<figure><img src="image_23.png" /><figcaption>Figure 4.4: MAE of PM<sub>2.5</sub> concentrations estimation for each sensing campaign</figcaption></figure>

<table><caption>Table 4.2: Average execution time of 1 iteration for KNN, MLR, XGBoost, and MLP</caption><thead><tr><th>Model</th><th>KNN</th><th>MLR</th><th>XGBoost</th><th>MLP</th></tr></thead><tbody><tr><td>Average execution time (seconds)</td><td>0.056</td><td>0.035</td><td>1.86</td><td>7.6</td></tr></tbody></table>

training set is plotted. Results indicate that the impact of the size of training set size could vary, depending on the model. For instance, MLR becomes less sensitive to the fraction of training set after 30%, while the MLP shows more variation. KNN and XGBoost have globally the same behavior with respect to the size of the training set.

<figure><img src="image_24.png" /><figcaption>Figure 4.5: MAE of PM<sub>2.5</sub> concentrations estimation in function of the training set fraction</figcaption></figure>

Based on these observations, we chose to use KNN for the coming tests with *k* = 4, because it performed better than the other algorithms in almost all campaigns, while having a significantly lower execution time, especially compared to XGBoost and MLP.
## 4.2.2 Sensor data reconstruction

Deploying low-cost WSNs often involves multiple challenges such as loss of connection, sensor failures, etc. In air quality monitoring applications, these events can have a huge impact on the performance of the application, whether it is a simple loss of communication or complete failure of the sensing probe. Thus, it is important to evaluate the capacity of reproducing a sensor's measurements based on the other available sensors of the network. We plotted in Figures 4.6 PM<sub>2.5</sub> concentrations for two different routes of two sensing campaigns. Figure 4.6 (a) shows measurements from sensors 1, 4, and 6 which were sampling the same route during the campaign of October 5th, 2019, while Figure 4.6 (b) depicts measurements from sensors 5 and 6 which were sensing the same route during the campaign of the 12th of October 2019. It can be seen from the plots that sensor readings follow globally the same trend, except for some differences that could be related to improper handling of the sensors or simply to the sensing probes' performance. The Pearson coefficient of correlation was calculated between these sensors, and it confirmed the visual observation as indicated in Table 4.3.

<figure><img src="image_25.png" /><figcaption>Figure 4.6: Example of PM<sub>2.5</sub> concentrations measured by our sensing nodes during the campaign of (a) October, 5th 2019 (b) October, 12th 2019</figcaption></figure>

<table><caption>Table 4.3: An example of Pearson's coefficient of correlation of PM<sub>2.5</sub> for sensors concentrations</caption><thead><tr><th>Date</th><th>Group of nodes</th><th>Pearson's coefficient</th></tr></thead><tbody><tr><td rowspan="3">October 5th, 2019</td><td>nodes 1 and 4</td><td>0.976</td></tr><tr><td>nodes 4 and 6</td><td>0.813</td></tr><tr><td>nodes 1 and 6</td><td>0.807</td></tr><tr><td>October 12th, 2019</td><td>nodes 5 and 9</td><td>0.783</td></tr></tbody></table>

In order to evaluate the possibility of predicting faulty sensor's data based on the remaining operational ones, we imagined a scenario in which the system receives no measurements from a node due to an operation problem. For this, performed a cross validation by taking one sensor's measurements for testing while using the other sensors to train the model. This process was repeated for each measurement campaign. The bar chart depicted by Figure 4.7 presents the MAE of predicting
PM₂.₅ concentrations for each sensor during the four sensing campaigns. The error of prediction varies depending on the considered sensor and the sensing campaign, with the largest MAE (10.31 µg/m³) reached during the campaign of October, 12th for sensor number 2. On the other hand, sensor number 10 gets the lowest error (2.18 µg/m³) during the same campaign.

<figure><img src="image_26.png" /><figcaption>Figure 4.7: PM₂.₅ concentration estimations MAE for cross-validation</figcaption></figure>

These results help to evaluate the performance of the low-cost sensors as they are likely to present some divergence in sensing, despite being of the same type, hence the need for frequent calibration. Moreover, it can be noted from Figure 4.7 that sensors number 10 and 11 for example have in general the lowest MAE across the four campaigns, meaning that they are well represented by the other sensors. It should be noted, however, that these results were obtained using a simple spatial interpolation method (KNN) and explanatory variables that are not completely up-to-date. They must be compared with other methods and using more recent datasets. Nevertheless, these observations can provide some indications of the approach to adopt with a dense network of sensors in function of the predictability of each sensor, such as fixing different sampling rates for different sensors or choosing a scheduling approach in which one sensor stops measuring when it is located in the vicinity of another sensor.

### 4.2.3 Sensing rate VS energy consumption

As explained in the previous chapter, energy consumption is of utmost importance when dealing with low-cost WSNs. In fact, sensing nodes and especially portable ones are often equipped with small batteries to meet multiple requirements in terms of size and budget, which can result in a greatly limited operating time. To cope with that, one can maximize the idle state of the nodes in order to extend the lifetime of the sensor nodes. However, this is often achieved at the expense of the spatio-temporal resolution, which has a significant impact on the knowledge of the phenomenon.

In section 3.3.3 of the previous chapter, we have presented the evaluation of the power consumption for our sensing nodes with multiple configurations. Results revealed that with sampling and transmission rates fixed at 20 seconds and 1 minute, respectively, the average energy consumption of our sensors is 231 mA. However, by
removing the PM sensor, the power consumption drops remarkably to 115 mA because of its integrated fan. Therefore, by carefully deactivating the PM sensor, one could considerably increase the operating time of the sensor.

Based on these results, we estimated the energy consumption for different configurations of sensing rate using a simple energy model. We introduced a sensing duty cycle *D* that represents the fraction of time during which the PM sensing probe is active over the sensing window. For example, if the sensor is active for 30 seconds and off for 10 seconds, then *D* = 3/4. The sensing duty cycle has to take into account the convergence time of the sensor, which is the time needed for a sensor to provide reliable measurements. This value varies from a sensor to another [122]. The formula of the energy consumption is given by:

$$I_{average} = I_{PMON} * D + I_{PMOFF} * (1 - D) \quad (4.1)$$

where *I*<sub>average</sub> is the average operating current of the 3M'air node, *I*<sub>PMON</sub> is the operating current of the node when the PM sensor is ON (231 mA in our case), and *I*<sub>PMOFF</sub> is the operating current when the PM sensor is turned off (115 mA in our case). It is worth mentioning that we do not turn off just the fan of the PM sensor, but the whole sensor instead. In addition, we do not turn off the other sensing probes because their energy consumption is much lower compared to the PM sensor. The GPS receiver is not turned off either because it needs a longer time to get to acquire satellite signals

Furthermore, in addition to evaluating the energy consumption of our nodes with a sensing rate of one sample every 20 seconds, we estimated their energy consumption for a sampling rate of 40, and 60 seconds using (4.1) and a convergence time of 30 seconds for the PM sensor, as indicated in its data sheet [96]. For a rate of 20 seconds, the PM sensor cannot be turned off because it does not have enough time to reach a steady state (*D* = 1). In contrast, the PM sensor can be turned off for 10 seconds and 30 seconds when the sampling rate is set to 40 (*D* = 3/4) and 60 seconds (*D* = 1/2) respectively. Table 4.4 shows the estimated operating current and time of the nodes for the three configurations using a 5100 mAh battery. It can be observed that a small change in the sampling rate can have a significant impact on the node's autonomy.

<table><caption>Table 4.4: 3M'Air node's operating time in function of the sampling rate</caption><tr><td>Sampling rate (seconds)</td><td>20</td><td>40</td><td>60</td></tr><tr><td>Average operating current (mA)</td><td>231</td><td>202</td><td>173</td></tr><tr><td>Average operating time (hours)</td><td>22.07</td><td>25.24</td><td>29.48</td></tr></table>

The performance of estimating PM concentrations was evaluated when reducing the sampling rate. For the first test, we randomly picked 80% of our sensors' data for each sensing campaign to train the model, and three configurations were tested on the training set. In the first configuration, we kept the initial sampling rate (i.e., 20 seconds). The sampling rate was then reduced to 40 seconds (by considering one measurement every two measurements) for the second configuration. Similarly, we lowered the rate to 60 seconds for the third configuration (i.e., taking one measurement every 3 measurements). The temporal resolution for the testing set was kept at 20 seconds and the process was repeated 40 times.
<figure><img src="image_27.png" /><figcaption>Figure 4.8: MAE of PM<sub>2.5</sub> concentrations estimation with three different sampling rates using 80% of data for training</figcaption></figure>

Figure 4.8 shows the MAE of the estimation model for all sensing campaigns, in function of the sensing rate. We observe that even when reducing the sampling rate by a factor of two to three, the estimation model can still achieve acceptable results compared to using the initial rate. Indeed, reducing the sampling rate to 40 seconds resulted in an error 1.57% larger while achieving 14.36% longer operating time. Moreover, by lowering the rate to one sample every 60 seconds the performance of estimation decreased by around 4.64%, but the node managed to save 33.57% more energy. Therefore, the gain in power could outweigh the loss in estimation quality, depending on the addressed application. Depending on the application, this extra battery autonomy may allow sampling more locations and hence, reduce the estimation error even further.

## 4.3 Air quality mapping using dense sensor networks

After highlighting the potential of low-cost environmental sensors through a data analysis of the pollutant concentrations collected during our sensing campaigns. In this part, we break the budget limit and consider a dense deployment of fixed low-cost WSN for air quality monitoring, while comparing regression approaches to data assimilation and taking into consideration multiple aspects, such as high density of sensors, numerical model errors, and sensing errors. However, to achieve this scaling towards data analysis of such dense network, the use of real data is impossible. Thus, the need to develop a generic framework to generate synthetic data and enable the performance evaluation based on these synthetic data.

### 4.3.1 Framework overview

In this section, we present an overview of the methodology that we followed in order to compare different regression and assimilation methods (see Figure 4.9). We propose to first generate multiple ground truth datasets based on simulated concentrations from a physicochemical model and an adequate estimation of the covariance matrix of the simulation errors. We explain in the next section the approach that we use to
estimate the covariance matrix of simulation errors.

Once the ground truth datasets are generated, we generate for each one a large number of observations (pollutant concentrations) in positions where low-cost sensors are deployed. This generation takes into account the ground truth realizations as well as the measurements' errors variance-covariance matrix. Based on the measurement realizations, the simulation values, and the ground truth realizations, multiple regression and assimilation methods can be implemented and compared.

<figure><img src="image_28.png" /><figcaption>Figure 4.9: Overview of the methodological flowchart used for the framework</figcaption></figure>

Regarding the datasets, we consider the simulated NO₂ concentrations in Lyon and the explanatory variables presented in Sections 4.1.4 and 4.1.3, respectively, while focusing on a 2.5 × 2.5 km² zone which corresponds to the center of Lyon and its immediate vicinity (see Figure 4.10).

<figure><img src="image_29.png" /><figcaption>Figure 4.10: The zone of interest (a) map of the center of Lyon and its immediate vicinity (b) annual averages of NO₂ concentrations in 2008 (simulated data)</figcaption></figure>
### 4.3.2 Characterization of the variance of simulation errors

In order to characterize the variance of the simulation errors, we used NO₂ concentration values provided by 16 reference stations in Lyon and compared them to the simulated concentrations of SIRANE. We considered monthly values in both cases. For each simulated value z, we computed the standard deviation of the errors associated to the simulated values in $z \pm 5\mu g/m^3$. Figure 4.11 shows that the model's error standard deviation $\sigma_b$ depends linearly on the pollutant concentration starting from a given threshold $z_0$:

$$ \sigma_b(z) = \alpha(z - z_0) + \beta, z \in [z_0, +\infty] \quad (4.2) $$

Where z is the simulated value. As shown in Figure 4.11, a linear regression with $z_0 = 24 \mu g/m^3$ results in $\alpha = 0.344$ and $\beta = 2.27 \mu g/m^3$ with a high $R^2$ value ($R^2 = 0.94$).

<figure><img src="image_30.png" /><figcaption>Figure 4.11: Error standard deviation vs Simulation Values</figcaption></figure>

### 4.3.3 Ground truth and measurements generation

Based on the variances of simulation errors and correlation coefficients $w_{ll'}$, we generate the variance-covariance matrix. We consider here the correlation coefficient as a function of the distance [123] given as: $w_{ll'} = e^{-\delta d_{ll'}}$, where $\delta$ is the attenuation coefficient of the correlation function, and $d_{ll'}$ is the euclidean distance between locations $l$ and $l'$. Assuming that the simulation error follows a multivariate normal distribution, we generate multiple ground truth datasets based on the simulated values and the computed variance-covariance matrix.

Once the ground truth datasets generated, we generate, for each ground truth set, a large number of observations (measurements) in positions where low-cost sensors are deployed. Sensor measurements are generated using a normal distribution, since the sensing errors are not correlated with each other. Hence, the measurement error variance-covariance matrix that we note $R$ is given by: $R = v_0 I$, where $v_0$ is the vector of measurements' error variance and $I$ is the identity matrix. For each sampled location $l$, the sensor reading is generated using a normal distribution of mean $y_l$ and variance $\sigma_m^2$, with $y_l$ the ground truth value, and $\sigma_m$ the standard deviation of the sensing error.
### 4.3.4 Evaluation of regression approaches

In this section, we compare different mapping strategies using four regression approaches, namely multiple linear regression, k-nearest neighbors, random forest and XGBoost, while taking into consideration the intrinsic characteristics of dense deployment of low-cost WSN for air quality monitoring. We compared these approaches on 30 realizations of ground truth obtained from a simulation with $\alpha = 0.05$.

#### Choice of the parameter *k* for the KNN algorithm

To choose a good value of *k*, we evaluated the MAE for different values of *k*. The results are presented in table 4.5. We noticed that the parameter *k* had no significant impact on the MAE for $k \in [3, 15]$. However, the minimum MAE was reached when *k* = 5. *k* was then set to 5 for the following comparisons.

<table><caption>Table 4.5: MAE vs *k* value of KNN regression ($\sigma_m = 2 \mu g/m^3$, Fraction of deployed sensors = 0.3)</caption><thead><tr><th>k</th><th>3</th><th>5</th><th>7</th><th>9</th><th>11</th><th>13</th><th>15</th></tr></thead><tbody><tr><td>MAE</td><td>3.246</td><td>3.230</td><td>3.234</td><td>3.240</td><td>3.245</td><td>3.251</td><td>3.256</td></tr></tbody></table>

#### Impact of the number of deployed sensors

The aim of this study is to evaluate the performance of regression methods in function of the percentage of deployed sensors, i.e., the fraction of sampled points. For that, we evaluate the estimation MAE of these methods when increasing the fraction of deployed sensors. For each ground truth dataset, all approaches are executed ten times with different measurement realizations (with $\sigma_m = 1\mu g/m^3$). The results, depicted in Figure 4.12, show that all methods present a better performance when the number of sensors increases. Indeed, the more deployed sensors, the lowest the error and the better the estimate. Random forest outperforms all the methods, reaching an MAE of $1.48\mu g/m^3$ when 40% of the points are sampled. It is followed closely by XGBoost, then KNN, and finally MLR.

<figure><img src="image_31.png" /><figcaption>Figure 4.12: MAE vs percentage of sampled points, with $\sigma_m = 1 \mu g/m^3$</figcaption></figure>
## Impact of sensor errors on regression methods

The main characteristic of low-cost environmental sensors, besides their cost and size, is their low-accuracy. Therefore, we assessed the impact of the sensing error by varying $\sigma_m$ from 1 to 7 $\mu g/m^3$, while fixing the number of deployed sensors to 30% of the total number of possible points. Results, plotted in Figure 4.13a, show that the MAE increases for all four methods as the sensing error increases, with XGBoost and random forest being close and outperforming KNN and MLR. However, we can observe different sensitivity to the $\sigma_m$ for each method. Indeed, from the different curve plots, we notice that XGBoost and the multiple linear regression are less sensitive to variations in sensing errors than random forest and KNN. Interestingly, although KNN is relatively close to XGBoost and random forest when the sensing error is small, it quickly deviates from the two as $\sigma_m$ starts increasing. Another interesting observation is that starting from a given error threshold, XGBoost and MLR can outperform random forest and KNN, respectively. These results give an idea regarding the choice of which estimation method to use in function of the sensing error.

<figure><img src="image_32.png" /><figcaption>Figure 4.13: (a) MAE vs standard error deviation, Fraction of deployed sensors = 0.3; (b) MAE of XGBoost method vs percentage of deployed sensors, $\sigma_m \in [1, 3, 7]$</figcaption></figure>

Moreover, we analyzed the behavior of regression approaches regarding the sensing error and the density of the network. In this vein, we computed the MAE of XGBoost while considering three different sensing standard deviation errors ($\sigma_m$ = 1, 3 and 7 $\mu g/m^3$) and varying the percentage of covered points from 1% to 40%. Results reveal in Figure 4.13b that when the sensing error is relatively high ($\sigma_m \ge 3\mu g/m^3$), increasing the number of deployed sensors does not necessarily improve the results, but instead decreases the performance of the mapping when the sensor density exceeds a certain value. In this case, when the sensing error is large, it is not useful to deploy more sensors. The results of the four regression approaches were similar with different sensing errors and sensor density.

### 4.3.5 Evaluation of data assimilation

In this part, we evaluate the performance of the data assimilation approach, namely the best linear unbiased estimation (BLUE), presented in Section 2.7 of Chapter 2, in function of the size of the network (number of deployed sensors), the simulation
error, and the sensing error. Similar to regression methods, the results were obtained
over 30 realizations of the ground truth.

## Impact of simulation errors

As seen in Chapter 2, data assimilation uses the output of numerical models as a base, then considers measurements collected in the field in order to correct the model's estimation. Indeed, these models are not perfect and present some errors due to the complexity of the studied phenomenon and the inputs that are usually averaged in space and time. With this respect, we computed the MAE of data assimilation for two models with $\alpha$ equals to 0.05 and 0.4, a sensing error $\sigma_m = 3$, and a percentage of sampled points ranging from 1% to 40%. We recall that the higher $\alpha$ is, the higher the error of the simulation model. Results depicted in Figure 4.14 show, as expected, that an assimilation with an accurate simulation output (i.e., small $\alpha$) performs better than an assimilation applied to a less accurate physical model. Nonetheless, the two systems benefit from the increasing number of deployed sensors to further correct the model. However, the decrease in MAE is more important for the network with $\alpha = 0.4$.

<figure><img src="image_33.png" /><figcaption>Figure 4.14: MAE of data assimilation in function of the sensing error and the percentage of covered points $\sigma_m = 3 \ \mu g/m^3$</figcaption></figure>

## Impact of sensing errors

Since data assimilation relies on sensor readings to correct the numerical simulations, the sensing errors certainly affect the final output. In order to evaluate this effect, we performed the BLUE assimilation with a fixed $\alpha$ (0.05) and a variable number of sensors, on four networks with a sensing error equals to 1, 3, 5, and 7 $\mu g/m^3$, respectively. As illustrated in Figure 4.15, the MAE decreases as we increase the fraction of deployed sensors. Interestingly, in contrast to regression methods, increasing the number of sensors deployed always helps to correct the model. Indeed, we can observe from the figure that even with a $\sigma_m=7$, the data assimilation still manages to reduce the error of the model, thanks to the consideration of both the simulation errors and the sensing errors.
<figure><img src="image_34.png" /><figcaption>Figure 4.15: MAE of data assimilation in function of the sensing error and the percentage of deployed sensors, $\alpha=0.05$</figcaption></figure>

### 4.3.6 Regression vs data assimilation

In this last simulation, we compare the four regression methods to the BLUE assimilation with different simulation errors. For that, we plot in Figure 4.16 the MAE of all methods while varying the value of $\alpha$ from 0.05 to 0.45. The results show that BLUE provides 40% better air quality estimation compared to random forest, which already have the best performance among the regression methods. This is mainly due to the consideration of the model's error as well as the sensing errors in the assimilation process. It is worth mentioning that BLUE performs very well when the simulation error covariance matrix and sensing error covariance matrix are well estimated.

<figure><img src="image_35.png" /><figcaption>Figure 4.16: MAE of all regression and data assimilation methods in function of $\alpha$, with $\sigma_m = 1 \mu g/m^3$ and 30% of points sampled</figcaption></figure>

The advantage of data assimilation is illustrated by Figure 4.17, which compares a reference heatmap of NO₂ concentrations (left) to two heatmaps estimated using random forests (right) and data assimilation (bottom). The estimated heatmaps were generated using a percentage of coverage of 20%, $\alpha = 0.05$, and $\sigma_m = 3\mu g/m^3$. From the figure, we can observe that data assimilation captures the variations of the pollutant concentrations with higher resolution than random forest, which tend to smooth the heatmap. Moreover, random forest visibly misses some hotspots compared
to BLUE.

<figure><img src="image_36.png" /><figcaption>Figure 4.17: NO₂ concentrations heatmap for the area of interest, (a) a realization of the ground truth (b) random forest estimation (c) BLUE estimation</figcaption></figure>

## 4.4 Conclusion

Through this chapter, we first provided spatial analysis of data collected during four sensing campaigns, using our lab-designed sensor nodes. This analysis allowed us to explore questions related to participatory sensing using low-cost WSN, such as the ability of the network to reconstruct missing data of a faulty sensor, or the gain/loss in energy and sensing quality in function of the sensing rate. We compared four statistical models and highlighted their ability to achieve acceptable performance despite lowering the sampling rate of the sensors. Moreover, we investigated in the last part, the use of dense low-cost WSN in air quality assessment. We proposed a general framework that allows the comparison of different regression and data assimilation approaches based on numerical simulations, while considering the size of the network, the simulation errors, and the sensing errors. We observed that data assimilation is less sensitive to the variations of measurement errors. In addition, we pointed out that
a large number of sensors with high errors is not always good for regression methods. However, this is not the case for data assimilation, especially when covariance matrices of the model's errors and the sensing errors are well estimated.

In the next chapter, we tackle another issue related to participatory sensing, which is the selection of participant routes. We propose two algorithms that take into consideration the accuracy of the sensors and the relationship between participants' routes, in addition to the distance constraint.
# Chapter 5

## Route selection in participatory
mobile sensing

Given the mobile nature of the crowd, mobile crowdsensing platforms need to implement adequate route planning/selection solutions to better guide the crowd through the area of interest and maximize the quality of monitoring.

Route planning and route selection are of great importance in mobile crowdsensing applications. On one side, participants in route planning delegate the construction of their route to the monitoring platform, which will drive participant movements by leading them through specific points of interest. On the other side, in route selection, each participant can have multiple favorite routes (generated using route planner algorithms), and the role of the system is to select the most appropriate path regarding the needs of the task. It is clear that the second method considerably limits the degree of freedom of sensing platforms as they are restricted to choose from already built candidate routes that may inevitably overlap, which results in the same point/area being sampled several times, and thus in data redundancy. Data redundancy can sometimes be beneficial when it comes to low-cost sensors, which by definition present low-accuracy properties. Some monitoring applications can be more tolerant regarding redundant data, in order to mitigate the impact of wrong sampling caused by faulty sensors.

In this chapter, we address the route selection problem in a participatory sensing context, while focusing on the application of air quality monitoring to validate our proposal. We first provide an overview of some related research work discussing route selection in crowdsensing applications. We then introduce the scenario we consider in this study. Afterwards, we present two route selection algorithms that take into consideration the low accuracy of the sensors. The first algorithm computes the similarity between the different possible routes in order to find a combination of participant routes that maximizes spatial coverage. The second algorithm takes advantage of clustering to evaluate the resemblance between the points of the area of interest in order to cover as many distinct points as possible. Finally, we evaluate the performance of the proposed algorithms and compare them to other baseline route selection algorithms.
## 5.1 Route selection : a paradigm of great interest in mobile crowdsensing

Mobile crowdsensing have raised questions regarding the movement of the crowd and how to maximize coverage while taking into consideration the constraints of the participants and the addressed application. This has opened the door for new research studies that harness citizen's mobility to improve the mapping of environmental phenomena.

Synthetic measurements were used in [124] to predict a map of NO₂ concentrations between the 12th and 25th of February 2018 in the city of Marseille, France, while considering up to 4500 bike-tracks randomly generated. The simulated concentrations were generated using a numerical model with a spatial resolution of 25 × 25km², while the explanatory variables (including number of trees, buildings, configuration of the road network, etc.) of the area were collected from “Open Street Map”. Fictive bike tracks were constructed using a cyclist route planner API, with a distance following a normal distribution of mean 2.5km and standard deviation 4.5km. The resulting dataset served to train and compare the performance of three estimation models, namely ordinary kriging, multilayer perceptron neural network, and a generalized additive model (GAM), which is the generalization of the linear regression to non-linear systems. The evaluation of the models used the simulated map generate by the model (without the measurements along the selected bike tracks) as a testing set. The performance assessment mainly evolved around the impact of the number of tracks and the spatial resolution of the measurements on the prediction error of the three models. The observed results showed that while the Mean Absolute Error (MAE) of kriging continued to decrease as the number of tracks increases, the RMSE for MLP and GAM showed almost no improvement with more than 100 tracks. The same observation was made for the coefficient of correlation.

In multiple crowdsensing applications, the system owner pays the participants for their collected data. In these use cases, the participants often perform sensing tasks at specific locations rather than continuous sampling. The payment is generally related to the number of accomplished tasks, their priority, the travel distance/duration, or the quality of the collected data. In this context, given a budget, the system owner optimizes the mobility of the crowd in order to maximize the number of completed tasks or the coverage of the study area, while satisfying the constraints of the participants.

A three-phase routing algorithm is proposed in [125] in a task assignment context. A task is characterized by its location and its added value to the system. In this work, the researchers do not rely on a third-party routing API, but instead, they construct the participant routes from scratch. The proposed algorithm is composed of three phases, where in the first phase the algorithm iteratively constructs the route from the origin to the destination, by considering at each step the task that has the largest added value. The task is assigned to the user if his device energy is enough to travel from the current location to the target task, then to the destination point. The second phase of the algorithm is similar to the first one, except that it starts the construction backwards (i.e., from the destination point to the starting point of the participant). The last part of the solution is the selection of the route with the highest added value. This solution is appropriate if we consider specific sensing locations and a limited budget constraint.

A crowd-based urban sensing framework is presented in [126] with the objective of maximizing area coverage for noise sensing. It uses a graph-based task assignment
algorithm and an objective function to maximize data coverage with a limited budget constraint. An urban sensing task is defined as a sequence of collecting locations with corresponding time intervals. The assignment of a task is done by estimating the travel time needed for the participant traveling from its origin to the task location and the travel time from this location to the destination of the participant. The authors also propose a recruitment mechanism that randomly selects participants from a candidate pool and then replaces them gradually by other candidates left in the pool, until no improvement of coverage is possible anymore.

Instead of suggesting a whole route to a participant, the work in [127] takes a different approach to maximize the coverage quality while avoiding redundant data. The study proposes a reverse greedy algorithm that selects only a subset of segments from the participant's route, based on the cost of the task and the available budget. The selected segments are those along which the participant performs the task. Thus, the participant will be rewarded according to the selected segments instead of his whole route. To achieve such result, the algorithm eliminates redundant segments. Two segments are considered redundant if the distance separating their respective endpoints is less than a predefined distance. After each round of trimming, if the total cost is greater than the available budget, a new round of trimming starts with a larger threshold distance to further reduce the number of selected segments. Two coverage metrics were used to evaluate the solution, namely the coverage percentage which indicates the ratio of covered to non-covered grids, and the uniform degree which describes the spatial distribution of selected segments.

TaskMe [128] identifies two bi-objective optimization problems. The first aims at maximizing the number of accomplished tasks and minimizing the total traveling distance in a situation where there are few participants and many tasks. To solve this problem, the authors present two algorithms based on Minimum Cost Maximum Flow models, where the first one selects all tasks as candidate whereas the other one takes into account only the nearest tasks to a given user. In contrast, the second optimization problem is designed for the case where there are more participants and only few tasks, and its objective is to find the set of participants to complete all tasks while minimizing the total incentive payments and the total traveled distance, knowing that the incentive is inversely proportional to the traveled distance. Since the two objectives are contradictory, the authors propose to convert the multi-objective problem to a single-objective one by either introducing weights to each objective or considering one of them as a constraint in the optimization model.

Gong et al. [129] addressed the path planning problem to maximize the total task quality in a scenario where users and tasks arrive dynamically. Each user has a limited distance budget and has to register his starting and destination points upon arrival. One of the proposed algorithms in this work selects the tasks that lead to the largest gain-cost ratio, one by one in a greedy manner, as long as they satisfy the travel distance budget of the user. An alternative solution tends to guide users to task-dense areas to maximize the cost-gain ratio. In addition to that, the authors designed an algorithm that takes into account the impact of a candidate task on the travel distance budget, in order to leverage tasks with low impact on the available budget. Given a candidate task, this algorithm evaluates the possibilities of the next step by computing the distance from this task to the others. The task that does not distract the participant from the rest of the tasks is selected. This algorithm yielded better performance compared to the other two solutions.

In [130], the goal is to maximize the sub-profit in each time slot to approximately approach the maximum profit of all slots for a given task. A task is represented as
a number of sensing locations during a certain period, the area of interest is divided into N cells, and the task duration into M time slots with the same length. Each participant in this scenario is guided to move along the shortest path. However, to avoid similar routes and maintain a stable dispersed distribution, the authors use 2D entropy (E) to guide participant distribution. The more decentralized the participants, the higher the entropy value. The solution first randomly selects a number of cells and compute E, then repeats the selection process for multiple. At last, the participant distribution with the largest E is selected as an approximately optimal solution.

Although the aforementioned studies explore mobile crowdsensing capabilities to accomplish sensing tasks, the majority of them focus more on specific sensing locations rather than sampling the entire zone. This prevents performing continuous sampling, which considerably limits the potential of the crowdsensing and significantly reduces the spatial resolution, especially in air quality monitoring applications where there is no sensing range. Moreover, the majority of these work do not take into consideration the inaccuracy of the sensors, which is one of the main challenges when it comes to monitoring air pollution using low-cost environmental sensors. Furthermore, in a participatory sensing context, participants may use their own sensors, which adds a new constraint that is sensor heterogeneity. Therefore, taking into account sensing errors during the participant recruitment phase and the route selection process should be highly considered.

## 5.2 Problem statement

In this section, we describe the scenario we focus on as well as the global objective we aim at. Afterwards, we give a mathematical formulation of the problem to solve it.

### 5.2.1 Scenario Description

In this part, we focus on a scenario in which multiple participants are equipped with heterogeneous low-cost environmental sensors to measure a specific phenomenon in a delimited area. Each participant has a starting point and wants to get a path to reach his destination using a routing service. The participant is also willing to take a path that contributes to the knowledge of the studied phenomenon, without deviating too much from the optimal path.

### 5.2.2 Objective

Our global mission, in this context, is to suggest to each participant a route that might not necessarily be the optimal/shortest path, but does improve the estimation while being acceptable in terms of journey distance/duration. This implies taking into consideration not only the length of the participant's routes and their relationships with other participant routes, but also the accuracy level of his sensor. In other words, our goal is to find a best combination of routes that allows the system to reduce the estimation error while still satisfying participants' constraints in terms of trip distance/duration.
### 5.2.3 Mathematical notation

Let $U = [u_1, u_2, ..., u_n]$ be the set of participants, $S = [s_1, s_2, ..., s_n]$ the set of their respective sensors, $\sigma_k$ the standard error of the k-th sensor, and $P_k = [p_{k1}, p_{k2}, ..., p_{kq}]$ the set of possible paths for the k-th participant. The goal is to select for each participant $u_k$ a path $p_{kj}$ from his possible $q$ routes, knowing that he is equipped with a sensor $s_k$ which has a standard error $\sigma_k$.

A brute force solution would be to test all the possible combinations (i.e., $q^n$ combinations). However, as the space of solutions grows exponentially with the number of participants, implementation of such solution in a real life scenario is impractical. To cope with that, we rely on heuristics that are not optimal, but have smaller solution spaces, hence running much faster than the exhaustive search.

## 5.3 Traditional route selection algorithms

Through this contribution, we address route selection algorithms in the context of participatory air quality sensing. The objective is to maximize the quality of the prediction where there are no measurements, using a spatial interpolation method and an efficient route selection algorithm.

Generally, route planning APIs always offer either the shortest path or $q$ possible paths, while taking into consideration multiple parameters (e.g., traffic condition, waypoints, type of path, etc.). Route selection consists of choosing for each participant a path among the $q$ proposed ones. This is a key element as it determines the geographical zones that will be sampled, which highly impacts the estimation quality. We consider in this part three traditional routing approaches as baseline:

* **Shortest-path-based routing (SPR):** This algorithm prioritizes participant comfort by reducing the travel distance for all participants, through suggesting the shortest route among the $q$ paths. In SPR, all route selections are performed independently for each participant.

* **Longest-path-based routing (LPR):** In contrast to SPR, this approach aims at maximizing the set of points to visit. It suggests to all participants the longest route possible among the $q$ suggested routes. It is to be noted that in this contribution, we consider the longest path within a predefined stretch factor with respect to the shortest path. Suggesting the longest-path for all participants will intuitively expand the set of collected measures, and hence, improve the estimation quality. However, the correlation between paths and especially the information provided by different points, which is very complex to evaluate, means that this method does not always maximize the quality of the spatial prediction.

* **Random routing (RR):** For each participant, randomly select a route among the $q$ possible ones without taking into consideration its length, the already selected routes for previous participants, or the accuracy level of the sensors.

All three algorithms cited above are easy to implement, but their downside is that they do not take into consideration the correlations between participant routes or the accuracy levels of the sensors. In fact, the routes can pass through similar points or even different points that provide redundant information (points with similar characteristics). These observations feed the need for more sophisticated techniques,
that will efficiently attribute routes to the participants that are not necessarily the longest or the shortest ones, but which bring as much diversity in the dataset as possible. This can be achieved by introducing metrics and techniques that help to choose the most distinct routes possible and thus bring more information.

## 5.4 Similarity-based route selection

The level of accuracy differs from a sensor to another, even between sensors of the same type. Therefore, to offer a good estimation, platforms should have an efficient routing approach by also taking into consideration the heterogeneity of sensors quality. In this regard, our algorithm processes first the participants with the most accurate sensors. Hence, instead of independently suggesting routes for the participants, the path provided to those with lower quality sensors are adapted according to the choices made for participants with more accurate sensors. In the example depicted in Figure 5.1, participant A has a better sensor than participant B. As a result, the path of A is chosen first before the path of B. In addition, the route of participant A overlaps with one of the two possible paths for B (along the bold black segment). Our similarity-based routing therefore selects the non-overlapping route B2 in order to maximize the coverage of the map.

<figure><img src="image_37.png" /><figcaption>Figure 5.1: An example of overlapping segments</figcaption></figure>

Given a pool of participants sorted according to their sensors' accuracy. The similarity-based algorithm considers the *q* possible routes of the first participant (q routes satisfying a given distance threshold between the shortest and the longest path) and selects the longest one. After that, the algorithm iterates over the next participants in the pool and chooses for each one the route that has the lowest similarity with the already selected routes from the previous participants. Every time a route is selected for a participant, the latter is removed from the pool and the algorithm moves to the next participant in a greedy manner, until the pool is empty. In Figure 5.1, the route *B*₁ would be privileged over *B*₂ for user *B* as it has a lower degree of similarity. This process is illustrated by Algorithm 1.

The calculation of the similarity percentage between routes has a major role in finding the most distinct routes possible, and can be computed in multiple ways. We have mainly explored two commonly used metrics in image segmentation, namely the
Jaccard index (also known as Intersection over Union or IoU) [131] and Sørensen-Dice coefficient (also known as Dice Similarity Coefficient or DSC) [132]. Considering two participant routes A and B, the formulas for these two coefficients are given as follows:

$$IoU_{A,B} = \frac{|A \cap B|}{|A \cup B|} \qquad (5.1)$$

$$DSC_{A,B} = \frac{2 \times |A \cap B|}{|A| + |B|} \qquad (5.2)$$

Where $|A|$ and $|B|$ are the areas of route A and route B, respectively, $|A \cap B|$ the area of the intersection between the two routes (i.e., area of overlapping), and $|A \cup B|$ the area of the union between route A and route B (with no duplicates). It is obvious that these two scores are positively correlated. Thus, we only consider the DSC in this study.

**Algorithm 1: Similarity-based route selection**

**Input:** $U$: The set of users

**Output:** $P$: the set of selected paths

**Initialization:** $P \leftarrow \emptyset$ // initialize the set of selected paths

// Order the set of users based on the accuracy of their sensors

$U \leftarrow order(U, descending\_accuracy)$

// take the longest path for the first user in $U$

$P \leftarrow longest\_path(u_0)$

for $u \in U$ with $u \neq u_0$ do

// select the path with the least similarity

$$s\_path \leftarrow \arg\min_{p \in Paths(u)} Similarity(p, P)$$

$$P \leftarrow P \cup s\_path$$

return $P$

## 5.5 Cluster-based route selection

Instead of computing the similarity between participant routes, this approach focuses on the similarity between the points of the map. The main idea is to regroup the points of the map not based on the spatial distance separating them but on explanatory variables related to surrounding conditions (such as distance to routes, meteorology, etc.). To achieve such a goal, we have opted for the agglomerative hierarchical clustering, which is a widely used technique of hierarchical clustering [133], in order to create groups of similar points that might be far from each other, but present similar properties. First, each point of the map is assigned to an individual cluster, and we calculate the distance between the clusters based on the independent variables. Then, clusters are merged successively while minimizing the sum of squared differences between the clusters being merged. As a result, all points of the map are classified into $c$ clusters. Then, for each route, we calculate the number of
## 5.6 Validation

### 5.6.1 Methodology

In order to validate our proposal, we followed the methodology presented in Figure 5.3, by considering simulated NO₂ concentrations as a reference map. We first start with a pool of participants, each with a starting and destination points. Each participant has a NO₂ sensor with its own standard error σₖ. Instead of constructing the different routes for each user, we rely on a routing service that provides us with several alternative paths whose length is within a predefined stretch factor of the shortest path. After that comes the route selection phase, during which the algorithm suggests a path for each participant based on the reliability of the sensors and the relationship between the routes. Following that, synthetic measurements are generated following the same approach explained in Chapter 4). For each location l visited by a participant's route, a synthetic measurement is generated using a normal distribution of mean yl and variance σ²ₖ, with yl being the reference concentration at the location l. Finally, these synthetic observations are passed to three spatial interpolation models presented in Chapter 2, namely multiple linear regression (MLR), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost), in order to produce a predicted map. The latter is then compared against the reference map to evaluate the impact and performance of the route selection approaches.

<figure><img src="image_38.png" /><figcaption>Figure 5.3: The general methodology used in validation</figcaption></figure>

### 5.6.2 Study area and reference map

We consider in this study an area of interest of 25km², which corresponds to the center of the city of Lyon and its immediate vicinity (see Figure 5.4a).

In order to build a reference map of NO₂ concentrations, we consider the two datasets presented in the previous chapter, i.e., the simulated NO₂ concentrations map of Lyon city (see Figure 5.4b) with a spatial resolution of 20m × 20m, and the set of 40 explanatory variables for each point on the map.
<figure><img src="image_39.png" /><figcaption>Figure 5.4: (a) Area of interest (b) reference heatmap of NO₂ concentrations (simulated data)</figcaption></figure>

### 5.6.3 Participant routes and sensor measurements generation

In our use case, a participant is represented as a triple consisting of his starting point, his destination, and the accuracy level of the sensor he carries. First, we generate 200 random participants with a distance between the starting and destination ranging from 1km to 5km. To match a realistic scenario in which participants have heterogeneous sensors, the accuracy of participant sensors varied between $1\mu g/m^3$ and $20\mu g/m^3$. Then, we use an existing routing API to generate a minimum of 5 alternative routes for each participant to meet a realistic scenario and to have a substantial search space. Furthermore, to avoid ending up with very long alternative routes, we only consider routes that are at most 30% longer than the shortest path.

In the second step, synthetic sensor measurements are generated using the standard errors of the sensors and a normal distribution at each point of the map that a participant's route passes through.

### 5.6.4 Computing the similarity

In order to implement the similarity-based algorithm, we need to compute the similarity metric between the different routes. This metric should consider segments that do not really overlap but are very close to each other. This may happen when two participants pass by the same road but in opposite directions or parallel sidewalks. For this reason, we build a buffer around each segment of a route, and then compute the similarity between the buffers. The size of the buffer highly influences the similarity metric. Indeed, the larger the buffer is, the higher the similarity value. This size should also be adapted to the spatial resolution of the available data. Figure 5.5 shows an example of two routes that do not exactly overlap but are close enough to be considered when computing the similarity metric. For the following validation tests, we choose a buffer size of 60 meters around the segment (i.e., 30 meters from each side of the segment).
<figure><img src="image_40.png" /><figcaption>Figure 5.5: An example of two routes not overlapping but close enough (a) routes not considered overlapping without a buffer (b) routes considered overlapping using a buffer</figcaption></figure>

### 5.6.5 Clusters construction

The construction of clusters is a crucial phase in the cluster-based route selection approach. It represents the foundation upon which the whole algorithm is built. Therefore, the choice of the optimal number of clusters to construct has a great importance. On one hand, choosing few clusters lowers the similarity threshold. As a result, more points are clustered together without showing much resemblance. On the other hand, a large number of clusters seeks very similar points. Hence, the clusters may gather very few points.

In order to choose an adequate number of clusters in our context, we have evaluated the MAE of the estimation while varying the number of clusters from 250 to 1500 with a step of 250 clusters and 20 iterations for each step. Results depicted in Table 5.1 show that the lowest MAE value is reached with 1250 clusters.

<table><caption>Table 5.1: MAE vs the number of clusters with 40 participants</caption><tr><td>Number of clusters</td><td>250</td><td>500</td><td>750</td><td>1000</td><td>1250</td><td>1500</td></tr><tr><td>MAE (µg/m³)</td><td>5.19</td><td>5.08</td><td>5.13</td><td>5.11</td><td>4.97</td><td>5.16</td></tr></table>

## 5.7 Performance evaluation and discussion

To evaluate the performance of our proposal, we first compared multiple spatial interpolation models using the same route selection strategy in order to get insights on which model performs better in estimating NO₂ concentrations. The best model was then used in the second part, which compares our proposed route selection strategies with the previously pressed baseline approaches, in terms of estimation error and traveled distance, using a variable number of participants increasing from 15 to 40 participants.
### 5.7.1 Comparison of the performance of MLR, KNN, and XG-Boost

We conducted multiple simulations with the MLR, KNN, and XGBoost, using the similarity-based route selection approach, while increasing the number of participants from 15 to 40 and randomly generating sensor errors between $1\mu g/m^3$ to $10\mu g/m^3$. The number of neighbors for KNN was fixed to 5, based on the previous results from Section 4.3.4. Figure 5.6 depicts the results of the average MAE in function of the number of participants, obtained after 20 iterations. We observe that as the number of participants increases, the prediction error decreases for all three models. XGBoost outperforms KNN by around 30% and MLR by nearly 42% in terms of MAE. Moreover, we present in Table 5.2 the average execution time of the three models for one iteration. The table shows that MLR is the fastest model, while KNN is the slowest among the three. The reason that KNN is much slower compared to the tests in Section 4.2.1 is that the dataset used to perform the prediction is much larger in this study, which makes the prediction step computationally intensive for KNN, because it needs to loop over the entire dataset to find the closest points to each unsampled one. The observations from Figure 5.6 along with those of Table 5.2 give hints about which model is more suitable for a real life scenario. In our case, we chose XGBoost for the upcoming evaluations.

<figure><img src="image_41.png" /><figcaption>Figure 5.6: MAE vs number of participants</figcaption></figure>

<table><caption>Table 5.2: Average execution time of 1 iteration For KNN, multiple linear regression, and XGBoost</caption><thead><tr><th>Estimation method</th><th>KNN</th><th>MLR</th><th>XGBoost</th></tr></thead><tbody><tr><td>Average Execution time (seconds)</td><td>12.95</td><td>0.31</td><td>0.73</td></tr></tbody></table>

### 5.7.2 Comparison of route selection algorithms

With the aim of evaluating the impact of route selection strategies on the estimation of NO₂ concentrations, we carried out multiple tests considering the shortest-path-based routing, the longest-path-based routing, and the two proposed route selection
algorithms, i.e., the similarity-based routing, and the cluster-based routing. The two proposed solutions have the same goal, that is, offering the most informative routes possible while taking into account the correlations between them. However, the difference lies in the evaluation of the relationship between the routes. The similarity-based routing computes the similarity between participant routes and tries to reduce the overlapping between the selected routes, while the cluster-based routing takes advantage of hierarchical clustering to form groups of similar points and then tries to maximize the set of visited groups.

The performance of the four route selection algorithms are evaluated using XG-Boost, based on the observations from the previous test. Figure 5.7 depicts the MAE of estimation in function of the number of participants. The experiments are performed with a number of participants increasing from 15 to 40 and repeated 20 times. At each iteration, we randomly select a new set of participants with sensor errors between $1\mu g/m^3$ to $20\mu g/m^3$. Results clearly show that increasing the number of users helps decreasing the estimation error due to the presence of more routes and hence a larger area being covered. An interesting observation is that while the shortest-path-based routing has the worst performance in terms of MAE of the estimation, the similarity-based and the cluster-based solutions outperform the longest-path-based routing. This means that choosing the longest path is not always a good decision to improve the estimation. Indeed, the similarity-based route selection performs up to 15.42% better than the shortest-path-based algorithm and 3.49% better than the longest-path-based solution, while the cluster-based solution surpasses the shortest-path-based and the longest-path-based by 16.84% and 5.22%, respectively.

<figure><img src="image_42.png" /><figcaption>Figure 5.7: MAE vs number of participants</figcaption></figure>

In addition, we investigate the impact of the different route selection approaches on the travel distance of participants. For this purpose, we calculate the total traveling distance for each route selection approach. Figure 5.8 shows the total travel distance accumulated for all participants. As expected, the shortest-path-based algorithm offers the shortest distance by definition and hence outperforms all the other solutions. Nonetheless, results reveal that our proposed solutions are also more efficient than the longest-path selection in terms of traveled distance. Indeed, the similarity-based algorithm improves the travel distance by up to 32.22% compared to the longest-path-based algorithm and 15.50% compared to the cluster-based solution, while being 13.68 % longer than the shortest-path. These promising results state that our proposed solutions give the lowest error while offering shorter routes compared to the best of baseline algorithms.
<figure><img src="image_43.png" /><figcaption>Figure 5.8: Total travel distance for the different route selection algorithms</figcaption></figure>

In addition, to get an idea on the impact of the sensing error over the estimation quality, we show in Figure 5.9 two heatmaps of the estimated NO₂ concentrations using the similarity-based algorithm, achieved with 40 users and sensor standard errors between 1µg/m³ and 5µg/m³ (left) and sensor standard errors ranging from 1µg/m³ and 20µg/m³ (right). We can visually observe that the estimation with lower sensing errors is more smooth and less noisy than the one with higher sensing errors.

<figure><img src="image_44.png" /><figcaption>Figure 5.9: Heatmap of NO₂ concentrations, estimated with 40 users and the similarity-based algorithm with (a) sensor standard errors between 1µg/m³ and 5µg/m³ (b) sensor standard errors between 1µg/m³ and 20µg/m³</figcaption></figure>

## 5.8 Conclusion

In this chapter, we addressed the problem of route selection in participatory sensing with low-cost sensors. We proposed two route selection algorithms that take into consideration the characteristics of low-cost sensors in the decision process. The similarity-based routing algorithm aims to give spatially dispersed routes to maximize coverage using the Sørensen-Dice coefficient, a commonly used metric in image segmentation. The cluster-based routing algorithm takes advantage of hierarchical
clustering to build groups of similar points, then tries to visit as many groups as
possible to increase the diversity of the collected information. Both algorithms per-
form in a greedy manner by suggesting at each iteration a route for the user with
the most accurate sensor. We compared our solutions to two baseline route selection
algorithms, namely the longest-path-based and the shortest-path-based algorithms.

We showed through the results that our route selection approaches can obtain
close results to the longest-path-based approach or even outperform it, while being
efficient regarding the travel distance. Our solution is adapted to a scenario in which
participants do not necessarily arrive at the same time. Moreover, the idea behind
the clustering-based approach can be used to find the safest route possible in terms
of exposure to air pollution, by reducing the number of high polluted locations to be
visited.

In the next chapter, we provide a general conclusion to summarize all the work
that has been achieved during this thesis, as well as some future research directions
that can be explored.
# Chapter 6

## Conclusion and perspectives

Today, mobile crowdsensing and low-cost wireless sensor networks form together an attractive and promising approach in various fields. Low-budget applications can make use of this solution and benefit from the cost-efficiency of low-cost WSNs and the crowd density in order to reach large-scale deployments and collect extensive data.

In this chapter, we summarize the contributions of this thesis and subsequently point out some perspectives on possible future research directions.

### 6.1 Main conclusion

Throughout this Ph.D. thesis, we addressed the use of low-cost WSN in mobile crowdsensing for air quality monitoring, while focusing on three key elements: 1) the design of low-cost participatory air quality monitoring systems, 2) the analysis of dense low-cost WSN data and their benefit in generating high-resolution air pollution maps, and 3) the selection of the participant routes in order to improve the knowledge of the phenomenon. We provided details on the design of the 3M'Air participatory air quality monitoring platform, along with some engineering guidelines that take into consideration the characteristics of the addressed application. We leveraged small, battery-powered, and low-cost sensor nodes equipped with three environmental sensing probes and long-range wireless communication. The proposed solution was compared to reference sensors, and the first results showed that our sensor readings are satisfactory. The 3M'Air platform operated during multiple sensing campaigns that were organized in the city of Lyon with the help of the 3M'Air project partners.

The collected measurements from the sensing campaign helped us to evaluate the performance of our nodes on the field and the consistency of their data. Moreover, this dataset allowed us to study the relationship between the energy consumption, the sensing quality, and the sensing rate. Furthermore, we considered a large-scale low-cost WSN deployment with the help of numerical simulations, and designed a general framework that enables the comparison of data assimilation and regression approaches, while taking into account the low accuracy of low-cost sensors. Through the obtained results, we observed that data assimilation outperformed all the considered regression methods, thanks to its capacity to consider both simulation and measurement errors. In addition, the results pointed out that increasing the number of deployed sensors is not always suitable for regression methods, especially when the sensing error is significantly large.

Our last contribution during this thesis focused on the problem of route selection in low-cost participatory sensing. We introduced two new route selection algorithms
that consider the low accuracy of low-cost sensors in their decision process. The first algorithm aims at reducing overlapping between participant routes using a metric inspired by the imagery field. The second algorithm leverages agglomerative clustering in order to maximize coverage in terms of distinct locations. Both algorithms were compared to baseline route selection approaches, and the impact of their strategy on the assessment of air quality was evaluated. The results indicated that our solution can help assessment models to achieve good performance, while suggesting shorter routes compared to the best baseline algorithm.

## 6.2 Extensions and future work

The contributions of this thesis form a basis on which we could build new solutions and address other challenges in order to improve our knowledge of air quality.

### 6.2.1 Future extensions of our participatory sensing platform

Our platform is operational and has been used in several measurement campaigns in Lyon. However, we believe that there is still room for improvement. To this aim, we are planning to improve our platform by adding the possibility to remotely manage the nodes. This will give us the ability to change the sampling/transmission parameters of the nodes and to put certain sensors/receivers in sleep mode. Therefore, the web application will not be used for data visualization only, but also for nodes administration. Regarding the node, we could add BLE support, which will give us the ability to use, when possible, the GPS of smartphones instead of the integrated GPS module, which could save us some energy. Another possible improvement is the support of downloading sensor data by just connecting the node to a computer or a smartphone via BLE.

Moreover, our sensing nodes are not limited to be used on foot, but can also be mounted on other mobile platforms, such as buses or bicycles [31]. However, some challenges need to be addressed in order for our node to operate properly. First, we need to evaluate the impact of the air flow on the reading of the sensing probes. In addition, we might need to adapt the design of the nodes to provide natural ventilation on 360° instead of the current 180°. Another parameter to be adapted is the sensing rate. In fact, buses and bicycles move faster than pedestrians, which means that with the current sensing rate of 20 seconds, the node will cover fewer locations as the traveled distance at each cycle is longer compared to the current scenario. Therefore, nodes should perform measurements at a higher rate to maintain a high spatial resolution. This could imply adopting another communication technology that is more adapted for this use case.

### 6.2.2 Expanding the network's lifetime

The sensing duty cycling consists of putting some sensors/receivers in sleep mode unless they are performing measurements. Such duty cycle behavior is limited by the convergence time of the sensors embedded in the node. The convergence time is indeed the duration that a sensor needs in order to reach a steady state in stationary conditions and then to output valid readings. Every sensor has its own stabilization time that depends not only on the type of the probe but may also depend on the duration of the sleep phase. Based on the data sheets, the convergence time of the
DHT22 is about 2 seconds [97], while the PM sensor's convergence time is about 30 seconds [96]. Based on the first experiments conducted in [42], the convergence time of the Alphasense NO₂ sensor in stationary conditions varies from around 50 seconds to ten minutes when the previous sleep phase goes from one minute to one hour, respectively.

Based on these observations and those on the impact of the sensing rate on the energy consumption and the sensing quality, we are planning to conduct more analysis in order to propose adaptive sampling [134] in function of the meteorological conditions, the node's trajectory, speed, and the convergence time of the sensors. This may lead to irregular sampling intervals in order to expand the node's lifetime, while enhancing the overall air quality estimation. We also believe that it would be interesting to investigate whether the GPS receiver can be deactivated at some sensing cycles and the location of a measurement predicted, based on the time of the measure and its value. This would make it possible to extend the battery life of the node even further. Another solution that can be leveraged to expand the operating time of the network is the notion of "rendez-vous" presented in [135]. Besides the problem of sensor calibration, this solution could be used to put on sleep mode some sensors when there are more than one in a given spatial buffer.

### 6.2.3 Extensions of our route selection solutions

The first simulations that we performed using our proposed route selection algorithms showed promising results. Yet, we still need to validate their performance in a real life scenario. Currently, the clustering-based algorithm takes a significant amount of time to find the routes, which is not suitable, especially in a scenario where routes are not known a priori. Therefore, we need to optimize the algorithm in order to reduce its computational cost. In addition, the similarity-based algorithm highly depends on the size of the buffer surrounding the routes. In turn, the size of the buffer depends on the sensing rate and the spatial resolution of the measurements. We believe that the impact of the buffer's size on the selection strategy should be investigated in order to achieve better results.

Furthermore, the temporal aspect is worth taking into consideration in the decision process. Indeed, in a near real-time monitoring scenario, where participants do not start the sensing task at the same time, only recent segments should be kept in memory for the route selection algorithm, i.e., the algorithm should take into consideration the freshness of the measurement. Additionally, route planning could be used in order to better lead the participants through locations where the estimation error is large.
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FOLIO ADMINISTRATIF

THESE DE L'UNIVERSITE DE LYON OPEREE AU SEIN DE L'INSA LYON

NOM : FEKIH
(avec précision du nom de jeune fille, le cas échéant)

DATE de SOUTENANCE : 04/02/2022

Prénoms : Mohamed Anis

TITRE : Low-cost Wireless Sensor Networks in Participatory Air Quality Monitoring

NATURE : Doctorat

Numéro d'ordre : 2022LYSEI007

Ecole doctorale : InfoMaths

Spécialité : Informatique

RESUME :

Mobile crowdsensing sensing is an emerging and promising paradigm that has attracted much attention in recent years, especially for environmental monitoring. Coupled with the power of low-cost wireless sensor networks (WSN), it leverages population density to collect extensive data in many applications, such as air pollution monitoring. In fact, air pollution is one of the main problems that still suffers from a lack of characterization due to the limitations of traditional assessment methods employed in terms of cost, network size, and flexibility. Mobile crowdsensing and WSN aim at filling this gap by enabling large-scale deployments in order to improve the local knowledge on the phenomenon.

In this thesis, we consider the air quality monitoring application using mobile crowdsensing, while focusing on three parts: 1) the design of low-cost participatory air quality monitoring systems; 2) the analysis of dense low-cost WSN data and their benefit in generating high-resolution air pollution maps; 3) the selection of the participants' paths in order to improve the characterization, while considering the constraints of travel distance and sensor errors. Through this work, we aim to show the potential of using low-cost WSN and participatory sensing in air quality monitoring. In this vein, we conduct substantial experimental work on the design of a participatory air quality monitoring system from scratch. We provide engineering guidelines regarding the design of low-cost participatory environmental monitoring platforms. Moreover, we propose a general framework that allows the comparison of different strategies based on numerical simulations and an adequate estimation of the simulation error as well as the sensing error. We also explore the impact of the sensing rate on the energy consumption and the characterization error. Furthermore, we propose two new algorithms for route selection in participatory sensing. Our approaches take into account the participants' constraints and the characteristics of low-cost WSN.

MOTS-CLÉS : Mobile crowdsensing, low-cost WSN, air quality, participatory sensing

Laboratoire (s) de recherche : CITI

Directeur de thèse :

- Hervé RIVANO, professeur des universités à INSA-Lyon. Directeur de thèse.

- Walid BECHKIT, maître de conférences à INSA-Lyon, co-directeur de thèse.

Président de jury :

Composition du jury :

- Nathalie MITTON, directrice de recherche INRIA (rapportrice)

- Nicolas MONTAVONT, professeur des universités à l'IMT Atlantique (rapporteur)

- Alexandre GUITTON, professeur des universités à l'Université Clermont Auvergne (examinateur)

- Nathalie REDON, maître de conférences à l'IMT Nord Europe (examinatrice)