Faculty Publications
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Publications by NITK Faculty
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Item CAMP: Congestion adaptive multipath routing protocol for VANETs(2012) Raviteja, B.L.; Annappa, B.; Tahiliani, M.P.Long congestion periods, frequent link failures and hand-offs in VANETs lead to more number of packets being dropped and incur high end to end delay, there by degrading the overall performance of the network. Congestion control mechanism, though mainly incorporated in transport protocols, if coupled with the routing protocols, can significantly improve overall performance of the network. In this paper we propose Congestion Adaptive Multipath Routing Protocol (CAMP) that aims to avoid congestion by proactively sending congestion notification to the sender. The proposed CAMP routing protocol is implemented in Network Simulator-2 (NS-2) and its performance is compared with Ad-hoc On Demand Multipath Distance Vector (AOMDV) in terms of Packet Drop due to Congestion, Packet Delivery Fraction, Throughput and Average End-to-End Delay. Simulation results show that CAMP routing protocol achieves significant performance gain as compared to that of AOMDV. © 2012 Springer-Verlag.Item A Detailed Case Study Report on a Section of Four-Lane National Highway Project of Bengaluru-Mangaluru(Springer Science and Business Media Deutschland GmbH, 2024) Srinivas, M.; Sinha, C.; Akshay, K.; Marella, D.A comprehensive study of the project titled “Four-laning of Bengaluru-Mangaluru from km 270 + 270 (Periya Shanthi) to km 318 + 755 (Bantwal)†was conducted to assess the project’s potential, necessities, and requirements. The evaluation also addressed the challenges encountered due to the project’s location in a mountainous terrain region. The major issue encountered and understood through this study was that the project is delayed more than usual due to many difficulties and challenges encountered by them under various circumstances. Based on the survey and research work done, it was understood that the region of Dakshina Kannada where the project is being done has high temperature during summers due to which ice cubes were used to maintain temperature for concrete batching. The objective of the project is to eliminate the hairpin bends which slowed down the traffic movement by straightening them to reduce congestion as there are many pilgrimage places, business hubs, and tourist places in the project vicinity. Some of the main reasons for the delay of the project was unseasonal rainfall, felling of trees, obstructing the Right of Way (ROW), the inability to move the utility units to the project site due to insufficient ROW, landslides, and many more reasons. Currently, the Extension of time (EOT) has been requested by the Contractor to NHAI, as the project has been delayed. This research paper further explores the detailed causes and gives a complete insight into the project. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Dynamic Traffic Assignment Using a Multi-class Continuum Model for Disordered Traffic(Springer Science and Business Media Deutschland GmbH, 2025) Nair, P.; Sreekumar, M.The traffic conditions in urban area consist of different vehicle types, which results in varying traffic dynamics for each vehicle class. The smaller vehicles can overtake larger vehicles and also move through the gaps between larger vehicles to traverse faster. The distinct dynamics of smaller and larger vehicles is a challenge to the traditional traffic assignment models which lack class-specific behavior. The routing of vehicles based on the travel time should capture these class-specific features to have a holistic view regarding typical urban traffic conditions. Traditional traffic assignment methods fail to reproduce conditions such as congestion, queue spill back, and bottleneck regions, thereby resulting in underestimation of real traffic scenarios. This study proposes a multi-class dynamic traffic assignment framework for disordered traffic to overcome the limitations of traditional traffic assignment. The framework is tested for different traffic conditions to deduce the class-specific behavior in multi-class traffic conditions. The results from the dynamic traffic assignment are compared to the traditional traffic assignment to account for the difference in travel time computations. The travel time plots obtained using dynamic traffic assignment shows that the vehicles can overcome low to mild levels of congestion by exhibiting overtaking and creeping behavior. This stands close to real traffic conditions where there is no much change in travel time unless heavy congestion. Thus, study justifies the necessity of class-specific features in travel time computation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item InDS: Intelligent DRL Strategy for Effective Virtual Network Embedding of an Online Virtual Network Requests(Institute of Electrical and Electronics Engineers Inc., 2024) Keerthan Kumar, T.G.K.; Addya, S.K.; Koolagudi, S.G.Network virtualization is a demanding feature in the evolution of future Internet architectures. It enables on-demand virtualized resource provision for heterogeneous Virtual Network Requests (VNRs) from diverse end users over the underlying substrate network. However, network virtualization provides various benefits such as service separation, improved Quality of Service, security, and more prominent resource usage. It also introduces significant research challenges. One of the major such issues is allocating substrate network resources to VNR components such as virtual machines and virtual links, also named as the virtual network embedding, and it is proven to be mathbb {N}mathbb {P} -hard. To address the virtual network embedding problem, most of the existing works are 1) Single-objective, 2) They failed to address dynamic and time-varying network states 3) They neglected network-specific features. All these limitations hinder the performance of existing approaches. This work introduces an embedding framework called Intelligent Deep Reinforcement Learning (DRL) Strategy for effective virtual network embedding of an online VNRs (InDS). The proposed InDS uses an actor-critic model based on DRL architecture and Graph Convolutional Networks (GCNs). The GCN effectively captures dependencies between the VNRs and substrate network environment nodes by extracting both network and system-specific features. In DRL, the asynchronous advantage actor-critic agents can learn policies from these features during the training to decide which virtual machines to embed on which servers over time. The actor-critic helps in efficiently learning optimal policies in complex environments. The suggested reward function considers multiple objectives and guides the learning process effectively. Evaluation of simulation results shows the effectiveness of InDS in achieving optimal resource allocation and addressing diverse objectives, including minimizing congestion, maximizing acceptance, and revenue-to-cost ratios. The performance of InDS exhibits superiority in achieving 28% of the acceptance ratio and 45% of the revenue-to-cost ratio by effectively managing the network congestion compared to other existing baseline works. © 2013 IEEE.