Volume XI, Issue I

Title– AN INTEGRATED AI-POWERED FRAMEWORK FOR NETWORKING AND PROCESSING IN INDUSTRIAL IOT APPLICATIONS

Author– Dr. Mahesh Sharma

Abstract

In the era of Industry 4.0, where digitalized production facilities heavily depend on intricate sensor networks, optimizing data utilization is imperative for bolstering production sustainability. This entails refining processes, minimizing downtime, curbing material wastage, and more. However, achieving intelligent, data-driven decisions within stringent time constraints necessitates the seamless integration of time-sensitive networks with robust data ingestion and processing infrastructure, complemented by versatile support for Machine Learning (ML) pipelines. Unfortunately, existing frameworks often grapple with the challenge of harmonizing and programming both networking and computing infrastructures while accommodating real-time ML decision-making based on collected data. To surmount this obstacle, this paper introduces AIDA, a cutting-edge holistic AI-driven network and processing framework meticulously crafted for real-time, reliable industrial IoT applications. AIDA adeptly orchestrates Time-Sensitive Networks (TSN) to facilitate instantaneous data ingestion across an observable AI-powered edge/cloud continuum. Furthermore, AIDA integrates adaptable and dependable ML components capable of rendering timely decisions tailored to the diverse needs of industrial IoT applications. This paper meticulously delineates the AIDA architecture, elucidates key components of the framework, and offers insights through the illustration of two distinct use cases.

Index Term– Internet of Things (IoT), Edge/cloud computing, Time-Sensitive Networks (TSN), Machine Learning.

DOI- 10.30696/IJEEA.XI.I.2023. 01-11

Reference to this paper should be made as follows:  Dr. Mahesh Sharma, (2023), “An Integrated AI-Powered Framework for Networking and Processing in Industrial IoT Applications” Int. J. Electronics Engineering and Applications, Vol. 11, Issue I, pp. 1-11.

Title– CLINICAL RESEARCH: MEDICAL IMAGE PROCESSING, ANALYSIS, AND VISUALIZATION

Author– Dr. Richa Vats

Abstract

Imaging plays a crucial role across various medical and laboratory disciplines, from cellular studies with 3D confocal microscopy to virologists reconstructing viruses from micrographs and radiologists identifying tumors in MRI and CT scans. Neuroscientists also utilize imaging for detecting metabolic brain activity. Formerly reliant on expensive UNIX workstations and custom software, the analysis and visualization of diverse image types can now be performed on cost-effective desktop computers. This paper introduces MIPAV (Medical Image Processing Analysis and Visualization), a platform-independent program tailored for the Internet-linked medical research community. MIPAV facilitates clinical and quantitative analysis of medical images over the Internet, enabling remote researchers and clinicians to collaborate seamlessly, enhancing their capabilities in studying, diagnosing, monitoring, and treating medical disorders.

Index Term– Internet of Things (IoT), Edge/cloud computing, Time-Sensitive Networks (TSN), Machine Learning.

DOI- 10.30696/IJEEA.XI.I.2023. 12-20

Reference to this paper should be made as follows:  Dr. Richa Vats, (2023), “Clinical Research: Medical Image Processing, Analysis, And Visualization” Int. J. Electronics Engineering and Applications, Vol. 11, Issue I, pp. 12-20.

Title– CONVERGING THREAT MODELING IN SMART FIREFIGHTING SYSTEMS

Author– Dr. Rajesh Kumar Tiwari

Abstract

This paper delves into security challenges within industrial automation technologies, particularly the realm of the Industrial Internet of Things (IIoT). Despite enhancing efficiency, IIoT introduces notable security risks, especially in smart cyber-physical systems (CPS). The study employs a smart firefighting use case, utilizing the MITRE ATT&CK matrix, and proposes a threat modeling framework for systematic risk analysis. Integrating system requirement collection (SRC) for asset information, the study maps the threat list onto NIST security and privacy controls. This demonstrates the applicability of these controls for mitigating security risks in smart firefighting systems, offering valuable insights for securing critical cyber physical systems in specific use cases.

Index Term– Threat modeling, Smart industrial system, Cyber–Physical System (CPS), Smart firefighting system, NIST controls, IoT, IIoT.

DOI- 10.30696/IJEEA.XI.I.2023. 21-30

Reference to this paper should be made as follows:  Rajesh Kumar Tiwari, (2023), “Converging Threat Modeling In Smart Firefighting Systems: Harmonizing Mitre Att&Ck Matrix With Nist Security Controls” Int. J. Electronics Engineering and Applications, Vol. 11, Issue I, pp. 21-30.

Title– DETECTING ANOMALY-BASED CYBERATTACKS IN SMART HOMES: A COMPREHENSIVE REVIEW OF THE LITERATURE

Author– Dr. Abu Salim K

Abstract

The paper explores the vulnerability of smart homes to cyberattacks due to the collection of sensitive data, emphasizing the need for effective anomaly detection. While existing literature often addresses IoT-related cyber threats, there is a notable gap in focusing on anomalies specific to smart homes. The study conducts a systematic literature review, offering an adapted taxonomy for classifying anomaly detection methods. The findings reveal a growing interest in utilizing anomaly-based models, particularly centralized and network based features, for detecting cyberattacks in smart homes. Popular techniques include ensemble and deep learning methods. However, challenges such as limited diversity in existing datasets and the absence of comprehensive datasets representing smart home complexity underscore the need for further research to enhance detection model generalizability.

Index Term– Anomaly detection, Machine Learning, Internet of Things (IoT), Smart home, Cyber-security, Cyber-attacks, Systematic literature review (SLR).

DOI- 10.30696/IJEEA.XI.I.2023. 31-41

Reference to this paper should be made as follows:  Abu Salim K, (2023), “Detecting Anomaly-Based Cyberattacks in Smart Homes: A Comprehensive Review of the Literature” Int. J. Electronics Engineering and Applications, Vol. 11, Issue I, pp. 31-41.

Title– ENABLING IPV6 OVER CROSS-TECHNOLOGY COMMUNICATION WITH WAKE-UP RADIO FOR ENHANCED CONNECTIVITY

Author– Dr. Pravin R. Gundalwar

Abstract

This paper addresses the interoperability challenges among IoT devices using different wireless technologies, such as IEEE 802.15.4 and IEEE 802.11. It introduces a bidirectional Cross Technology Communication with Wake-up Radio (WuR-CTC) approach to enable connectivity without a gateway. The key innovation is the design, implementation, and evaluation of an adaptation layer that supports IPv6 over WuR-CTC, utilizing the IETF Static Context Header Compression and fragmentation (SCHC) framework. Experimental results demonstrate successful transmission of a 127-byte IPv6 packet from an IEEE 802.15.4 device to an IEEE 802.11 device in 69 ms on average, without the need for a gateway. This solution is particularly beneficial for latency-sensitive applications in smart environments requiring real-time device interaction.

Index Term– Internet of Things, Cross-Technology Communication, Wake-up radio, SCHC, IPv6, Compression, Fragmentation, LPWAN.

DOI- 10.30696/IJEEA.XI.I.2023. 42-54

Reference to this paper should be made as follows:  Dr. Pravin R. Gundalwar, (2023), “Enabling IPv6 over Cross-Technology Communication With Wake-up Radio for Enhanced Connectivity” Int. J. Electronics Engineering and Applications, Vol. 11, Issue I, pp. 42-54.