# HyPM: Hybrid Performance Metric Transmission in Low-Power Wireless Networks

Fabian Graf, Esteban Norena Arroyave, Thomas Watteyne, Michael Villnow

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Fabian Graf, Esteban Norena Arroyave, Thomas Watteyne, Michael Villnow. HyPM: Hybrid Performance Metric Transmission in Low-Power Wireless Networks. ISCC 2025 - 30th IEEE Symposium on Computers and Communications, Jul 2025, Bologna, Italy. hal-05030266

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# HyPM: Hybrid Performance Metric Transmission in Low-Power Wireless Networks

Fabian Graf
Siemens AG

Siemens Technology
Erlangen, Germany

fabian.graf@siemens.com

Esteban Noreña Arroyave
Siemens AG

Siemens Technology
Erlangen, Germany

esteban.norena-arroyave-ext
@siemens.com

Thomas Watteyne
Inria

AIO Team
Paris, France

thomas.watteyne@inria.fr

Michael Villnow
Siemens AG

Siemens Technology
Erlangen, Germany

michael.villnow@siemens.com

*Abstract*—Low-power wireless networks are increasingly deployed in Industrial Internet of Things (IIoT) environments to ensure reliable and secure operation of machinery. One of the key requirements for these networks is reliability, which hinges on the ability to continuously assess the network's health – a challenge addressed by Application Performance Monitoring (APM). Within APM, transmitting performance metrics, particularly in low-power networks, must be efficient to preserve battery life. This paper addresses various APM approaches, including In-band Network Telemetry (INT), which leverages multi-hop topologies to append telemetry data to existing packets. We propose HyPM, a novel hybrid method that combines active monitoring with INT to meet user requirements for telemetry scope and frequency while improving energy efficiency by 31%. Simulations in the Contiki-NG network simulator demonstrate the superiority of HyPM in balancing energy consumption and data reporting accuracy.

## I. INTRODUCTION

Wireless Sensor Networks (WSNs) networks are commonly deployed in Industrial Internet of Things (IIoT) environments to control the flawless operation of machines and entities in the factory. Customers of such networks consider reliability as the most important feature, followed by security, standards, range and battery changes [1]. Low-power wireless mesh networks based on IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) [2] aim to meet all of these requirements. 6TiSCH is a networking protocol designed by the Internet Engineering Taskforce (IETF) to enable reliable, low-power, and deterministic wireless IIoT communication by combining IPv6 networking with time slotted channel hopping (TSCH). Reliability is not simply defined by a high packet delivery ratio (PDR) but also by being able to continuously assess and monitor the network's health. This challenge is tackled by Application Performance Monitoring (APM). APM is an emerging topic in IIoT due to the demand for resilient systems, which are capable of reacting on changes in a highly dynamic environment in an autonomous fashion. The central design premise of APM is to deliver a verbose and timely report on the system's condition, while keeping the impact on the network's actual performance as small as possible. Sampling performance metrics on the motes and exporting them via the network manager, the Backbone Border Router (BBR), is more efficient than transmitting long logging messages.

To ensure capturing a complete picture of the system's health condition, it is crucial to collect metrics from all categories, i.e. hardware (HW)- and software (SW)-related metrics, as well as networking key performance indicators (KPIs). Surveys show that APM metrics go far beyond the generic ones such as PDR, receive signal strength indicator (RSSI) and battery state of charge (SoC) [3], [4]. Besides identifying a meaningful set of metrics, the challenge of APM is to efficiently report these metrics to the user or an autonomous control system. Active monitoring defines the process of sending this set as dedicated message frame, which are often called “health reports”. Since the radio is typically one of the most power-hungry parts of the mote, reducing the number of messages sent is crucial to ensure long battery lifetimes. In-Band Network Telemetry (INT) is a very energy-efficient manner of transferring performance metrics from the motes to the network BBR. INT exploits the multi-hop topology by appending telemetry, i.e., performance metrics, to packets with free space left, as they traverse through the hops in the network on their way to the BBR.

In this paper, we compare the efficiency of various APM approaches by counting the number of bytes transmitted from each node to the BBR and measuring the energy consumption in each case. We obtain a fair comparison by evaluating the required energy per byte of telemetry. We simulate different network topologies and traffic pattern in Contiki-NG's network simulator Cooja, which implements the 6TiSCH standard [5]. We highlight the dependency between network traffic and the verbosity of the performance metrics. We show that, for an insufficient amount of application traffic, INT cannot serve the user requirements in terms of telemetry data scope and timeliness. We therefore propose HyPM, a novel hybrid approach that combines active monitoring with INT. HyPM is based on a pre-defined user requirement on the scope of the telemetry data as well as on the reporting period. Simulation results show that this approach outperforms active monitoring in terms of energy efficiency by 31%, while, unlike INT, fully satisfying the user demand.

The remainder of this paper is organized as follows. Section II summarizes related work on strategies for transporting performance metrics, provides a detailed explanation of INT
in 6TiSCH networks and shows where these existing strategies fall short. Section III introduces HyPM and explains its functionality. Section IV describes the simulation setup. Section V compares the existing approaches of related work with each other and evaluates the performance of HyPM. Section VI concludes this paper, and discusses future work on security and latency.

## II. RELATED WORK

At first, a taxonomy of existing APM strategies in literature is presented. After that, we highlight the INT approach by Karaagac et al. [6] in detail and refer to it as K-INT in the remainder. K-INT shall serve as a reference baseline for further investigations. Finally, drawbacks and limits of the presented related work are discussed.

### A. Taxonomy of APM Strategies

Related work on exporting performance metrics from the motes to the BBR can be grouped in six categories, which we survey here.

**Category 1: Active Monitoring.** In this traditional method, nodes periodically send health information in additional packets. While this approach is effective for failure detection and symptom alerting, it generates extra overhead. This negatively impacts system performance by increasing energy consumption and using additional bandwidth. A notable example is SmartMesh IP [7], a commercial solution implementing a 6TiSCH-like protocol that relies on performance metrics being reported in "health reports" packets.

**Category 2: Passive Monitoring.** This method avoids adding dedicated traffic to infer the system's health, rather relies on monitoring ongoing communication. The advantage is that it doesn't requires more packets, hence energy. The disadvantage is that the BBR may not have enough data to accurately provide a basis for evaluating the health of its network. Snif [8] is one of the early passive monitoring solutions, utilizing a separate diagnostic sensor network to track communication without contributing to the network's traffic load.

**Category 3: Hybrid Approaches.** These combine the strengths of both active and passive monitoring. A significant framework is the Hybrid Monitoring Platform (HMP) [9], which actively collects information directly from sensor nodes and passively from the wireless network, balancing low overhead with comprehensive observability.

**Category 4: Piggybacking.** Instead of generating separate packets for health data (as in active monitoring), piggybacking embeds this information within the payload of regular application packets. This reduces energy consumption while still providing valuable performance metrics. Dunkels et al. [10] first demonstrate the benefits of piggybacking by showing that it significantly reduces energy costs when combining multiple beacons into a single transmission. This concept has since been extended to 6TiSCH networks by Gaillard et al. [11] who also study the overhead caused by piggybacking for various monitoring frequencies.

**Category 5: Alternate-Marking Performance Measurement (AM-PM).** This involves adding "markers" to packets so they serve to extract performance metrics of the network, including delay and packet loss. This means AM-PM does not generate additional traffic, rather adds a handful of bits to existing packet, thereby having very little impact on network throughput and battery lifetime. AM-PM was originally developed for wired networks [12], and later adapted to low-power wireless networks [13]. Karaagac et al. re-purpose otherwise unused bits in the IEEE 802.15.4 header.

**Category 6: In-Band Network Telemetry (INT).** Here, telemetry data is embedded directly within the packet as it moves through the network. INT building upon the P4 language [14]; it was originally designed for wired networking equipment such as switches, network interface cards (NICs) and routers. Unlike piggybacking, INT collects and appends telemetry data at each hop during a packet's journey. Based on the original dataplane specification [15], INT has been successfully adapted for wireless networks – particularly 6TiSCH – by Karaagac et al. [6]. A recent study by van Leemput et al. [16] introduces an analytical traffic model for 6TiSCH networks utilizing INT. This model enables the calculation of network traffic overhead by using INT to accurately predict the number of transmitted and received bytes within the network.

### B. K-INT: Karaagac et al.'s Approach to In-Band Network Telemetry in 6TiSCH Networks

The objective of INT is to collect metrics for each mote at each hop and append those to the packets traversing the network. The device generating the application traffic initiates the metric collection process by embedding INT commands inside the packet. Intermediary nodes can add metrics as they forward packets. All telemetry information is received by the BBR.

The INT implementation described in this section builds upon the previous work by Karaagac et al. [6] and is referred to as K-INT in the following. The authors introduce the "INT engine" sublayer between TSCH (MAC layer) and routing protocol for low-power and lossy networks (RPL) (networking layer). The INT engine processes all outgoing and incoming packets, and determines whether metrics can be appended. In K-INT, commands and metrics are carried as IEEE 802.15.4 Information Elements (IEs), as outlined in IETF standard RFC8137 [17]. That is, first, the "Present IEs" field in the IEEE 802.15.4 header is set. If no Header IEs are present, the "Header Termination 1 IE" (2 B) is embedded to indicate the start of Payload IEs. Then, IE descriptor (2 B) is added, with Group ID 0x05. The INT sub-IEs are appended, including the "INT Header" and "INT Entries" containing telemetry data from each node. A Payload Termination IE (2 B) is added at the end of the payload IEs to indicate their completion.

In K-INT, the IE's subtype identifier is to 202 (0xca), a value in the "reserved for experimental use" range. The "INT Control" byte is hardcoded to support only the Hop-by-Hop (HBH) mode. The "HBH Mode" is arbitrarily set to 10, with the actual mode (Probabilistic or Opportunistic) defined by a
<figure><img src="image_2.png" /><figcaption>Figure 1: INT insertion into MAC layer frame structure.</figcaption></figure>

variable in the code. The encoding mode is always “Bitmap mode”: the bitmap never changes. We also implement the “Overflow” feature that tells subsequent hops to ignore the packet for the insertion of new INT entries. The “Loopback” and “Query” features are not included in our implementation, and thus their bits are always set to 0. Additionally, the “Node Bitmap” feature, which allows nodes to add their own bitmaps, is not implemented. The result is that, once INT fields are initialized by a node, subsequent hops append their INT entries with the same size and telemetry structure as defined by the hard-coded bitmap.

Fig. 1 shows the resulting frame structure when embedding INT as a sub-IE, where a single INT header is followed by the INT entries added by each hop. IEEE 802.15.4 frames are at most 127 bytes long, which limits the number of INT entries present. We add INT information only to data frames generated by the application. That is, no INT information is present in broadcast frames, RPL packet (including Destination Oriented Directed Acyclic Graph (DODAG)), and 6TiSCH control frames (including 6P messages and Enhanced Beacons). Since messages generated by the 6TiSCH Operation Sublayer (6top) sublayer are ignored by the INT sublayer, using 6top and minimal scheduling function (MSF) does not affect the outcome of the INT implementation. K-INT keeps working even when MSF is used. Furthermore, IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) fragmented messages do not have INT fields appended, as fragmentation implies the packet is already full.

1) **K-INT with Opportunistic Logic:** The INT engine of K-INT determines at each hop whether INT entries should be appended to the frame, provided there is space. If the INT engine follows an opportunistic logic, it means appending metrics whenever possible. This results in an out-of-balance delivery of metrics at the BBR, as motes closer to it more frequently encounter frames that are already fully packed, leaving no free space for appending INT. This is not a problem if the application only sends a handful of bytes. For larger payload sizes, however, as illustrated by Fig. 2 “Payload B”, opportunistic INT penalizes motes with small hop-depths. Insufficient reporting of metrics by motes close to the BBR may lead to undetected critical failures and an incomplete picture of the network’s health condition.

2) **K-INT with Probabilistic Logic:** To mitigate this issue, K-INT also offers a probabilistic logic. Nodes adopt a

<figure><img src="image_3.png" /><figcaption>Figure 2: Overview of INT insertion strategies.</figcaption></figure>

probabilistic approach: each decides to insert or skip INT entries based on dynamically calculated probabilities in a distributed manner. While this approach can lead to a reduction in the overall volume of telemetry data, it ensures a fairer distribution of telemetry data across the nodes. The INT insertion probability is computed as

$$p_i = 100 \cdot \frac{\left| \frac{MTU - S_f}{S_{int+}} \right|}{\left| \frac{R}{\Delta r_{min}} \right|}, \quad (1)$$

where MTU is the maximum transmission unit, i.e. 125 B in IEEE 802.15.4 (excluding the 2 B Frame Check Sequence (FCS) field), S<sub>f</sub> is the current frame size (including headers, payload and INT entries from previous hops), S<sub>int+</sub> is the size of the newly to be appended INT entry. In the denominator of (1), the remaining hop count is calculated from the current RPL DODAG rank R and the minimum RPL rank increase configuration Δr<sub>min</sub>. Fig. 2 shows in Payload C to E that the probabilistic logic intentionally decides to skip inserting INT entries, so the reported amount of metrics is equal for all hops.

### C. Drawbacks and Limits of Existing Strategies

The strategies discussed in Section II-A differ in verbosity and associated costs, impacting the overall performance of the application.

Active Monitoring offers the flexibility to report metrics with high verbosity but requires sending dedicated packets, which increases energy consumption and reduces the device’s battery life. Passive Monitoring introduces no overhead but is difficult to implement and inaccurate regarding capturing meaningful performance metrics. Piggybacking is more energy-efficient than active monitoring, but its verbosity is constrained by the available space in frames and the frequency of frame generation, both dictated by the underlying application traffic. AM-PM avoids the issue of full frames by utilizing specific bits in the header, though this severely limits the granularity of the data. INT appends telemetry at each hop, enabling more frequent metric reporting than piggybacking. However, the length and frequency of packets still rely heavily on the application’s traffic. Obviously, a hybrid approach combining the benefits of the different strategies is desirable.

## III. HYPM: HYBRID PERFORMANCE METRIC TRANSMISSION

Indeed, we understand telemetry data in a different manner compared to the presented literature about INT in Section II-A.
The scope of telemetry data in that context is mostly limited to instantaneous metrics concerning the current frame, such as RSSI, channel and timestamp. However, we want to exploit the INT strategy to export a large set of metrics which goes far beyond the mentioned ones. Those sets are not in the scope of this work and the interested reader is referred to related survey papers [3], [4].

The metrics are assumed to be stored in a buffer and to be sampled in reasonable time intervals ensuring that always current values are reported to the BBR. The size of this buffer is certainly allowed to exceed the MTU of 125 B, because the metrics are extracted from the buffer in a consuming fashion and fragmentation is desired. Eventually, we define the user requirement by the size of telemetry $B_{user}$ that the user expects to receive in a certain time interval $t_{req}$.

From the presented approaches, active monitoring is the only one guaranteeing to meet such a user requirement. The design premise of a novel hybrid approach, dubbed as HyPM, is to be more efficient than active monitoring by using INT and supporting with active monitoring packets only when motes can not fulfill the user's needs. The idea of HyPM is to periodically set up a timer of duration $t_{req}$. Initially at the beginning of such an interval, the values of all performance metrics are sampled and stored in the telemetry data buffer, which is typically smaller than $B_{user}$. Once the buffer is fully depleted, a resampling process for the metrics is initiated. Consequently, when the last metric is extracted from the buffer, exporting of all metrics begins anew. For every packet generated by the mote or traversing the mote, the INT engine tries to append $S_{int+}$ bytes of metrics from this buffer. $S_{int+}$ is the size of a single INT entry to be appended at each hop and needs to be chosen so that all hops theoretically have the chance to insert their telemetry. Therefore, $S_{int+}$ can be determined based on the average application traffic payload size and the maximum hop depth in an adaptive manner. After $t_{req}$, the timer elapses and the algorithm checks if the user requirement has already been fulfilled, i.e., if more than $B_{user}$ bytes of telemetry have been exported, yet. If INT has not been able to serve the user requirement, an active monitoring packet is sent out containing the remaining metrics.

## IV. SIMULATION ENVIRONMENT

The evaluation of HyPM is based on simulations in the Cooja network simulator, which imitates the behavior of a wireless network of motes running the Contiki-NG firmware [5]. In this paper, we consider two different network topologies shown in Fig. 3. In Topology A, the motes are arranged in a straight line down to a hop depth $N$. In Topology B, there is more than one paths with the BBR as destination. These topologies may be regarded as atomic building blocks of arbitrary topologies, so results and lessons learnt extracted for these topology carry over to a random general case.

The evaluation metric used for this comparison is energy cost per telemetry byte. Thus, the amount of telemetry bytes reported to the BBR by each node is tracked within the simulation. Since the motes are merely emulated by Cooja, the

<figure><img src="image_4.png" /><figcaption>Figure 3: The topologies used for the evaluation of HyPM.</figcaption></figure>

energy consumption needs to be estimated. Therefore, Contiki-NG offers the Energest module, counting the ticks spent in transmit (TX) and receive (RX) mode. Ticks serves as the basic unit of time for the operating system (OS), enabling task scheduling and time management. The charge drawn in the respective state can then be calculated by

$$C_{state} = \frac{ticks \cdot I}{RT_{arch}}. \qquad (2)$$

The results in this paper are calculated based on the specifications of Zolertia Z1 [18]. The current $I$ is assumed to be 17.4 mA in TX and 18.8 mA in RX mode, respectively. $RT_{arch}$ is the number of ticks per second and is given by $10^6$ s$^{-1}$ in this architecture. Based on $C_{state}$ the energy spent in this state is obtained by multiplying with the battery voltage, which is assumed to be 3V.

$$E_{state} = C_{state} \cdot V \qquad (3)$$

## V. SIMULATION RESULTS

At first we compare the efficiency of the existing approaches from Section II-A. We restrict this comparison to active monitoring, piggybacking and K-INT, as passive monitoring and AM-PM deliver an insufficient amount of telemetry to work with. The second simulation evaluates the performance of HyPM.

### A. Comparison of Existing APM Strategies

The first comparison is carried out over topology A with $N$ ranging from 2 to 6 motes, as this topology effectively demonstrates how each strategy scales with increasing hop depth. Furthermore, this first scenario is intended to be not demanding in terms of payload size and therefore 2 B of application payload are transmitted every 10 s by every mote. For entries inserted by K-INT we also consider just a small amount of $S_{int+} = 4$ B of telemetry. Thus, no full packets are expected to reach the BBR in this simulation and therefore the performance of K-INT Probabilistic coincides with K-INT Opportunistic due to an insertion probability of $p_i = 100\%$. Obviously, the nodes closer to the BBR will transmit larger amounts of telemetry using K-INT. To ensure a fair comparison, we set the active monitoring reporting interval so that at
<figure><img src="image_5.png" /><figcaption>Figure 4: Comparison of APM methods for Topology A.</figcaption></figure>

least mote $N$ transmits a comparable amount of bytes at the end of the simulation time (20 min). In the case of $N = 6$, active monitoring would trigger sending a 4 B large payload of metrics every 7 s. Piggybacking uses the existing traffic generated at each mote and appends metrics at the end of the Constrained Application Protocol (CoAP) options field. The difference to K-INT is, that piggybacking only adds metrics at packet creation and not at intermediate hops. To indicate the presence of telemetry in piggybacking, we decide to introduce a telemetry marker and telemetry size field before the actual metrics inside the CoAP options field.

The simulation results are shown in Fig. 4. Active Monitoring appears to have the highest costs concerning energy per telemetry byte. This cost also grows rapidly with network size $N$. Piggybacking consumes significantly less than active monitoring and the costs grow at a much smaller scale, too. The results obtained by K-INT are remarkable due to various reasons. Firstly, because K-INT consumes magnitudes less energy in contrast to the other methods while delivering the same amount of metrics and even more at the motes closer to the BBR. Furthermore, the energy cost per telemetry byte barely grows for larger network sizes. Consequently, K-INT is the obvious choice for scenarios with small application payload and telemetry sizes, respectively.

## B. Performance Evaluation of HyPM

For the second comparison, the simulation parameters are tightened to align even more with real-world use cases. This means that the application traffic is not fixed anymore, but allowed to vary in a random fashion with larger payload sizes. Furthermore, telemetry data is generated based on a user requirement. The combination of both leads to full packets, i.e., Piggybacking and K-INT struggle to sufficiently append metrics and meet this requirement.

We evaluate the performance of HyPM and compare it to the existing methods on both topologies, whereas using $N = 5$ for topology A. We arbitrarily choose the user requirement to

be $B_{user} = 70$ B and $t_{req} = 60$ s. As application traffic, we assume CoAP packets every 10 s containing a payload of 10,20,30,40,45 and 50 bytes. The payload size varies in a uniformly random fashion. For active monitoring, we assume to send out a health report packet every 15 s. To ensure a fair comparison between K-INT and HyPM, the size of a single INT entry $S_{int+}$ is fixed to 19 B.

The simulation results after 30 min on both topologies are shown in Fig. 5 and Fig. 6, respectively.

Fig. 5 plots the amount of metrics exported per minute for each mote. The area below the user requirement of $B_{user} = 70$ B is marked red. It can be observed, that the HyPM and active monitoring are the only methods which meet the requirement. Furthermore, the performance of K-INT opportunistic and K-INT probabilistic appears to be the reversed. Indeed, the formula for the insertion probability for K-INT probabilistic is not designed for varying application payload sizes. Therefore, the insertion at more distant hops is skipped more often, which results in undesired preference of motes closer to the BBR.

The comparison in terms of energy cost per telemetry byte in Fig. 6 shows that HyPM is 31 % more efficient than active monitoring for topology A and 29 % for topology B, respectively. In general, the energy costs are higher for topology A as the packets have a longer way to the BBR on average. Surprisingly, active monitoring even outperforms K-INT, which exhibits low efficiency under demanding conditions, as it struggles to transmit a significant amount of telemetry data. The energy cost per telemetry byte of HyPM is similar to piggybacking for topology B and just slightly higher for topology A. However, the amount of exported telemetry in Fig. 5 shows that piggybacking is also not able to fulfill the user's needs.

In summary, HyPM exhibits the highest rate of exported telemetry while having the relative lowest energy cost per telemetry byte.

# VI. CONCLUSION

This study analyzes the potential of K-INT for APM in low-power wireless networks, comparing it primarily to existing approaches. We implement K-INT for 6TiSCH networks in Contiki-NG's Cooja simulator. An initial simulation for a low-demand scenario in terms of payload and telemetry size shows remarkable results regarding the energy consumption overhead introduced by K-INT compared to Active Monitoring and Piggybacking. Even with increasing network sizes, K-INT's energy demand grows negligibly. However, K-INT reaches its limits in more demanding environments, where motes near the BBR face fully packed frames. To address this, we propose a novel hybrid approach, called HyPM, combining INT with Active Monitoring, fulfilling user requirements for scope and timeliness while being more energy-efficient. In two testbeds, the energy cost per telemetry byte is about 30 % lower than with Active Monitoring.

The INT engine introduces latency by deciding at each hop whether to append metrics to traversing frames. Additionally,
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>Figure 5: User requirement and amount of exported telemetry bytes per minute for both topologies.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 6: Energy-efficiency evaluation of HyPM.</figcaption></figure>

INT poses a security risk, as motives with malicious software could append false metrics, triggering incorrect alarms and unintended system behavior. Addressing these challenges, i.e. latency and security, falls outside the scope of this study and remains an area for future research.

## ACKNOWLEDGMENT

This document is issued within the frame and for the purpose of the OpenSwarm project. This project has received funding from the European Union's Horizon Europe Framework Programme under Grant Agreement No. 101093046. Views and opinions expressed are however those of the author(s) only and the European Commission is not responsible for any use that may be made of the information it contains.

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