Faculty Publications

Now showing 1 - 3 of 3

Media Independent Handover and Mobile IPv6-Based UDP Performance Evaluation Suite for Heterogeneous Wireless Networks
(Springer, 2023) Chandavarkar, B.R.
Simulation is a cost-effective, simple, and straightforward approach to implementing a system for exhaustive analysis. Many commercial and open-source simulators, such as NS2, NS3, OMNET++, OPNET, QualNet, etc., exist in the literature to simulate wired and wireless networks. However, the major challenge in dealing with open-source simulators is analysing the results and presenting their performance metrics. Further, the ever-increasing demands of the users in terms of higher data rates with uninterrupted connections resulted in a heterogeneous wireless network (HWN) that supports the integration of WiFi, WiMAX, LTE, etc. Amongst all network simulators available in the literature, NS2 and NS3 are the most popularly used by the research community because of their immense support for implementing and verifying innovative networking algorithms. Furthermore, with the contribution of the National Institute of Science and Technology (NIST), NS2 supports the simulation of WiFi and WiMAX heterogeneous wireless networks with Media Independent Handover and Mobile-IPv6 which is yet to be supported entirely by NS3. However, the major shortcoming of NIST’s contribution is the ease of developing a simulation script followed by result analysis. In continuation with the NIST’s contribution, this paper proposes a Graphical User Interface-based evaluation suite (ES) for the simulation of User Datagram Protocol applications’ in HWN, referred to as ES-HWN. With the support of this suite, the research community can quickly develop the heterogeneous wireless network simulation script followed by the textual and graphical results of handover, packets sent and received, throughput, packet delay, and jitter. The proposed ES-HWN supports the configuration of 10 WiFi and WiMAX interface mobile nodes with two WiFi-Access Points and a WiMAX-Base Station. Besides, it supports the configuration of UDP-based applications’ packet size and transmission rate. Finally, over many experiments, ES-HWN exhibited 100% reliable results. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Integration of Synergetic IoT Applications with Heterogeneous Format Data for Interoperability Using IBM ACE
(Springer, 2024) Sandeep, M.; Chandavarkar, B.R.
Data interoperability is a crucial requirement in IoT to improve services and enhance business opportunities and innovation. Integrating synergetic applications with heterogeneous data formats is a critical issue that needs to be addressed to achieve interoperability. The use cases indicate IBM ACE is promising in resolving integration issues among on-premises and cloud applications. Further, many efforts are observed to address the interoperability issue apart from the IBM ACE approach. However, they are complex, restricted to few data formats, and use proprietary solutions. To address these above-mentioned issues, this paper proposes the Integration of Synergetic IoT applications with Heterogeneous format data for Interoperability using IBM ACE (ISHII). Further, an intelligence-based data recognition module in the proposed ISHII is trained with standard features defined in RFC 7111, 8259, 8996, JSON-LD of W3C, and Google’s Protobuf. Subsequently, recognized heterogeneous format data are integrated and translated to interoperable format using Data Format Description Language (DFDL) with Extended SQL codes on IBM ACE. Finally, the performance of ISHII has been evaluated with synthetically generated patient monitoring and room ambiance datasets with reference to accuracy, time required for integration, and translation efficiency. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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.

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