian domain. Regardless of the application, the placement of routing UAV nodes (especially in networks spanning long distances) is crucial in determining network performance parameters such as network lifetime and data transmission delay. In this paper, an Energy-efficient, Fault-Tolerant, and Area-optimized UAV placement scheme (EFTA) is proposed for search operations. A cluster-based UAV network is considered, in which the Cluster Members (CMs) are mobile and scan the geographic area of interest. The Cluster Heads (CHs) are quasi-static and route information from the CMs to the Ground Control Station (GCS). A multi-objective Cuckoo Search Algorithm is used to determine the placement of the CHs while minimizing energy consumption, maximizing area coverage, and maximizing tolerance to node failures. Further, a comprehensive analysis was performed against a state-of-theart UAV placement algorithm. The analysis showed that EFTA gives a significant performance improvement when compared to the competing placement scheme in fault tolerance, power consumption, network lifetime, end-to-end delay, and packet delivery ratio.
Energy and latency aware mobile task assignment for green cloudlets
(Elsevier, 2022-07) Chalapathi, G.S.S.; Chamola, Vinay
Edge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, many cloudlets are required in such an edge computing network, leading to a tremendous increase in carbon emissions of computing networks globally. This increase in carbon emission envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). But GCNs must deal with the problem of the unpredictability of green energy available to them while optimizing the performance (in terms of latency) delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs to address this issue. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green-energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reduction in the latency while ensuring up to 50.15% of reduction in the energy consumption than other comparable task-assignment schemes.Thus, Ge-LATA suggests that it leads to an optimal task assignment by considering the various factors mentioned above during the task assignment process. Thus, Ge-LATA considers the above-mentioned extensive set of parameters during the task allotment process. It also proposes an efficient green energy allotment scheme that adapts itself to actual weather and network conditions, leading to optimal task assignment decisions in GCNs.
Enhancing data privacy: a comprehensive survey of privacy-enabling technologies
(IEEE, 2025-02) Chalapathi, G.S.S.
Privacy is a fundamental human right, especially crucial in our modern digital age. With the rapid advancement of technology, ensuring individuals’ privacy has become increasingly complex. Our survey paper aims to shed light on various privacy engineering technologies that play a crucial role in protecting personal data. We delve into four key areas: data anonymization, data encryption, synthetic data generation, and differential privacy. These technologies serve as essential tools in safeguarding online privacy. Data anonymization, for instance, includes removing or modifying identifiable information from datasets to protect individuals’ identities. Encryption secures data by converting it into a code that can only be decoded by authorized parties. Synthetic data generation creates artificial data that closely resembles real data but doesn’t contain any identifiable information. Differential privacy adds a small amount of controlled noise to protect sensitive information. Throughout our exploration, we not only explain the principles and techniques behind these technologies but also the tools used for each of these techniques and evaluation criteria and also examine their practical applications. By understanding their strengths, limitations, and real-world implementations, we gain valuable insights into how they contribute to the broader goal of ensuring privacy in our digital world.
A Framework for Secure Vehicular Network using Advanced Blockchain
(IEEE, 2020) Chamola, Vinay; Chalapathi, G.S.S.
Vehicular Ad-hoc Network (VANET) poses to be a promising technology for the future since it increases the comfort level of the drivers while also enhancing the safety measures for them. The main aim of VANETs is to enable communication among vehicles and roadside units (RSUs) using vehicle-to-vehicle (V2V) and vehicle-to-RSU (V2R) networks. VANET applications have a vast potential for growth owing to the increasing number of smart cities around the globe and advancement taking place in the technology sector. However, with all their benefits, VANETs also face several security challenges. The sensitive nature of data being transferred turns VANETs prone to malicious attacks. To overcome the security challenges, this paper proposes a distributed Directed Acyclic Graph (DAG) enabled vehicular network comprising several requesting vehicles and RSUs. The proposed model is based on advanced blockchain and therefore provides a strong level of security and data immutability. Furthermore, the interactions between the requesting vehicles and the RSUs have been modeled using an auction-based game-theoretic smart contract deployed on the blockchain.
From information overload to lucidity: a survey on leveraging gpts for systematic summarization of medical and biomedical artifacts
(IEEE, 2024-12) Chalapathi, G.S.S.; Singh, Amit Rajnarayan
In medical research, the rapid proliferation of condition-specific studies has led to an information overload, making it challenging for researchers and practitioners to stay abreast of the latest findings. This paper presents a comprehensive survey on leveraging Generative Pretrained Transformers (GPTs) to summarize medical and biomedical artifacts systematically. We delve into the current applications of GPTs in this domain, discussing their role in understanding and summarizing research papers, medical dialogues, and medical records. Through a comparative analysis of recent studies and methodologies, we highlight the effectiveness of GPTs in distilling complex medical information into concise, understandable summaries. Our survey underscores the potential of GPTs as a tool for navigating the information overload in medical research and bringing clarity to healthcare professionals. This transformation will enhance patient care and outcomes, such as improving the accessibility and comprehensibility of medical research, assisting in rapid information retrieval, and facilitating the summarization of complex medical studies for broader audiences.
Hardware validated efficient and simple Time Synchronization protocol for clustered WSN
(IEEE, 2016) Chalapathi, G.S.S.; Chamola, Vinay
Time Synchronization in WSN is a well-researched topic. But there are very few algorithms which have been proven to be scalable and efficient on a hardware platform. We consider cluster-based network and try to improve the efficiency of the existing algorithms. Our algorithm called as Simple Algorithm for Time Synchronization (SATS) is based on an existing receiver-sender synchronization algorithm [1]. We implement it in a cluster-based topology and do its performance analysis on a large WSN test bed. Its performance is compared with commonly used regression based synchronization protocol both analytically and on the hardware platform. Our experiments show that our algorithm shows a significant improvement in terms of average synchronization error and computational efficiency over regression based method.
Hardware-Based Implementation of Target Tracking in Unmanned Aerial Vehicles (UAVs)
(IEEE, 2023) Chalapathi, G.S.S.
Unmanned Aerial Vehicles (UAVs) have gained sig-nificant attention in various fields, including surveillance, search and rescue, and monitoring applications. One important application for UAV s is target tracking, which requires detecting and tracking a specific object of interest in real time. This paper surveys work done so far in the area of target-tracking in UAV s. It then presents a comprehensive hardware-based framework for target tracking in UAV s. This work utilizes the State-of-the-Art YOLOv8 (You Only Look Once) algorithm for target detection, an efficient high-speed target tracking model, and a Proportional Derivative (PD) control algorithm for precise drone movement control. YOLOv8 provides fast, accurate, and real-time detection of the object of interest, allowing the UAV to detect and identify the target object quickly and reliably. Subsequently, a robust tracking algorithm tracks the identified object across consecutive frames, ensuring accurate localization and trajectory estimation. Furthermore, a PD control algorithm is integrated into the system to enable precise and smooth drone movement. The proposed framework is integrated and used for target tracking in UAV s. Further, this framework is implemented on a UAV. The results demonstrate the effectiveness and robustness of the proposed framework, showcasing its potential for real-world applications.
Industrial Internet of Things (IIoT) Applications of Edge and Fog Computing: A Review and Future Directions
(Springer, 2021-01) Chamola, Vinay; Chalapathi, G.S.S.
With rapid technological advancements within the domain of Internet of Things (IoT), strong trends have emerged which indicate rapid growth in the number of smart devices connected to IoT networks and this growth cannot be supported by traditional cloud computing platforms. In response to the high volume of data being transferred over these networks, the edge and fog computing paradigms have emerged. These paradigms are extremely viable frameworks that shift computational and storage resources from the centralized cloud servers to distributed LAN resources and powerful embedded devices at the edge of the network. These computing paradigms, therefore, have the potential to support massive IoT networks of the future and have fueled the advancement of IoT systems within industrial settings, leading to the creation of the Industrial Internet of Things (IIoT). IIoT is revolutionizing industrial processes in a variety of domains. In this chapter, we elaborate on the impact and viability of edge and fog computing paradigms in IIoT through a use-case approach. Finally, we conclude with the future research directions like security and privacy for edge and fog computing in IIoT, relevance of Blockchain for IIoT, programmability and task partitioning, virtualization, etc.
Integrated Cooperative Synchronization for Wireless Sensor Networks
(IEEE, 2019-06) Chalapathi, G.S.S.
A precise decentralized clock synchronization method, referred to as integrated cooperative synchronization (ICS) is proposed in this letter. ICS is a delay compensating method that relies on mean-field message passing, where the measurement phase is integrated into the message passing phase. By selecting an extended factorization of the underlying joint a-posteriori distribution of the clock parameters and an appropriate message scheduling for link initialization, ICS is conceptually much simpler than conventional message-passing methods while achieving similar accuracy and reduced computational complexity. Moreover, it has only slightly higher implementation requirements compared to less accurate consensus methods.
Latency aware mobile task assignment and load balancing for edge cloudlets
(IEEE, 2017) Chamola, Vinay; Chalapathi, G.S.S.
With the various technological advances, mobile devices are not just being used as a means to make voice calls; but are being used to accomplish a variety of tasks. Mobile devices are being envisioned to practically accomplish any task which could be done on a computer. This is hurdled by the limited computational resources available with the mobile devices due to their portable size. With the mobile devices being connected to the Internet, leveraging cloud services is being seen as a promising solution to overcome this hurdle. Computationally intensive tasks can be offloaded to the Cloud servers. However, owing to the latency and cost associated with using cloud services, edge devices (termed cloudlets) stationed near the mobile devices are being seen as a prospective alternative to replace/assist the Cloud services. The mobile devices have an easier access to the cloudlets being situated in their vicinity and can offload their task requests to them to be served at a lower cost. This paper considers a network of such connected cloudlets which provide service to the mobile devices in a given area. We address the issue of task assignment in such a scenario (i.e. which cloudlet serves which mobile device) aimed towards improving the quality of service experienced by the mobile devices in terms of minimizing the latency. Through numerical simulations we demonstrate the performance gains of the proposed task assignment scheme showing lower latency as compared to the traditional scheme for task assignment.
LoRa-Based Wireless Sensor Network Testbed for Precision Agriculture Application
(IEEE, 2024) Chalapathi, G.S.S.
Over the past few years, Wireless Sensor Network (WSN) has seen many improvements, and there are various applications of WSNs in various domains. Most communication technologies have a trade-off between distance and power consumption, i.e., to reach a longer distance, high power is consumed. LoRa overcomes this problem by consuming less power and transmitting small data packets for a long distance. The objective of this research work is to use LoRa technology in Precision Agriculture applications to help the stakeholders in better decision-making. A small experimental testbed is set up for precision agriculture applications. This testbed had sensors to monitor pH, soil moisture, soil temperature, and NPK parameters. An Automatic Weather Station (AWS) is set up to monitor ambient weather parameters-temperature, humidity, rainfall, wind speed and direction, barometric pressure, solar radiation, and leaf wetness. These sensor parameters were collected at the LoRa Gateway and forwarded to a network server hosting the The Things Network (TTN) LoRa stack. Transmission statistics are collected and analyzed for this application for remote monitoring of agricultural farms for quick and efficient decision-making.
Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques
(Springer Nature, 2025-07) Srinivas, Rallapalli; Chalapathi, G.S.S.; Singh, Amit Rajnarayan
Modeling the spatial variability and uncertainty of soil fertility parameters is crucial for sustainable agriculture but remains a challenge due to complex interactions between soil properties. Traditional models often assess individual parameters, such as pH or nitrogen (N), without considering their combined influence and uncertainty. This study develops a fuzzy logic and geoinformatics-based approach to simultaneously assess multiple soil fertility parameters. The model integrates 80 fuzzy rules to evaluate macro- and micronutrients, incorporating 250 soil samples analyzed using the PUSA Soil Test and Fertilizer Recommendation Meter (STFR). Experimental results showed soil fertility parameter ranges: pH (7.46–8.26), ECe (0.267–0.807 dS m−1), organic carbon (0.24–0.56%), N (85.56–146.32 kg ha−1), P (21.99–34.28 kg ha−1), K (116.41–156.16 kg ha−1), S (5.60–20.86 mg kg−1), Fe (1.065–5.095 mg kg−1), Mn (2.058–2.637 mg kg−1), Zn (0.748–1.105 mg kg−1), B (0.372–0.530 mg kg−1), and Cu (0.230–0.788 mg kg−1). The fuzzy model-derived fertility scores ranged from 41.55 to 52.60, with pH, organic carbon, nitrogen, phosphorus, potassium, and iron as critical parameters influencing fertility. Geostatistical kriging interpolation estimated fertility values at unsampled locations, generating a continuous, high-resolution soil fertility map for precision agriculture. Validation with crop yield data ranked suitability as: Pearl millet (0.919) > Mustard (0.890) > Wheat (0.863) > Barley (0.861). Multi-criteria decision analysis confirmed pearl millet as the most suitable crop based on fertility and yield potential. The study categorizes soil into low and moderate fertility zones across Jhunjhunu, Rajasthan, ensuring a systematic assessment for optimal nutrient management. By integrating fuzzy logic with GIS-based spatial modeling, this study enhances soil fertility classification, site-specific nutrient recommendations, and sustainable crop planning, reinforcing the role of fuzzy-GIS frameworks in precision agriculture.
Medical image segmentation using advanced UNETt: VMSE-Unet and VM-Unet CBAM+
(2025-07) Chalapathi, G.S.S.
In this paper, we present the VMSE U-Net and VM-Unet CBAM+ model, two cutting-edge deep learning architectures designed to enhance medical image segmentation. Our approach integrates Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) techniques into the traditional VM U-Net framework, significantly improving segmentation accuracy, feature localization, and computational efficiency. Both models show superior performance compared to the baseline VM-Unet across multiple datasets. Notably, VMSEUnet achieves the highest accuracy, IoU, precision, and recall while maintaining low loss values. It also exhibits exceptional computational efficiency with faster inference times and lower memory usage on both GPU and CPU. Overall, the study suggests that the enhanced architecture VMSE-Unet is a valuable tool for medical image analysis. These findings highlight its potential for real-world clinical applications, emphasizing the importance of further research to optimize accuracy, robustness, and computational efficiency.
MPLS based hybridization in SDN
(IEEE, 2017) Shekhawat, Virendra Singh; Chalapathi, G.S.S.; Sinha, Yash
The new paradigm of Software Defined Networking (SDN) although has great potential to address the complex problems presented by enterprise networks, it has its own deployment and scalability issues. Further, a full SDN deployment has its own business and economic challenges. A smooth transition from legacy networks to SDN (disruption free, accommodating budget constraints, with progressive improvement in network management) requires a hybrid networking model as an inevitable intermediate step; that allows heterogeneous paradigms to function together while the full transition is realized in phases. Therefore, the need of the hour is to develop an incremental deployment strategy that caters to the needs of the organization. We present here a class-based hybrid SDN model for Multi Protocol Label Switching (MPLS) networks. We discuss the model, design, components, their interactions, advantages and drawbacks. We also present a n implementation and evaluation of a prototype. In legacy networks, MPLS architecture closely resembles SDN paradigm in terms of separation of control and data planes, flow-abstraction etc. Moreover, ISPs have preferred MPLS over the years due to benefits of virtual private networks and traffic engineering. The central idea is to partition traffic using forwarding equivalence classes at the ingress router, the rules of which can be updated via a centralized controller using OpenFlow. Therefore, we aim to use the standard MPLS data-plane together with a control-plane based on OpenFlow to come up with a systematic incremental deployment methodology as well as a hybrid operation model
Multi-Objective MDP-Based Routing in UAV Networks for Search-Based Operations
(IEEE, 2024-05) Chalapathi, G.S.S.; Chamola, Vinay
Unmanned aerial vehicle (UAV) systems have gained widespread recognition due to their versatility and autonomy. Their deployment for disaster mitigation and management operations is seen as one of their most important applications over the past decade. In such UAV networks, routing plays a crucial role in determining network performance parameters such as network lifetime, data transmission latency, and packet delivery ratio. This paper presents a novel routing mechanism - Multi-Objective Markov Decision Based Routing (MOBMDP) for UAV networks carrying out search-based operations. MOBMDP models routing decisions in a Markov Decision Process (MDP) framework and uses Q-learning to take decisions. It compares routing paths using three metrics, viz., Remaining Energy of the Minimum Energy Node (REMEN), Power Distance ratio (PD), and Expected Delay (ED). Amongst these metrics, PD is a novel metric proposed by this work. PD simultaneously optimizes the energy efficiency and energy distribution in the network. Further, MOBMDP uses a novel reinforcement learning inspired method to estimate transmission delay in a given path. Intensive simulation studies compare MOBMDP to four state-of-the-art routing protocols. Results show a significant improvement in network lifetime, packet delivery ratio, energy efficiency, average data transmission delay, and error in delay estimation.
Non-Fungible Tokens (NFTs)—Survey of Current Applications, Evolution, and Future Directions
(IEEE, 2023-12) Chalapathi, G.S.S.
Non-fungible tokens (NFTs) have become an exciting technology that provides a fresh perspective on asset ownership, provenance, and value exchange. NFTs, a blockchain-based technology, are distinct and indivisible cryptographic tokens used to confirm and record the ownership of digital and physical assets in an immutable and transparent way. The fundamental block of NFT is a smart contract built on a blockchain network. This contract contains specific information about the asset it represents, such as its unique identifier, metadata, and ownership details. The information is kept private and tamper-proof due to the decentralized and distributed structure of the blockchain, boosting faith in the token’s authenticity. The NFT is gaining popularity, but it is still in the developing stage. There is a need for a comprehensive survey to guide future research and development in NFTs. Thus, this paper presents the technical components of NFTs, their features, and the minting process. Further, this survey paper describes different token standards for NFTs. It presents various applications of NFTs in healthcare, supply chain, gaming, identity verification, agriculture, intellectual property, smart cities, charity and donation, and education. The article also emphasizes the significant difficulties faced currently in implementing NFT technology from the viewpoints of ownership, governance, and property rights, as well as security, privacy, and environmental effects. This work also elucidates the future directions to overcome the challenges in adopting NFTs in various applications.
An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets
(Elsevier, 2020-01) Chalapathi, G.S.S.; Chamola, Vinay
Over the past decade, there has been an increasing demand for mobile devices to perform computationally intensive tasks. However, the computational capability of these devices is limited due to memory, power and portability constraints. One of the feasible and attractive ways to enhance the performance of the resource-limited mobile devices is to offload their computationally intensive tasks on to the cloud servers when internet connectivity is available. However, when cloud servers are involved in processing, the latency and cost of computation increases. To mitigate these problems, devices with high computational resources, called cloudlets, can be deployed in the locations close to the mobile users/devices. The mobile devices can then offload their computationally intensive tasks on to them. Due to easier access and nearness of the cloudlets, the cost and latency in processing the tasks decreases. In this work, we focus on task assignment problem in a multi-cloudlet network connected via a wireless SDN network, which services the task offload requests from mobile devices in a given locality. The aim of the proposed solution is to minimize latency and thus enhance the quality of service for mobile devices. We prove the optimality of the proposed solution mathematically and employ an admission control policy to maintain this optimality even in heavily loaded networks. We also perform numerical simulations for two scenarios of small and large networks and evaluate the performance for varying traffic and network parameters. The results demonstrate that the proposed task assignment method offers reduced latency compared to state-of-the-art task assignment approaches and hence improves the quality of service offered to mobile devices.
Privacy Utility Tradeoff Between PETs: Differential Privacy and Synthetic Data
(IEEE, 2024-11) Chalapathi, G.S.S.
Data privacy is a critical concern in the digital age. This problem has compounded with the evolution and increased adoption of machine learning (ML), which has necessitated balancing the security of sensitive information with model utility. Traditional data privacy techniques, such as differential privacy and anonymization, focus on protecting data at rest and in transit but often fail to maintain high utility for machine learning models due to their impact on data accuracy. In this article, we explore the use of synthetic data as a privacy-preserving method that can effectively balance data privacy and utility. Synthetic data is generated to replicate the statistical properties of the original dataset while obscuring identifying details, offering enhanced privacy guarantees. We evaluate the performance of synthetic data against differentially private and anonymized data in terms of prediction accuracy across various settings—different learning rates, network architectures, and datasets from various domains. Our findings demonstrate that synthetic data maintains higher utility (prediction accuracy) than differentially private and anonymized data. The study underscores the potential of synthetic data as a robust privacy-enhancing technology (PET) capable of preserving both privacy and data utility in machine learning environments.