Solutions for enabling agentic sensing with network optimization
draft-bernardos-nmrg-agentic-network-optimization-00
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Carlos J. Bernardos , Alain Mourad , Muhammad Awais Jadoon | ||
| Last updated | 2026-03-02 | ||
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draft-bernardos-nmrg-agentic-network-optimization-00
NMRG CJ. Bernardos
Internet-Draft UC3M
Intended status: Experimental A. Mourad
Expires: 3 September 2026 InterDigital
M. A. Jadoon
InterDigital Europe Ltd
2 March 2026
Solutions for enabling agentic sensing with network optimization
draft-bernardos-nmrg-agentic-network-optimization-00
Abstract
Integrated Sensing and Communications (ISAC) represents a paradigm
shift in wireless networks, where sensing and communication functions
are jointly designed and optimized. By leveraging the same spectral
and hardware resources, ISAC enables advanced capabilities such as
environment perception, object tracking, and situational awareness,
while maintaining efficient and reliable data transmission. There
are sensing scenarios and use cases that involve a distributed
sensing task, in which multiple sensors participate and contribute
with (raw or pre-processed) sensing data, which is processed by a
sensing service (e.g., fusing sensing measurements from the different
sensors). This sensing service needs to be executed on some kind of
sensing processing/computing function which receives raw (or
preprocessed) data from multiple sources, potentially of different
(heterogeneous) kinds (e.g., RF and non-RF sensing, or RF from
different radio technologies). This processing might impose time
synchronization constraints on the reception of the different parts
of data, as well as potentially specific computing and/or AI/ML
capabilities on the processing node.
The joint selection of sensing entities, processing locations, and
network configuration under time-varying conditions results in a
large, coupled, and non-stationary decision space. These
characteristics motivate the use of agentic AI to enable distributed,
closed-loop configuration and reconfiguration of sensing and
networking resources.
This document presents initial considerations and potential solution
directions for an architecture that enables the use of agentic AI for
sensing (as an exemplary use case) supporting network optimization.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Enabling agentic AI distributed sensing with network
optimization . . . . . . . . . . . . . . . . . . . . . . 5
4. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 13
5. Security Considerations . . . . . . . . . . . . . . . . . . . 13
6. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 13
7. Informative References . . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 14
1. Introduction
Integrated Sensing and Communications (ISAC) is emerging as a key
enabler for next-generation wireless networks, integrating sensing
and communication functionalities within a unified system. By
leveraging the same spectral, hardware, and computational resources,
ISAC enhances network efficiency while enabling new capabilities such
as high-resolution environment perception, object detection, and
situational awareness. This paradigm shift is particularly relevant
for applications requiring both reliable connectivity and precise
sensing, such as autonomous vehicles, industrial automation, and
smart city deployments. Given its strategic importance, ISAC has
gained significant traction in standardization efforts. The ETSI
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Industry Specification Group (ISG) on ISAC has been established to
explore technical requirements and use cases, while 3GPP has
initiated discussions on ISAC-related features within its ongoing
research on future 6G systems. Furthermore, research initiatives
within the IEEE and IETF are investigating how ISAC can be integrated
into network architectures [I-D.ietf-green-use-cases], spectrum
management, and protocol design, making it a critical area of
development in the evolution of wireless networks.
There are sensing scenarios and use cases that involve a distributed
sensing task, in which multiple sensors participate and contribute
with (raw or pre-processed) sensing data, which is processed by a
sensing service (e.g., fusing sensing measurements from the different
sensors). This sensing service needs to be executed on some kind of
sensing processing/computing function which receives raw (or
preprocessed) data from multiple sources, potentially of different
(heterogeneous) kinds (e.g., RF and non-RF sensing, or RF from
different radio technologies). This processing might impose time
synchronization constraints on the reception of the different parts
of data, as well as potentially specific computing and/or AI/ML
capabilities on the processing node.
The selection of the nodes that participate as sensors and sensing
processing functions in a given distributed sensing task and the
configuration of the network to facilitate the sensing task, and
optimize both the sensing and the network operation, are not
independent. However, achieving an overall optimal configuration is
not a trivial task, especially when multiple optimization metrics
and/or constraints are considered.
In distributed sensing, sensing KPIs (e.g., accuracy, refresh rate,
confidence level, latency) are tightly coupled with radio, compute,
and transport configurations. Moreover, mobility, traffic load, and
environmental dynamics continuously alter the relationship between
configuration and achieved sensing performance. Static or centrally
pre-computed deterministic configurations can therefore become
suboptimal or infeasible at run time. An agentic AI approach enables
distributed decision-making, coordination among sensing and
networking entities, and adaptive reconfiguration to sustain sensing
KPIs under dynamic conditions
We assume a generic network architecture, where IETF CATS and GREEN
architectural considerations and solutions can be of application,
though the solution can be generalized to scenarios based on
different architectures.
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We assume that there is a network function in charge of the
coordination and configuration of the distributed sensing task, aware
of which nodes in the network can participate as sensor nodes, and
potentially of the capabilities of potential sensing processing
nodes. This network function can be, for example, the Gateway
Sensing Function (GSF)/ the Sensing Control Function (SCF) as
introduced by 3GPP.
We also assume that there is a network function in charge of managing
the network configuration of the network, such as an SMF/AMF in a
3GPP 5G architecture.
We assume that there are AI agents, which might run on network nodes
(such as terminals, radio access nodes or infrastructure nodes), of
two types: AI agents for Sensing (AIaS) and AI agents for Network
(AIaN). These agents can run tasks aimed at finding an optimal
configuration for sensing and connectivity, respectively and can
interact among them to pursue these goals.
A given network function or application function might request a
specific sensing task (with associated requirements, e.g., in terms
of accuracy) to the SCF directly or indirectly via the NEF and/or
GSF, which can then request several AI agents for Sensing to select a
sensing configuration and interact with the AI network agents to
ensure the network is configured as needed. Note that the sensing
task request might have some associated requirements, specific to the
task (such as accuracy, or privacy) but also global ones, such as
energy consumption, etc.
2. Terminology
The following terms are used in this document:
AIaS: AI agent for Sensing.
ISAC: Integrated Sensing and Communications.
SCF: Sensing Control Function, responsible of configuring and
triggering distributed sensing performed by a group of sensors.
SF: Sensing Function, participates in a distributed sensing
function as a sensor.
SPF: Sensing Processing Function, participates in a distributed
sensing function processing raw (or pre-processed) sensing data.
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3. Enabling agentic AI distributed sensing with network optimization
We describe next an example of operation and signaling for a
distributed sensing task to be configured and dynamically optimized
based on agentic AI for sensing and networking. An AI agent for
Sensing and an AI agent for Networking run on several network nodes
(terminals, access nodes and processing nodes) and might interact to
agree on a sensing and networking configuration that overall meets
the sensing requirements while optimizing other metrics (such as
privacy and energy consumption).
/_\ AI agent for Sensing
_
|_| AI agent for Networking
_________
| _ |
| |_| /_\ +-----\
|_________| \ ____________________________
Access Network #1 \ | |
_________ \| _________ |
| _ | | | _ | |
| |_| /_\ | | | |_| /_\ | |
|_________| | |_________| _________ |
terminal #1 | Processing | _ | |
______________ | node #1 | |_| /_\ | |
( ) | |_________| |
_________ ( object ) | SCF |
| _ | (______________) | |
| |_| /_\ | | _________ |
|_________| | | _ | |
terminal #2 | | |_| /_\ | |
/| |_________| |
_________ / | Processing |
_________ | _ | / | node #2 |
| _ | | |_| /_\ +- |____________________________|
| |_| /_\ | |_________|
|_________| Access Network #2
terminal #3
Figure 1: Exemplary scenario and architecture
Figure 1 shows a high-level picture of the architecture.
In the following, we describe an exemplary procedure showing how
different agents can interact to configure a distributed sensing
task. The focus is on the interactions, the information exchanged
and what actions might be triggered.
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_________ _________ _________ _________ _____ _____
| _ | | _ | | _ | | _ | | _ | | |
| |_| /_\ | | |_| /_\ | | |_| /_\ | | |_| /_\ | | |_| | | /_\ |
|_________| |_________| |_________| |_________| |_____| |_____|
terminal #1 terminal #2 AN #1 SPF #1 netw. SCF
| | | | | | | | ctrl. |
| | (0.AI agents discovery and registration) | 1.Sensing task
| | | | | | | | | request
| | 3.Agentic sensing | | 2.Sensing task request |<---
| | task request | |<-----------------------------|
| |<----------------------| | | | |
4a.Agentic net req. 4b.Agentic net req. 4c.Agentic net req. |
|<--| | | |<--| |<--| | |
4a.Connectivity request | | | | | |
|------------------------------------------------->| |
| | | | |4b.Connectivity request | |
| | | | |------------------------->| |
| | | | | | |4c.Connectivity request
| | | | | | |---------->| |
| | | | | | | | (network |
| | | | | | | | config.) |
| | | | | 5b.Connectivity response | |
| | | | |<-------------------------| |
| | | | | | 5c.Connectivity response |
6a.Agentic net resp. | | |<----------| |
|-->| | 6b.Agentic net resp. 6c.Agentic net resp. |
| | | | |-->| |-->| | |
| | | | | |7.Agentic sensing task resp. |
7.Agentic sensing task resp. |<-------------| | |
| |---------------------->| | | | |
7.Agentic sensing task resp.| | | | |
| | | |---------->| 8.Sensing task response |
| | | | | |----------------------------->|
| | | | | | | | | |
| (9.Monitoring actions to trigger reconfig. if needed) |
| | | | | | | | | |
Figure 2: Exemplary signaling of agentic AI interactions for
optimized distributed sensing
Figure 2 shows the message sequence chart of an agentic AI-enabled
multi-sensor distributed sensing which is explained next:
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0. We assume AI agents for Sensing have been trained with some
data, possibly based on their own local knowledge and
potentially enriched with additional data. We also assume they
have some knowledge about other neighboring agents and that
there could be some type of centralized/distributed repository
where they are registered.
1. The network receives a sensing request. A sensing managing
entity, such as the SCF, receives this request and decides to
delegate that request, or part of that request to some AI agents
for Sensing running in the network. For example, the initial
request received by the network may be for a XR/multi-media
service (which explicitly or implicitly require sensing
services), while the sensing request that is delegated to AI
Agents may be created to meet the sensing requirements of the
initial request. SCF would decide which specific sensing
request should be processed via AI agents and which part of the
sensing could be done using sensing "traditional" (i.e., not
using agentic AI) methods.
2. Based on the information and metadata associated to the sensing
request (e.g., location of the intended sensing), the SCF
determines to use AI Agents for executing the sensing task, and
sends a request to the AI agent for Sensing at the chosen
sensing entity (Access Node #1 [AN#1], in our example). In this
step the SCF may determine to use AI Agents based on any
combination of:
a. an indication received in the initial request that AI Agents
can/must be used.
b. Received policy information, indicates that AI Agents can/
must be used.
c. Fast reactions to changes in contextual information and
availability of resources is required.
d. The overall service requirements allow for delays and errors
incurred due to potential conflicts among agents and
performance oscillations. These may be specified as
threshold values.
This request might include the following information:
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a. Sensing task description: metadata indicating what is the
sensing task, sensing accuracy (spatial and temporal),
whether the sensing task involves stationary or mobile
targets, Time information associated with the sensing task
(e.g., a time period), etc.
b. Sensing data governance requirements about privacy, security
and trustworthiness such as for example what sensing data
can be processed where in the system and what pre-processing
and processing might be allowed to happen with the data, its
fusion framework with other data and its exposure to
application function or network function. Examples (non-
limiting) of encoding of this are:
i. Processing of raw sensing data: only local at the
originating node / local or remote processing allowed
/ partial processing allowed remotely.
ii. Trustworthiness of data: any generated data is trusted
/ only data from a list of explicit sources is trusted
/ data has to be signed by a trusted source to be
trusted.
iii. Confidentiality of exchanged data: all data (processed
or not) must be encrypted (specifying the mechanism to
be used to encrypt it), data (raw/partially processed/
processed) must be encrypted, all data can be sent in
clear.
iv. List of trusted generating entities/administrative
domains.
Allowed types of sensing fusion, e.g., a combination of the
following possible options: only with raw data of some kind,
mixing data partially processed with raw data, mixing
different types of sensing technologies, mixing different
levels of trustworthiness, etc. If the nodes involved in
the sensing task belong to different administrative domains,
additional mechanisms might need to be used to guarantee/
prove that the processing and/or confidentiality of the
sensing data is enforced. An example would be the use of a
private or public blockchain.
c. Allowed level of agentic AI interactions. This parameter,
which might be expressed in different ways, indicates how
different agents are allowed to interact towards completion
of the intended task. For example, the requester may
indicate that the agent receiving the request has to perform
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the required actions without interacting with other sensing
agents, or without interacting with networking agents, or
which limitations to apply in regards of other agentic
interactions (e.g., agent ownership limitations). It also
includes whether the involved agents are responsible for
monitoring the sensing task to trigger alerts and propose
reconfiguration actions if needed. This parameter may also
indicate the maximum size of the agent-to-agent
communication hops, or tiers or number of worker Agents used
for executing part of a task or in whole. This may be seen
as the maximum size or depth of the agent-to-agent network
(graph). When this parameter is sent from one agent to
another agent (as in step 3.d), it may adjust the value to
be the (maximum size) relative to that specific agent node/
hop (for example, allowed_level_of_agentic_AI_interactions =
allowed_level_of_agentic_AI_interactions - 1).
d. Additional network requirements, such as energy consumption
metrics.
e. List of AI agents available (optional, as this might be
known by the receiving AI agent based on its local context
and/or other AI agent discovery mechanisms).
3. The receiving AI agent for Sensing (in this example AIaS@AN#1)
processes the request and based on the parameters received and
its knowledge of the local context and prior training, decides
whether it can honor the received request and whether it can
interact with other agents. In this example, the agent decides
to interact with three additional AI agents for sensing
(@terminal#1 and @terminal#2, @processing node/SPF #1) to
basically configure a multistatic active sensing (involving
terminals #1 and #2 and AN#1) with the sensing processing done
at processing node/SPF #1. AIaS@AN#1 sends an Agentic sensing
task request, which includes the following parameters:
a. Sensing task description: metadata indicating what is the
intended sensing task, sensing accuracy (spatial and
temporal), whether the sensing task involves stationary or
mobile targets, etc.
b. Intended sensing task configuration, including parameters
such as:
i. Static/Multi-static.
ii. Active/passive sensing.
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iii. Sensing technology (e.g., WiFi, 5G).
iv. Other participant node's IDs.
c. Sensing data governance requirements about privacy, security
and trustworthiness such as for example what sensing data
can be processed where in the system and what pre-processing
and processing might be allowed to happen with the data, its
fusion framework with other data and its exposure to
application function or network function. Examples (non-
limiting) of encoding of this are:
i. Processing of raw sensing data: only local at the
originating node / local or remote processing allowed
/ partial processing allowed remotely.
ii. Trustworthiness of data: any generated data is trusted
/ only data from a list of explicit sources is trusted
/ data has to be signed by a trusted source to be
trusted.
iii. Confidentiality of exchanged data: all data (processed
or not) must be encrypted (specifying the mechanism to
be used to encrypt it), data (raw/partially processed/
processed) must be encrypted, all data can be sent in
clear.
iv. List of trusted generating entities/administrative
domains.
Allowed types of sensing fusion, e.g., a combination of the
following possible options (non limiting): only with raw
data of some kind, mixing data partially processed with raw
data, mixing different types of sensing technologies, mixing
different levels of trustworthiness, etc. If the nodes
involved in the sensing task belong to different
administrative domains, additional mechanisms might need to
be used to guarantee/prove that the processing and/or
confidentiality of the sensing data is enforced. An example
would be the use of a private or public blockchain.
d. Allowed level of agentic AI interactions. This parameter,
which might be expressed in different ways, indicates how
different agents are allowed to interact towards completion
of the intended task. For example, the requester may
indicate that the agent receiving the request has to perform
the required actions without interacting with other sensing
agents, or without interacting with networking agents, or
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which limitations to apply in regards of other agentic
interactions (e.g., agent ownership limitations). It also
includes whether the involved agents are responsible for
monitoring the sensing task to trigger alerts and propose
reconfiguration actions if needed. An example of a possible
encoding of the allowed level of agentinf AI interaction is
the following:
0. no delegation,
1. local AIaS may interact with local AIaN,
2. local AIaS may delegate/talk to other sensing nodes, but
those cannot delegate it further,
3. local AIaS may delegate/talk to other sensing nodes,
which can delegate it to N-2 levels.
e. Additional network requirements, such as energy consumption
metrics.
How an agent decides that additional sensing tasks need to be
performed in order to honor/complete the received sensing task
is out of the scope of this document. It is up to the specific
agents' implementation and the knowledge they have of the local
context.
4. The receiving agents process the request, and similarly to what
was done in the previous step, decide whether they need to do
additional agent interactions (this can only happen if the
received "allowed level of agentic AI interactions" is > 1, on
each level the "allowed level of agentic AI interactions" is
decreased by 1). (Non-limiting) examples of these sub-tasks
are:
* Perform an isolated sensing task targeting a specific goal
(i.e., track an object in a given geographic area, with an
intended accuracy, sensing technology and energy contraints).
* Configure the network and/or specific network elements to
support a given connectivity level to transport the data
generated by another agents.
* Find out which types of sensing, and with which level of
trustworthiness/security/privacy are allowed to be used by
certain nodes and/or in a given geographic area.
* Etc.
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Let's assume for the sake of this example, that the following
actions are required:
i. AIaS@terminal#1 needs to interact with its local AI agent
for Networking (AIaN@terminal#1) to ensure that the radio
interface of terminal#1 is configured as required by the
sensing task.
ii. AIaS@AN#1 needs to interact with its local AI agent for
Networking@AN#1 to request a guaranteed communication path
to the processing node#1. As a result of this, AIaN@AN#1
sends the required request to the networking control
entity (in this example SMF/AMF), which then performs the
required configuration.
iii. AIaS@processing-node#1 needs to interact with its local AI
agent for Networking@processing-node#1 to request a
guaranteed communication path to AN#1 and terminals #1 and
#2. As a result of this, AIaN@processing-node#1 sends the
required request to the networking control entity (in this
example SMF/AMF), which then performs the required
configuration.
Note that it might also be possible that the request for
guaranteed communication paths (e.g., between the processing
node #1 and terminals #1 and #2, could also be triggered by AI
agents running on the terminals.
5. As a result of the requests (4b and 4c) for guaranteed
communication paths to the AMF/SMF, the AMF/SMF performs the
required configurations and responds back to the AI agents for
networking (5b and 5c).
6. The AI agents for networking respond back to the AI agents for
sensing, after completing their tasks.
7. Each involved AI agent that was tasked a given action responds
back to the initiating AI agent. In this example,
AIaS@terminal#1, AIaS@terminal#2 and AIaS@processing-node#1
responds to AIaS@AN#1, including the result of the operation.
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8. Once all the required configurations are completed, the initial
agent (AIaS@AN#1) responds back to the SCF with a sensing task
response, providing the result of the sensing task request
(success/failure) and about the resulting sensing (and
networking) configuration. At this point, the distributed
sensing task is ongoing. This may also include information of
the established Agent Network, e.g., a graph of agents and their
capabilities.
9. SENSING and CONNECTIVITY MONITORING. Depending on whether the
agents were instructed to perform continuous monitoring or not,
different options are possible:
a. Monitoring performed by the agents. In this case, agents
monitor the activity and local context for variations that,
according to its previous training and knowledge, might
require corrections. If that is the case, the conducted
actions are notified back to the SCF, through the chain of
involved agents. Examples of monitoring include: (i)
Measuring (passively or actively) connectivity between
sensing sources and processing node, (ii) Measuring
estimated sensing precision, (iii) Measuring energy
consumption associated with the sensing task. The
monitoring can include additional parameters, such as: (i)
specific thresholds for each/some monitored parameters and
associated actions if those threshold are passed, (ii)
Frequency of monitoring.
b. Monitoring performed by the network. In this case, "legacy"
monitoring mechanisms are used, which might trigger
reconfiguration actions (in a similar fashion to the initial
sensing task request).
4. IANA Considerations
N/A.
5. Security Considerations
TBD.
6. Acknowledgments
The work of Carlos J. Bernardos in this document has been partially
supported by the Horizon Europe MultiX (Grant Agreement No.
101192521) and DISCO6G-CM.
7. Informative References
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[I-D.ietf-green-use-cases]
Stephan, E., Palmero, M. P., Claise, B., Wu, Q.,
Contreras, L. M., Bernardos, C. J., and X. Chen, "Use
Cases for Energy Efficiency Management", Work in Progress,
Internet-Draft, draft-ietf-green-use-cases-01, 22 January
2026, <https://datatracker.ietf.org/doc/html/draft-ietf-
green-use-cases-01>.
Authors' Addresses
Carlos J. Bernardos
Universidad Carlos III de Madrid
Av. Universidad, 30
28911 Leganes, Madrid
Spain
Phone: +34 91624 6236
Email: cjbc@it.uc3m.es
URI: http://www.it.uc3m.es/cjbc/
Alain Mourad
InterDigital Europe Ltd
London
United Kingdom
Email: Alain.Mourad@InterDigital.com
URI: http://www.InterDigital.com/
Muhammad Awais Jadoon
InterDigital Europe Ltd
London
United Kingdom
Email: muhammad.awaisjadoon@interdigital.com
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