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Item Reliable cross layer design for e-health applications—iot perspective(Springer Science and Business Media Deutschland GmbH, 2018) Sarwesh, P.; Shet, N.S.V.; Chandrasekaran, K.Of late, there has been many applications are developed by the aid of IoT technology, such as smart city, e-health, smart home, industrial automation etc. In that, e-health is one of the efficient idea that is decidedly developed for healthcare sectors. IoT devices used in e-health applications are run by battery powered smart objects and low frequency links, which says energy constrained and unreliable nature of IoT network. Thus, providing potent healthcare service (regularly following and reporting the patients’ health information) in energy constrained network environment (battery power smart objects and low frequency links) is the prime need in resource constrained networks environment. In this chapter, reliable cross layer design is introduced to prolong the lifetime of IoT devices and reliable data transfer in IoT e-health applications. In proposed cross layer model, network layer and data link layer (MAC layer) are integrated. Reliability related parameter are included in route discover process and later MAC based power control technique make use of routing information, to obtain the suitable transmission power. Our results show that proposed cross layer design is reliable and energy efficient and it is more suitable for IoT e-health applications. © Springer International Publishing AG 2018.Item Topology Control in Wireless Sensor Networks: A Survey(Springer, 2019) Mahapatra, R.K.; Shet, N.S.V.Topology control is found to be a prominent strategy, to prolong the lifetime of WSNs. It helps to control the power consumption of the sensor nodes. In this paper, topology construction and topology maintenance are taken into account as a part of review of topology control. Topology construction algorithms encompass to frame the reduced form of topology. Topology maintenance helps in providing a reduced topology intermittently, as soon as the current topology becomes no longer optimum. Simulation results demonstrate that sensor node battery lifetime can be prolonged by the appropriate use of topology control. © 2019, Springer Nature Singapore Pte Ltd.Item Envisioned Network Architectures for IoT Applications(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2019) Sarwesh, P.; Shet, N.S.V.; Chandrasekaran, K.Internet of Things is the auspicious technology that connects different internet enabled devices in single network architecture. IoT contributes effective service in various applications such as industrial automation, health care sectors, and home automation. Availability of low cost devices makes IoT as innovative paradigm in large-scale wireless network research. Challenges in IoT applications vary from each other. For example, in smart grid applications QoS is more important, whereas for land slide monitoring applications, energy efficiency and reliability are the major requirements. Thus, in this chapter, we come up with various network architectures that are suitable for IoT applications. The network architectures are designed by combining different optimization techniques into single network design, to satisfy specific network requirements. This chapter elaborates the major issues that affect the network performance and suitable solutions for those issues by means of efficient network architectures. © 2019, Springer International Publishing AG, part of Springer Nature.Item Congestion control mechanisms in vehicular networks: A perspective on Internet of vehicles (IoV)(Elsevier, 2022) Patil, A.; Muthuchidambaranathan, P.; Shet, N.S.V.Developing congestion control in highly mobile vehicular networks is a challenging task. The network of vehicles or heavy vehicles uses different data for communication depending on the required application. These networks are one of the main components of the Internet of Things (IoT), and the aim is to connect every vehicle to every other vehicle for the purpose of improving the user’s quality of life. To provide better network accessibility, channel utilization, and speedy delivery of the information over these networks, congestion control plays a significant role. In this chapter, we present various congestion control mechanisms for vehicular networks by considering different applications in these networks. The decentralized and centralized mechanisms are presented and their use in different types of vehicular networks is also suggested. In the end, we have listed some challenges to help researchers to expand their research in this area. © 2022 Elsevier Inc.Item Hybrid Malicious Encrypted Network Traffic Flow Detection Model(Springer Science and Business Media Deutschland GmbH, 2023) Hublikar, S.; Shet, N.S.V.Encrypted communication technology has evolved as the network, and Internet applications have advanced. Malicious communication, on the other hand, employs encryption to bypass standard detection and security protection. The existing security prevention and detection technologies are unable to identify harmful communication that is encrypted. The growth of artificial intelligence (AI) in these days has enabled to employ machine learning (ML) as well as deep learning approaches to identify encrypted malicious communications without decryption, with remarkably precise detection outcomes. At this moment, research on detecting harmful encrypted traffic is mostly focused on analyzing the features of encrypted data and selecting neural network (NN) techniques. Hybrid ML is proposed in this study by merging two well-performing data mining algorithms with natural language processing tasks. Here, a new traffic flow detection method is performed by the hybrid ML technique. At first, the benchmark data is collected from public sources. The features are extracted using the convolutional layer of deep convolutional neural network (DCNN). Then, the weighted feature extraction is performed by grasshopper optimization algorithm (GOA). Employed the hybrid machine learning-based malicious detection with the “support vector machine (SVM) and neural network (NN)” is utilized in this model to detect the traffic affected by malicious activities, where the hidden neuron count of NN and kernel of SVM are tuning by the same GOA for increasing the accuracy and precision. This research provides findings from experiment, encouraging various researchers to develop the research as future work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.