Photo of Delaram Amiri

Delaram Amiri

Ph.D.2015 – 2020
Research Interests:
  • Healthcare IoT
  • Sensor Control
  • Optimization
  • Edge Computing
Education:
  • 2020
    Ph.D. in Computer Science
    University of California, Irvine, U.S.
  • 2015
    M.S. in Electrical Engineering, Communications
    Indiana University–Purdue University Indianapolis, U.S.
  • 2013
    B.S. in Electrical Engineering, Communications
    Shiraz University, Iran
Website: LinkedIn

I received my B.S. from Shiraz University, Iran in Electrical Engineering. Later on, I got my M.S. in Electrical Engineering in Signal and Image Processing at Indiana University, Purdue University, Indianapolis (IUPUI). I am now a Ph.D. Candidate at University of California Irvine, studying Electrical Engineering. My research focuses on Internet of Cognitive Things in Healthcare. I study on edge based optimization approaches as a function of contexts of a patient to control energy constraint sensors. I try to design algorithms using both accuracy of measurements in sensors and activity of an individual to build more energy efficient sensor control methods to monitor a patient for longer time while fulfilling certain levels of risk of health deterioration. Working under supervision of my advisor, Prof. Levorato and Co-advisor, Prof. Nikil Dutt at UC Irvine, I implemented edge based context-aware algorithms to control body sensors. My project is in collaboration with University of Turku, Finland and VTT Technical Research Centre of Finland. We implement health-care IoT for a better life.

Publications

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Book Chapters

  • Delaram Amiri
    Arman Anzanpour
    Iman Azimi
    Amir M. Rahmani
    Pasi Liljeberg
    Nikil Dutt
    Marco Levorato
    energy-efficient fog computing
    energy-efficient sensor control
    healthcare Internet-of-Things
    Markov decision process
    myopic strategy
    wearable sensors
    D. Amiri, A. Anzanpour, et al.I. Azimi, A. M. Rahmani, P. Liljeberg, N. Dutt, and M. Levorato, Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control, pp. 245 – 268, 2020. Release
    @article{amiri-anzanpour-fog20,
      author = {Amiri, Delaram and Anzanpour, Arman and Azimi, Iman and Rahmani, Amir M. and Liljeberg, Pasi and Dutt, Nikil and Levorato, Marco},
      title = {Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control},
      booktitle = {Fog Computing},
      chapter = {9},
      pages = {245-268},
      publisher = {John Wiley & Sons, Ltd},
      year = {2020},
      doi = {10.1002/9781119551713.ch9},
      eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119551713.ch9},
      isbn = {9781119551713},
      keyword = {energy-efficient fog computing, energy-efficient sensor control, healthcare Internet-of-Things, Markov decision process, myopic strategy, wearable sensors},
      url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119551713.ch9},
    }

Journal Articles

  • Delaram Amiri
    Arman Anzanpour
    Iman Azimi
    Marco Levorato
    Pasi Liljeberg
    Nikil Dutt
    Amir M. Rahmani
    abnormality detection
    wearable electronics
    Internet of Things
    Health monitoring
    energy efficiency
    edge/fog computing
    edge-assisted control
    context awareness
    D. Amiri, A. Anzanpour, et al.I. Azimi, M. Levorato, P. Liljeberg, N. Dutt, and A. M. Rahmani, Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems, ACM Trans. Comput. Healthcare, vol. 1, no. 2, article no. 7, Mar. 2020. Release

    Healthcare applications supported by the Internet of Things enable personalized monitoring of a patient in everyday settings. Such applications often consist of battery-powered sensors coupled to smart gateways at the edge layer. Smart gateways offer several local computing and storage services (e.g., data aggregation, compression, local decision making), and also provide an opportunity for implementing local closed-loop optimization of different parameters of the sensor layer, particularly energy consumption. To implement efficient optimization methods, information regarding the context and state of patients need to be considered to find opportunities to adjust energy to demanded accuracy. Edge-assisted optimization can manage energy consumption of the sensor layer but may also adversely affect the quality of sensed data, which could compromise the reliable detection of health deterioration risk factors. In this article, we propose two approaches: myopic and Markov decision processes (MDPs)—to consider both energy constraints and risk factor requirements for achieving a twofold goal: energy savings while satisfying accuracy requirements of abnormality detection in a patient’s vital signs. Vital signs, including heart rate, respiration rate, and oxygen saturation, are extracted from a photoplethysmogram signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor’s sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable photoplethysmogram sensor in an IoT system, and show that compared to nonadaptive methods, myopic reduces an average of 16.9

    @article{amiri-azimi-acmhealth20,
      author = {Amiri, Delaram and Anzanpour, Arman and Azimi, Iman and Levorato, Marco and Liljeberg, Pasi and Dutt, Nikil and Rahmani, Amir M.},
      title = {Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems},
      journal = {ACM Trans. Comput. Healthcare},
      volume = {1},
      number = {2},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      month = mar,
      year = {2020},
      articleno = {7},
      doi = {10.1145/3351286},
      issn = {2691-1957},
      issue_date = {April 2020},
      keyword = {abnormality detection, wearable electronics, Internet of Things, Health monitoring, energy efficiency, edge/fog computing, edge-assisted control, context awareness},
      numpages = {25},
      url = {https://doi.org/10.1145/3351286},
    }
  • Arman Anzanpour
    Delaram Amiri
    Iman Azimi
    Marco Levorato
    Nikil Dutt
    Pasi Liljeberg
    Amir M. Rahmani
    edge computing
    Internet of Things
    wearable electronics
    Health monitoring
    early warning score
    edge-assisted control
    A. Anzanpour, D. Amiri, et al.I. Azimi, M. Levorato, N. Dutt, P. Liljeberg, and A. M. Rahmani, Edge-Assisted Control for Healthcare Internet of Things: A Case Study on PPG-Based Early Warning Score, ACM Trans. Internet Things, vol. 2, no. 1, article no. 1, Oct. 2020. Release

    Recent advances in pervasive Internet of Things technologies and edge computing have opened new avenues for development of ubiquitous health monitoring applications. Delivering an acceptable level of usability and accuracy for these healthcare Internet of Things applications requires optimization of both system-driven and data-driven aspects, which are typically done in a disjoint manner. Although decoupled optimization of these processes yields local optima at each level, synergistic coupling of the system and data levels can lead to a holistic solution opening new opportunities for optimization. In this article, we present an edge-assisted resource manager that dynamically controls the fidelity and duration of sensing w.r.t. changes in the patient’s activity and health state, thus fine-tuning the trade-off between energy efficiency and measurement accuracy. The cornerstone of our proposed solution is an intelligent low-latency real-time controller implemented at the edge layer that detects abnormalities in the patient’s condition and accordingly adjusts the sensing parameters of a reconfigurable wireless sensor node. We assess the efficiency of our proposed system via a case study of the photoplethysmography-based medical early warning score system. Our experiments on a real full hardware-software early warning score system reveal up to 49

    @article{anzanpour-amiri-acmiot20,
      author = {Anzanpour, Arman and Amiri, Delaram and Azimi, Iman and Levorato, Marco and Dutt, Nikil and Liljeberg, Pasi and Rahmani, Amir M.},
      title = {Edge-Assisted Control for Healthcare Internet of Things: A Case Study on PPG-Based Early Warning Score},
      journal = {ACM Trans. Internet Things},
      volume = {2},
      number = {1},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      month = oct,
      year = {2020},
      articleno = {1},
      doi = {10.1145/3407091},
      issn = {2691-1914},
      issue_date = {February 2021},
      keyword = {edge computing, Internet of Things, wearable electronics, Health monitoring, early warning score, edge-assisted control},
      numpages = {21},
      url = {https://doi.org/10.1145/3407091},
    }
  • Mahmoud Keshavarzi
    Delaram Amiri
    Amir Mansour Pezeshk
    Forouhar Farzaneh
    M. Keshavarzi, D. Amiri, A. M. Pezeshk, and F. Farzaneh, A Novel Method of Deinterleaving Pulse Repetition Interval Modulated Sparse Sequences in Noisy Environments, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. 97, no. 5, pp. 1136 – 1139, May 2014. Release
    @article{keshavarzi-amiri-2014-tfeccs,
      author = {Mahmoud Keshavarzi and Delaram Amiri and Amir Mansour Pezeshk and Forouhar Farzaneh},
      title = {A Novel Method of Deinterleaving Pulse Repetition Interval Modulated Sparse Sequences in Noisy Environments},
      journal = {IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences},
      volume = {97},
      number = {5},
      pages = {1136--1139},
      month = may,
      year = {2014},
      doi = {10.1587/transfun.e97.a.1136},
    }
  • Mahmoud Keshavarzi
    Amir Mansour Pezeshk
    Forouhar Farzaneh
    Delaram Amiri
    M. Keshavarzi, A. M. Pezeshk, F. Farzaneh, and D. Amiri, A Robust Method for Recognition of Complicated Pulse Repetition Interval Modulations, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. 96, no. 11, pp. 2306 – 2310, Nov. 2013. Release
    @article{keshavarzi-pezeshk-2013-tfeccs,
      author = {Mahmoud Keshavarzi and Amir Mansour Pezeshk and Forouhar Farzaneh and Delaram Amiri},
      title = {A Robust Method for Recognition of Complicated Pulse Repetition Interval Modulations},
      journal = {IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences},
      volume = {96},
      number = {11},
      pages = {2306--2310},
      month = nov,
      year = {2013},
      doi = {10.1587/transfun.e96.a.2306},
    }
  • Delaram Amiri
    Mahmoud Keshavarzi
    Kourosh Amiri
    D. Amiri, M. Keshavarzi, and K. Amiri, A Simple Method for Pulse Repetition Interval Estimation and Tracking Radar Pulse Trains With Complex Pulse Repetition Interval Modulations, Advanced Science Letters, vol. 19, no. 8, pp. 2262 – 2265, Aug. 2013. Release
    @article{amiri-keshavarzi-2013-asl,
      author = {Delaram Amiri and Mahmoud Keshavarzi and Kourosh Amiri},
      title = {A Simple Method for Pulse Repetition Interval Estimation and Tracking Radar Pulse Trains With Complex Pulse Repetition Interval Modulations},
      journal = {Advanced Science Letters},
      volume = {19},
      number = {8},
      pages = {2262--2265},
      publisher = {American Scientific Publishers},
      month = aug,
      year = {2013},
      doi = {10.1166/asl.2013.4954},
    }

Conference Proceedings

  • Delaram Amiri
    Arman Anzanpour
    Iman Azimi
    Marco Levorato
    Amir M. Rahmani
    Pasi Liljeberg
    Nikil Dutt
    D. Amiri, A. Anzanpour, et al.I. Azimi, M. Levorato, A. M. Rahmani, P. Liljeberg, and N. Dutt, Edge-Assisted Sensor Control in Healthcare IoT, in Proceedings of the IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, Dec. 2018. Release

    The Internet of Things is a key enabler of mobile health-care applications. However, the inherent constraints of mobile devices, such as limited availability of energy, can impair their ability to produce accurate data and, in turn, degrade the output of algorithms processing them in real-time to evaluate the patient’s state. This paper presents an edge-assisted framework, where models and control generated by an edge server inform the sensing parameters of mobile sensors. The objective is to maximize the probability that anomalies in the collected signals are detected over extensive periods of time under battery-imposed constraints. Although the proposed concept is general, the control framework is made specific to a use-case where vital signs — heart rate, respiration rate and oxygen saturation — are extracted from a Photoplethysmogram (PPG) signal to detect anomalies in real-time. Experimental results show a 16.9% reduction in sensing energy consumption in comparison to a constant energy consumption with the maximum misdetection probability of 0.17 in a 24-hour health monitoring system.

    @inproceedings{amiri-anzanpour-2018-globecom,
      author = {Delaram Amiri and Arman Anzanpour and Iman Azimi and Marco Levorato and Amir M. Rahmani and Pasi Liljeberg and Nikil Dutt},
      title = {Edge-Assisted Sensor Control in Healthcare {IoT}},
      booktitle = {Proceedings of the IEEE Global Communications Conference (GLOBECOM)},
      address = {Abu Dhabi, United Arab Emirates},
      month = dec,
      year = {2018},
      doi = {10.1109/glocom.2018.8647457},
    }
  • Delaram Amiri
    Mohamed El-Sharkawy
    Brian King
    D. Amiri, M. El-Sharkawy, and B. King, Fast Bilateral Filter Technique for 3D-HEVC Standard, in Proceedings of the International Conference on Advances in Computing, Electronics and Electrical Technology (CEET), Nov. 2016, pp. 88 – 91. Release
    @inproceedings{amiri-elsharkawy-2016-ceet,
      author = {Delaram Amiri and Mohamed El-Sharkawy and Brian King},
      title = {Fast Bilateral Filter Technique for {3D}-{HEVC} Standard},
      booktitle = {Proceedings of the International Conference on Advances in Computing, Electronics and Electrical Technology (CEET)},
      pages = {88--91},
      month = nov,
      year = {2016},
      doi = {10.15224/978-1-63248-109-2-12},
    }
  • Delaram Amiri
    Mohamed El-Sharkawy
    Brian King
    D. Amiri, M. El-Sharkawy, and B. King, Adaptive Loop Filter Technique for 3D-HEVC Standard, in Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), Dec. 2015, pp. 531 – 534. Release
    @inproceedings{amiri-elsharkawy-2015-csci,
      author = {Delaram Amiri and Mohamed El-Sharkawy and Brian King},
      title = {Adaptive Loop Filter Technique for {3D}-{HEVC} Standard},
      booktitle = {Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI)},
      pages = {531--534},
      month = dec,
      year = {2015},
      doi = {10.1109/csci.2015.19},
    }

M.S. Thesis

  • Delaram Amiri
    D. Amiri, Bilateral and Adaptive Loop Filter Implementations in 3D-High Efficiency Video Coding Standard, Master’s thesis, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana, Dec. 2015.
    @mastersthesis{amiri-2015-iupui,
      author = {Delaram Amiri},
      title = {Bilateral and Adaptive Loop Filter Implementations in {3D}-High Efficiency Video Coding Standard},
      school = {Indiana University–Purdue University Indianapolis},
      address = {Indianapolis, Indiana},
      month = dec,
      year = {2015},
    }