arXiv subjects

Sridhar Iyer

Publications and source records attributed to Sridhar Iyer.

14 recordsLinked to original sources

arXiv2026

TinyML for On-Device and Edge Analytics in Wireless Networks: A Survey of Deployments, Opportunities, and Concept-Drift Mitigation

Ubiquitous intelligence is essential for enabling real-time, adaptive, autonomous, and scalable operations in the next generation of wireless networks. However, this poses significant challenges in data management and energy consumption on the end-device/edge side, specially under dynamic environmental conditions. This has driven the adoption of tiny machine learning (tinyML), which offers data-driven optimization at the end-device/edge side. In this work, we survey and thoroughly discuss various tapped/untapped deployment possibilities of tinyML in wireless networks. We identify existing frameworks, accustomed to design tinyML algorithms, that could be utilized to solve a range of wireless network problems. We present a federated learning-based tinyML model update procedure, for both battery-powered and batteryless end-devices, to resolve the concept drift problem faced by tinyML models. Furthermore, we discuss the update-aware checkpointing, fault-tolerant bootloader, and intermittent-aware modify operation, which could support federated learning-based tinyML model update in the case of batteryless end-devices. Overall, this paper spells out several areas where end-device/edge intelligence can be utilized in the next generation of wireless systems, as well as ways to mitigate the concept drift problem faced in the case of end-device intelligence.

eess.SY↗

arXiv2026

Semantic Communication for 6G Networks: A Trade-off between Distortion Criticality and Information Representability

In this work, a self-attention based conditional generative adversarial network (SA-cGAN) framework for the sixth generation (6G) semantic communication system is proposed, explicitly designed to balance the trade-off between distortion criticality and information representability under varying channel conditions. The proposed SA-cGAN model continuously learns compact semantic representations by jointly considering semantic importance, reconstruction distortion, and channel quality, enabling adaptive selection of semantic tokens for transmission. A knowledge graph is integrated to preserve contextual relationships and enhance semantic robustness, particularly in low signal-to-noise ratio (SNR) regimes. The resulting optimization framework incorporates continuous relaxation, submodular semantic selection, and principled constraint handling, allowing efficient semantic resource allocation under bandwidth and multi-constraint conditions. Simulation results show that, although SA-cGAN achieves modest syntactic bilingual evaluation understudy scores at low SNR to approximately 0.72 at 20 dB, it significantly outperforms conventional and JSCC-based schemes in semantic metrics, with semantic similarity, semantic accuracy, and semantic completeness consistently improving above 0.90 with SNR. Additionally, the model exhibits adaptive compression behavior, aggressively reducing redundant content while preserving critical semantic information to maintain fidelity. The convergence of training loss further validates stable and efficient learning of semantic representations. Overall, the results confirm that the proposed SA-cGAN model effectively captures distortion-invariant semantic representations and dynamically adapts transmitted content based on distortion criticality and information representability for meaning-centric communication in future 6G networks.

eess.SP↗

arXiv2024

Cell Balancing Paradigms: Advanced Types, Algorithms, and Optimization Frameworks

The operation efficiency of the electric transportation, energy storage, and grids mainly depends on the fundamental characteristics of the employed batteries. Fundamental variables like voltage, current, temperature, and estimated parameters, like the State of Charge (SoC) of the battery pack, influence the functionality of the system. This motivates the implementation of a Battery Management System (BMS), critical for managing and maintaining the health, safety, and performance of a battery pack. This is ensured by measuring parameters like temperature, cell voltage, and pack current. It also involves monitoring insulation levels and fire hazards, while assessing the prevailing useful life of the batteries and estimating the SoC and State of Health (SoH). Additionally, the system manages and controls key activities like cell balancing and charge/discharge processes. Thus functioning of the battery can be optimised, by guaranteeing the vital parameters to be well within the prescribed range. This article discusses the several cell balancing schemes, and focuses on the intricacies of cell balancing algorithms and optimisation methods for cell balancing. We begin surveying recent cell balancing algorithms and then provide selection guidelines taking into account their advantages, disadvantages, and applications. Finally, we discuss various optimization algorithms and outline the essential parameters involved in the cell balancing process.

eess.SY↗

arXiv2024

QMViT: A Mushroom is worth 16x16 Words

Consuming poisonous mushrooms can have severe health consequences, even resulting in fatality and accurately distinguishing edible from toxic mushroom varieties remains a significant challenge in ensuring food safety. So, it's crucial to distinguish between edible and poisonous mushrooms within the existing species. This is essential due to the significant demand for mushrooms in people's daily meals and their potential contributions to medical science. This work presents a novel Quantum Vision Transformer architecture that leverages quantum computing to enhance mushroom classification performance. By implementing specialized quantum self-attention mechanisms using Variational Quantum Circuits, the proposed architecture achieved 92.33% and 99.24% accuracy based on their category and their edibility respectively. This demonstrates the success of the proposed architecture in reducing false negatives for toxic mushrooms, thus ensuring food safety. Our research highlights the potential of QMViT for improving mushroom classification as a whole.

cs.CV↗

arXiv2024

Cell Balancing for the Transportation Sector: Techniques, Challenges, and Future Research Directions

Efficient and reliable energy systems are key to progress of society. High performance batteries are essential for widely used technologies like Electric Vehicles (EVs) and portable electronics. Additionally, an effective Battery Management System (BMS) is crucial to oversee vital parameters of battery. However, BMS can experience cell imbalance due to charging/discharging dynamics, which reduce battery capacity, lifespan, and efficiency, and raise critical safety concerns. This calls for effective cell-balancing techniques. Notably, the existing literature on cell balancing is limited, urgently necessitating a thorough survey to pinpoint key research gaps and suggest prompt solutions. In this article, cell balancing and corresponding techniques are reviewed. Initially, we detail comparison of passive cell balancing techniques and assess their respective advantages, drawbacks, and practical applications. Then, we discuss the strengths and weaknesses of active cell balancing methods and applicability of cell balancing for both, series and parallel-connected cells. Additionally, we examine the need for cell balancing in commonly used batteries, and applications in EVs. Lastly, we present detailed prospects which include challenges and directions for future research.

eess.SY↗

arXiv2023

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.

cs.NI↗

arXiv2023

TinyML: Tools, Applications, Challenges, and Future Research Directions

In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has limited their use mainly to high-capability devices such as network nodes. However, with many advancements in technologies such as the Internet of Things (IoT) and edge computing, it is desirable to incorporate ML techniques into resource-constrained embedded devices for distributed and ubiquitous intelligence. This has motivated the emergence of the TinyML paradigm which is an embedded ML technique that enables ML applications on multiple cheap, resource- and power-constrained devices. However, during this transition towards appropriate implementation of the TinyML technology, multiple challenges such as processing capacity optimization, improved reliability, and maintenance of learning models' accuracy require timely solutions. In this article, various avenues available for TinyML implementation are reviewed. Firstly, a background of TinyML is provided, followed by detailed discussions on various tools supporting TinyML. Then, state-of-art applications of TinyML using advanced technologies are detailed. Lastly, various research challenges and future directions are identified.

cs.LG↗

arXiv2022

A Comprehensive Survey on Spectrum Sharing Techniques for 5G/B5G Intelligent Wireless Networks: Opportunities, Challenges and Future Research Directions

The increasing popularity of Internet of Everything and small-cell devices has enormously accelerated traffic loads. Consequently, increased bandwidth and high data rate requirements stimulate the operation at the millimeter wave and the Tera-Hertz spectrum bands in the fifth generation (5G) and beyond 5G (B5G) wireless networks. Furthermore, efficient spectrum allocation, maximizing the spectrum utilization, achieving efficient spectrum sharing (SS), and managing the spectrum to enhance the system performance remain challenging. To this end, recent studies have implemented artificial intelligence and machine learning techniques, enabling intelligent and efficient spectrum leveraging. However, despite many recent research advances focused on maximizing utilization of the spectrum bands, achieving efficient sharing, allocation, and management of the enormous available spectrum remains challenging. Therefore, the current article acquaints a comprehensive survey on intelligent SS methodologies for 5G and B5G wireless networks, considering the applications of artificial intelligence for efficient SS. Specifically, a thorough overview of SS methodologies is conferred, following which the various spectrum utilization opportunities arising from the existing SS methodologies in intelligent wireless networks are discussed. Subsequently, to highlight critical limitations of the existing methodologies, recent literature on existing SS methodologies is reviewed in detail, classifying them based on the implemented technology, i.e., cognitive radio, machine learning, blockchain, and multiple other techniques. Moreover, the related SS techniques are reviewed to highlight significant challenges in the B5G intelligent wireless network. Finally, to provide an insight into the prospective research avenues, the article is concluded by presenting several potential research directions and proposed solutions.

cs.NI↗

arXiv2022

A Survey on Semantic Communications for Intelligent Wireless Networks

With deployment of 6G technology, it is envisioned that competitive edge of wireless networks will be sustained and next decade's communication requirements will be stratified. Also 6G will aim to aid development of a human society which is ubiquitous and mobile, simultaneously providing solutions to key challenges such as, coverage, capacity, etc. In addition, 6G will focus on providing intelligent use-cases and applications using higher data-rates over mill-meter waves and Tera-Hertz frequency. However, at higher frequencies multiple non-desired phenomena such as atmospheric absorption, blocking, etc., occur which create a bottleneck owing to resource (spectrum and energy) scarcity. Hence, following same trend of making efforts towards reproducing at receiver, exact information which was sent by transmitter, will result in a never ending need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (context) of received data as opposed to only reproducing correct transmitted data. This in turn will require less bandwidth, and will reduce bottleneck due to various undesired phenomenon. In this respect, current article presents a detailed survey on recent technological trends in regard to semantic communications for intelligent wireless networks. We focus on semantic communications architecture including model, and source and channel coding. Next, we detail cross-layer interaction, and various goal-oriented communication applications. We also present overall semantic communications trends in detail, and identify challenges which need timely solutions before practical implementation of semantic communications within 6G wireless technology. Our survey article is an attempt to significantly contribute towards initiating future research directions in area of semantic communications for intelligent 6G wireless networks.

cs.NI↗

arXiv2022

Survey on Wireless Information Energy Transfer (WIET) and Related Applications in 6G Internet of NanoThings (IoNT)

This article contains an overview of WIET and the related applications in 6G IoNT. Specifically, to explore the following, we: (i) introduce the 6G network along with the implementation challenges, possible techniques, THz communication and related research challenges, (ii) focus on the WIET architecture, and different energy carrying code words for efficient charging through WIET, (iii) discuss IoNT with techniques proposed for communication of nano-devices, and (iv) conduct a detailed literature review to explore the implicational aspects of the WIET in the 6G nano-network. In addition, we also investigate the expected applications of WIET in the 6G IoNT based devices and discuss the WIET implementation challenges in 6G IoNT for the optimal use of the technology. Lastly, we overview the expected design challenges which may occur during the implementation process, and identify the key research challenges which require timely solutions and which are significant to spur further research in this challenging area. Overall, through this survey, we discuss the possibility to maximize the applications of WIET in 6G IoNT.

cs.NI↗

arXiv2022

A Survey on Brain-Computer Interface and Related Applications

BCI systems are able to communicate directly between the brain and computer using neural activity measurements without the involvement of muscle movements. For BCI systems to be widely used by people with severe disabilities, long-term studies of their real-world use are needed, along with effective and feasible dissemination models. In addition, the robustness of the BCI systems' performance should be improved so they reach the same level of robustness as natural muscle-based health monitoring. In this chapter, we review the recent BCI related studies, followed by the most relevant applications of BCI systems. We also present the key issues and challenges which exist in regard to the BCI systems and also provide future directions.

cs.HC↗

arXiv2022

Survey on Internet of Things enabled by 6G Wireless Networks

The 6G wireless technology is visualized to revolutionize multiple customer services with the Internet of Things (IoT), thereby contributing to a ubiquitous intelligent society comprising autonomous systems. In this chapter, we conduct a detailed survey on the IoT networks with 6G wireless networks and investigate the trending possibilities provided by the 6G technology within the IoT networks and the related utilization; Firstly, we detail the breakthrough IoT technologies and the technological drivers which are anticipated to strengthen IoT networks in future. Next, we present the relevant use cases detailing the discussion on the role of the 6G technology within a broad spectrum of IoT potential applications. Lastly, we highlight the several research scope and challenges and list the potential research needs and encourage further research within the thrust area of IoT enabled by 6G networks.

cs.NI↗

arXiv2022

A Survey of Machine Learning Algorithms for 6G Wireless Networks

The primary focus of Artificial Intelligence/Machine Learning (AI/ML) integration within the wireless technology is to reduce capital expenditures, optimize network performance, and build new revenue streams. Replacing traditional algorithms with deep learning AI techniques have dramatically reduced the power consumption and improved the system performance. Further, implementation of ML algorithms also enables the wireless network service providers to (i) offer high automation levels from distributed AI/ML architectures applicable at the network edge, (ii) implement application-based traffic steering across the access networks, (iii) enable dynamic network slicing for addressing different scenarios with varying quality of service requirements, and (iv) enable ubiquitous connectivity across the various 6G communication platforms. In this chapter, we review/survey the ML techniques which are applicable to the 6G wireless networks. and also list the open problems of research which require timely solutions.

cs.NI↗

arXiv2022

A Survey on Technological Trends to Enhance Spectrum Efficiency in 6G Communications

The research community has already identified that, by 2030, 5G networks will reach the capacity limits, and hence, will be inadequate to support next generation bandwidth-hungry, ubiquitous, intelligent services, and applications. Therefore, in view of sustaining the competitive edge of wireless technology and stratifying the next decade's communication requirements both, industry and research community have already begun conceptualizing the 6G technology. This article presents a detailed survey on the recent technological trends which address the capacity issues and enhance the spectrum-efficiency in 6G Communications. We present these trends in detail and then identify the challenges that need solutions before the practical deployment to realize 6G communications. Our survey article attempts to significantly contribute to initiating future research directions in the area of spectrum-efficiency in 6G communications.

cs.NI↗