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. The edge node serves users' requests by deciding whether to command the...

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Veröffentlicht in:IEEE transactions on communications Jg. 70; H. 11; S. 7446 - 7463
Hauptverfasser: Hatami, Mohammad, Leinonen, Markus, Chen, Zheng, Pappas, Nikolaos, Codreanu, Marian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Age of information (AoI)
Batteries
constrained Markov decision process (CMDP)
Constraints
Costs
Energy harvesting
energy harvesting (EH)
Energy measurement
Internet of Things
Iterative algorithms
Iterative methods
Markov processes
Monitoring
Nodes
Optimal scheduling
Optimization
Random processes
Sensors
energy harvesting (EH)
constrained Markov decision process (CMDP)
Age of information (AoI)
ISSN:0090-6778, 1558-0857, 1558-0857
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Zusammenfassung: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. The edge node serves users' requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. First, we derive a Markov decision process model and propose an iterative algorithm that obtains an optimal policy. Then, we develop an asymptotically optimal low-complexity algorithm - termed relax-then-truncate - and prove that it is optimal as the number of sensors goes to infinity. Simulation results illustrate that the proposed relax-then-truncate approach significantly reduces the average on-demand AoI compared to a request-aware greedy policy and a weighted AoI policy, and also depict that it performs close to the optimal solution even for moderate numbers of sensors.
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content type line 14
ISSN:0090-6778
1558-0857
1558-0857
DOI:10.1109/TCOMM.2022.3208873