Victor Scholler
email: victor.scholler@orange.fr

Alain Groslambert

Sidney Grosprêtre

Frederic Grappe

Neural efficiency and ability to produce accurate efforts in different perceived intensity zones

Keywords: Electroencephalography (EEG), cycling, α/β ratio, pacing, effort

This study aimed to investigate the relationship between neural efficiency and the ability of an athlete to produce accurate efforts in different perceived intensity zones during a racing scenario. The α/β ratio was used to quantify the neural efficiency during cycling, as it traduced the degree of participants information processing activity with lower cortical activity possible. Twelve trained competitive male cyclists delimited their perceived intensity zones 2 to 6 on a scale to assess the rating of exercise intensity. Then, they performed a 30 min racing scenario during which they had to produce different perceived intensities. The ability of athletes to produce perceived effort with accuracy and their neural efficiency was quantified during the racing scenario. The increase in the neural efficiency with the increase in the effort intensity could partly explain the improvement in athletes' ability to produce accurately perceived efforts from intensity zones 3 to 6. Moreover, the neural efficiency during the racing scenario was significantly correlated to the ability to produce perceived effort with accuracy at submaximal intensities.

mean relative PO at the lower limit of IZ (2 to 6) PO IZmiddle mean relative PO at the middle limit of IZ (2 to 6) PO IZhigh mean relative PO at the higher limit of IZ (2 to 6) PO mean mean PO during a racing scenario block PO% difference between and PO mean and PO IZmiddle for each IZ FFT Fast Fourier transformed PSD Power spectral density TTE Time trial to exhaustion Introduction:

Performance in cycling has often been related to biomechanical and metabolic factors, such as the muscle fiber type, pedalling efficiency, aerobic and anaerobic capacity, or oxygen uptake . However, a growing body of research reported positive effects of cognitive strategies, such as attentional focus or motivational state [START_REF] Barwood | Improvement of 10-km time-trial cycling with motivational self-talk compared with neutral self-talk[END_REF][START_REF] Barwood | A motivational music and video intervention improves high-intensity exercise performance[END_REF], on the performance.

All these cognitive strategies impact the cortical activities [START_REF] Comani | Attentional focus and functional connectivity in cycling: An EEG case study[END_REF][START_REF] Di Fronso | Focusing attention on muscle exertion increases EEG coherence in an endurance cycling task[END_REF] and influence the pacing strategies of athletes [START_REF] Ouvrard | The influence of pleasure and attentional focus on performance and pacing strategies in elite individual time trials[END_REF], which is an important factor of performance in an endurance sport such as cycling [START_REF] Baron | The global model of pacing process for long and ultra-long distance[END_REF][START_REF] Groslambert | Influencing factors of pacing variations and performance in a 44-kilometer mountain trail race[END_REF]. Pacing results from continuous adjustments of the power output (PO), using feedforward and feedback mechanisms from the external and internal afferent signals [START_REF] St Clair Gibson | The Role of Information Processing Between the Brain and Peripheral Physiological Systems in Pacing and Perception of Effort[END_REF]) that impact the perceived effort. Indeed, the perception of effort, defined as 'the sensation of how hard, heavy and strenuous a physical task is' [START_REF] Borg | Borg's Perceived Exertion And Pain Scales (Human kinetics)[END_REF], could impact performance during an incremental cycling exercise to exhaustion.

Therefore, exercise was terminated when the perception of maximal effort was reached [START_REF] Fontes | Brain activity and perceived exertion during cycling exercise: An fMRI study[END_REF].

This termination of effort is induced by a decrease in the cortical functions that allow for the preservation of the integrity of the body [START_REF] Fontes | Modulation of cortical and subcortical brain areas at low and high exercise intensities[END_REF][START_REF] Robertson | Prefrontal and motor cortex EEG responses and their relationship to ventilatory thresholds during exhaustive incremental exercise[END_REF]. Therefore, this suggests that some cortical areas in the brain are involved in the perception of effort that would probably affect the ability of the athlete to produce effort in different perceived intensity zones (IZ).

However, studies employing EEG techniques during a cycling exercise reported no consensus concerning motor cortex (MC), prefrontal cortex (PFC), and parietal cortex (PC) activity during high-intensity exercise. These areas of the brain are involved in motor, cognitive, and sensorimotor activities, respectively [START_REF] Cheron | Brain oscillations in sport: Toward EEG biomarkers of performance[END_REF]. During incremental exercise performed by trained cyclists, there is an increase in the PFC α, and β activity from 50% of the O 2 peak to the respiratory compensation point (RCP) and a decrease in the PFC activity until the end, after the RCP, while MC activity is maintained [START_REF] Robertson | Prefrontal and motor cortex EEG responses and their relationship to ventilatory thresholds during exhaustive incremental exercise[END_REF]. RCP describes the onset of hyperventilation, above which arterial P CO2 starts to decline due to lactic acidosis. The same activation of the PC and MC were observed during an intermittent incremental cycling exercise performed by recreational cyclists and sportsmen at high intensity, at a rate of perceived exertion (RPE) from 13 to 17 on the 6-20 Borg scale [START_REF] Fontes | Modulation of cortical and subcortical brain areas at low and high exercise intensities[END_REF]. However, during a time trial cycling exercise, where trained cyclists performed at 85% of maximal aerobic power until exhaustion, there was a constant increase in the MC, PFC, and PC activity [START_REF] Enders | Changes in cortical activity measured with EEG during a high-intensity cycling exercise[END_REF].

Athletes' physiological levels would be one factor influencing the variability of brain cortical efficiency during exercise. It has been well established that experts used their brains more efficiently during cognitively demanding tasks, [START_REF] Neubauer | Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain[END_REF]. Such phenomenon referred to the neural efficiency [START_REF] Haier | Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography[END_REF]. A broad range of neurophysiological markers has been used during the different cognitive tasks demands to quantify the neural efficiency. Often, a good neural efficiency resulted in a more focused cortical activation on relevant cortical areas, and a decrease in total cortical activation when performing a task [START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF][START_REF] Neubauer | Intelligence and neural efficiency: Measures of brain activation versus measures of functional connectivity in the brain[END_REF]. Cycling is a cognitively demanding task that required dealing with many external (environmental conditions, other competitors) and internal (bodies state) signals [START_REF] Gutmann | Effects of Physical Exercise on Individual Resting State EEG Alpha Peak Frequency[END_REF], which can be very aversive especially during high-intensity exercise, leading to a hard perception of the effort. Because α is dominant during mental inactivity and β rhythm is a good marker of information processing and cognitive activity, it is suggested using the α/β ratio to quantify the neural efficiency during cycling [START_REF] Cheron | Brain oscillations in sport: Toward EEG biomarkers of performance[END_REF]. A high α/β ratio while cycling will be the indicator of a good neural efficiency, meaning that the participants processed the sensory information's with the lower cortical activity possible.

Such phenomenon was already measured during cycling in athletes with higher O 2 max (55.6 ± 2.8 ml.min -1 .kg -1 ) who had a greater neural efficiency during incremental exercise to exhaustion compared to athletes with lower O 2 max (46.4 ± 4.1 ml.min -1 .kg -1 ) [START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF]. Indeed, [START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF] used the α/β ratio to quantify the neural efficiency during cycling. The authors suggested that these differences were due to the ability of the participants to inhibit all task-irrelevant cognitive processes.

We suggest that dealing with such aversive signals would be irrelevant during cycling. To manage aversive situations induced by cycling, strategies as the inhibition of brain cognitive areas such as the PFC were reported by [START_REF] Radel | Saving mental effort to maintain physical effort: A shift of activity within the prefrontal cortex in anticipation of prolonged exercise[END_REF]. Such abilities were also reported for highly trained military personnel that allowed to reduce insula activity during aversive breathing stimuli compared to less trained participants [START_REF] Haase | When the brain does not adequately feel the body: Links between low resilience and interoception[END_REF][START_REF] Haase | Mindfulness-based training attenuates insula response to an aversive interoceptive challenge[END_REF][START_REF] Simmons | Altered insula activation in anticipation of changing emotional states: Neural mechanisms underlying cognitive flexibility in special operations forces personnel[END_REF]. It should be relevant to explore whether the α/β ratio could be associated with the capacities of cyclists to deal efficiently with the external and internal afferent signals during cycling, which would affect their pacing strategies.

Therefore, this study aims to investigate the relationship between the neural efficiency quantified with the α/β ratio, and the ability of an athlete to accurately produce efforts in different perceived intensity zones during a racing scenario. The scale for the rating of subjective exercise intensity (RSEI) [START_REF] Grappe | Utilisation de la perception de l'effort comme outil d'estimation de l'intensité de l'exercice[END_REF][START_REF] Grappe | Utilisation de la perception de l'effort comme outil d'estimation de l'intensité de l'exercice[END_REF] was used to determine six different IZs with the perception of the effort, that is without any external guidance such as PO or heart rate (HR).

Methods:

Participants

To determine the sample size, we performed a sample size calculation with the G*Power software (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) that adjusted for the ANOVA within factor test.

Significance level () was fixed at 0.05, power () was fixed at 0.95 and the effect size (ES) was fixed at 0.5.

With this parameter, power analysis indicated that at least 9 participants were needed for this study. Therefore, twelve trained competitive male cyclists (age: 25.5 ± 9.1 years, height: 175.3 ± 6.2 cm; body mass: 69.3 ± 8.8 kg, body mass index: 22.5 ± 2.2, maximal aerobic power (MAP) = 360 ± 48.4, hours/week: 11.1 ± 5.4H)

volunteered to participate in this study. Only males were selected to avoid gender impact on cortical activity [START_REF] Ludyga | Do Male and Female Cyclists' Cortical Activity Differ Before and During Cycling Exercise[END_REF]. All participants practiced road cycling, from amateur to professional level. Prior to participating in the experiment, each participant provided written informed consent, and the study was conducted in accordance with the ethical principles of the Declaration of Helsinki ( 1983) and approved by the regional ethics committee.

Procedure

Each competitive participant visited the laboratory on three days, separated by at least one day [START_REF] Kunzler | Intense Cycling Exercise Improves Acute Cognitive Responses[END_REF]. The three visits consisted of three different cycling exercises on the participant's bicycle mounted on a home-trainer (Elite Drivo, Elite, Fontaniva, Italy). During the first visit, participants performed an incremental test to determine MAP. During the second visit, they performed a 30 min pedalling exercise aimed at establishing the boundaries of the participant's IZ 2 to 6 on the RSEI scale [START_REF] Grappe | Utilisation de la perception de l'effort comme outil d'estimation de l'intensité de l'exercice[END_REF][START_REF] Grappe | Utilisation de la perception de l'effort comme outil d'estimation de l'intensité de l'exercice[END_REF]. This scale is graduated from 1 to 7 (1light intensity and 7maximal intensity) and allows the quantifying of exercise intensity, using the subjective perception of effort to classify exercise intensity in different intensity zones according to different physiological perceived items (Supplementary Material 1).

XXXXSupplementary Material 1XXXX

Supplementary Material 1: The Scale of Rating of Subjective Exercise Intensity (RSEI) presented to the subjects during the second visit

This method has been validated by the French cycling federation and has been used by them and a large number of professional cyclists for 15 years. For each IZ, the scale associates acute physiological cues that can be perceived by the athlete in full effort, allowing him to identify the intensity zone of his effort. The third visit was a road race scenario, in which the participant had to follow a race profile projected on a TV screen and pedal in different IZs of the RSEI scale. This session was aimed at studying the abilities of participants to regulate their effort at different intensities, according to the RSEI scale, while following the race scenario. Therefore, the cyclist's perceptual finesse is engaged in each IZ [START_REF] Grappe | Puissance et performance en cyclisme. S'entraîner avec des capteurs de puissance[END_REF].

1.First Day Laboratory Visit: MAP Test

During the first visit, a MAP incremental test was performed. The initial workload consisted of pedalling for 6 min at 100 W, and this was incrementally increased by 30 W every 2 min. Cadence (CAD, rpm) was freely selected by participants during the test. The test was performed until exhaustion, with both the RPE, determined by Borg's category-ratio scale (CR-10), being between 9 and 10, and a significant decrease in CAD. The MAP was calculated by considering the mean PO of the lower and the final level [START_REF] Bouillod | Physiological and biomechanical responses between seated and standing positions during distance-based uphill time trials in elite cyclists[END_REF]:

With being the value of the last complete workload (W), the value of the last incomplete workload (W), and the time of the last incomplete workload (sec).

2.Second Day Laboratory Visit: IZ discrimination Test

During the second visit, a 30 min pedalling exercise was performed in the different IZs of the RSEI scale. All the participants were familiar with this scale but were reminded of the proper use of the scale before the experiment.

For each IZ, a description of the perception of the effort expected, the maximal range of time that can be maintained, and a characteristic picture showing the type of effort expected (for example, a rider on a TT bike to depict an effort in the IZ4 close to the anaerobic threshold) were presented to the participants. The 30 min exercise was composed of different blocks of exercises, characterized by their duration and intensity on the RSEI scale (from IZ1 to IZ6), and the level of effort in the IZ (middle, upper, or lower boundary). The exercise plan was presented to the participants before the experiment (Figure 1). Moreover, they were verbally and visually informed during the session regarding the time and intensity of each block by the instructor. Slides also showed the IZ to be discriminate and the level of intensity inside the IZ (lower, middle or upper boundary) with pictures illustrating the corresponding effort and the time remaining in each IZ. They were not informed about the PO or HR.

Figure 1 : Intensity zones (IZ) discrimination test. The 30 min session consisted of establishing individual IZs of the RSEI scale (IZ2, IZ3, IZ4, IZ5, and IZ6). For each IZ (except IZ6), participants had to pedal 45 sec in the middle of the IZ, 45 sec at the upper boundary of the IZ, and 45 sec at the lower boundary of the IZ, twice (from 3 to 23 min). Two min of exercise in the middle of the IZ2 were performed between IZ4 and IZ5 boundaries delimitation. For IZ6, the participant pedal in the middle, at the upper, and the lower boundary of the IZ during the 20 sec after pedalling 1min 45s in the middle of IZ2 (from 23 to 30min).

PO and CAD were continuously sampled during the 30 min pedalling session. After 3 min of pedalling in the middle of IZ1, participants had to pedal 45 sec in the middle, the upper, and the lower boundary of IZ2, twice.

For each intensity change, except IZ1, the participants were asked to slowly increase or decrease their pedalling cadence until they felt that they had reached the superior or inferior limit of IZ2. Indeed, in an IZ, the PO was essentially adjusted with the change in CAD. Changes in gear ratio were only allowed when the IZ changed. The same IZ delimitation was performed twice for IZ3, IZ4, and IZ5. Between IZ4 and IZ5, 2 min of pedalling in the center of IZ2, a fundamental endurance intensity that solicited mainly the aerobic glycolysis and lipolysis (see Supplementary Materials) was inserted to allow participants to recover. Because IZ6 corresponds to a submaximal intensity which corresponds to a lactic tolerance exercise with an accumulation of H+ ions in the muscles, that can be maintained between 7 and 30s, participants only produced a 20-sec effort at the middle, upper, and lower boundaries to delimit this IZ, separated by 1 min 30 sec of relaxing pedalling in the center of IZ1 to allow recovery. For PO and CAD analyses, the first 15 sec after each intensity change were not considered. Instead, this time was used to allow participants to adjust the intensity to the required IZ. The upper and lower boundaries of the IZ1 were not investigated because this intensity corresponds to a decontraction effort (recreational) that did not induce enough physical strain to be discriminated against. The mean relative PO (% MAP ) between both efforts performed at the lower (PO IZlow ), middle (PO IZmiddle ), and upper (PO IZhigh ) limits of IZ2, IZ3, IZ4, and IZ5 was measured. Due to the severe difficulty of effort required for IZ6, only the 20-sec blocks performed at each limit were considered to quantify PO IZlow , PO IZmiddle, and PO IZhigh .

Third-day laboratory visit: Racing scenario

XXXXFigure2XXXX

Figure 2 : Race profile describing IZ blocks and slope gradient presented to the participants before the experiment. Unexpected scenarios underlined in grey were added. These consisted of suddenly modifying the IZ to IZ4, IZ5, or IZ6 to simulate race pace elevation, attacks, and final sprint, respectively. Participants had to perform the effort in the middle of the IZ.

During the third visit, participants performed a 30 min exercise session, during which the intensity of effort was adjusted in the middle of different IZs, mimicking a race profile. This race profile was composed of different blocks, defined by their duration and intensity, based on the RSEI scale (from 1 to 6, all blocks performed in the middle of the IZ). The race profile was presented to the participants before the experiment (Figure 2). The percentages (%) of the climbs were simulated by the Elite Direto Trainer, which automatically adjusts according to the race profile scheduled on My-Elite software (My-Elite, Elite, Fontaniva, Italy). Cyclists were kept informed visually of their progress throughout the protocol. They were also informed of the time and intensities they had to produce through slides displayed on a TV screen placed in front of them. Slides also showed characteristic pictures of the corresponding IZ. To reproduce real racing conditions, sudden modifications of the intensity of effort were added to blocks IZ4, IZ5, or IZ6 to simulate the pace elevation, attacks, and final sprint, respectively. Participants were not informed about these unexpected scenarios prior to the experiment. Instead, they were only informed 10 sec before the unexpected scenario occurred. Oral guidelines, pictures representing pace elevation, attack in a climb, or final sprint, and the intensity to produce were used to inform participants about unexpected and expected changes in block intensity. The words 'PACE ELEVATION', 'ATTACK' and 'FINAL SPRINT' in large police size in orange and surrounded by a flash draw were displayed to inform the participants 10 sec before a shift in IZ4, IZ5, and IZ6, respectively. They were not informed about the duration for which they had to maintain the unexpected efforts. The race scenario guidelines that participants followed with the oral interventions are presented in the appendix. In total, participants performed 8 min in the middle of IZ1, 6 min in the middle of IZ2, 5 min in the middle of IZ3, 5 min in the middle of IZ4, 5.5 min in the middle of IZ5, and only 30 sec in the middle of IZ6 to avoid participants failure during this heavy intensity effort [START_REF] Ekkekakis | The pleasure and displeasure people feel when they exercise at different intensities: Decennial update and progress towards a tripartite rationale for exercise intensity prescription[END_REF].

The mean PO (PO mean ) produced in each one of the 11 blocks (between IZ2 and IZ6) of the racing scenario was measured. IZ1 was not considered in the PO to analyze because it corresponds to a relaxation effort that was used as a warming-up or recovery during the racing scenario. To measure the ability of the cyclist to produce accurate efforts in the different perceived IZ, the difference (PO%) between the PO mean and the PO of the middle zone of the discrimination test was calculated.

Participants had to rate their RPE every 5 min on Borg's CR10 rating scale [START_REF] Borg | Borg's Perceived Exertion And Pain Scales (Human kinetics)[END_REF]. To investigate participants' brain responses during the expected or unexpected racing scenario, electroencephalography (EEG) measurements were done for 30 sec at the end of each intensity block. Therefore, for IZ6, records were performed during the entire duration of the block. The methodology of EEG acquisition and analyse is described in the following section.

Electroencephalography (EEG):

The third-day laboratory test started with the participants being seated and an EMOTIV EPOX Flex (EMOTIV EPOC Flex, EMOTIV, San Francisco, California, USA) BioSemi system headset placed on their heads. Thirtyfour electrodes were used in our configuration (FCz, Fz, FP1, FP2, AF3, AF4, F3, F4, F7, F8, FC3, FC4, FC7, FC8, C3, C4, T7, T8, CP3, CP4, TP7, TP8, P3, P4, P7, P8, P9, P10, Pz, POz, O1, O2, O9, and O10), according to the international 10%-system (Sharbrough, G.E. Chatrian, & R. Lesser, 1991). The EEG was recorded with the EMOTIV App Pro at 128 Hz. A saline solution (BioTrue, Bausch Health Companies Inc., Laval, Quebec, CANADA) was used to wet the electrodes to attain a minimum of 98% contact quality, according to the guidelines of the EMOTIV App Pro. The montage was a common electrical reference that used an average of AFz and FCz values as reference. In this BioSemi system, CMS and DRL electrodes situated on AFz and FCz respectively replaced the ground electrodes. Indeed, according to the BioSemi guidelines is recommended to set the CMS electrodes in the middle of the measuring electrodes and the DRL away from the measuring electrodes.

Before each EEG acquisition, the participants were asked to avoid head and upper-body movements while cycling. The raw EEG data were exported as an EDF file from the EMOTIV PRO App to be processed and analysed with EEGLAB, an open-source MATLAB toolbox for the analysis of electrophysiological data (EEGLAB, Swartz Centre for Computational Neuroscience, University of California, California, USA). Records performed during each IZ block (expect IZ1) were first filtered with a 1-40 Hz bandpass filter and decomposed in segments of 30 s length. Data were then inspected and cleaned for eyeblink and muscular artifacts, using an automated ICA algorithm proposed by the SASICA toolbox [START_REF] Chaumon | A practical guide to the selection of independent components of the electroencephalogram for artifact correction[END_REF].

Decontaminated data were then Fast Fourier Transformed (FFT) into power spectra on the whole 30s of each file recorded at a sampling rate per channel of 128Hz. Each FFT window lasted 1-s and contained 128 data points.

No tapering, zero-padding neither overlapping was applied on these windows. Such FFT parameters generated a frequency precision of 1Hz and temporal precision of 7.8ms. Power spectral densities (PSD) were calculated for the α (8-13 Hz) and β (13-30 Hz) frequency bands. Prior to analysis, the EEG data were divided into three specific regions: the MC (C3, C4, and Cz), the PFC cortex (F3, F4, and Fz), and the PC (P3 and P4). To quantify the neural efficiency, the α/β ratio [START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF] for each record of each intensity block was calculated by dividing the PSD of the α bandwidth by the PSD of the β bandwidth for the MC, PFC, and the PC. The α/β ratio was then calculated for each of the regions and averaged for the same IZ blocks (except IZ6) to obtain the mean α/β ratio for each IZ. Topographical maps on the α PSD, β PSD, and α/β ratio for IZ2 to IZ6 were provided in supplementary material 2.

XXXXSupplementary Material 2XXXX

Supplementary Material 2: Topographical maps of the mean α and β PSD, and the mean α/β ratio for each intensity zones (IZ)

Statistical Analyses:

Statistical analyses were performed with JASP (JASP, Amsterdam, Netherlands). The Shapiro-Wilk statistical test revealed that all the data were normally distributed. A sphericity test was also performed with Mauchly's tests, and a Greenhouse-Geisser correction was applied if the sphericity was violated. To compare PO% at each IZ, a one-way repeated measure ANOVA (IZ) was performed. Moreover, to investigate differences in the α/β ratio between intensities at each brain region, a two-way repeated measure ANOVA (IZ × region) was performed. If significant interactions were found posthoc Conover's post-hoc tests were applied to examine significant differences between pairs of means. Conover's post-hoc test was performed when the sphericity was violated. Linear regression was performed between the mean α/β ratio of the diffuse cortical activity and the PO % for each of the IZs (2 to 6). The level of significance was set at p < 0.05.

ES were calculated for each set of data [START_REF] Cohen | Set Correlation and Contingency Tables[END_REF]). Cohen's d classification of effect size magnitude was used, whereby d < 0.2 = negligible effect; d = 0.2-0.49 = small effect; d = 0.50-0.8 = moderate effect and d > 0.8 = large effect. Statistical analyses were performed with JASP (JASP, Amsterdam, Netherlands).

Results:

The participants performed the racing scenario at a mean RPE of 3.9 ± 0.7 U.A. and a mean PO of 220 ± 28 W. The mean PO% was 10.7 ± 9.9 %. The mean relative PO during the IZ discrimination test for the low, middle, and high boundaries are presented in Table 2. The repeated measures ANOVA revealed a significant effect of intensity on PO% (p = 8.7x10-14;

F(1.7,18.9) = 11.3; η 2 = 0.4). Moreover, the two-way repeated measure ANOVA reported a significant effect of IZ on the mean α/β ratio (p = 0.01; F(1.5, 16.7) = 6.1; η 2 = 0.27), but no effect of the region (p = 0.057; F(1.5, 16.7) = 3.2; η 2 = 0.3), neither the region*IZ interaction (p = 0.3; F(2.2, 23.9) = 1.1; η 2 = 0.01) on the mean α/β ratio was reported. Conover's post-hoc test revealed that PO% is significantly smaller for IZ6 compared to IZ3 (-69.8 ± 60.6%, p < 0.001, d = 1.1), IZ4 (-50.7 ± 38.9%, p < 0.001, d = 1.3) and IZ5 (-14.6 ± 22.8%, p < 0.01, d = 1.6) (Figure 3). Moreover, the Conover post-hoc test revealed that the mean α/β ratio increased significantly between IZ3 and IZ5 (+43.4 ± 36.2%; p = 0.045; d = 1.1), and between IZ3 and IZ6 (+64.2 ± 60.5%; p = 0.028; d = 0.8) for all brain regions (Figure 4). Moderate size effects were also reported between IZ2 and IZ4 (+11.7 ± 13.1%; d = 0.56). Finally, large size effect was reported for the mean α/β ratio between the MC and the PFC (+168.5 ± 91%; d=0.85), and moderate effect was reported between the MC and the PC (+162.1 ± 101.8%; d=0.6).

Moreover, significant regression was found between the mean α/β ratio in the MC and the power output variation (PO % ) at IZ4 (p<0.05, r = 0.65, R 2 = 0.42, d=1.1, ) and in the PFC (p<0.05, r = 0.65, R 2 = 0.42, d=1.1, ), and large size effect were found between the mean α/β in the PC and the PO % at IZ4 (r = 0.54, R 2 = 0.28, d=1, ) (Figure 5). Finally, significant regression was found between the mean α/β ratio in the PC and the PO % at IZ5 (p<0.05, r = 0.65, R 2 = 0.42, d=0.9, ), and large size effect were found between the mean α/β and PO % at IZ5 in the MC (r = 0.51, R 2 = 0.26, d=0.8, ) and the PFC (r = 0.31, R 2 = 0.57, d=0.9, ) (Figure 6).

XXXXFigure3XXXX

Figure 3 Difference between the PO mean during the racing scenario and the PO of the middle zone of the discrimination test (PO % ) for each intensity zones (IZ). * defined p < 0.05

XXXXFigure4XXXX

Figure 4 : Mean α/β in the diffuse cortex for each intensity zone (IZ) performed during the racing scenario. * defined p < 0.05

XXXXFigure5XXXX

Figure 5 : Linear regression between the mean α/β ratio in the MC, PFC, and PC and the power output variation (%PO) at intensity zone 4 (IZ4).

XXXXFigure6XXXX

Figure 6: Linear regression between the mean α/β ratio in the MC, PFC, and PC and the power output variation (%PO) at intensity zone 5 (IZ5).

Discussion:

This study aimed to investigate the link between the ability of a cyclist to produce accurate efforts in different perceived intensity zones during a racing scenario and his neural efficiency. The more important finding of this study is that the increase in the α/β ratio together with effort intensity could partly explain the decrease in PO% from IZ3 to IZ6. The low PO% at IZ2 and IZ6 were also reported by [START_REF] Arnold | Etude de la perception de l'intensité à travers l'expertise chez des cyclistes en condition de laboratoire[END_REF] and could be due to the polarized training methods practiced by most of the participants. Moreover, the mean α/β activity in the PFC and MC while pedalling in IZ4, and the mean α/β activity in the PC during IZ5 efforts would partly explain the participant's abilities to produce accurately perceived in these IZ during the racing scenario.

The significant increase of the α/β ratio with IZ (Figure 4) could be due to the growing demand for sensorimotor, cognitive, and motor activity in the cycling task [START_REF] Maceri | EEG responses to incremental self-paced cycling exercise in young and middle aged adults[END_REF]. Indeed, the β and the α cortical activities are both associated with psychological activities during aerobic exercise [START_REF] Cheron | Brain oscillations in sport: Toward EEG biomarkers of performance[END_REF][START_REF] Di Fronso | Focusing attention on muscle exertion increases EEG coherence in an endurance cycling task[END_REF][START_REF] Enders | Changes in cortical activity measured with EEG during a high-intensity cycling exercise[END_REF][START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF][START_REF] Zainuddin | Brainwave biomarkers of brain activity, physiology and biomechanics in cycling performance[END_REF]. Our results are in line with [START_REF] Enders | Changes in cortical activity measured with EEG during a high-intensity cycling exercise[END_REF], who reported an increase in both α and β bandwidth during a time trial to exhaustion (TTE) at 85% MAP . The same increase of α and β bandwidth activities was reported during a time-to-exhaustion incremental cycling exercise [START_REF] Robertson | Prefrontal and motor cortex EEG responses and their relationship to ventilatory thresholds during exhaustive incremental exercise[END_REF]. Authors associated these changes with greater motor planning activity associated with the increased sensory input toward the end of the exercises. Moreover, the increase in the α/β ratio has also been associated with an increase in arousal as the intensity of exercise increased [START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF][START_REF] Maceri | EEG responses to incremental self-paced cycling exercise in young and middle aged adults[END_REF]. Therefore, the significant reduction in PO% at IZ6 compared to IZ3, IZ4, and IZ5 would be due to a more important allocation of cognitive resources to deal with exercise-related cues, leading to more efficient processing of sensory signals [START_REF] Ludyga | Four weeks of high cadence training alter brain cortical activity in cyclists[END_REF].

The lack of significance between regions for the α/β ratio suggests similar variations of the α and β activities in brain regions. This result is consistent with the fact that the MC, PFC, and PC are all similarly involved in the sensorimotor pathways during aerobic effort. Indeed, it has been reported that the PFC cortex is involved in the sensory treatment during a cycling exercise composed of six blocks of two minutes at 1.25 W/kg [START_REF] Fontes | Brain activity and perceived exertion during cycling exercise: An fMRI study[END_REF]. This allows for an estimation of the sensory feedback that is associated with the sensory signals to generate an awareness state [START_REF] Fontes | Brain activity and perceived exertion during cycling exercise: An fMRI study[END_REF]. This estimation by the PFC allows the regulating of MC activity through the different afferent feedback [START_REF] Craig | Interoception: The sense of the physiological condition of the body[END_REF]. The PC has to synchronize with the premotor cortex to integrate the sensory information and execute motor responses during a cycling exercise at 60% O 2 peak until exhaustion [START_REF] Hilty | Cerebral Processes Mediating Muscle Fatigue in Healthy Humans[END_REF] occurred between both MC and PFC. The growing solicitation of the cortex with the increase in IZ causes a growing solicitation of the efferent pathways. These results agree with those of [START_REF] Marcora | Perception of effort during exercise is independent of afferent feedback from skeletal muscles, heart, and lungs[END_REF] who reported that the relationship between the perception of effort and the efferent pathways are driven by sensory processing. These relationships between MC, PFC, and PC during aerobic exercise agree with the similar variations of the α/β ratio with the intensities between these brain regions.

However, the ability of some cyclists to produce accurate effort according to the IZ performed seemed to be due to different mechanisms set by the brain. Indeed, for IZ4, significant negative linear relationships were found between the mean α/β ratio and the PO% in the MC and the PFC. These results seemed to support a more motor execution and sensory activity in the MC and PFC respectively. Same differences in the α/β ratio were also reported by [START_REF] Ludyga | The Athlete's Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise[END_REF] between athletes with higher O 2 max (55.6 ± 2.8 ml/min/kg) and low O 2 max (46.4 ± 4.1 ml/min/kg) during an incremental cycling exercise until exhaustion. They explained that cycling becomes automatic for highly trained athletes, leading to a decrease in the degree of arousal by inhibiting cognitive activity that is not involved with the cycling task [START_REF] Hüttermann | Does the inverted-U function disappear in expert athletes? An analysis of the attentional behavior under physical exercise of athletes and non-athletes[END_REF], and therefore optimizing the motor execution [START_REF] Callan | Neural Processes Distinguishing Elite from Expert and Novice Athletes[END_REF]. Moreover, [START_REF] Callan | Neural Processes Distinguishing Elite from Expert and Novice Athletes[END_REF] explained that the neural efficiency could be also due to a reduction in the sensory and motor cortex activity. Such inter-participant variability in these cognitive abilities could be due to an improvement in cognitive abilities, such as memory, learning, attention, or processing information [START_REF] Comani | Attentional focus and functional connectivity in cycling: An EEG case study[END_REF][START_REF] Groslambert | Influencing factors of pacing variations and performance in a 44-kilometer mountain trail race[END_REF][START_REF] Wagner | Changes in fMRI activation in anterior hippocampus and motor cortex during memory retrieval after an intense exercise intervention[END_REF][START_REF] Won | Semantic memory activation after acute exercise in healthy older adults[END_REF] with moderate and high-intensity cycling exercises.

However, in IZ5, significant negative linear relationships were found between the mean α/β ratio and the PO% only in the PC. This result would highlight sensorimotor mechanisms set by some participants to improve efficiency. This hypothesis was supported by the reduction in insula's reactivity to an aversive breathing stimulus for navy SEALs or trained adventurers compared to normal participants [START_REF] Haase | When the brain does not adequately feel the body: Links between low resilience and interoception[END_REF][START_REF] Simmons | Altered insula activation in anticipation of changing emotional states: Neural mechanisms underlying cognitive flexibility in special operations forces personnel[END_REF]. Indeed, as the PC, insular is mainly involved in sensorimotor activities [START_REF] Haase | Mindfulness-based training attenuates insula response to an aversive interoceptive challenge[END_REF]. Such abilities can be improved by mindfulness-based training [START_REF] Haase | Mindfulness-based training attenuates insula response to an aversive interoceptive challenge[END_REF].

Therefore, such control of the PC during IZ5, which can be considered as an aversive situation due to the high intensity of exercise, would improve the ability to accurately produce effort in this IZ.

Further research is needed to investigate the effect of cognitive training on sensorimotor and cognitive activity, neural efficiency, and performance determinants, such as the ability to produce perceived effort with accuracy.

Conclusion:

Table 1 :

 1 List of abbreviations

	PO	Power output
	IZ	Intensity zones
	MC	Motor cortex
	PFC	Prefrontal cortex
	PC	Parietal cortex
	RCP	Respiratory compensation point
	RPE	Rate of perceived exertion
	RSEI	Rate of subjective exercise intensity
	HR	Heart rate
	ES	Effect size
	MAP	Maximal aerobic power
	CAD	Cadence
	PO

Table 2 :

 2 mean ± SD of the mean PO (in % MAP) during the IZ discrimination test for the low, middle, and high

	boundaries of IZ2 to IZ6
	IZ/	Mean ±
		low	middle	high
	SD PO (%MAP)
	IZ2	34.5 ± 9.5	38.8 ± 8.6	40 ± 9.4
	IZ3	46.5 ± 12.2	49.7 ± 12.5	53.3 ± 13.3

The authors didn't receive any funding for this work. Each participant wrote informed consent, and the study was conducted following the ethical principles of the Declaration of Helsinki (1983) and approved by the

All data and material are available.

The results of this study suggest that an increase in the neural efficiency with the increase of IZ appeared to positively impact the ability of a cyclist to produce efforts in different perceived intensity zones.

Moreover, the degree of neural efficiency of an athlete during IZ4 and IZ5 seemed to impact his ability to accurately produce effort at different perceived intensity zones.

Funding:

The authors declare that they have no funding.


domain: info spi

text:
Francisco A A Gomes

Paulo A L Rego
email: pauloalr@ufc.br

Fernando A M Trinta
email: fernando.trinta@dc.ufc.br

José De Souza

José N De Souza

Comparative Analysis of Service Mesh Platforms in Microservices-Based Benchmark Applications

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The development of monolithic systems brings several challenges related to the maintenance and lack of autonomy of the development teams to adopt new technologies that differ from the adopted architecture [START_REF] Bennett | Software maintenance and evolution: A roadmap[END_REF]. There is currently a trend in systems development to use the composition of microservices, that is, small pieces of cohesive and autonomous software. With the microservice architecture, the application is divided into independently deployable services that can be rapidly developed on any infrastructure resource, and each service runs in an isolated process (container) [3]. Additionally, these services communicate through an HTTP API 1 . Thus, this architectural style allows developers to decompose software into small units, facilitating the scalability of only the most demanded services at that moment, which strongly contrasts with the architectures of corporate systems that are usually a single implementable component.

Several studies point to challenges related to microservices, for example, methodologies and tools for multilayer monitoring, functional adaptation algorithms at runtime, advanced adaptation features at runtime, and high availability support [START_REF] Casalicchio | The state-of-the-art in container technologies: Application, orchestration and security[END_REF]. Furthermore, developing technology for microservices also increases the operational complexity associated with modern applications. This led to the emergence of service mesh, a promising approach to mitigate this situation, which introduces a dedicated infrastructure layer on top of microservices without imposing modifications on service implementations.

The proposed work aims to present a survey about service mesh platforms and its application in a benchmark of applications based on microservices. The benchmark works as a reference to an application that has several microservices related to each other and reflects the idea of a corporate application. Furthermore, the work carried out a performance experiment to analyze the impact of these platforms.

The rest of this article is organized as follows: Section 2 presents the background: microservices, service mesh and platforms. Section 3 presents the results of experiments carried out. Finally, Section 4 concludes the article and exposes possible future works.

Background

The microservices emerged empirically from architectural patterns used in the real world, where systems are composed of services that collaborate to achieve their goals, communicating through lightweight mechanisms (e.g., Web APIs ) [3]. The idea of microservices is to build small applications, developed independently, which tend to present efficient processing and interoperability aspects, allowing the continuous deployment/delivery of large and complex applications [START_REF] Newman | Building microservices: designing fine-grained systems[END_REF]. Given these characteristics, it is possible to implement each microservice with a different technology stack. According to Zimnermann (2017) , microservices are a way of implementing and deploying services in SOA using state-of-the-art software engineering practices (i.e., development and deployment paradigms and technologies).

A microservice that communicates with other services incorporates business logic and network communication logic. Each microservice contains a significant part of its network communication-related code, independent of the service's business logic. Implementing functionality related to service-to-service communication from scratch is costly [START_REF] Indrasiri | Service mesh[END_REF]. Instead of focusing on business logic, the developer needs to spend a lot of time creating service-to-service communication functionality. Service mesh is a communication infrastructure between services. With a service mesh, a given microservice will not communicate directly with the other microservices. Instead, all service-to-service communication occurs in a software component called a Proxy sidecar. The sidecar is a software component co-located with the service in the same Virtual Machine or pod (Kubernetes). The Proxy sidecar layer is known as the Data Plane. All these Proxies sidecars are controlled via a control plane. This is where all settings related to inter-service communications apply.

Service mesh provides built-in support for networking functions like resiliency, service discovery, etc. Therefore, service developers can focus more on business logic, while most of the work related to network communication is offloaded to service mesh. The microservice for sidecar communication always takes place via standard protocols such as HTTP1.x/2.x, gRPC, etc. Thus, service mesh is a technology and language-independent. Regarding the control plane, all service mesh sidecars are centrally managed by a control plane. This is useful when supporting service mesh features such as access control, observability, service discovery, etc. All changes made to the control plane are sent to the sidecars.

Several platforms offer service mesh for microservice applications. This section will introduce two of them: Istio and Linkerd. Istio 2 is an open-source platform for connecting, managing, and securing microservices. It provides an infrastructure for communication between microservices, with resiliency, routing, load balancing, service-to-service authentication, observability, and more, without requiring any changes to the service code. By deploying Sidecar, the developer can add the service to Istio's service mesh . Deploying Istio is deeply tied to Kubernetes, but deploying it to other systems is also possible. Linkerd3 is a service mesh for Kubernetes. It makes running services easier and safer by offering run-time debugging, observability, reliability, and security, all without requiring changes to source code. Linkerd is fully open source, licensed under Apache v2, and is a graduate project of the Cloud Native Computing Foundation.

Experiments and Results

To verify the use of service mesh platforms and to analyze the impact of these platforms on benchmarks applications in microservices, the work carried out a performance experiment taking into account the latency of a request.

For the experiment, a software called Apache JMeter4 was used, a tool that allows the performance of load and stress tests in Web applications. The tests were performed on a machine with the following configuration: a node with a 4th generation 1.8 GHz i3 processor, with 2 cores, 8 GB of RAM, 128 GB of SSD, running on the Mint 20 Operating System. In addition, Kubernetes version 1.21 was installed.

For the present research, the test consists of analyzing the Teastore application5 in three scenarios: with Kubernetes only, Kubernetes with Istio, and Kubernetes with Linkerd. With JMeter, the Teastore application was submit tests considering the return of stored data and calculating the average latency. Each scenario was subjected to different request rates: 600, 1200, and 2400 requests per minute. 10 users were used to running each scenario in concurrency at each request rate.

Figure 1 presents the results of the tests carried out. Baseline is the Kubernetes-only scenario. The obtained results show a better performance of Linkerd compared to Istio in all request rates. When verified at the rate of 2400 requests per minute, the increase in latency was 12.9% comparing the two platforms (Linkerd -482,2 and Istio -426,8). Istio even showed better results in some tests than Linkerd, but its overall average was impaired due to the high incidence of errors during the execution of the tests. This can be explained by the fact that Istio has a higher CPU and memory consumption than Linkerd. Another result found is about the impact of service mesh platforms. When comparing Istio with Baseline, the average latency value is up to 6.15 times higher when subjected to a rate of 2400 requests per minute (Baseline -67,35 and Istio -482,2).

Conclusion and Future Works

The article presented a survey about relevant topics, such as microservices, service mesh and tools, benchmark application, and a performance test performed. Important concepts and definitions for understanding the work were presented, as well as tools that can be used to carry out analyzes in microservices applications. Service mesh brings advantages to the developer because instead of focusing on business logic, it was necessary to spend time creating serviceto-service communication functionalities, and with the service mesh is no longer necessary. The results showed that despite the benefits of service mesh, it impacts the latency of applications. The conclusion is that, in terms of performance, Linkerd is better than Istio, which is explained by its lighter system, that consumes fewer resources. However, there are still reasons why a developer might prefer to use Istio, more popularity and greater community support.

Figure 1 :

 1 Figure 1: Comparative Analysis of Tests

https://istio.io/

https://linkerd.io/

https://jmeter.apache.org/

https://github.com/DescartesResearch/TeaStore


text:
André Teixeira De Aquino

José Ailton Leão

Barboza Júnior

Nicolas De

Araujo Moreira

Paulo Peixoto Praça
email: paulopp@dee.ufc.br

José Ailton Leão Barboza

Nícolas De

Araújo Moreira

Impacts of GPS module on energy consumption and machine-learning based battery lifetime estimation

Keywords: Machine Learning, Internet of Things, Wireless Sensor Networks

come   L'archive ouverte pluridisciplinaire

Introduction

The Internet of Things (IoT) is the connection between different types of sensors, devices, objects, machines, vehicles, and buildings using Wireless Sensor Networks (WSNs). IoT is a key concept in the evolution of communication, industry, and agriculture, for example, [START_REF] Wei | Robustness against interference in Internet of Things[END_REF][START_REF] Yan | Robustness against interference in sensor networks[END_REF][START_REF] Nidia | Smart and green: An internet-of-things framework for smart irrigation[END_REF].

The nodes of this network are self-organized and decentralized intelligent sensors able to communicate with each other through radio links [START_REF] Massouri | Modélisation comportamentale SystemC-AMS d'interfaces RF et liaisons radio multipoint pour réseaux de capteurs[END_REF][START_REF] Carle | Energy-efficient area monitoring sensor networks[END_REF]. WSN are characterized by complexity and heterogeneity: it integrates different analog and digital blocks, such as Analog-Digital Converters (ADCs), Digital Signal Processors (DSPs), and Radio-Frequency Circuits [START_REF] Massouri | Modélisation comportamentale SystemC-AMS d'interfaces RF et liaisons radio multipoint pour réseaux de capteurs[END_REF]. Usually, the communication of WSNs is multi-path asymmetric, presenting a many-to-one data flow, i.e., the nodes send data to a monitoring station [START_REF] Massouri | Modélisation comportamentale SystemC-AMS d'interfaces RF et liaisons radio multipoint pour réseaux de capteurs[END_REF].

Wireless Sensor Networks (WSNs) have been used widely in many scientific, industrial, and commercial applications, such as ecological control (forest fire detection, pollution control), intelligent houses, etc., acquiring environmental parameters (e.g. temperature, illumination, etc.) [START_REF] Massouri | Modélisation comportamentale SystemC-AMS d'interfaces RF et liaisons radio multipoint pour réseaux de capteurs[END_REF]. WSN must be low cost and have strong energy constraints: depending on the scenario, it is quite difficult to recharge or replace the battery, as for example, applications with a huge number of nodes spread over a large area (forest or farm monitoring, for instance) or applications in distant and dangerous locations (such as volcano monitoring) [START_REF] Massouri | Modélisation comportamentale SystemC-AMS d'interfaces RF et liaisons radio multipoint pour réseaux de capteurs[END_REF].

The uneven use of livestock pastures has a significant impact on productivity, the ecosystem, biodiversity, and function. The use of collars with Global Positioning System (GPS) allows for obtaining a large amount of data about the position and activity of the animals, giving new possibilities to researchers [START_REF] Lauro | Detecting autocorrelation problems from gps collar data in livestock studies[END_REF]. Similarly to the previously described scenarios, one of the limitations related to using these devices is their autonomy, more precisely, the battery lifetime. Battery replacement or recharging impacts the logistics of animal handling. So, it is important to have an estimate of battery lifetime to have a more adequate maintenance schedule for these networks. A second relevant point is related to the way the data is accessed. The current work is inserted in the context of a project of a collar for a location using satellite, data acquisition, and transmission using LoRa for remote data access for real-time surveying of pastures. This paper has discussed the impact of the GPS module, to track bovines on farms, activation on current consumption, and two machine-learning-based estimation techniques are used to estimate the current consumed and then compared. The remainder of this paper is as follows: the next section [START_REF] Coutray | Wireless sensor network node global energy consumption modeling[END_REF] gives an overview of existing works related to this research. The following section (3) presents the two machine-learning techniques used: Long-Short Term Memory (LSTM) Networks and Decision Trees with AdaBoost. Section 4 presents the used hardware. Then, section 5 presents and discusses the results of measurements and estimation.

Literature Review and State of the Art

As discussed before, IoT has applications in a wide variety of areas. A considerable number of researches discusses also how to increase the battery lifetime and study factors that affect it, such as the impact of interference on energy consumption. Applications of IoT in precision irrigation for agriculture are explored in [START_REF] Kamienski | Smart water management platform: Iot based precision irrigation for agriculture[END_REF][START_REF] Nidia | Smart and green: An internet-of-things framework for smart irrigation[END_REF]. The author in [START_REF] Toldov | Adaptive MAC layer for interference limited WSN[END_REF] explores the use of IoT in wildlife tracking.

The author in [START_REF] Toldov | Experimental evaluation of interference impact on the energy consumption in wireless sensor networks[END_REF] presents a platform named SYNERGIE to measure the energy consumption on nodes of WSNs and quantify the impact of interference on energy consumption. It measures the consumption of microcontroller, memory, sensor, and radio-frequency circuit at a rate of 1,500 samples per second through General Purpose Input-Output (GPIO) lines and a resistor interface. The measurements are transmitted to a computer through a serial link. This platform is based on commercially available low-cost and low-power components: AT-mega328p microcontroller, which contains an embedded 10-bit Analog-to-Digital-Converter (ADC) and five operational amplifiers XCT1086 able to acquire up to five independent measurements. A similar power meter system is presented in [START_REF] Zhou | Nemo: a high fidelity noninvasive power meter system for wirelss sensor networks[END_REF]: the authors present a real-time power metering system for a wireless sensor network called Nemo, a noninvasive and plug-and-play device that can be easily installed without wires. Nemo has a circuit that dynamically adjusts the resistance of shunt resistors according to the current load (shunt resistor switch). Nemo presents a high measurement accuracy (average measurement error of 1.34%) on TelosB devices.

The author in [START_REF] José | Adaptive MAC layer for interference limited WSN[END_REF], in addition to the introduction of SYNERGIE platform, also explores the problem of energy efficiency and discusses a model of energy consumption on WSN nodes, presenting software that allows generating automatically an energy consumption model based on Principal Component Analysis (PCA) and an algorithm for node lifetime estimation based on Markov chain.

The authors in [START_REF] Coutray | Wireless sensor network node global energy consumption modeling[END_REF] also present a model for global energy consumption for WSN nodes using a simulator called Ns-2 and iMote2 hardware. The results show energy consumption simulated close to measured values on real devices under the same experimental conditions for radio-frequency circuits. A similar work is shown in [START_REF] Zhou | Modeling of node energy consumption for wireless sensor networks[END_REF], however, based on an event-trigger mechanism for each component (processor, radio-frequency circuit, sensors). Energy modeling in sensor networks is also discussed in [START_REF] Schmidt | Energy modelling in sensor networks[END_REF].

Theoretical Introduction: Machine Learning Algorithms

In this section, the two algorithms used to estimate the current consumption will be introduced: Long-Short-Term Memory (LSTM) Networks and Decision Trees with AdaBoost.

Long-Short-Term Memory (LSTM) Networks

Long Short-Term Memory Network (LSTM) is a kind of deep learning algorithm called Recurrent Neural Networks (RNN) -dense neural networks connected to themselves: this feedback loop allows the information to be stored [START_REF] Medsker | Recurrent neural networks: design and applications[END_REF][START_REF] Josefowic | An empirical exploration of recurrent neural networks[END_REF]. LSTM networks have a high capability of predicting time series and it differs from other types of neural networks due to the introduction of a forget gate, that controls which states are remembered or forgotten.

A gate is a structure that adds, removes, or transmits data, being composed by a sigmoid neural network layer (here denoted by σ), that outputs a number within the interval [0, 1] -where 1 denotes that all information is re-transmitted and 0 denotes to not allow the transmission of any information, and a Hadamard product (denoted by •). Let's denote by U and V the matrices connecting inputs and recurrent outputs, respectively, and , x t , h t-1 , the input and the output of the previous cell, thus:

h g = σ(U x t + V h t-1 ). ( 1
)
Each cell is composed of: (i) cell state (c t ), the internal memory of the cell that stores long and short-term memories; (ii) hidden state (c h ), which decides to retain short and/or long-term information on state cell to predict; (iii) input gate (i t ), which conditionally decides which input values will be updated on state memory; (iv) forget gate (f t ), which decides how much information of current input and from previous state cell is forwarded to current state cell; and, finally, (v) the output gate (o t ), which conditionally decides which will be the output according to input and memory block.

A piece of data is forgotten only when new data replaces it. LSTM starts deciding which data will be forgotten and then which new data will be stored.

The previous cell c t-1 is updated into a new cell c t and then the old state is multiplied by a forgetting factor f t . A new possible value is generated, so it is necessary to decide what will be transmitted to output based on the cell state applying a sigmoid layer. The tanh layer forces the value to be mapped in the interval [-1, 1]. Denoting U g , V g the weights for input and previous cell output, respectively, and b g an input bias, we have:

g = tanh(b g + x t U g + h t-1 V g ).
(

) 2
The input gate is a hidden layer of sigmoid activation nodes with weighted inputs x t , h t-1 .

Then, the expression for the input gate can be written as:

i = σ(b i + x t U i + h t-1 V i ). (3)
Denoting by s t the internal state of an LSTM cell, the forget gate is a set of sigmoid activation nodes multiplied by s t-1 to determine what must be remembered (output of forget gate close to 1) and what must be forgotten (output of forget gate close to 0), allowing LSTM to learn according to the scenario. So, the mathematical expression for the forget gate is given by:

f = σ(b f + x t U f + h t-1 V f ). (4)
So, the output of the forget gate works as a weight for the internal states. Thus, the output of this state s t is expressed by:

s t = s t-1 • f + g • i. ( 5
)
The last stage of the LSTM cell is the output gate, which is composed of a sigmoid layer and hyperbolic tangent (tanh) layer, responsible to create a vector with new possible values to be added to a ct state. The output is given by:

o = σ(b o + x t U o + h t-1 V o ). ( 6
)
thus, cell output is expressed by:

h t = tanh(s t ) • o. ( 7
)
Figure 1: LSTM Architecture.

Decision Tree

A tree is a set T of positive integers with two functions l(•), r(•) ≥ 0 denoting the left and right nodes respective with equality indicating a terminal node and from T to T ∪ {0} where each element of T denotes a node in the three. There is a unique parent of each node [START_REF] Webb | Statistical Pattrern Recognition[END_REF]. Decision trees are capable of modeling complex nonlinear decision boundaries. Associated with each internal node of the tree are a variable and a threshold and with each leaf (terminal node), is a class label. The decision tree divides the decision into simpler decisions at each node.

A sub-tree is a non-empty subset (T k ⊂ T ) associated to two functions l k , r r . Denoting {u(t), t ∈ T } a partition of the data space R p (u(t) is a subspace of R p ) and denoting ω j(t) ∈ {ω 1 , ..., ω C } denote one of the class labels [START_REF] Webb | Statistical Pattrern Recognition[END_REF] then, a classification tree is a tree T associated with the class labels {ω j(t),t∈ T } and the partition {u(t), t ∈ T } [START_REF] Webb | Statistical Pattrern Recognition[END_REF].

A classification tree is built using a labeled data set, L = (x i , y i ), i = 1, ..., n where x i are the data samples and y i the corresponding class labels [START_REF] Webb | Statistical Pattrern Recognition[END_REF]. Example: Properties {taste,color,shape,size}, pattern x={sweet,yellow,long,medium} is classified as banana.

AdaBoost

Boosting is a technique for improving the accuracy of learning algorithms [START_REF] Freund | A short introduction to boosting[END_REF] and is a classifier in the form [START_REF] Wu | Top 10 algorithms in data mining[END_REF]:

F T (x) = T t=1 f t (x) (8)
where each f t is a weak learner that takes an object x and returns the class of the object. At each iteration of the training process, a weight w i,t is assigned to each sample in the training set equal to the current error on that sample. AdaBoost (Adaptive Boosting) is a machine learning algorithm [START_REF] Wu | Top 10 algorithms in data mining[END_REF], an important ensemble method, and is a particular method of training a boosted classifier. It is very accurate, simple to implement, and presents good generalization [START_REF] Wu | Top 10 algorithms in data mining[END_REF]. AdaBoost often tends to be empirically resistant to overfitting [START_REF] Gao | On the doubt about margin explanation of boosting[END_REF], however, presents a sub-optimal solution. The AdaBoost algorithm was introduced by Freund and Schapire in 1995 [START_REF] Gao | On the doubt about margin explanation of boosting[END_REF][START_REF] Wu | Top 10 algorithms in data mining[END_REF]. AdaBoost training process selects only features known to improve the prediction, reducing dimension and improving computation time once irrelevant features are not used [START_REF] Wu | Top 10 algorithms in data mining[END_REF]. The AdaBoost algorithm aims to build a strong classifier by doing a linear combination of weak learners (classifier) h t (x) : ξ → {-1, 1} [START_REF] Gao | On the doubt about margin explanation of boosting[END_REF]:

f (x) = T t=1 α t h t (x) (9)
AdaBoost is capable of reducing bias and variance of weak classifiers, has a good generalization property, and its output converges to the algorithm of the likelihood ratio. AdaBoost can be seen as a feature selector and is close to sequential decision-making (it produces a sequence of gradually more complex classifiers).

Taking as input a training set (x i , y i ), i = 1, ..., m where x i belongs to some domain and y i is a label on some label set Y . For instance, assume Y = {-1, 1}. Let's consider t = 1, ..., T the number of rounds. One of the ideas of the algorithm is to maintain a distribution or a set of weights over the training set. The weight of this distribution on training example i on round t is denoted D t (i). Initially, all weights are set equally, but on each round, the weights of incorrectly classified examples are increased to force the focus on the examples harder to learn. The objective of the learner is to find a weak hypothesis h t : X → {-1, 1} appropriate for the distribution D t . The goodness of a weak hypothesis is measured by its error [START_REF] Freund | A short introduction to boosting[END_REF]:

ϵ t = P r i∼Dt [h t (x i ) ̸ = y i ] = i:ht(xi)̸ =yi D t (i) (10)
First, it assigns equal weights to all the training examples. Denoting D t the distribution of the weights at the t-th learning round. From the training set and D t , the algorithm generates a weak learner h t : X → Y and then uses the training examples to test h t . The weights of the incorrect classified examples will be increased. Such a process is repeated for T rounds. The final model is derived by weighted majority voting of the T weak learners [START_REF] Wu | Top 10 algorithms in data mining[END_REF]. The steps of the algorithm are shown below [START_REF] Freund | A short introduction to boosting[END_REF][START_REF] Gao | On the doubt about margin explanation of boosting[END_REF][START_REF] Wang | On the margin explanation of boosting algorithms[END_REF][START_REF] Rojas | Adaboost and the super bowl of classifiers: A tutorial introduction to adaptive boosting[END_REF][START_REF] Wu | Top 10 algorithms in data mining[END_REF]:

Algorithm 1 AdaBoost 1: procedure AdaBoost((x i , y i ), i = 1, ..., N, i ∈ X, y i ∈ Y = {-1, 1}) 2: Initialize D 1 (i) = 1/m 3:
for t = 1, ..., T do:

4:
Train the weak learner using distribution D t

5:

Get weak hypothesis h t : X → {-1, 1} with error

ϵ t = P r i∼Dt [h t (x i ) ̸ = y i ] 6:
Choose α t = 0, 5 ln(ϵ -1 t (1 -ϵ t ))

7:

Update: D t+1 (i) = Dt(i) exp(-αtyiht(xi))

Zt

, where Z t is a normalization factor 8:

end for 9: Output the final hypothesis: H(x) = sign T t=1 α t h t (x) 10: end procedure 4 Hardware

The system is composed of a microcontroller module, a WiFi Lora 32 (V2) module, a u-blox Gy-neo6mv2 GPS module, and an ADS 1115 16 analog-digital (A/D) converter. To activate and deactivate the GPS module, a BC 548 transistor was used to cut the ground (GND) of GPS module when a logic low level is applied to its base terminal that is connected to the microcontroller (See Fig. 3).

The current measurement is done through the voltage drop on a 1 Ohm resistor connected between the battery and the system. Two channels of A/D converter are connected between the terminals of the resistor. The microcontroller receives data from the converter and calculates the voltage between terminals. The obtained value is the current, once the resistance is equal to 1 ohm.

The system operates in cycles. The microcontroller activates the GPS and waits until the reception of a valid location. After receiving the valid location, GPS is deactivated and the coordinates are sent using the radio and the system waits five minutes to restart the cycle. The current reading is done each second.

Results and Discussion

The consumed current was estimated using LSTM networks (Figure 4) and a Decision Tree with AdaBoost (Figure 5). As is possible to see, 11,784 current samples were measured. 67% of the data were used for training and the 33% remaining were used for testing. The cycles of GPS activation/deactivation are quite evident on the graphics, showing the 5min period to restart the GPS data acquisition. When the GPS is activated, the circuit consumes 151.48 mA (average), while when deactivated, the circuit consumes 70.666 mA(average). It means that the activation of GPS module increases 114.36% the energy consumption.

In Figure 4, the blue curve represents the measured current, the orange one is the result of the estimation of LSTM during the training period and the green one is during the test period. The loss function used was Mean Squared Error and the Adam algorithm was used as the optimization function. Adam optimization is a stochastic gradient descent method adequate for large data and parameters problems based on adaptive estimation of first and second-order moments and which is computationally efficient, that consumes low memory, and which is invariant to diagonal re-scaling of gradients [START_REF] Diederik | Adam: A method for stochastic optimization[END_REF]. The calculated Root Mean Square (RMS) error for LSTM was 9.88. In figure 5, is possible to see, the blue curve represents the training samples (real data), the green one is the estimation with 1 estimator, and the red one with 300 estimators. The maximum depth of the tree was set to 10. Clearly, the accuracy when using only one estimator is much better, having an RMS error 0.0086.

As is possible to see, both algorithms can predict the behavior (profile of curve) of current consumption. So, the Decision Tree with AdaBoost with 1 estimator has better accuracy than LSTM and LSTM has better accuracy than the Decision Tree with AdaBoost with 300 estimators considering a depth equal to 10. However, if we increase the depth of the Decision Tree with AdaBoost to 20, the curve fits almost perfectly (see figures 6 and 7 for 1 estimator and 300 estimators, respectively) with a small advantage for the case using 300 estimators, presenting and RMS error equal to 0.00015.

While the architecture presented in [START_REF] José | Adaptive MAC layer for interference limited WSN[END_REF][START_REF] Toldov | Experimental evaluation of interference impact on the energy consumption in wireless sensor networks[END_REF] is based on an AT-mega328p microcontroller, which contains an embedded 10-bit ADC, the present work is based on an ESP32 microcontroller with a 16-bit ADC. None of the papers mentioned in section 2 focuses specifically on GPS consumption or on energy consumption forecasting. The models presented in [START_REF] José | Adaptive MAC layer for interference limited WSN[END_REF] (PCA and Markov Chain) have errors between the experimental and the model varying between 1.09% and 15.19%.

Conclusion and Future Works

WSN has a wide number of applications. In our study, we used GPS to have a near real-time bovines location on large farms. The maintenance of the devices in distant and hard access areas may represent an important challenge for WSNs. In this context, is important to have an estimation of the energy consumption of WSN devices to predict their lifetime and have better maintenance schedules.

This paper showed the impact of the GPS module on current consumption and compared two approaches to predict the current consumption of WSN devices, LSTMs and Decision Tree with AdaBoost, with the last one presenting a better accuracy, mainly considering a depth equal to 20.

Possible interesting approaches to be explored in future works are RANdom SAmple Consensus (RANSAC) and Support Vector Machines (SVM).

Figure 2 :

 2 Figure 2: Generic example of a decision tree.

Figure 3 :

 3 Figure 3: Circuit implemented.

Figure 4 :

 4 Figure 4: Current consumed estimated using LSTM.

Figure 5 :

 5 Figure 5: Current consumed estimated using Decision Tree with AdaBoost considering depth equal to 10.

Figure 6 :

 6 Figure 6: Current consumed estimated using Decision Tree with AdaBoost considering depth equal to 20 and 1 estimator only.

Figure 7 :

 7 Figure 7: Current consumed estimated using Decision Tree with AdaBoost considering depth equal to 20 and 300 estimators.


text:
Wilson R S Silva

Diego L Cardoso

Aerial Base Station Allocation Strategy

Unmanned aerial vehicles (UAV) serving as aerial base stations (ABSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, point-failure in existing infrastructure, or disasters. However, positioning, signal coverage is still a challenge for this technology. In this work we propose a UAVbs allocation strategy to provide wireless signal to dynamically distributed users. Experimental results show that the proposed using swarm algorithm design maximizing the number of connected devices inthe UAV-BS. * Designed and implemented the class style † Did numerous tests and provided a lot of suggestions

Introduction

Aerial Base Stations (UAVs) serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices. The adjustable altitude and mobility of UAVs make them suitable candidates for flexible deployment as aerial base stations (UAV-BSs) [START_REF] Omoniwa | Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning[END_REF] UAVs are one of the supplements of 6G. We can use drones in 6G as mobile hot spots during disaster situations, when the ground base stations (BSs) or even the electrical infrastructure are damaged and when the users are out of service, according to work [START_REF] Malkawi | 3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices[END_REF]. Recent popularity of UAVs and all of these potential applications, integrating UAVs in mobile networks is still a challenging topic. In this context, several issues still remain, such as determining the optimal deployment location of multiple UAVs, designing their optimal trajectory [START_REF] Ozturk | Clustering Based UAV Base Station Positioning for Enhanced Network Capacity[END_REF].

In this paper we propose a solution to determine the optimal placement of multiple UAVs scenario. Based on that, a simulation scenario is built, in which an urban area is considered and user traffic demands are generated. we propose the utilization swarm algorithm to find the optimal placement of multiple UAVs BS.

The remainder of this paper is as follows. Section II introduces the proposed System Model. Section III shows the simulation results, while Section IV concludes the paper.

Proposed System Model 2.1 UAV Base Station

According to work [START_REF] Ozturk | Clustering Based UAV Base Station Positioning for Enhanced Network Capacity[END_REF] the UAV is carrying a directional antenna, and it has a coverage wireless. This is illustrated in Fig. 1, where is the directivity angle, H is the UAV height and R is the radius of coverage. The model as which considers free space path loss between the user and the UAV, which depends on whether or not there is line of sight (LOS) in the link between the UE and the UAV. Where f is the carrier frequency, d is the link distance, c is the light speed, as shown in work [START_REF] Ozturk | Clustering Based UAV Base Station Positioning for Enhanced Network Capacity[END_REF].

In the work of [START_REF] Wa'ed Malkawi | 3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices[END_REF], he considers that there is a transmission between a user allocated and a drone. Then each user's data rate is that found from.

Problem formulation

UAVs are equipped with antennas capable of providing network access to mobile users. According to the height of the UAV, it will have a certain cellular coverage area. The problem is finding the ideal height, coverage radius and power to provide access to mobile users on the ground. This is an optimization problem. Based on this, the following cost function is proposed approach .

min u u=1 (h) (1)
subject to: p < P ower (2)

r < Radius ( 3
)
Where U is sum the of UAVs bases stations. P is the signal Power. h is the Height of the uav and r is comunication Radius. To provide access to the network for users, the UAVs must remain at a certain height, signal strength and communication radius.

Allocation Strategy

To solve the above optimization problem, which consists of finding the ideal height, power and radius of the UAVS BS. The proposed algorithm consists of: 1. user cluster using kmeans; 2. for each cluster (i) 3. optimize uav height, power and radius 4. end for 5. plot the solution

Simulation and results

In our experiments, We consider 5 UAVs BS with backhall dedicated. Assume that there are 500 stationary users in the specified geographical area of 500x500 distributed in a uniform. The simulation are performed in Matlab®2022.

The figure below shows the allocation of users in the chosen scenario. There is a central BS that provides the backhall for the UAVs. Each UAV BS provides wireless signal to distributed users.

Conclusion

In this paper, the problem the allocation optimal for multiple UAVs BS in is studied. We formulate the problem which finds the optimal placements, radius and power for multiple UAVs BS using swarm algorithm. This propost, allocation more than 98 percent of users in the chosen scenario. In future, we will investigate others algorithm to optimal of UAVs to cover the movable user in the area remotes.

Publication Agreement for Papers Accepted for

Publication in ADVANCE '2020 Proceedings

Please fill in this form and submit it in a pdf format (with your final version of the paper) using your easychair account: If any of the above terms and conditions does not apply to your work or you do not agree with any of these provisions, please inform the ADVANCE Workshop Organizing Committee immediately at the following address: E-mail: advance2020@easychair.org

Figure 1 :

 1 Figure 1: UAV coverage wireless.

Figure 2 :

 2 Figure 2: UAV coverage wireless [4].

Figure 3 :

 3 Figure 3: Allocation the UAVs BS.

10th

  International Workshop on ADVANCEs in ICT Infrastructures and Services

Figure 4 :

 4 Figure 4: Power UAV BS coverage wireless.

Figure 5 :

 5 Figure 5: Percent of users served by each UAV BS.

  : UAVBS Allocation Strategy Using Swarm Algorithm AUTHOR(S): Wilson Rogério Soares e Silva PUBLICATION TITLE: Proceedings of ADVANCE Conference By submitting your work for publication in the Proceedings of Sixth International Workshop on ADVANCEs in ICT (ADVANCE), you have agreed, by default, to the following terms and conditions: (a) The author and coauthor(s) must complete this form and submit it via easychair along with the camera-ready. If the paper is a work made for hire and the author or coauthor(s) do not have the authority to sign the paper, the owner of the paper must also sign the form. (b) You grant the publisher of ADVANCE Proceedings a right to publish your work and to distribute the published work according to the relevant laws and regulations governing publications. (c) You must be either the owner of the work or have obtained permission from the owner of the work (e.g. your employer) to publish the work. You have agreed to take the responsibility to solve any disputes that may arise anytime in the future with regard to the ownership of the work and the right of publishing the work, including penalty and compensation payment. (d) If excerpts from copyrighted works are included in your paper, you must have obtained written permission from the copyright owners and show credit to the sources in your paper. You have agreed to take the responsibility to solve any disputes that may arise anytime in the future with regard to copyright materials that you have used in your work.

10th International Workshop on ADVANCEs in ICT Infrastructures and Services


text:
Joyce Quintino
email: joycequintinoalves@alu.ufc.br

Carina T De Oliveira
email: carina@lar.ifce.edu.br

Rossana M C Andrade
email: rossana@ufc.br

Blockchain Security in the Internet of Things: Literature Review

Keywords: Internet of Things, Security, Blockchain, Consensus Protocols

The Internet of Things (IoT) allows different devices in our daily routines, such as refrigerators, light bulbs and autonomous cars, to be part of the Internet. Accordingly, the amount of data circulating in the network increases. Thus, a secure and reliable IoT environment becomes essential to avoid security vulnerabilities. In IoT, resources are limited and existing security solutions used in the traditional Internet can be ineffective, making data security still a challenge. In this scenario, Blockchain emerges as a promising technology to improve security in IoT, as it enables the recording of data in a decentralized, encrypted and immutable way with the consensus of the network participants in a packet data structure called a block. In Blockchain, the consensus is a protocol responsible for maintaining the state of the network and allowing the addition of new blocks with data. However, many consensus protocols require high computational power and power consumption to solve the mathematical problems that make possible data records in the blocks. Since many devices have low processing power in the IoT environment, developing new consensus approaches is needed to use Blockchain in IoT effectively. Therefore, this paper presents the results of a literature review about the use of Blockchain technology, specifically consensus protocol approaches, in IoT security.

Introduction

The Internet of Things (IoT) is a network of objects and embedded devices connected to the Internet [START_REF] Minerva | Towards a definition of the internet of things (iot)[END_REF]. IoT connects devices of our everyday life, such as refrigerators, light bulbs, wearables devices and autonomous cars, through the Internet. According to [START_REF] Fan | Iotdefender: A federated transfer learning intrusion detection framework for 5g iot[END_REF], the number of these devices will reach about 25 billion by 2025. Data that comes from IoT devices can be useful, for example, to connect patients to doctors, enable more efficient farming, aid decision-making in smart environments, optimize the industrial sector, and contribute to economic growth. So, the IoT has significant benefits that contribute to society.

On the other hand, as the amount of connected devices increases, so does the amount of data traversing the network and, thus, a secure and reliable IoT environment becomes essential to avoid security vulnerabilities [START_REF] Ahmad | Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using unsw-nb15 data-set[END_REF]. However, the limitations of IoT devices in computing power, storage, and power consumption hinder the development of a secure IoT. Existing security solutions used in the traditional Internet can be ineffective [START_REF] Makhdoom | Anatomy of threats to the internet of things[END_REF]. In this way, IoT devices are subject to several security threats, considering generic vulnerabilities for Internet-connected devices and specific threats inherent to the technology of these devices [START_REF] Waheed | Security and privacy in iot using machine learning and blockchain: Threats and countermeasures[END_REF].

In this scenario, Blockchain emerges as a promising technology to improve security in IoT, as it enables the recording of data in a decentralized, encrypted and immutable way with the consensus of network participants in a packet data structure called block [START_REF] Nasir | A survey on blockchain technology: Evolution, architecture and security[END_REF]. Considering that many devices have low processing power in the IoT environment, there is a need to develop new approaches to consensus protocols, as the current versions are not suitable for IoT due to the high processing power required [START_REF] Singh | A survey and taxonomy of consensus protocols for blockchains[END_REF].

This paper presents the results of a literature review for using Blockchain technology, specifically consensus protocol approaches, to improve the Internet of Things security. We hope to bring to the community the state of the art about consensus protocols, the low-cost consensus approaches, applications and mechanisms, which have been developed to improve security in IoT environment, as well as the most frequent attacks related to Blockchain consensus, where consensus solutions are applied, and what tools are used for Blockchain simulations, development, and testing.

Our literature review follows the precepts of a systematic mapping [START_REF] Kitchenham | A systematic review of systematic review process research in software engineering[END_REF] and the PICOC (Population, Intervention, Comparison, Outcome, and Context) structure, which is a conceptual model to support the research questions [START_REF] Wohlin | Experimentation in Software Engineering[END_REF]. Furthermore, the Parsif.al1 tool was used to document the review process.

We selected three well-known digital libraries to find the relevant papers for this research: ACM Digital Library2 , Scopus3 and IEEE Xplore4 . In the research for the most relevant papers published in recent years, this review considered papers published from 2019 to October 2022. To find the papers that are part of this review, a search string was formulated and can be viewed on the github repository 5 . We defined five research questions (RQ) to address in the literature review that are presented in Section 3.

The selection process of the papers was initiated considering the defined inclusion and exclusion criteria. The inclusion criterias are papers about low-cost consensus used to improve security in blockchain-based IoT and english papers. The exclusion criterias are extended abstracts, unrelated papers about low-cost consensus used to improve security in blockchain-based IoT, secondary studies (surveys and systematic mappings) and papers published before 2019. Also, others two filters were considered to improve the selection of the papers. The first filter involves reading the papers' titles, abstracts, and keywords. The second filter is the complete reading of the papers to fill in the form with the research questions. RQ1 -What are the approaches/applications or low-cost consensus algorithm/protocol mechanisms for improving security in the Blockchain-based Internet of Things? We categorized the research according to the focus of the paper's proposal. The categories defined are application, mechanism, or approach. The application category in this work means using a consensus protocol already known in the literature to test its performance or compare it with others in the IoT context. Mechanism represents new proposed consensus protocols. Finally, the approach represents a solution based on some consensus protocol modified for IoT. Among the 54 selected, 33 papers were classified as mechanisms, 16 were classified as approaches, and 5 were classified as applications. Figure 2(a) shows the number of approaches, applications, and mechanisms.

Results

RQ2 -What is the main focus of the low-cost consensus approach/mechanism/application? Some consensus solutions found are aimed at integrity, authenticity, and access control. According to [START_REF] Stallings | Cryptography and Network Security: Principles and Practice[END_REF], integrity is defined as the property in which information has not been altered unauthorizedly. Authenticity is defined as the property of being genuine and capable of being verified and trusted. Access control is limiting and controlling access to systems and applications through links. According to our research, it was observed that 6 of the papers focus on Authenticity, 4 papers on Access Control, and 1 on Integrity. Also, 43 papers have this diversified response and were classified as Others. RQ3 -What level/layer is the low-cost consensus solution applied? This question identifies at which level/layer the consensus solution is applied. The results show that only 4 specified the layer. More specifically, 1 of the solutions applied at 3 different layers (Physical, Blockchain, and Application), and 4 papers focused on the Blockchain layer. The remaining 49 papers did not specify which layer they suit. Figure 3(a) shows how many papers have specified the layer.

RQ4 -What are the attacks associated with blockchain consensus? The results show that 21 papers cited one or more types of attacks, while 33 cited no types of attacks at all. The most frequent attacks are Denial of Service (DoS), Distributed Denial of Service (DDoS), Sybil, Majority Attack (Majority, also known as 51% Attack), Eclipse, Double-spending, Spoofing, Replay, and Man in the Middle (MITM). Twenty-one papers have mostly cited DDoS and Sybil attacks. In the future, a study based on these two types of attacks can be conducted to determine how they affect Blockchain-based IoT security. Figure 3 RQ5 -What are the tools used? It was observed that some of the papers used tools to test, implement, and validate the solution. Among the 54 accepted papers, 25 papers used one or more tools. To facilitate the presentation of the tools, we have grouped them according to their context of use. The tools are grouped into the following categories: blockchain platforms, simulators, Integrated Development Environment (IDE), software testing, network testing, and browser extension. Table 1 shows the classification of the tools according to these categories.

Category

Final Remarks

The results of this literature review suggest that new consensus solutions should be developed to run on lowprocessing IoT devices. Another recommendation is to study how new consensus solutions can prevent Sybil and DDoS attacks to avoid future vulnerabilities. For future work, we intend to study how the consensus solutions proposed by the selected papers improve the IoT security, and to identify which methodologies are used for developing consensus protocols.

Figure 1 (

 1 Figure 1(a) shows the total number of papers selected from each base and the number of papers accepted at the end of the selection process. It is noticeable that most of the accepted papers are from IEEE Xplore. With the support of the Parsif.al tool, 35 duplicates papers were identified. As a way to validate the 35 duplicates, the titles of each paper were checked. After the duplicates were removed, 249 papers remained, which went through the remaining stages of the review. After the selection phases, 195 papers were rejected, and 54 were accepted.Figure 1(b) presents the distribution of the accepted papers per year. Among the 54 accepted papers, 8 papers were published in 2019, 12 papers in 2020, 14 papers in 2021, and 20 papers in 2022. This result shows a growth in the number of publications in recent years on low-cost consensus protocols for improving the security of the Blockchain-based Internet of Things.

Figure 1 (

 1 Figure 1(a) shows the total number of papers selected from each base and the number of papers accepted at the end of the selection process. It is noticeable that most of the accepted papers are from IEEE Xplore. With the support of the Parsif.al tool, 35 duplicates papers were identified. As a way to validate the 35 duplicates, the titles of each paper were checked. After the duplicates were removed, 249 papers remained, which went through the remaining stages of the review. After the selection phases, 195 papers were rejected, and 54 were accepted.Figure 1(b) presents the distribution of the accepted papers per year. Among the 54 accepted papers, 8 papers were published in 2019, 12 papers in 2020, 14 papers in 2021, and 20 papers in 2022. This result shows a growth in the number of publications in recent years on low-cost consensus protocols for improving the security of the Blockchain-based Internet of Things.

Figure 1 :

 1 Figure 1: Number of accepted papers.

Figure 2 (

 2 b) represents the division of the categories by solution focus. This part of the study shows a trend in research toward authenticity focused solutions.

  (a) Applications, approaches, and mechanisms (b) Main focus of the solutions

Figure 2 :

 2 Figure 2: Research Questions RQ1 and RQ2.

  (b) shows the frequency of the mentioned attacks.

  (a) Level/layer of the low-cost consensus solution (b) Frequency and types of attacks

Figure 3 :

 3 Figure 3: Research Questions RQ3 and RQ4.

Table 1 :

 1 Tools categories.

		Tools
	Blockchain Platforms	Ethereum, Hyperledger Fabri, Hyperledger Sawtooth and Ganache
	Simulators	NS-3, NS-2 and TeraSim
	IDE	Remix IDE online
	Software Testing	HPE LoadRunner and Caliper
	Network Testing	Fabric test blockchain
	Browser extension	Metamask

* CNPq's Productivity Scholarship in Technological Development and Innovative Extension -DT -Level 1D

https://parsif.al/

https://dl.acm.org/

https://www.scopus.com/

https://ieeexplore.ieee.org/Xplore/home.jsp

https://github.com/JoyceQuintino/masterResearch/blob/literaturereview/searchstring