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Found 13 papers, 0 papers with code
Optimal Semantic-aware Sampling and Transmission in Energy Harvesting Systems Through the AoII
We study a real-time tracking problem in an energy harvesting status update system with a Markov source under both sampling and transmission costs.
Status Updating under Partial Battery Knowledge in Energy Harvesting IoT Networks
We study status updating under inexact knowledge about the battery levels of the energy harvesting sensors in an IoT network, where users make on-demand requests to a cache-enabled edge node to send updates about various random processes monitored by the sensors.
On the Age-Optimality of Relax-then-Truncate Approach under Partial Battery Knowledge in Energy Harvesting IoT Networks
We consider an energy harvesting (EH) IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an EH sensor.
Query-Age-Optimal Scheduling under Sampling and Transmission Constraints
This letter provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel.
Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes
Finally, we consider unknown environment and devise a learning-based transmission policy by relaxing the CMDP problem into an MDP problem using the DPP method and then adopting the deep Q-learning algorithm.
Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge
We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node.
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling
The CMDP problem is solved by transforming it into an MDP problem using the Lagrangian relaxation method.
On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors
We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor.
Minimizing AoI in Resource-Constrained Multi-Source Relaying Systems with Stochastic Arrivals
We propose an algorithm that obtains a stationary deterministic near-optimal policy, establishing a benchmark for the system.
Semantic Communications in Networked Systems: A Data Significance Perspective
We present our vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems.
AoI Minimization in Status Update Control with Energy Harvesting Sensors
Users send requests to the edge node where a cache contains the most recently received measurements from each sensor.
Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks
In such a quantized compressed sensing (QCS) context, we address remote acquisition of a sparse source through vector quantized noisy compressive measurements.
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning
We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between the users and sensors.
