Average age of information with hybrid ARQ under a resource constraint

Scheduling the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information (AoI) at the destination under a constraint on the average number of transmissions at the source node. After each transmission, the source receives an instantaneous ACK/NACK feedback, and decides on the next update without prior knowledge on the success of future transmissions. First, the optimal scheduling policy is studied under different feedback mechanisms when the channel statistics are known; in particular, the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Then, for an unknown environment, an average-cost reinforcement learning (RL) algorithm is proposed that learns the system parameters and the transmission policy in real time. The effectiveness of the proposed methods are verified through numerical simulations

Non-marine Ostracoda in Sağlık plain, Kahramanmaraş, Turkey, since the Late Glacial to mid-Holocene

We present the freshwater ostracod stratigraphy of former lake, in Sağlık plain, South Central Anatolia, Turkey, since the Last Late Glacial until the mid-Holocene. Podocopoid (non-marine) ostracods were identified in Sağlık II (SAĞ II) core whose the lowermost part goes back to 15500 years ago. Both smooth and noded forms of Cyprideis torosa (Jones, 1850), Candona sp. (Baird, 1845), Ilyocypris sp. (Brady & Norman, 1889), Darwinula stevensoni (Brady & Robertson, 1870), Plesiocypridopsis newtoni (Brady & Robertson, 1870), and Prionocypris zenkeri (Chyzer&Toth, 1858) were the observed specie

Reinforcement learning to minimize age of information with an energy Harvesting sensor with HARQ and sensing cost

The time average expected age of information (AoI) is studied for status updates sent from an energy-harvesting transmitter with a finite-capacity battery. The optimal scheduling policy is first studied under different feedback mechanisms when the channel and energy harvesting statistics are known. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the status update policy in real time. The effectiveness of the proposed methods is verified through numerical results

In vitro susceptibility of quinolone-resistant Escherichia coli isolates with and without producing extended-spectrum beta-lactamase to fosfomycin trometamol

Yo

Kalman prediction based proportional fair resource allocation for a solar powered wireless downlink

Optimization of a Wireless Sensor Network (WSN) downlink with an energy harvesting transmitter (base station) is considered. The base station (BS), which is attached to the central controller of the network, sends control information to the gateways of individual WSNs in the downlink. This paper specifically addresses the case where the BS is supplied with solar energy. Leveraging the daily periodicity inherent in solar energy harvesting, the schedule for delivery of maintenance messages from the BS to the nodes of a distributed network is optimized. Differences in channel gain from the BS to sensor nodes make it a challenge to provide service to each of them while efficiently spending the harvested energy. Based on PTF (Power-Time-Fair), a close-to-optimal solution for fair allocation of harvested energy in a wireless downlink proposed in previous work, we develop an online algorithm, PTF-On, that operates two algorithms in tandem: A prediction algorithm based on a Kalman filter that operates on solar irradiation measurements, and a modified version of PTF. PTF-On can predict the energy arrival profile throughout the day and schedule transmission to nodes to maximize total throughput in a proportionally fair way