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Author: search.filters.author.Gupta, ShashankSubject: search.filters.subject.Artificial intelligence (AI)

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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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    Role of machine learning and deep learning in securing 5G-driven industrial IoT applications
    (Elsevier, 2021-12) Chamola, Vinay; Gupta, Shashank
    The Internet of Things (IoT) connects millions of computing devices and has set a stage for future technology where industrial use cases like smart cities and smart houses will operate with minimal human intervention. IoT’s cross-domain amalgamations with emergent technologies like 5G and blockchain affects human life. Hence, increase in reliance over IoT necessitates focus on its privacy and security concerns. Implementing security through encryption, authentication, access control and communication security is the need of the hour. These needs can be best catered with the use of machine learning (ML) and deep learning (DL) that can help in realizing secure intelligent systems. In this work, the authors present a comprehensive review for securing Industrial-IoT (I-IoT) devices to contribute to the development of security methods for I-IoT deployed over 5G and blockchain. The survey provides a general analysis of the state-of-the-art security implementation and further assesses the product life cycle of IoT devices. The authors present numerous virtues as well as faults in the machine learning and deep learning algorithms deployed over the fog architecture in context with the security solutions. The potential security algorithms can help overcome many challenges in the IoT security and pave way for implementation with emerging technologies like 5G, blockchain, edge computing, fog computing and their use cases for creating smart environments.