1. Introduction
Wireless Sensors Networks (WSN) are an expression of Internet of Things and are a powerful, promising and low-cost platform for several different applications [
1], e.g., the development of an intelligent and automated system for irrigation [
2], for the monitoring of cultural assets [
3], or to the management of the container terminals logistic [
4]. Basically, WSN are systems composed of radio-frequency (RF) transceivers, sensors, micro-controllers or processing units, and power sources [
5,
6]. Compared to the traditional wired sensors networks, WSN technology is cheaper, typically presents short deployment time and makes use of a higher number of sensors of various nature, e.g., temperature, humidity, acceleration, and concentration of chemicals [
1,
6]. Therefore, with WSN, it is possible to perform a multi-variable monitoring of a given industrial process of interest [
6]. Moreover, the deployment of the sensors and nodes can cover large scale areas or be mobile [
7]. The WSN can also ensure a robust digital transmission of information, with acceptable data rate [
5,
6,
7].
Given these advantages, the WSN technology was proposed as a tool for the development of Intelligent Transportation Systems (ITS), i.e., solutions which can provide drivers relevant information about their ride, allow setting up convenient services to reduce traffic congestion, increasing the road capacity, and enhancing the efficiency in using transportation resources [
8,
9]. Especially in the case of urban transportation systems, the WSN potentialities satisfy the ITS requirement of being an efficient, cost-effective method for collecting and transfer information [
8]. To date, WSNs were tested for intelligent parking systems and tracking of buses and taxi [
8,
10]. Some WSNs, designed and developed for automotive applications, are oriented to the monitoring of air quality and the road and traffic conditions [
11,
12]. Moreover, the commercially available WSN nodes and solutions monitor parameters such as the air temperature, light, acceleration, magnetic fields, and position [
13]. For instance, the signal acquired from accelerometer can be employed to derive information about road bumps and to estimate the vehicle instantaneous speed [
14]. Furthermore, WSN can be employed in intra-vehicle communication between heterogeneous devices [
15], thus acting as a management system. However, for specific needs and issues, an ad hoc WSN architecture must be designed [
13,
16,
17]. To this aim, an old analysis and comparison of the available hardware architectures for WSN in automotive applications and ITS can be found in [
13,
16,
17,
18]. From these surveys, it stands out that flexible and effective tools are required. Therefore, open hardware devices have been studied and investigated. Among the different available solutions, the employment of Raspberry Pi unit as core element in nodes [
19,
20], given its low-cost, low power consumption, and small dimensions that allow an easy placement inside a vehicle [
21]. For example, the use of this embedded platform was explored in the management of traffic through the setup of dedicated vision tool systems for the obstacle detection and for enhancing the correct handling of the travel time [
21]. The various SBC employed are often compared in terms of CPU, memory, operating system, and price [
21]. However, from a practical point of view, to use one of these devices for automotive applications, a ECE-R10 certification for Electromagnetic Compatibility (EMC) is also required, according to ISO norms and CSN EN standards [
22,
23,
24].
In this work, cost-effective open hardware and software are used to develop a simple but reliable, flexible, and powerful WSN for automotive application, in particular for the public transport case. Several available hardware solutions are compared. The selected device was tested against possibly risky, harsh, and adverse environmental and operational conditions, i.e., temperature, humidity, and vibrations due to the vehicle motion. With a multi-purpose and flexible design, the environmental and traveling data gathered from the WSN can be also exploited to improve the quality of service during bus ride thanks to an automatic alert system developed using open-source softwares. Furthermore, as explicitly required by the public transportation company, the WSN also manages the exchange of multimedia data (e.g., messages and videos) from a server and make them available by displaying on the bus screens. The influence of environmental and operative parameters on the WSN operations was investigated from a quantitative point of view.
4. Results
In
Figure 7, as examples, are reported the curves for the physical quantities monitored by the designed WSN node. These curves are the average of the data gathered during January, February, and November 2019. The dynamic of relative humidity, in percent, and temperature, in
C, for 12 h of bus ride are shown in
Figure 7a,b. From these graphs, it is possible to notice that, on average, at the PCB location, humidity presents a relatively narrow variation during the observation period (e.g., between 8% and 18.3%). These relatively low values are due to the node position, which is very close to the air conditioning system in a dedicated box above the driver seat. As regards the temperature, the range of variations is wider and, on average, it can vary between a minimum of 18
C and a maximum of 30
C. However, the automatic monitoring system detected values above the threshold of 36
C, especially in the summer season, and then sent the alert messages, as shown in
Figure 2. It is worth noting the inverse trend of the relative humidity with respect to the temperature, i.e., the colder is the air temperature, the higher is the relative water content retained in the environment, and vice versa [
39]. The intra-vehicle monitoring is an aspect often neglected in the literature related to WNS for automotive applications [
11].
The results of the elaboration (see
Section 3,
Figure 6) of the accelerometer signals are shown in
Figure 7c. The average monthly curves in
Figure 7c represent the acceleration experienced by the hardware of the WSN node. This acceleration, as explained in
Section 3 and
Figure 6, is caused by the high-frequency vibrations of the vehicle, and they represent a potential threat for the hardware and its functioning. It can be noticed that the sensor, during an average ride, is subject to vibrations with acceleration values generally higher than 9.8 ms
, i.e., the gravitational acceleration value. Therefore, the force to which the WSN node is subjected during a typical bus route can be about two times higher than the force it experiences at rest, when the gravitational force is acting. In particular, the value of
, during several days, can overcome the threshold level of 20 ms
. Therefore, alarm messages are frequent for this physical quantity (see
Figure 2). Finally, in
Figure 7d, the rate of download of multimedia files,
, in MBs
, is presented. The videos and massages have a variable size and they were chosen according to the need of the public transportation company.
It is possible to observe that, for seven random days during January, February, and November 2019, the download rate can have a constant trend of 3 MBs
, or can be as high as 10 MBs
. However, several sudden variations can be noticed. Focusing on the period of February 2019, the possible differences in the download of messages and videos was investigated. The results for the eight tests for messages with different number of characters are shown in
Table 2. On average, the update time is 1.5 s for about 32 characters, i.e., 128 bits, for an average transfer rate of 85.3 bits per second. Then, the potentially more complicated transfer of video was tested. As derived form the results presented in
Table 3, the average transfer time is of about 94.8 s for an average size of 21.12 MB. The results from
Table 2 and
Table 3 are coherent with the findings in
Figure 7d. Moreover, these are relatively good performances compared to those of other WSN tested with vehicle in movements [
40].
It must be reported that, during both the tests and during the operating period some issues with the download occurred. Due to the variability of the physical quantities and the different routes, a complete and systematic understanding can be troublesome. However, some theoretical or adaptive strategies can be implemented to avoid these adverse situations [
41,
42,
43]. This is why, as explained in
Section 3.2, the Pearson’s correlation coefficient between the aforementioned variables and the download rate was investigated for the three months of monitoring. The average correlation values for whole period are reported in
Table 4. As suggested by the curves in
Figure 7c,d, it can be noticed that there exists a strong negative correlation (−0.6601) between temperature and humidity. As regards the relationship between the download rate and the temperature, the positive correlation value of 0.6321 indicates that, if the temperature increases,
increases. On the other hand, the correlation between the download rate and the relative humidity is relatively lower and negative, equal to −0.3683. This means that, a lowering in the humidity value calls for an increase in the download rate. Therefore, in merit to the correlation between the download rate and the norm of the acceleration due to the vehicle vibration, since a value of −0.2653 was found, it is possible to infer that, the lower is the bus vibration, the better is the multimedia content management by the WSN.
With the knowledge retrieved from the rather simple correlation analysis, the data analysis was deepened to investigate if a linear relationship between the download rate and the environmental and vehicle parameters exist. A multivariate regression was performed according to Equation (
7). The coefficients for
, averaged on the three months of observations, are
=
,
= 4.9100,
=
, and
= 1.7046, found with
= 0.6272. The results cannot be presented due to the 4D nature of the function. The value of the residual sum of squares indicates that the hypothesis of a linear relationship is a well posed one. Indeed, values very close to one can indicate a bias due to the combination of data [
37,
38]. However, since the Pearson’s correlation coefficient
is high, it is possible that one of these variable may not be significant to describe the variations of the download rate during the bus rides.
Therefore, the multivariate regression of Equation (
7) was performed again imposing
= 0 in order to identify the level of significance of humidity and the norm of the acceleration due to the bus vibrations. It was found that
= 9.9947,
= 0.06, and
=
, with
= 0.1357. The results of the multivariate linear regression for this case are presented in
Figure 8a. Then, the model was fitted imposing
= 0, to exclude the humidity variable. The resulting surface with coefficients
=
,
= 4.2130, and
=
is shown in
Figure 8b. In this case, in a way similar and very close to the three variable models, the residual sum of squares is 0.6046. The derived model is fit to the experimental data. Therefore, from this last finding, it is possible to infer that the download rate of the proposed WSN architecture is slightly affected by the environmental parameters (especially the temperature) and by the strength of the acceleration of the vehicle vibrations. These analyses and results justify the proposed design and study, while encouraging to further develop, enhance, characterize, and investigate this architecture in order to develop a robust, reliable engineering tool for the automotive field of public transportation.
5. Conclusions
Wireless Sensors Network are recognized as a powerful expression of the Internet of Things. However, despite the great advantages offered by this technology, their application has been partially investigated in automotive applications, especially public transportation and intelligent transportation systems. A first goal was the investigation of whether open-hardware software can be safely and effectively employed in a peculiar scenario such as a bus. Moreover, the aim of this study was to develop a cost-effective WSN node that can monitor several physical parameters (i.e., acceleration, temperature, and relative humidity), while managing the download and streaming of multimedia content, as well as set up an automatic alert system. Therefore, in this work, several off-the-shelf, open, and cost-effective hardware components were compared to identify a suitable choice for the node and WSN design to be employed in the automotive field. The Raspberry Pi unit is a good candidate, given the fact that the EMC has been recently studied [
19,
23]. The WSN was set up in the bus vehicles and it was tested for three months, i.e., January, February, and November 2019. During this period, the physical quantities of interest were monitored and analyzed. After preliminary test of the efficiency of the download rate of multimedia contents, given the variability of this quantity, a correlation analysis was performed. Then, a linear multivariate regression was investigated to derive a model that could describe the variation of this relevant quantity with respect to the environmental and operative parameters. The findings indicate that the lower is the magnitude of the vibrations experienced by the WSN node, the higher and the better is the download rate. Therefore, as a conclusion, the proposed solution can be employed in automotive applications.
The proposed WSN architecture was also carefully analyzed in terms of Strengths, Weakness, Opportunities, and Threats (SWOT), as shown in
Figure 9 [
44,
45]. The engineering tool investigated and characterized in this work offers a versatile multimedia management, combined with a real time messaging system. Furthermore, the system is selective, since it can easily track and discriminate the nodes and vehicles, implying that the overall quality of service can be improved. As regards the cost-effectiveness of the system, considering the functionalities, the choice of the Raspberry Pi as SBC allows reducing the cost by about 30%, with respect to the Nano-ULT3 and the VB0X-3120, as shown in
Table 5. This low-cost solution for automotive application can be appealing for the automotive field because of the open source hardware employed, which is a flexible, reliable, easy to update, and portable platform. The flexibility and high modularity add new functionalities with respect to the other devices currently available, at a lower price (see
Table 1). However, despite the advantages, strengths, and promising findings of the proposed WSN architecture, it must be pointed out that the design and results are at a preliminary stage. Hence, one major limitation is that the automotive certification for the Raspberry Pi unit is not available at the moment, but some recent literature findings encourage the possibility to easily obtain it [
19,
23,
46]. Furthermore, even though it has been demonstrated that the temperature, humidity, and bus vibrations do not affect the management of multimedia content, the data transmission in upload and download should be optimized and enhanced to avoid instabilities, since this is a primary requirement for the case company. To these weaknesses, it should be stressed that the threat of WSN security is a pivotal aspect that deserves to be investigated in the future. Future works may deal with the empowerment of the proposed WSN node. For instance, a thermographic or optical camera [
11,
12] could be integrated to derive useful features and information to measure some parameters related to the quality of the service or the security during the transportation, e.g., the count of passengers or the detection of passengers smoking on the bus.