Department of Computer Science and Information Systems

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Subject: search.filters.subject.Internet of Things (IoT)

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Now showing 1 - 10 of 22
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    Leveraging precision agriculture techniques using UAVs and emerging disruptive technologies
    (Elsevier, 2024-07) Gupta, Shashank
    The next great innovation in Unmanned Aerial Vehicles (UAV) technology is smart UAVs, which aim to provide new possibilities in numerous applications. There is an increasing usage of UAVs in various fields of civil applications including live tracking, wireless connectivity, distribution of goods, remote sensing, protection and surveillance, precision agriculture, and review of civil infrastructure. UAVs or drones have a tremen- dous potential to provide smart farming with various productive solutions. Internet of Things (IoT) technologies together with UAVs are anticipated to transform agriculture, allowing decision- making in days rather than weeks, offering substantial cost savings and yield increases. These technologies are employed in a number of different ways, from monitoring crop status and amount of moisture in soil in real time to using drones to help with activities such as the application of pesticide spray. Nonethe- less, the employment of such IoT and smart networking technol- ogy, exposes the smart farming ecosystem to cyber security risks and vulnerabilities. This survey gives a detailed understanding of UAV applications in Precision Agriculture (PA). In this survey, we demonstrate a comprehensive analysis on security and privacy in a smart farming scenario. In this complex and dispersed cyber- physical environment, we describe how Blockchain technology along with 5 G in UAVs communication network can dissipate the security issues of the network. The survey addresses possible scenarios for cyber threats and the advancement in the fields of machine learning and artificial intelligence that can boost cybersecurity. At last, the survey outlines open research issues and future directions in the field of cybersecurity in UAVs and PA.
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    Dyswitch: dynamic switching to enable secure and energy efficient data communication in resource constrained iot environmentattack detection in data plane
    (Springer, 2025-04) Haribabu, K.
    Numerous applications spanning smart health, smart cities, smart parking, smart agriculture, smart homes, and smart transportation rely extensively on internet of things (IoT) systems. These systems depend on the periodic sensing of the physical environment, employing wireless sensor networks (WSNs) to collect vast amounts of data. Given the importance of safeguarding this data against diverse attacks, traditional security mechanisms may prove impractical for resource constrained WSN devices. Lightweight cryptographic algorithms emerge as a fitting solution for such environments. This paper introduces a system proposed to dynamically transition between available lightweight cryptographic algorithms, guided by factors such as the desired security level, network status (e.g. bit error rate), and user requests. Through this dynamic adaptive approach, the proposed system ensures swift adaptability to evolving security requirements and network conditions. Moreover, this methodology highlights a nuanced integration of cryptographic algorithms, catering to the evolving needs of modern IoT environments.
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    Evolution and Adoption of Next Generation IoT-Driven Health Care 4.0 Systems
    (Springer, 2022-07) Gupta, Shashank
    The uprising of Internet of Things (IoT) has dramatically influenced the world’s technology in terms of interoperability, connectivity and interconnectivity with help of smart sensors, devices, objects, data and applications. General population aging, dearth of healthcare resources and upsurge in healthcare costs makes IoT advancements in healthcare all the more essential in order to confront these challenges. The revolution in IoT healthcare is redeveloping the healthcare sector in every aspect – social, technical and economical. This article presents a comprehensive survey on upcoming technologies helpful in healthcare 4.0 systems where the major focus is on emerging technologies like fog computing, cloud computing, machine learning and Bigdata analytics and that are all based on IoT based healthcare applications. In addition, the authors also provided an exhaustive survey on Wide Body Area Network (WBAN)-based IoT health care systems and discussed their network topology, architecture, platform, services and their applications. In addition, this study analyses IoT healthcare security challenges, possible threats, attack taxonomies and how blockchain technology can be helpful in countering these challenges. Lastly, exhaustive state-of-the-art technologies, challenges identified so far and possible future scope of this domain is also discussed in this survey.
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    Leveraging big data analytics in 5G-enabled IoT and industrial IoT for the development of sustainable smart cities
    (2022-07) Gupta, Shashank
    There has been an exponential growth in the number of low-cost heterogeneous sensor devices that are connected to the internet in the existing infrastructure of smart cities in the past decade. These sensors and actuator devices are employed in various industries to collect invaluable data that greatly impacts business decisions. State-of-the-art research is being carried out to process the high-volume data collected in high velocity streams with high variability in order to draw meaningful insights to cater to business needs in various domains. Big data analytics finds diversified opportunities in 5G-enabled Internet of Things (IoT) and industrial IoT environment to study the data patterns and produce new results which provides big organizations a conducive environment to take informed decisions. This article presents an exhaustive investigation of the various applications and algorithms of the big data analytics in 5G-driven IoT and industrial IoT systems with a detailed taxonomy of the existing analytical systems and also the challenges specific to the applications in an IoT environment. A holistic understanding on the importance of security and privacy of big data during the development of smart city infrastructure in high velocity streaming process has been explained for a broad understanding of the big data streaming processes. A thorough analysis of important data mining algorithms such as classification, association rule mining, clustering, and prediction methods for big IoT data from the recent literature also has been presented to provide a clear understanding of existing issues in handling big data in 5G-enabled IoT for the development of sustainable smart city infrastructure.
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    Drones as a service (DaaS) for 5G networks and blockchain-assisted IoT-based smart city infrastructure
    (Springer, 2024-04) Gupta, Shashank
    Recent ground-breaking research in the Internet of Things (IoT) and drone technology has brought about revolutionary advancements in automation, remote sensing, and remote operations. Drones have gained massive popularity in IoT applications, like remote monitoring and task automation. Consequently, an innovative business model called Drones-as-a-Service (DaaS), which aims at leasing out drones for commercial use, is gaining momentum in the market. This spike in demand for drones is credited to their ease of deployment, flexibility of operation, and risk-free functioning. To fully utilize the advantages that drones provide and further delve into the flourishing field of drone research, it is essential to acquaint ourselves with the scope of this field and understand the capabilities of a drone. The methodology employed in this survey paper encompassed a thorough literature review to establish a foundation, followed by the design and implementation of a comprehensive survey instrument. As a result, this survey provides a means for the reader to explore diverse drone research topics, ranging from basic drone types and deployment architectures to complex drone networking and communication systems. Intelligence-based enhancements for drone communications and Blockchain-based security solutions have also been discussed. We have conducted detailed comparisons between different classes and architectures of drones, drone communication protocols (WiFi, LTE, 5G, satellites), and drone-enabled 5G networks (Ad hoc networks and Software Defined Networks). Blockchain-based and Intelligence-assisted solutions for 5G-related drone network security concerns have also been discussed. We also highlight the utility of drones in present-day Smart City Infrastructure, ranging across multitudes of applications, as well as in the Precision Agriculture domain and their future scope. Finally, we outline significant security challenges and cyber-attacks faced by drones and their countermeasures, as proposed in the state-of-the-art literature. In conclusion, we end this survey by discussing the future scope and directions of drone research.
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    Enabling AI in Agriculture 4.0: A Blockchain-Based Mobile CrowdSensing Architecture
    (Springer, 2024-04) Bhatia, Ashutosh; Tiwari, Kamlesh
    Agriculture 4.0 relies on extensive data for predictive services, necessitating effective data collection. Mobile CrowdSensing (MCS), with its cost-effectiveness and scalability, addresses this need but faces centralization limitations. Blockchain-based frameworks have been proposed to mitigate these issues but often focus solely on data collection, lacking a comprehensive end-to-end architecture for smart agriculture. Recent literature has explored the integration of the Internet of Things (IoT), edge computing, fog computing, and cloud computing capabilities to establish centralized end-to-end architectures. Nonetheless, these architectures come with their own set of centralized limitations. In the context of contemporary technologies, the integration of blockchain and digital twin (DT) holds the potential to revolutionize the field of smart agriculture. This paper introduces a holistic end-to-end, layered, and service-oriented architecture for Agriculture 4.0, integrating mobile crowdsensing, blockchain, and DT. Unlike existing architectures, this approach aims to overcome centralization limitations, leveraging the strengths of emerging technologies. The proposed architecture extends current capabilities for more efficient and secure Agriculture 4.0 practices. We deploy the suggested architecture onto the Ethereum blockchain, demonstrating its practicality through the obtained results.
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    Enhancing security through continuous biometric authentication using wearable sensors
    (Elsevier, 2024) Bhatia, Ashutosh; Tiwari, Kamlesh
    The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.
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    A way forward towards a technology-driven development of industry 4.0 using big data analytics in 5G-enabled IIoT
    (Wiley, 2021-10) Dua, Amit; Gupta, Shashank
    The evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G-enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G-enabled IIoT. The paper presents the state-of-the-art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT-driven 5G technology. We have also highlighted the revolution of IIoT-driven 5G framework which satisfies the demands of IIoT applications.
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    Leveraging AI-enabled 6G-driven IoT for sustainable smart cities
    (Wiley, 2023-08) Dua, Amit
    Many scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low-latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost-efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain-enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in-depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.
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    Graphical Flow-based Spark Programming
    (Springer, 2020-01) Mahapatra, Tanmaya
    Increased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software frameworks and systems. The inherent complexity in programming Big Data applications is also due to the presence of a wide range of target frameworks, with different data abstractions and APIs. The paper aims to reduce this complexity and its ensued learning curve by enabling domain experts, that are not necessarily skilled Big Data programmers, to develop data analytics applications via domain-specific graphical tools. The approach follows the flow-based programming paradigm used in IoT mashup tools. The paper contributes to these aspects by (i) providing a thorough analysis and classification of the widely used Spark framework and selecting suitable data abstractions and APIs for use in a graphical flow-based programming paradigm and (ii) devising a novel, generic approach for programming Spark from graphical flows that comprises early-stage validation and code generation of Spark applications. Use cases for Spark have been prototyped and evaluated to demonstrate code-abstraction, automatic data abstraction interconversion and automatic generation of target Spark programs, which are the keys to lower the complexity and its ensued learning curve involved in the development of Big Data applications.