Faculty Publications
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Item Analysis of the Standard Objective Functions of RPL(Springer Science and Business Media Deutschland GmbH, 2022) Kamble, S.; Chandavarkar, B.R.Low power and Lossy Network (LLN) is a network widely used to connect objects nowadays, and routing in such type of network is a new research area to work. ROLL-WG developed a routing protocol for LLN called Routing Protocol for low power and lossy network (RPL). Here, the Objective Function (OF) defines the way the node should select the parent node. The two standard OFs are OF-0, and MRHOF developed in 2010 and 2012. The paper explains these OFs and presents the experimental results of the two OFs in Contiki-3.0. The experiments were carried out by varying the number of nodes and varying data rates. The experimental results show that due to low hop count and fewer nodes getting involved in OF-0, it performs better than MRHOF, as MRHOF requires more stable links to transmit the data. Thus, OF-0 is useful in the scenarios where there are fewer nodes, and the destination is closer. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Novel Fuzzy-based Objective Function for routing protocol for low power and lossy networks(Elsevier B.V., 2023) Kamble, S.; Bhilwar, P.; Chandavarkar, B.R.In both wired and wireless networks, data routing along the best path is constantly a top priority for the research community. Currently, the most widely adopted routing algorithms in the Internet of Things is the Routing Protocol for Low Power and Lossy Networks (RPL). In RPL, selecting the optimal path primarily depends on the Objective Function (OF). Multi-attribute OFs are thought to be a viable method for choosing the best path, but the main concern here is how these attributes must be coupled. Such issues are being taken care by fuzzy based technologies as it deals with imprecise or distorted attribute values. Most of the fuzzy logic-based OFs are mainly based on the Mamdani method, which has low accuracy. However, a better accuracy is observed in Takagi–Sugeno method. Hence the paper develops a Fuzzy-based Takagi and Sugeno Objective Function (FTSOF) for RPL. The FTSOF technique combines the routing metrics to give a crisp value based on which the parent node is selected. The proposed approach is implemented in the Contiki operating system. The simulation was carried out on different network densities and various data rates. The performance parameters considered are packet delivery ratio, latency, network setup time, and control message overhead. The results shows that FTSOF outperforms the state-of-the-art techniques such as OF-0, MRHOF, and the Fuzzy-based Mamdani Objective Function (FMOF). © 2023 Elsevier B.V.Item Heterogeneous data format integration and conversion (HDFIC) using machine learning and IBM-DFDL for IoT(Springer Nature, 2024) Sandeep, S.; Chandavarkar, B.R.; Khatri, S.The future of the Internet of Things (IoT) demands the integration of synergetic applications to cater to societal needs. Examples of IoT-based confederated applications include Ambient Assisted Living with Active Healthy Ageing, CasAware with Smart Energy, Smart Gas Distribution Networks with GIS systems, and more. However, the data heterogeneity hinders integration, as these systems follow different standards, data formats, semantic models, and representations. Further, this leads to data interoperability issues in IoT. The major concern of academia and industry in the smooth integration of heterogeneous applications is interpreting different data formats and representing them in a common schema for further analysis. Existing solutions, such as message payload translation, middleware/cloud format, and Inter-IoT, are complex, time-consuming, and ineffective. Hence, this paper proposes the heterogeneous data format integration and conversion (HDFIC), a machine learning-based system to identify data formats using a Random Forest classifier and integrate them using the Data Format Description Language (DFDL). The content-based data format identification in the proposed HDFIC is trained with the standard features defined in RFC 7111, 8259, and 8996. Subsequently, the data is integrated into a single XML Schema Definition and converted into the required data format using the IBM App Connect Enterprise tool and DFDL. Finally, the performance of HDFIC is evaluated with the synergetic patient body vitals and room ambiance dataset for accuracy, data integration time, and conversion efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.Item EWRPL: entropy-based weighted RPL(Springer, 2025) Kamble, S.; Chandavarkar, B.R.The Internet of Things (IoT) refers to a network of interconnected devices communicating through the internet. Routing is a crucial aspect of IoT for effective communication between devices since intermediate nodes collect and transmit sensor data for processing. IoT networks face limitations like poor connectivity, and devices are constrained in power, bandwidth, and memory. The Internet Engineering Task Force developed the Routing Protocol for Low power and lossy networks (RPL) to address these issues. In RPL, a Destination Oriented Directed Acyclic Graph (DODAG) is formed to start the data transfer. Rank is computed using routing metrics and an objective function during DODAG formation. The paper studies the approaches developed for rank calculation and proposes a new technique called the Entropy-based Weighted approach for RPL (EWRPL). The proposed approach combines the routing metrics and computes the rank for parent selection. In EWRPL, we have assigned weights to the routing metrics using the entropy method. The proposed approach considers four routing metrics-hop count, ETX, delay, and energy. The EWRPL approach was experimented with using the Cooja simulator and Contiki-OS. The study indicates that utilizing the EWRPL approach results in a higher Packet Delivery Ratio (PDR), lower latency, and reduced control message overhead compared to the two standard objective functions and the state-of-the-art equal weight approach. The technique can be used in various domains, such as home automation, healthcare, and industrial usage. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.