Browsing by Author "Tripathi, Sharda"

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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.
Assessment of power system stability using reduced-rate synchrophasor data
(IEEE, 2015) Tripathi, Sharda
In this paper, we investigate a data driven approach based on o-Support Vector Regression (o-SVR) to identify the dependence of present sample of power-line frequency on past few samples. In standard practice, the Phasor Measurement Units (PMUs) measure the frequency samples continuously from various bus locations in the power grid and transmit them at a fixed rate, typically at 25 samples/sec, to the Phasor Data Concentrator (PDC). Objective of the proposed strategy is to reduce the sampling rate at a PMU or transmission rate of the fixed-rate samples from a PMU to the PDC such that any impending disturbance in the power system can be detected early without compromising stability of the power system. We evaluate the performance of our proposed model by quantifying the sample data rate reduction and obtaining the average prediction error during the steady state as well as disturbed state conditions in the power gird.
Channel-Adaptive Transmission Protocols for Smart Grid IoT Communication
(IEEE, 2020-08) Tripathi, Sharda
This article presents a new paradigm for channel dynamics adaptive transmission of intermittent data in smart grid IoT communication networks, wherein novel channel prediction frameworks using stochastic modeling as well as data-driven learning of channel variability are proposed. A probing-based transmission is also proposed as a benchmark. These prediction frameworks are complemented with an adaptive channel coding scheme to increase the transmission reliability of time-critical grid monitoring data over a wireless channel. Through analyzing the prediction and packet loss performance at varying SNR and fading conditions, it is noted that the stochastic modeling framework is efficient when the fading correlation in the channel is high while the learning-based approach is more adaptive to channel dynamics as the correlation reduces. The proposed frameworks are easily implementable on low-cost end nodes, owing to the optimal selection of parameters for low runtime complexity. When compared to probing-based data transmission for a given fading in the channel, the packet loss probability of the learning-based transmission closely matches while with stochastic model loss probability is found to be 12.3% higher. However, their respective signaling overheads are 38% and 98% lower with respect to the probing-based approach, which is a significant gain at the cost of marginally additional computation complexity.
A Context-Aware Radio Resource Management in Heterogeneous Virtual RANs
(IEEE, 2022-03) Tripathi, Sharda
New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. Here, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness, we develop a testbed for proof-of-concept. Experimental results demonstrate that CAREM enables an efficient radio resource allocation under different settings and traffic demand. Also, compared to the closest existing scheme based on neural network and the standard LTE, CAREM exhibits an improvement of one order of magnitude in packet loss and latency, while it provides a 65% latency improvement relatively to the contextual bandit approach.
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.
Data-driven Secure Authentication for Smart Grid IoT Networks
(IEEE, 2023-07) Tripathi, Sharda
Internet of Things (IoT) has found wide-spread usage in cyber physical systems. We consider the security aspects related to cyber physical systems, particularly the smart grid, wherein IoT devices are deployed for monitoring and control, often in the open without any active surveillance, thereby increasing their vulnerability to security attacks. To this end, device authentication is an essential security feature for smart grid IoT networks. A desirable authentication protocol designed for IoT devices should not only be robust when the device is compromised by an adversary, it also needs to be computationally efficient in the wake of limited storage and computational capabilities of IoT devices. In this work, we address the critical issue of device security in smart grid IoT networks, and propose a data-driven, yet lightweight and privacy preserving authentication scheme for IoT devices. The performance analysis based on our experiments show that the proposed scheme is robust against heterogeneous network attacks, and significantly reduces computational load.
Dynamic Prediction of Powerline Frequency for Wide Area Monitoring and Control
(IEEE, 2018-07) Tripathi, Sharda
This paper presents a novel data driven framework based on ϵ -Support Vector Regression to reduce the bandwidth requirement for transmission of phasor measurement unit (PMU) data. This is achieved by judicious elimination of redundant data at the PMU before transmission. Simultaneously, the missing samples are predicted at PDC to ensure faithful identification of impending disturbances in the power system. Due to inherent nonstationary nature of PMU data, the hyperparameters are dynamically recomputed as necessary, thereby maintaining the accuracy of prediction and robustness of the algorithm. Performance of the proposed algorithm is evaluated via large scale simulations using powerline frequency data. A trade-off between prediction quality and runtime of the algorithm is observed, which is addressed by suitable selection of hyperparameters. Compared to the competitive data reduction scheme, the proposed algorithm saves around 60% bandwidth and identifies power system disturbances 73% more accurately.
An Efficient Data Characterization and Reduction Scheme for Smart Metering Infrastructure
(IEEE, 2018) Tripathi, Sharda
In this paper, a novel characterization of smart meter data based on Gaussian mixture (GM) model is presented. It is shown that compared to the existing characterization models, the proposed GM model provides a significantly better fit for smart meter data. Furthermore, at each smart meter, sparsity of data is exploited to devise an adaptive data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter data transmission is reduced with minimum loss of information. When compared to the closest competitive scheme, the proposed compressive sampling based data reduction algorithm is found to be noise robust and offers 12.8% and 7.4% higher bandwidth saving, respectively, at 1 s and 30 s sampling intervals for comparable reconstruction accuracy. Proposed scheme is tested in real-time using RT-LAB.
EnRoute: A DQN based Energy Efficient Routing for URLLC in Next Generation Networks
(IEEE, 2023) Tripathi, Sharda
5G networks and beyond (B5G) will support a wide range of services which demand large data rates, massive connection densities, high reliability and low latency. Catering to such needs will require a dense deployment of gNodeBs (gNBs), leading to enormous network energy consumption, thereby contributing to the global carbon emissions. Conventional network energy saving techniques, such as gNB sleep scheduling, significantly hamper the latency performance of delay-sensitive data. To this end, we propose EnRoute for energy efficient route selection of delay-sensitive traffic in B5G networks. We formulate an integer programming problem to optimally select a routing path for delay sensitive data such that its target delay is met while maximizing the energy efficiency of gNBs. Since the problem is NP-complete, we resort to DQN-based learning framework for designing EnRoute. Our simulation results confirm an energy efficient route selection and the key performance indices meeting the required QoS targets. We further compare our approach with two benchmark schemes, one that minimizes the latency, and other that maximizes the energy efficiency of gNBs (instead of tackling them together) for the route selection, and show that our latency performance matches the former (99.7%), with a marginal deficit in energy efficiency with respect to latter (10%).
Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services
(IEEE, 2023-03) Tripathi, Sharda
The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks
Green Sensing and Communication: A Step Towards Sustainable IoT Systems
(Springer, 2020-04) Tripathi, Sharda
With the advent of Internet of Things (IoT) devices, their reconfigurability, networking, task automation, and control ability have been a boost to the evolution of traditional industries such as health-care, agriculture, power, education, and transport. However, the quantum of data produced by the IoT devices poses serious challenges on its storage, communication, computation, security, scalability, and system’s energy sustainability. To address these challenges, the concept of green sensing and communication has gained importance. This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications. Further, a few case studies are presented that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality. Challenges associated with these green techniques, various open issues, and future research directions for improving the energy efficiency of the IoT systems are also discussed.
MERGE: Meta Reinforcement Learning for Tunable RL Agents at the Edge
(IEEE, 2023-12) Tripathi, Sharda
The efficient allocation of radio resources is an essential trait of 5G/6G radio access networks (RANs), as they are called to meet diverse QoS requirements of highly demanding applications. To equip RANs with such an ability and, at the same time, meet their function split constraints, we envision a distributed learning approach for radio resource allocation that makes the most out of the Central Unit (CU) and Distributed Unit (DU) components by effectively exploiting their synergy. On the one hand, our solution, named MERGE, leverages the knowledge of the radio connectivity dynamics that each DU can acquire through the local use of a deep reinforcement learning radio agent. On the other hand, it lets the CU collect such agents in a crowdsourcing fashion, and, then, thanks to a meta-learning policy, properly select and aggregate them to create up-to-date radio agents of the right size (hence, complexity level) to fit the computing constraints of the individual DUs. Our results show that MERGE can match the performance of the highest-complexity radio model in [1] with 25% less computational requirements, and, for a given computational resource, it outperforms a single pruned model with a 19% increase in QoS.
A multi-modal smart switching based image transmission using semantic communication
(IEEE, 2025-02) Tripathi, Sharda; Joshi, Sandeep
The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon’s classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction.
Pathway and Future of IoE in Smart Cities: Challenges of Big Data and Energy Sustainability
(CRC, 2021) Tripathi, Sharda
This chapter deals with the IoT node-level and network energy sustainability and IoE stability aspects, wherein the benefits from node-level and network intelligence are presented. Some field IoT sensing applications, such as in smart cities, pollution monitoring, and agricultural automation, are characterized by high energy consumption, node lifetime criticality, and energy sustainability issues. More discussion on smart meter data compressibility and novel approaches to dealing with big data concerns caused by millions of smart meter installation as a part of smart city monitoring exercise are presented in the chapter. It has been demonstrated how the challenges of big data and energy sustainability are addressed via application-specific unique approaches. The research state-of-the-art and open issues on mitigating big data challenges and energy sustainability have been highlighted. Further, the role of smart grid on uninterrupted IoE operation, the benefits of distributed energy generation through IoE, and the newer challenges of power grid stability/controllability have also been discussed.
A quality-of-service-centric uplink rate-splitting approach for next-generation multiple access
(IEEE, 2025-06) Tripathi, Sharda
Recently, Rate-Splitting Multiple Access (RSMA) has emerged as a powerful paradigm for meeting the demanding performance requirements of 6G wireless networks through non-orthogonal high-rate data transmission. However, uplink access in RSMA necessitates optimizing the decoding order, which can lead to significant search latency. Besides, the process overlooks the Quality-of-Service (QoS) constraints of different traffic types, making current RSMA methods inadequate, especially for low-latency communication. Here, we address this issue by proposing QORA, short for QoS-aware One-shot Rate-splitting multiple Access, a multi-agent Deep Q-Network (DQN) framework that leverages a novel QoS-aware transmit power allocation and decoding order policy in uplink RSMA that achieves remarkable performance improvements while maintaining low latency and high admission rates.
An RL Approach to Radio Resource Management in Heterogeneous Virtual RANs
(IEEE, 2021) Tripathi, Sharda
5G networks are primarily designed to support a wide range of services characterized by diverse key performance indicators (KPIs). A fundamental component of 5G networks, and a pivotal factor to the fulfillment of the services KPIs, is the virtual radio access network (RAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of virtual RANs in non-stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the non-trivial interdependence of network and channel conditions. In this paper, we propose CAREM, an RL framework for dynamic radio resource allocation, which selects the best link and modulation and coding scheme (MCS) for packet transmission, so as to meet the KPI requirements in heterogeneous virtual RANs. To show its effectiveness in real-world conditions, we provide a proof-of-concept through actual testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for any of the considered time periodicity of the decision-making process
Smart IoT Communication: Circuits and Systems
(IEEE, 2020) Tripathi, Sharda
In a smart IoT system, multi-sensing at a field node is a typical scenario. The examples considered in this study are pollution monitoring and smart energy metering. In such applications, energy sustainability and communication and storage resource usage optimization are two of the key issues of interest. In this study, on one hand it is intended to develop indigenous beyond state of the art multi-sensing boards with the inherent smartness in energy replenishment and sensing/communication activities. On the other hand, smart data collection and processing at the end node (fog node or edge node) is of interest primarily from efficient communication bandwidth usage perspective. On the first exercise towards energy sustainable IoT sensing and communication board design, we have designed a prototype for a 5G capable environmental air pollution monitoring system. The system measures concentrations of NO2, ozone, CO and SO2 using semiconductor sensors. Further, the system gathers other environmental parameters like temperature, humidity, PM1, PM2.5 and PM10. The prototype is equipped with a GPS sub-system for accurate geo-tagging. The board communicates through Wi-Fi and NB-IoT. The board is also equipped with energy harvesting power management, and is powered through solar energy and battery backup. On the second exercise, a working model of a smart IoT device with a data pruning subsystem is designed, where a smart energy meter is considered for an example application. As a proof of concept we plan to demonstrate data compression at the edge to save bandwidth required for data transmission to a remote cloud. At each smart meter, sparsity of data is exploited to devise an adaptive data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter data transmission is reduced with minimum loss of information. The Smart Energy Meter is WiFi and NB-IoT enabled.
Sustainable marine surveillance sensor network aided by swipt-enabled auvs
(IEEE, 2025-08) Tripathi, Sharda
Oceans are vital for Earth's climate stability, oxygen production, and as sources of food and energy for countless organisms. However, human-induced climate change significantly disrupts marine ecosystems, emphasizing the need for advanced underwater monitoring. The Internet of Underwater Things (IoUT), composed of marine sensor networks, offers a promising solution, nonetheless, challenges such as limited communication range and constrained power supplies. To address these issues, this work proposes using simultaneous wireless information and power transfer (SWIPT) from autonomous underwater vehicles (AUVs) to enhance sensor node efficiency. We formulate an integer linear programming (ILP) problem aimed at optimizing AUV trajectories through marine sensor networks, minimizing propulsion energy and mission duration while ensuring adequate energy harvesting at each node. The problem is proven to be NP-hard, resembling the well-known traveling salesman problem (TSP). Further, we introduce CLEAR, a multi-agent deep-Q-network (DQN) framework that effectively selects optimal path for AUV-based data muling. Experimental results demonstrate that CLEAR significantly improves network energy-efficiency, reduces mission duration, boosts harvested energy, and decreases age-of-information (AoI) of sensor data.
VERA: Resource Orchestration for Virtualized Services at the Edge
(IEEE, 2022) Tripathi, Sharda
The combination of service virtualization and edge computing allows mobile users to enjoy low latency services, while keeping data storage and processing local. However, the network edge has limited resource availability, and when both virtualized user applications and network functions need to be supported concurrently, a natural conflict in resource usage arises. In this paper, we focus on computing and radio resources and develop a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions. Through our testbed, we demonstrate the effectiveness of our solution in resource-limited scenarios, and show an improvement of around 60% in the CPU budget violation rate with respect to RL based standard multi-agent framework.
Versatile Multivariate Data Pruning in Smart Grid IoT Networks
(IEEE, 2020) Tripathi, Sharda
With wide scale sensor deployments in smart grid IoT networks, there has been a manyfold increase in the variety and quantity of data generated in the network. In this work, the problem of data reduction in smart grid IoT network is addressed to enhance the resource utilization without hampering the required quality of service. A novel versatile algorithm for multivariate data pruning at the edge devices in smart grid IoT networks is presented. This is achieved via a two stage data reduction mechanism which first exploits the inter-variable correlation to cut down on the number of transmitted variables, followed by adaptive data compression in temporal domain using adaptive compressive sampling. It is shown that with the application of the proposed algorithm at the edge nodes, around 23% savings in bandwidth requirement can be achieved with minimum loss of information.