Department of Electrical and Electronics Engineering

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Start date: 2010Subject: search.filters.subject.Internet of Things (IoT)

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Now showing 1 - 10 of 24
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    Quantum computing applications for Internet of Things
    (IET, 2023-11) Chamola, Vinay
    The rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum-secured smart lock etc.
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    Three-Tier Indirect Tracing Model for Enhancing Epidemic Surveillance
    (IEEE, 2024-08) Chamola, Vinay
    In the wake of recent global health crises, effective contact tracing has emerged as a key tool in controlling infectious disease outbreaks. However, traditional contact tracing methods predominantly focus on direct tracing, often overlooking crucial indirect contacts. This study aims to address this gap by exploring scenarios where conventional tracing fails to identify all potential contacts. We argue for the necessity of indirect tracing, a component typically absent in traditional schemes, and demonstrate its importance across different stakeholders: end users, service providers, and healthcare professionals. To this end, we have designed an end-to-end application, available on GitHub, which significantly enhances the efficacy of contact tracing. Our approach effectively doubles or triples the maximum number of traceable individuals compared to traditional direct contact tracing methods, thereby offering a more comprehensive and effective tool for epidemic surveillance and control. This may lead to significant improvements in contact-tracing applications, thereby containing virus outbreaks more efficiently. In addition to the comprehensive analysis and development of the robust architecture, this study also emphasizes the broader implications and potential impact of incorporating indirect tracing into contact tracing efforts.
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    A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues and More
    (IEEE, 2024-12) Chamola, Vinay
    Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Cloud Computing, and Big Data Analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and Cloud Computing driving further expansion and application of Digital Twin technologies
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    Performance of Integrated IoT Network With Hybrid mmWave/FSO/THz Backhaul Link
    (IEEE, 2024-01) Zafaruddin, S.M.
    Establishing end-to-end connectivity of Internet of Things (IoT) network with the core for collecting sensing data from remote and hard-to-reach terrains is challenging. In this article, we analyze the performance of an IoT network integrated with a wireless backhaul link (BHL) for data collection. We propose a solution that involves a self-configuring protocol for aggregate node (AN) selection in an IoT network, which sends the data packet to an unmanned aerial vehicle (UAV) over radio frequency (RF) channels. We adopt a novel hybrid transmission technique for wireless backhaul employing opportunistic selections combining (OSC) and maximal ratio combining (MRC) that simultaneously transmits the data packet on mmWave (mW), free space optical (FSO), and terahertz (THz) technologies to take advantage of their complementary characteristics. We employ the decode-and-forward (DF) protocol to integrate the IoT and BHLs and provide physical layer performance assessment using outage probability and average bit-error rate (BER) under diverse channel conditions. We also develop simplified expressions to gain a better understanding of the system’s performance at a high-signal-to-noise ratio (SNR). We provide computer simulations to compare different wireless backhaul technologies under various channel and SNR scenarios and demonstrate the performance of the data collection using the integrated link
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    RPL*: An Explainable AI-based routing protocol for Internet of Mobile Things
    (Elsevier, 2024-10) Shenoy, Meetha V.
    The Internet of Mobile Things (IoMT) is an emerging paradigm of Internet of Things (IoT) with special focus on enabling mobility to the ‘things’. Several IoMT applications such as group of robots or drones performing collaborative search and rescue operation, identification of mines, warehouse management, goods delivery, etc can be considered as examples of IoMT systems. In the applications mentioned above, the nodes may send the information in a multi-hop manner to the root or coordinator node which may be static or mobile. While the Routing Protocol for Low Power and Lossy Networks (RPL) is extensively utilized in static IoT networks, it encounters significant limitations in handling mobility and providing resilience against routing attacks in mobile IoT networks. In this work, we propose a modified RPL, RPL* which is robust to handling mobility in nodes and is resilient towards routing attacks. In RPL*, any deviation from the normal behaviors of the network are identified as anomalies using an unsupervised Explainable Artificial Intelligence (XAI) strategy. In RPL*, we propose a novel mobility detection mechanism that will identify the mobility in the network in an energy efficient manner without incurring additional communication overhead. To maintain the connectivity with parent node, we propose a novel proactive connectivity management mechanism in RPL* which will ensure a smooth transition from one parent to another if required, thus avoiding the network partitioning due to mobility. The performance analysis of the system has demonstrated an improvement in packet delivery ratio of the mobile nodes by 40% due to the proposed RPL* when compared to RPL. Also, the proposed XAI strategy provided an F1-score of over 95% for the detection of sink hole and black hole attacks in the tested IoMT network scenarios. It was observed that RPL* improves the performance of the IoMT network when compared to RPL. However it may be noted that the mechanisms introduced to support mobility does not lead to a drop in PDR or increase in control packet overhead for static networks. Hence, RPL* can be considered as an alternative to RPL for IoT as well as IoMT networks.
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    Heterogeneous CMOS-MEMS based Boost Converter for 2.4 GHz RF energy Harvester
    (IEEE, 2024) Rao, V. Ramgopal
    Internet of Things (IoT) has experienced a significant growth in last few years. Billions of battery-powered wireless sensors are expected to be employed as the IoT becomes an integral part of our daily lives. Therefore, ambient energy resources such as light, RF source, EM radiation, thermal energy can be utilized to prolong the lifetime of batteries for sensors. In this work, ambient RF energy source is used for energy harvesting to power up the wireless sensors and low power electronic devices. For the first time, we experimentally demonstrated RF energy harvester to scavenge 2.45 GHz from Wi-Fi sources using commercially available CMOS-MEMS (micro electromechanical switch) hybrid switches. The use of MEMS switches in the boost converter instead of conventional NMOS switches reduces the leakage current, stabilize the ON-state resistance, and improves the overall efficiency. Our experimental result indicates that the use of MEMS switches increases the efficiency of the energy harvester more than 15% as compared to its NMOS counterpart.
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    Smart Energy Harvesting System for IoT & Cyber Physical Devices
    (IEEE, 2022) Rano, Dinesh
    The modern IoT & cyber-physical systems often rely on wireless or independent energy sources that do not need battery change or manual recharging. Recharging of such systems is often delivered by rectifying antennas. In this paper we propose an energy harvesting system for IEEE 802.11 standard that can work within Wi-Fi frequency range (2412 MHz-2472 MHz and 5160-5825 MHz). The proposed system is based on phased patch-antennas with focusing and scanning of electron beam. The use of such a system increases the gain, power characteristics and, consequently, the productivity of a smart energy harvester compared to energy harvesting systems with a single antenna.
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    CNFET Based Ultra-Low-Power Schmitt Trigger SRAM for Internet of Things (IoT) Applications
    (Springer, 2021-09) Vidhyadharan, Sanjay
    This paper presents Carbon Nanotube FET (CNFET) based ultra-low-power Schmitt trigger SRAM designs which can operate at voltage levels as low as 200 mV, with high Static Noise Margins (STM) of 100–120 mV. The hysteresis in the STM curve of the CNFET Schmitt SRAM has been achieved through proper adjustment of the threshold voltage Vth of the different CNFETs used to implement the SRAM. The Vth of the CNFET can be set to the required level by selecting the appropriate chiral vectors of the CNFET. The CNFET based SRAM consumes merely 3.2 pW of power as compared to 19.5 pW of power required by the same SRAM implemented with MOSFET devices. The CNFET SRAM also has an average propagation delay of 31 ps, which is significantly lower than the delay of 250 ns experienced in CMOS-based SRAM. A simplified multi-Vth 6T CNFET SRAM design is also proposed, which consumes merely 0.1 pW of power, thus enabling a 99% reduction in total power consumption in contrast to the conventional CMOS SRAM design. The device characteristics of the CNFET has been benchmarked with 45 nm CMOS devices. The improvement in the performance of the CNFET based SRAMs can be attributed to the 10 times higher ION:IOFF ratio and 18 times higher ION:CGG ratio of the CNFET as compared to the MOSFETs.
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    Data-driven optimizations in IoT: a new frontier of challenges and opportunities
    (Springer, 2019-03) Tripathi, Sharda
    Internet of Things (IoT) has gained tremendous popularity with the recent fast-paced technological advances in embedded programmable electronic and electro-mechanical systems, miniaturization, and their networking ability. IoT is expected to change the way of human activities by extensively networked monitoring, automation, and control. However, widespread application of IoT is associated with numerous challenges on communication and storage requirements, energy sustainability, and security. Also, IoT data traffic as well as the service quality requirements are application-specific. Through a few practical example cases, this article presents IoT data driven unique communication approaches and optimization techniques to reduce the data handling footprint, leading to communication bandwidth, cloud storage, and energy saving, without compromising the service quality. Subsequently, it discusses newer challenges that are needed to be tackled, to make the IoT applications practically viable for their wide-ranging adoption.
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    Adaptive Multivariate Data Compression in Smart Metering Internet of Things
    (IEEE, 2021-02) Tripathi, Sharda
    Recent advances in electric metering infrastructure have given rise to the generation of gigantic chunks of data. Transmission of all of these data certainly poses a significant challenge in bandwidth and storage constrained Internet of Things (IoT), where smart meters act as sensors. In this work, a novel multivariate data compression scheme is proposed for smart metering IoT. The proposed algorithm exploits the cross correlation between different variables sensed by smart meters to reduce the dimension of data. Subsequently, sparsity in each of the decorrelated streams is utilized for temporal compression. To examine the quality of compression, the multivariate data is characterized using multivariate normal-autoregressive integrated moving average modeling before compression as well as after reconstruction of the compressed data. Our performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver. The proposed algorithm is tested in a real smart metering setup and its time complexity is also analyzed.