Jyotirmoy Bhardwaj

• Bhardwaj, Jyotirmoy & Cenkeramaddi, Linga Reddy (2026). Unsupervised Detection of Multivariate Water Quality Anomalies via Residual Feature Embeddings. IEEE Access. 14, p. 97948–97961. doi: 10.1109/ACCESS.2026.3704534. Full text in Research Archive Show summary

  Monitoring water quality in water utilities via multiple distributed sensors is critical for detecting diverse pollution events. Multivariate water-quality time series, generated by such water quality sensors, often exhibit overlapping seasonal patterns, long-term trends, and irregular disturbances, making anomaly detection challenging without extensive labeled data. In this work, we present an anomaly detection approach based on unsupervised machine learning (ML) using residual feature embeddings and multivariate clustering. To extract anomalies, for each sensor, we compute candidate seasonal periods and fit a polynomial trend, yielding residual vectors that serve as feature embeddings for Isolation Forest (IF), an unsupervised ML approach. IF was used to identify anomalous observations through residual embeddings, and K-Means clustering was used to group residual vectors into similar water-quality events. To aid interpretability, the clusters were further analyzed statistically to identify the potential signatures of anomalous events. Validation is performed on data annotated by domain experts. This approach enables robust unsupervised anomaly detection by effectively handling multiple overlapping seasonality and trends that might otherwise obscure anomalies. An experimental evaluation of multivariate water-quality datasets demonstrates that the proposed method successfully identifies unusual events and provides valuable insights for water utilities.

• Bhardwaj, Jyotirmoy; Harman, Christopher Peter; Pussewalage, Harsha Gardiyawasam & Cenkeramaddi, Linga Reddy (2023). Real-Time Missing Data Estimation in Water Networks, 2023 IEEE Applied Sensing Conference (APSCON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665461634. doi: 10.1109/APSCON56343.2023.10101123. Full text in Research Archive Show summary

  Estimating missing data (e.g., pressure) in geographically distributed nodes is one of the fundamental challenges in a water distribution network (WDN). The Model-Based Kalman Filter is a simple solution that can estimate missing data in systems with a linear Gaussian state model. However, in large WDN, the state model is non-linear, and accurate system dynamics are unknown. Thus, we investigate the use of Data-Driven Kalman Filter (DDKF), in which the Recurrent Neural Network (RNN) updates the Kalman Gain. This data-driven approach has the potential to reduce approximation errors caused by the state model’s nonlinearity. As a result, this work demonstrates the application of a DDKF in a WDN. In addition, we demonstrate numerically that the DDKF can overcome approximation errors caused by flow equations and can estimate missing data in a WDN.

• Singh, Umesh; Bhardwaj, Jyotirmoy & Kanaujia, Binod Kumar (2023). An Efficient Beamformer and Power Splitter Design for SWIPT in Spectrum Sharing Network, IC3-2023: Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing. Association for Computing Machinery (ACM). ISSN 9798400700224. p. 638–642. doi: 10.1145/3607947.3608073. Full text in Research Archive Show summary

  In this paper, we investigate the joint design of a beamformer and power splitter for simultaneous wireless information and power transfer (SWIPT) in spectrum-sharing networks. The goal of this work is to minimize the total transmitted power at the secondary transmitter (ST) while ensuring minimum signal-to-interferenceplus-noise ratio (SINR) and energy harvesting (EH) threshold at each secondary receiver (SR) while maintaining the interference power level at the primary user (PU) below the predefined interference threshold power in multiple co-existing networks. To solve this problem, we derive an optimization problem. However, the optimization formulation turns out to be non-convex, given the non-linear constraints. We address this challenge via the semidefinite relaxation (SDR) technique. The simulation results demonstrate the critical role of transceiver design in achieving an optimal balance among various network parameters. Specifically, the results emphasize the significance of balancing the transmit power at the ST, the SINR, the EH at the SR, and the interference threshold at the PU in co-existing networks.

• Bhardwaj, Jyotirmoy; Krishnan, Joshin Parakkulangarayil & Beferull-Lozano, Baltasar (2021). Data-Driven Pump Scheduling for Cost Minimization in Water Networks. In NN, NN (Eds.), 2021 IEEE International Conference on Autonomous Systems (ICAS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728172897. p. 197–201. doi: 10.1109/ICAS49788.2021.9551168. Full text in Research Archive Show summary

  Pumps consume a significant amount of energy in a water distribution network (WDN). With the emergence of dynamic energy cost, the pump scheduling as per user demand is a computationally challenging task. Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. However, MIO formulations are NP-hard in general and solving such problems is inefficient in terms of computation time and memory. Moreover, the computational complexity of solving such MIO formulations increases exponentially with the size of the WDN. As an alternative, we propose a data-driven approach to estimate the decision variables of pump scheduling using deep neural networks (DNN). We evaluate the performance of our trained DNN relative to a state-of-the-art MIO solver, and conclude that our DNN based approach can be used to minimize the pump switching and cost incurred due to dynamic energy in a given WDN with much lower complexity.

• Deininger, Anne; Martin, Angela Helen; Pardo, Juan; Berg, Paul Ragnar; Bhardwaj, Jyotirmoy & Catarino, Diana Sofia de Oliveira [Show all 15 contributors for this article] (2021). Coastal Research Seen Through an Early Career Lens—A Perspective on Barriers to Interdisciplinarity in Norway. Frontiers in Marine Science. 8. doi: 10.3389/fmars.2021.634999. Full text in Research Archive Show summary

  The value of interdisciplinarity for solving complex coastal problems is widely recognized. Many early career researchers (ECRs) therefore actively seek this type of collaboration through choice or necessity, for professional development or project funding. However, establishing and conducting interdisciplinary research collaborations as an ECR has many challenges. Here, we identify these challenges through the lens of ECRs working in different disciplines on a common ecosystem, the Norwegian Skagerrak coast. The most densely populated coastline in Norway, the Skagerrak coast, is experiencing a multitude of anthropogenic stressors including fishing, aquaculture, eutrophication, climate change, land runoff, development, and invasive species. The Skagerrak coastline has also been the focus of environmental science research for decades, much of which aims to inform management of these stressors. The region provides a fantastic opportunity for interdisciplinary collaboration, both within and beyond the environmental sciences. This perspective article identifies the barriers ECRs in Norway face in establishing interdisciplinary and collaborative research to inform management of coastal ecosystems, along with their root causes. We believe our discussion will be of broad interest to all research institutions who employ or educate ECRs (in Norway and worldwide), and to those who develop funding mechanisms for ECRs and interdisciplinary research.

• Bhardwaj, Jyotirmoy; Krishnan, Joshin Parakkulangarayil; Marin, Diego F. Larios; Beferull-Lozano, Baltasar; Cenkeramaddi, Linga Reddy & Harman, Christopher Peter (2021). Cyber-Physical Systems for Smart Water Networks: A Review. IEEE Sensors Journal. ISSN 1530-437X. 21(23), p. 26447–26469. doi: 10.1109/JSEN.2021.3121506. Full text in Research Archive Show summary

  There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.

• Bhardwaj, Jyotirmoy (2018). Bhardwaj, J., Gupta, K. K., & Gupta, R. (2018). Towards a cyber-physical era: soft computing framework based multi-sensor array for water quality monitoring. Drinking Water Engineering and Science, 11(1), 9-17. Drinking Water Engineering and Science (DWES). ISSN 1996-9457. 11(1), p. 9–17. doi: 10.5194/dwes-11-9-2018. Full text in Research Archive
• Bhardwaj, Jyotirmoy; Gupta, Karunesh & Khatri, Punit (2018). Real Time Assessment of Potable Water Quality in Distribution Network based on Low Cost Multi-Sensor Array. IOP Conference Series: Materials Science and Engineering. ISSN 1757-8981. 331(1), p. 1–5. doi: 10.1088/1757-899x/331/1/012027. Full text in Research Archive

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• Bhardwaj, Jyotirmoy & Cenkeramaddi, Linga Reddy (2025). Scalable Online Anomaly Detection in Multi-Parameter Sensor Data Stream for Water Networks. Full text in Research Archive
• Bhardwaj, Jyotirmoy; Harman, Christopher Peter; Pussewalage, Harsha Gardiyawasam & Cenkeramaddi, Linga Reddy (2023). Real-Time Missing Data Estimation in Water Networks. Full text in Research Archive

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