All Publications


  • Informational Cascades With Nonmyopic Agents IEEE TRANSACTIONS ON AUTOMATIC CONTROL Bistritz, I., Heydaribeni, N., Anastasopoulos, A. 2022; 67 (9): 4451-4466
  • Smart Greedy Distributed Energy Allocation: A Random Games Approach IEEE TRANSACTIONS ON AUTOMATIC CONTROL Bistritz, I., Ward, A., Zhou, Z., Bambos, N. 2022; 67 (5): 2208-2220
  • Consensus-Based Stochastic Control for Model-Free Cell Balancing IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS Bistritz, I., Bambos, N. 2021; 8 (3): 1139-1150
  • Wireless Body Area Network Control Policies for Energy-Efficient Health Monitoring SENSORS Bar David, Y., Geller, T., Bistritz, I., Ben-Gal, I., Bambos, N., Khmelnitsky, E. 2021; 21 (12)

    Abstract

    Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines the time between battery charges of the wearable sensors, which is a key performance factor (and can be critical in the case of implantable devices). In this paper, we study the inherent trade-off between the power consumption of the sensors and the probability of misclassifying a patient's health state. We formulate this trade-off as a dynamic problem, in which at each step, we can choose to activate a subset of sensors that provide noisy measurements of the patient's health state. We assume that the (unknown) health state follows a Markov chain, so our problem is formulated as a partially observable Markov decision problem (POMDP). We show that all the past measurements can be summarized as a belief state on the true health state of the patient, which allows tackling the POMDP problem as an MDP on the belief state. Then, we empirically study the performance of a greedy one-step look-ahead policy compared to the optimal policy obtained by solving the dynamic program. For that purpose, we use an open-source Continuous Glucose Monitoring (CGM) dataset of 232 patients over six months and extract the transition matrix and sensor accuracies from the data. We find that the greedy policy saves ≈50% of the energy costs while reducing the misclassification costs by less than 2% compared to the most accurate policy possible that always activates all sensors. Our sensitivity analysis reveals that the greedy policy remains nearly optimal across different cost parameters and a varying number of sensors. The results also have practical importance, because while the optimal policy is too complicated, a greedy one-step look-ahead policy can be easily implemented in WBAN systems.

    View details for DOI 10.3390/s21124245

    View details for Web of Science ID 000666452800001

    View details for PubMedID 34205774

  • Game of Thrones: Fully Distributed Learning for Multiplayer Bandits MATHEMATICS OF OPERATIONS RESEARCH Bistritz, I., Leshem, A. 2021; 46 (1): 159–78
  • Online Learning for Load Balancing of Unknown Monotone Resource Allocation Games Bistritz, I., Bambos, N., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021
  • My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits Bistritz, I., Baharav, T. Z., Leshem, A., Bambos, N., Daume, H., Singh, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020
  • Distributed Scheduling of Charging for-On-Demand Electric Vehicle Fleets Bistritz, I., Klein, M., Bambos, N., Maimon, O., Raagopal, R. ELSEVIER. 2020: 472-477
  • Distributed Learning for Channel Allocation Over a Shared Spectrum Zafaruddin, S. M., Bistritz, I., Leshem, A., Niyato, D. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2019: 2337–49
  • Multiagent Autonomous Learning for Distributed Channel Allocation in Wireless Networks Zafaruddin, S., Bistritz, I., Leshem, A., Niyato, D., IEEE IEEE. 2019
  • Informational cascades can be avoided with non-myopic agents Heydaribeni, N., Bistritz, I., Anastasopoulos, A., IEEE IEEE. 2019: 655–62
  • Asymptotically Optimal Distributed Gateway Load-Balancing for the Internet of Things Bistritz, I., Bambos, N., Cianfrani, A., Riggio, R., Steiner, R., Idzikowski, F. IEEE. 2019: 98–101
  • Controlling Contact Network Topology to Prevent Measles Outbreaks Bistritz, I., Bambos, N., Kahana, D., Ben-Gal, I., Yamin, D., IEEE IEEE. 2019
  • The Power of Consensus: Optimal Distributed Multichannel Wireless Transmitter Power Control Bistritz, I., Bambos, N., IEEE IEEE. 2019
  • Smart Greedy Distributed Allocation in Microgrids Bistritz, I., Ward, A., Zhou, Z., Bambos, N., IEEE IEEE. 2019
  • Online EXP3 Learning in Adversarial Bandits with Delayed Feedback Bistritz, I., Zhou, Z., Chen, X., Bambos, N., Blanchet, J., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019
  • Distributed Multi-Player Bandits - a Game of Thrones Approach Bistritz, I., Leshem, A., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Characterizing Non-Myopic Information Cascades in Bayesian Learning Bistritz, I., Anastasopoulos, A., IEEE IEEE. 2018: 2716–21