Faculty Publications
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Publications by NITK Faculty
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Item Mitigating Localization and Neighbour Spoofing Attacks in Underwater Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2020) Chandavarkar, B.R.; Gadagkar, A.V.The location information of a node is one of the essential attributes used in most underwater communication routing algorithms to identify a candidate forwarding node by any of the sources. The exact location information of a node exchanged with its neighbours' in plain text and the absence of node authentication results in some of the attacks such as Sybil attack, Blackhole attack, and Wormhole attack. Moreover, the severe consequence of these attacks is Denial of Service (DoS), poor network performance, reduced network lifetime, etc. This paper proposes an anti-Spoof (a-Spoof) algorithm for mitigating localization and neighbour spoofing attacks in UASN. a-Spoof uses three pre-shared symmetric keys to share the location. Additionally, location integrity provided through the hash function. Further, the performance of a-Spoof demonstrated through its implementation in UnetStack with reference to end-to-end packet delay and the number of hops. © 2020 IEEE.Item A framework for residual energy model in unetstack simulator for underwater sensor networks(Institute of Electrical and Electronics Engineers Inc., 2020) Chandavarkar, B.R.; Gadagkar, A.V.In recent years, Underwater Acoustic Sensor Networks (UASN) has gained much attention from researchers because of its diverse applications. UASNs face several issues and challenges like limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and energy constraints. Unlike the nodes in terrestrial wireless sensor networks (TWSNs), UASNs suffer from energy constraints, severely affecting the network lifetime and throughput. Simulation of UASNs is a common aspect of researchers. It facilitates analysis of the working and performance of a UASN before it is implemented and deployed, which incurs substantial time and cost. Among the different simulation platforms available for simulating UASNs, UnetStack is one, which is an efficient and well-known tool available for simulating UASN, with significant benefits. But, the present UnetStack does not provide direct functionality for monitoring the energy of nodes during simulations, which is crucial. This paper presents the design and implementation of the residual energy model framework in UnetStack. Additionally, through the experimental simulations, the number of frames transmitted received, and the depletion of node energy over time presented. Further, the implemented energy model framework able the researchers in the design of energy-aware routing protocols and load balancing methods. © 2020 IEEE.Item Expectation-Based Multi-Attribute Multi-Hop Routing (EM2R) in Underwater Acoustic Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2020) Chandavarkar, B.R.; Gadagkar, A.V.Underwater acoustic sensor networks (UASNs) have been a recommended technology for acquiring details from underwater. These networks has underwater sensors that have energy constraints and use acoustic communication medium. Routing in UASN is one of the primary issues, as the data need to be forwarded utilizing minimum energy and higher packet delivery rate. Deciding the next forwarding node play a significant role in routing algorithms for UASN and directly impact packet delivery and energy consumed by the nodes. This paper proposes an expectation-based multi-attribute multi-hop routing (EM2 R) in underwater acoustic sensor networks. EM2 R uses node's residual energy and distance as a multi-attribute criterion in selecting next-hop for routing. Further, the detailed implementation of EM2 R in industry-standard underwater network simulator referred to as UnetStack is presented. Additionally, the performance of EM2 R is presented with reference to the selection of the forwarding node and their energy depletion, delay, and throughput. © 2020 IEEE.Item Security of Data with Enhanced Technique of DiDrip Protocol for Distributed Systems in Mobile Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Begum, S.; Gadagkar, A.V.; Kumar, S.; Koli, M.Mobile Wireless Sensor Network do not have any protocols, hence the protocols from MANET are chosen for MWSN. Data discovery and dissemination is to disseminate small configuration parameters, variables, queries, and commands in packets. The data gathered by the sensors should be transmitted to the base station or to any destination that requires data.Existing systems are based on the centralized approach; where in it does not hold good for multiple owner-multiple users. These protocols have certain security issues due to which adversaries can easily hack the systems and can cause failure to the network. Many works have been proposed for the data dissemination but none of them could provide security for the network.The work presented in this paper proposes the first reliable and secure protocol called DiDrip, that enables an effective data discovery and dissemination in a distributed system. The proposed protocol is robust and efficient. It allows multiple users (intermediate nodes) to get registered with network administrator and grants special privileges to the users so that users can directly disseminate data into the sensor nodes. © 2021 IEEE.Item A Comprehensive Review on Wireless Technologies and Their Issues with Underwater Communications(Institute of Electrical and Electronics Engineers Inc., 2021) Gadagkar, A.V.; Chandavarkar, B.R.Since many years, the life and environment in underwater especially the oceans are being explored, yet there are many parts of oceans remained unexplored due to the particularities of underwater environment. The increasing interest in underwater exploration has paved way to the development of different technologies for underwater communication. Underwater wireless communication plays a vital role in the study of marine life, water pollution, monitoring of natural calamities, coastal surveillance, and to monitor various phenomena in underwater environment. Hence, the underwater wireless communication has become a key area of research for exploring underwater environment for a variety of applications. The primary underwater wireless technology uses acoustic waves, electromagnetic waves in RF band, and optical waves as communication carriers to transmit data in an underwater environment. This paper provides a survey of these communication technologies discussing their physical characteristics, compatibility and technical issues, thus enabling to understand use of these technologies for underwater communications. © 2021 IEEE.Item Intrusion Detection of Sinkhole Attack in Underwater Acoustic Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Palisetti, S.; Chandavarkar, B.R.; Gadagkar, A.V.Underwater networks have the potential to allow previously unexplored applications as well as improve our ability to observe and forecast the ocean. Underwater acoustic sensor networks (UASNs) are often deployed in unprecedented and hostile waters and face many security threats. Applications based on UASNs such as coastal defense, pollution monitoring, assisted navigation to name a few, require secure communication. A new set of communication protocols and cooperative coordination algorithms have been proposed to enable collaborative monitoring tasks. However, such protocols overlook security as a key performance indicator. Spoofing, altering, or replaying routing information can affect the entire network, making UASN vulnerable to routing attacks such as selective forwarding, sinkhole attack, Sybil attack, acknowledgement spoofing and HELLO flood attack. The lack of security against such threats is startling if it is observed that security is indeed an important requirement in many emerging civilian and military applications. In this work, the sinkhole attack prevalent among UASNs is looked at and discuss mitigation approaches that can feasibly be implemented in UnetStack3. © 2021 IEEE.Item An Improved Expectation-Based Multi-attribute Multi-hop Routing (IEM2 R) in Underwater Acoustic Sensor Networks(Springer Science and Business Media Deutschland GmbH, 2023) Gadagkar, A.V.; Chandavarkar, B.R.Underwater Acoustic Sensor Networks (UASNs) uses acoustic signals as a physical layer medium for communication underwater. The distinct characteristics of acoustic communication channel make reliable routing a challenging task in underwater applications. To handle this, an Expectation-Based Multi-Attribute Multi-Hop Routing (EM R) was proposed, which selects the best next-hop using an expectations-based multi-attribute ranking based on multiple attributes that are node’s residual energy and distance. However, in EM R node mobility was not considered, which is inevitable underwater. Also, fetching the attributes incurs communication overhead and is prone to attacks. Further, it is crucial to optimize the timing of executing expectation-based multi-attribute ranking in deciding the best next hop. To address these issues, this paper proposes an extension to the previous work called an Improved Expectation-Based Multi-Attribute Multi-Hop Routing (IEM R). The IEM R introduces node mobility and implements a prediction model which uses distance and predicted energy as attributes and predicts the best next hop. The proposed method implemented in the industry standard underwater network simulator UnetStack3 is described. Additionally, results show the predicted and actual next-hop selection, energy depletion, average end-to-end delay, and throughput. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item A Survey on Semantic Segmentation Models for Underwater Images(Springer, 2023) Anand, S.K.; Kumar, P.V.; Saji, R.; Gadagkar, A.V.; Chandavarkar, B.R.Semantic segmentation remains a key research field in modern day computer vision and has been used in a myriad of applications across various fields. It can be extremely beneficial in the study of underwater scenes. Various underwater applications, such as unmanned explorations and autonomous underwater vehicles, require accurate object classification and detection to allow the probes to avoid malicious objects. However, the models that work well for terrestrial images rarely work just as well for underwater images. This is because underwater images suffer from high blue light intensity as well as other ill effects such as poor lighting and contrast. This can be fixed using preprocessing techniques to manually improve the image characteristics. Trying to improve the model to account for bad image quality is not a great method as the model may misidentify noise as an image characteristic. In this chapter, 6 different deep learning semantic segmentation models—SegNet, Pyramid Scene Parsing Network (PSP-Net), U-Net, DNN-VGG (Deep Neural Network-VGG), DeepLabv3+, and SUIM-Net—are explored. Their architectures, technical aspects with respect to underwater images, advantages, and disadvantages are all investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Bridging Energy Holes in Underwater Acoustic Sensor Networks: A Call for Adaptive Routing Protocols(Institute of Electrical and Electronics Engineers Inc., 2024) Gadagkar, A.V.; Chandavarkar, B.R.Underwater Acoustic Sensor Networks (UASNs) has significantly attracted the attention of researchers with many of its uses in aquatic environments, such as tactical surveillance, pollution monitoring, underwater resource exploration, and catastrophe detection. An important and difficult problem with UASNs is the rapid depletion of energy due to the high power requirements for acoustic communication, while the battery capacity of the sensors is restricted. However, if the battery energy consumption is unbalanced, it can cause nodes to deplete their energy reserves quickly. This results in the formation of energy sink hole, which divide the whole network and damage the accuracy of the monitoring data. In severe cases, it may even cause the entire network to fail. However, ensuring that energy is evenly distributed across nodes may significantly increase the longevity of these networks. Therefore, it is imperative to implement energy-conserving protocols that can efficiently handle the restricted energy allocation while considering the distinctive characteristics of the underwater environment. © 2024 IEEE.Item Semantic Segmentation of Underwater Images with CNN Based Adaptive Thresholding(Springer Science and Business Media Deutschland GmbH, 2025) Anand, S.K.; Kumar, P.V.; Saji, R.; Gadagkar, A.V.; Chandavarkar, B.R.Semantic segmentation remains a key research field in modern day computer vision and has been used in a myriad of applications across various fields. It can be extremely beneficial in the study of underwater scenes. Various underwater applications, like unmanned explorations and autonomous underwater vehicles, require accurate object classification and detection to allow the probes to avoid malicious objects. However, the models which work well for terrestrial images rarely work just as well for underwater images. This is because underwater images suffer from high blue light intensity as well as other ill-effects such as poor lighting and contrast. Trying to improve the model to account for bad image quality is not a great method as the model may misidentify noise as an image characteristic. In this paper, a unique CNN-based approach for post-processing image thresholding is proposed, on top of 3 models used for the semantic segmentation itself–Segnet, U-Net, and Deeplabv3+. The models’ outputs are then subject to the CNN-based post-processing technique to binarize the outputs into masks, and provides improved segmentation results compared to the base models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.