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Article

Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring

by
Mariusz Rychlicki
1,
Zbigniew Kasprzyk
1,
Małgorzata Pełka
2 and
Adam Rosiński
3,*
1
Division of Air Transport Engineering and Teleinformatics, Faculty of Transport, Warsaw University of Technology, 75 Koszykowa St, 00-662 Warsaw, Poland
2
Motor Transport Institute, 80 Jagiellońska St, 03-301 Warsaw, Poland
3
Division of Electronic Systems Exploitations, Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, 2 Gen. S. Kaliski St, 00-908 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(20), 9243; https://doi.org/10.3390/app14209243
Submission received: 26 August 2024 / Revised: 21 September 2024 / Accepted: 9 October 2024 / Published: 11 October 2024
(This article belongs to the Special Issue Research and Estimation of Traffic Flow Characteristics)
Browse Figures
Figure 1
<p>Typical LoRaWAN architecture. (Source: authors’ image based on [<a href="#B51-applsci-14-09243" class="html-bibr">51</a>,<a href="#B52-applsci-14-09243" class="html-bibr">52</a>,<a href="#B53-applsci-14-09243" class="html-bibr">53</a>]).</p> "> Figure 2
<p>Architecture of the proposed solution. (Source: authors’ own image based on [<a href="#B51-applsci-14-09243" class="html-bibr">51</a>,<a href="#B52-applsci-14-09243" class="html-bibr">52</a>,<a href="#B53-applsci-14-09243" class="html-bibr">53</a>]).</p> "> Figure 3
<p>Road system in the Stare Babice commune (source: authors’ own image based on [<a href="#B61-applsci-14-09243" class="html-bibr">61</a>]).</p> "> Figure 4
<p>Topographic map of the Stare Babice commune. (Source: authors’ own image based on [<a href="#B71-applsci-14-09243" class="html-bibr">71</a>]).</p> "> Figure 5
<p>“Area” sub-path attenuation method. (Source: authors’ own image based on [<a href="#B72-applsci-14-09243" class="html-bibr">72</a>]).</p> "> Figure 6
<p>CompleTech ComAnt CAS+ antenna radiation characteristics [<a href="#B73-applsci-14-09243" class="html-bibr">73</a>].</p> "> Figure 7
<p>Impact of h<sub>GW</sub> transmitter station location height (10, 15, 20, and 25 m) on radio coverage.</p> "> Figure 8
<p>Impact of h<sub>EN</sub> receiving antenna height-wise positioning (2, 4, 6, and 8 m).</p> "> Figure 9
<p>Locations of transmitting stations (GWs) and distribution of area boundaries and roads.</p> "> Figure 10
<p>Radio coverage areas and values for six transmitting stations (GWs) within the preset area.</p> "> Figure 11
<p>Areas of radio coverage by individual GW transmitting stations.</p> "> Figure 12
<p>Area radio coverage with a signal exceeding the preset value.</p> ">
Versions Notes

Abstract

:
This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time.

1. Introduction

The modern world is built on information—its acquisition, accumulation, processing, and exchange. Due to the particular importance of information exchange, tele-IT systems have been included in the broader concept of information and communication technologies (ICTs) [1], often alternatively referred to as information and telecommunication, telecommunications and information technology, and information technologies. The practical mass-scale implementation of these tasks is enabled and executed by ICT systems, also known as information systems, which are integrated sets of components designed to collect, store, and process data, as well as to provide information, knowledge, and digital products and services [2]. These systems have evolved from straightforward data processing solutions to complex information management systems that support both business operations and strategic decision-making [3]. This development has been driven by technological progress, particularly in the advancement of computers, software, and telecommunications networks.
Current trends in the field of ICT systems include the ongoing development of technologies such as artificial intelligence, machine learning, big data analytics, cloud computing, and the Internet of Things, all of which have the potential to further transform the way ICT systems are used. One of the primary users, due to its significance for the economy of every developed and industrialized country, is the transport sector [4,5,6,7], with a particular emphasis on sustainable transport [8]. ICT plays a key role in transport, particularly in improving road safety. One of the most common applications is traffic management systems, which monitor traffic volumes and automatically adjust traffic lights to facilitate smoother vehicle flow and minimize the risk of accidents. For instance, intelligent transport systems use sensors and cameras to collect traffic data and inform drivers in real time about traffic jams, accidents, or weather conditions that may impact safety. Another important application of ICT in transport is advanced driver assistance systems (ADAS), which employ technologies such as radar, cameras, and lidar to warn drivers of potential dangers and even intervene in emergency situations. These systems can automatically brake, keep the vehicle in its lane, or warn of an impending collision. Such solutions significantly reduce the risk of accidents, especially in conditions of limited visibility or on busy roads. In addition, ICT supports the development of intelligent monitoring systems for trucks and buses, enabling the control of their speed, technical condition, and drivers′ working hours. This helps to reduce accidents caused by driver fatigue or vehicle malfunctions. These systems can also diagnose faults remotely, allowing for quick repairs before they lead to more serious breakdowns or dangerous situations on the road. ICT is also utilized in road infrastructure management, where sensors and monitoring systems assess the condition of road surfaces and bridges in real time. This enables quick responses to potential hazards, such as road damage or dangerous weather conditions, which can impact traffic safety.
Classical, static methods of measuring vehicle speed are based on various techniques, including radar, lasers, and induction loops. Radar meters work by emitting radio waves that bounce off a moving vehicle, and based on the change in frequency of the waves (Doppler effect), the device calculates the vehicle′s speed. Laser meters use a laser beam to measure the time it takes for the reflected beam to return to the device, allowing for an accurate determination of the vehicle′s distance and speed. Inductive loops, on the other hand, are installed directly in the road surface—when a metal vehicle passes over the loop, it alters the electromagnetic field, enabling the speed to be calculated based on the time taken to pass through successive loops spaced along a stretch of road.
While each of these methods differs in terms of application and precision, all are widely used for speed control on roads. However, they share a common element and limitation: they are only used at specific locations, either fixed or selected for random speed control by police patrols. Sectional speed measurement, which is conducted over a designated and typically small segment of a single road, does little to address this limitation. As a result, the positive impact of speed control on improving road safety is confined to these specific control sites and their immediate surroundings. Additionally, such methods provide limited information on traffic volumes and make it difficult to monitor changes in traffic flow.

2. State of the Art

Modern technologies have been supporting efforts to improve road traffic safety for years [9,10,11,12]. The most obvious and popular examples include speedometers, originally radar-based and later laser-based as the technology advanced. Solutions such as video cameras and drones also provide additional support. The development and application of new solutions necessitate the adoption of new data transmission standards and technologies, such as Bluetooth [13]. Increasing user requirements and expectations have led to the implementation of ICT solutions that enable the creation of vast wireless networks supporting a variety of devices and sensors. These include a wide range of possibilities and specific process solutions offered by LPWAN (Low Power Wide Area Network), a type of wireless network designed for long-distance, low-power-consumption communication. It is an ideal solution for the Internet of Things (IoT), where devices often need to operate on batteries for extended periods and may be distributed over vast areas, including both urban and rural environments. LPWANs thus present an alternative to hard-wired solutions based on fiber optic cables [14]. The main features of LPWAN include the following:
  • Low energy consumption: Devices can run on batteries for years without the need for replacement or recharging.
  • Long range: LPWANs can cover an area with a radius of up to several dozen kilometers.
  • Low throughput: LPWANs are optimized to transfer small data volumes, typical of many IoT applications.
  • High capacity: The networks can handle a large number of devices within their range.
LPWAN technologies include the following solutions:
  • LoRaWAN (Long Range Wide Area Network): This technology uses LoRa modulation to achieve long range and penetration in harsh environments [15,16,17,18].
  • Sigfox: This technology employs Ultra Narrowband (UNB) radio technology to achieve a long range with very low power consumption [19,20,21].
  • NB-IoT (Narrowband IoT): A communication standard developed by 3GPP that uses narrowband LTE radio communication [22,23,24].
  • LTE-M (LTE Cat-M1): A version of LTE adapted for IoT purposes, offering balanced coverage, power consumption, and throughput parameters [25,26].
Road transport LPWAN applications that are particularly important in the context of road traffic safety include solutions designed for real-time traffic monitoring, as reviewed in [27,28], or even those that detect undesirable events such as traffic accidents [29].
The LoRaWAN wireless data transmission technology is increasingly being employed in various branches of the economy [30,31,32]. Its benefits, such as long-distance data transmission and low power consumption, are driving its use in the fields of smart cities [33,34,35] and intelligent transport systems [36,37,38,39,40]. The particular significance of the potential application of LoRaWAN technology in road transport stems, among other factors, from the search for new solutions aimed at increasing the safety level of both drivers and pedestrians. Efforts in this regard cannot be undertaken on a local scale through stationary vehicle speed control but instead require the implementation of area-based solutions. One such option is employing LoRaWAN technology in combination with V2X (Vehicle to Everything) communication [41]. Test results demonstrated in [42] confirm the viability of deploying such solutions.
Another potential application of LoRaWAN technology aimed at increasing traffic safety is a wireless motion detection system. The authors of [43] developed such a system and conducted tests focused on determining when traffic intensity decreases or is suspended, which is particularly important on expressways, as it increases the risk of traffic accidents. The conducted tests led to the conclusion that it is possible to determine vehicle speed using this system.
LoRaWAN technology has also been applied in managing parking spots [44,45,46] for motor vehicles and bike-sharing systems [47,48]. Despite the introduction of various solutions, there is an ongoing search for increasingly effective parking management systems that also ensure the security of implemented functions. The application of wireless data transmission through devices with low power consumption enables increased efficiency in smart parking systems. LoRaWAN is the primary LPWAN technology that can be considered promising and with significant potential for applications in road transport. This also applies to scenarios discussed in [49], where vehicles are out of GSM and Internet network coverage.

3. LoRa/LoRaWAN Standard

LoRa (Long Range) is a wireless data transmission and exchange standard designed for long-range communication with low power consumption, dedicated to transmitting small data volumes. These features make it an ideal solution for creating vast networks comprising different sensor types, providing data for control, monitoring, and surveillance systems [50]. This technology does not support image transmission, especially at a quality that meets today’s standards. However, it is possible to send processed and extracted camera data, such as vehicle license plates, weight, or category. LoRa sits between standards like WiFi, Bluetooth, LTE, or ZigBee, offering unique advantages such as cost reduction or increased coverage. LoRaWAN, on the other hand, is a bidirectional MAC (Medium Access Control) protocol developed with high efficiency, long range, and low power consumption in mind. Therefore, LoRa and LoRaWAN are not identical. LoRaWAN optimizes power consumption and supports mechanisms for optimizing traffic between nodes. Establishing secure connections is guaranteed by a point-to-point encryption mechanism. The protocol also supports the wireless registration of new devices in the network and multicast transmission (one-to-many communication) [51].
The LoRa standard is formally managed by the LoRa Alliance, an open non-profit association that has become one of the largest and fastest-growing technology sector alliances since its establishment in 2015. Its members collaborate closely and share their experiences to promote and drive the development of the LoRa standard, which they consider to be a leading, open-source, global standard for secure IoT connectivity [52].

3.1. LoRaWAN Architecture

Figure 1 illustrates a typical architecture of a LoRaWAN. It consists of four basic components [51,52,53]:
  • EN (end nodes);
  • GW (gateways—base stations and routers);
  • NS (network server);
  • AS (application server).
End nodes (ENs, terminal devices) are distributed network elements that implement specific sensor and measurement functions. They have very limited computing power but are equipped with a wireless communication module that they use to send data to gateways. The node-to-gateway transmission is called Uplink, while transmission in the opposite direction is called Downlink. Depending on the volume of transmitted data and power consumption, nodes are classified into classes A, B, and C.
Gateways (GWs—modems and access points) receive data sent by end nodes (EN) via LoRaWAN. By design, they are transparent and have limited computing power. Their primary role is converting the transmission medium and packets to enable onward transmission via a traditional IP network.
A network server (NS) is the recipient of all data sent by the nodes via the gateways. It has significant computational power and performs complex data processing operations. The network server can be based on a direct physical infrastructure or be located in a computing cloud. It is often integrated with a database center.
An application server (AS) supports the base application, which implements specific tasks and provides a particular service. It should be noted that there may be more than one application server, and the applications and services can vary, although they can be based on the same network and share some or all of the same data.

3.2. LoRa Radio Interface

The strength and popularity of LoRa technology stem, among other factors, from the fact that its transmission is based on the following unlicensed ISM (Industrial, Scientific, Medical) megahertz bands:
  • 169 MHz (Asia);
  • 433 MHz (Asia);
  • 868 MHz (Europe);
  • 915 MHz (North America).
The transmission distances between communicating devices can reach several kilometers, depending, of course, on the terrain, land type, and the type and location of the antennas. However, achieving such coverage comes at a cost, specifically the small data transmission volume, which varies depending on modem configuration, ranging from kilobytes to single bytes per second. Despite this limitation, achieving such long distances with minimal power consumption would be impossible without the application of appropriate radio signal modulation. LoRa uses CSS (Chirp Spread Spectrum) modulation for this purpose, which involves modulating the data stream with a linearly increasing frequency signal [44,53,54].
The implementation of the CSS signal modulation process depends on three main parameters:
  • BW (modulation bandwidth) describes the extent of modulation frequency variation;
  • SF (spread factor) determines the rate of modulation frequency variation;
  • CR (code rate) introduces redundancy while correcting errors that arise during transmission.
These parameters impact the maximum coverage and throughput of a LoRa link and are described by Relationship (1):
R b b / s = S F ∗ C R ∗ B W [ H z ] 2 S F
where the individual parameters can take the following values [44,54]:
  • BW {7.8 kHz, 10.4 kHz, 15.6 kHz, 20.8 kHz, 31.25 kHz, 41.7 kHz, 62.5 kHz, 125 kHz, 250 kHz, 500 kHz};
  • SF {6, 7, 8, 9, 10, 11, 12};
  • CR {4/5, 4/6, 4/7, 4/8}.
In LoRa technology, the possible combinations of SF (spreading factor), CR (coding rate), and BW (bandwidth) depend on the specifications and hardware capabilities of specific devices, as well as the regulatory requirements for the use of frequency bands in a given region. Examples of combinations of SF, CR, and BW values and their impact on coverage and airtime are shown in the table [55].
When performing calculations based on Relationship (1), the term “small data volume” can be quantified, with a determined minimum and maximum data transfer ranging from just 11.5 b/s to 37.5 kb/s. This includes not only user data but also control and monitoring data.
In the LoRaWAN standard, key technical parameters (SF, CR, and BW) significantly affect communication performance, including range, throughput, reliability, and energy consumption. A higher SF results in lower data transmission speeds, as the longer transmission time means that fewer data can be sent in a given period. In comparison, lower SF values (e.g., SF7) offer higher throughput but reduce range and resilience to interference. In practice, selecting an SF value is a trade-off between range and transmission speed. The error correction rate (CR) can range from 4/5 to 4/8, with higher CR values adding more control bits, increasing error resistance but reducing effective data transmission speed. A higher CR improves connection reliability in challenging transmission environments, such as those with significant signal interference, but it also increases transmission time and energy consumption. Thus, choosing a CR involves balancing reliability needs with bandwidth and battery life requirements. A smaller bandwidth (BW) (e.g., 125 kHz) makes the signal more concentrated, enhancing resistance to noise and allowing longer transmission distances, though at the cost of reduced throughput. Conversely, a larger bandwidth (e.g., 500 kHz) increases throughput, enabling faster data transmission, but shortens the range and makes the signal more susceptible to interference. In practice, bandwidth selection depends on whether the priority is greater range or faster data transmission.
All of these parameters—SF, CR, and BW—are interrelated and collectively impact communication performance in a LoRaWAN network. The choice of values depends on specific application requirements, such as the expected range, data volume, reliability, and energy consumption. In long-range, low-throughput networks like LoRaWAN, it is essential to find the optimal balance between these parameters to maximize the technology′s potential while minimizing energy usage and ensuring adequate transmission quality.
An example case study of LoRaWAN use in transportation could be a medium-sized city that decided to implement a smart fleet management system for city buses. The goal was to improve operational efficiency, monitor fuel consumption, and optimize routes in real time. Traditional systems based on GSM technology proved too costly, and the GPS transmitters in the vehicles consumed significant amounts of energy. By using spreading factors (SF9 and SF10), a suitable compromise was achieved between throughput and transmission range. In the urban environment, where transmissions occurred over relatively short distances, a medium SF provided stable connections and allowed near-real-time information transmission. A bandwidth of 125 kHz was sufficient for transmitting small data packets, such as GPS updates and fuel consumption information, while effectively managing interference.

4. LoRaWAN Area-Based Speed Control

As outlined above, excessive vehicle speed is one of the main factors adversely impacting road traffic safety (RTS). For this reason, activities aimed at effectively enforcing and monitoring vehicle speed limit compliance are crucial and strategic in improving RTS [56,57]. This problem has also been acknowledged by the European Parliament, which introduced several regulations dedicated to newly marketed vehicles, such as Regulation No. 2019/2144, mandating the inclusion of Intelligent Speed Assistants (ISA) in motor vehicles starting from 6 July 2022 for new vehicle types, and from 7 July 2024 for all new vehicles [58]. However, considering the standard delays in implementing such regulations and the average age of vehicles used and imported into Poland, the full dissemination of this solution is expected to take 20 or more years. Moreover, during the initial period, the ISA is intended to be informative and advisory, rather than strictly enforcing compliance.
Therefore, traditional speed checks will remain a crucial element of monitoring and improving road traffic safety for the foreseeable future. However, technological development enables continuous advancements and the introduction of new dimensions to speed monitoring, including the application of Artificial Intelligence (AI).

4.1. LoRaWAN Radio Interface

In an attempt to address the aforementioned considerations, the authors proposed an area-based speed control system using LoRaWAN technology. The system architecture, illustrated in Figure 2, is based on the standard LoRaWAN system architecture shown in Figure 1. Additional elements are included due to the system′s intended purpose and the extra functions and capabilities required.
The system is designed to consist of a number of end nodes (ENs—sensors and speedometers) arranged along roads throughout the monitored area, measuring the speed of passing vehicles in real time. The data are sent via gateways (GWs) to a network server (NS), from where it is forwarded to an application server (AS) that runs core software. Additionally, the data are sent to a database (DB), from which it can be accessed by other application servers and services. In the initial phase of the research, off-the-shelf, outdoor-ready 24 GHz radar modules were used as EN terminal devices. These were connected via an RS485 link to commercially available LoRa terminals operating in the 868 MHz band. The entire setup was powered by 12 V batteries. Ultimately, the plan is to integrate the 24 GHz radar and LoRa transmission modules into a custom monolithic device. Additionally, it is envisioned that the device will be adapted to run on renewable energy sources, primarily solar power, but also wind energy.
Data from the gateways to the network server are transmitted via a fixed cable link. Although data transmission over the GSM LTE network is possible, it is used as a backup solution to enhance system reliability. The use of the GSM LTE network incurs fixed subscription fees for SIM card maintenance, which are significantly higher when using telemetry cards than for private applications, making this a substantial obstacle. For the same reason, the EN end devices do not use the GSM LTE network at all in the proposed solution. In this case, the LoRa-based solution demonstrates a clear and distinct advantage.
Data come from speed sensors as well as metadata on the current network transmission parameters. Processed and visualized data are sent via the GSM LTE network to end users, both stationary (e.g., surveillance centers) and mobile (e.g., police patrols). Data visualization enables immediate traffic safety assessment relative to the current state of speed limit compliance and traffic volume. This makes it possible to dispatch a patrol car to conduct static speed checks at the specific location currently presenting the highest hazard to traffic safety. Mobile users are supported by a GPS location system, which requires nothing more than simple classic GPS receivers, as demonstrated in previous studies [59].
It seems that with a sufficiently long system operation time and the collection of relevant data, it will be possible not only to monitor traffic in the classic way—traffic volume and vehicle speed—but also to use machine learning or even artificial intelligence to predict traffic safety risks, as well as when and where the greatest risks are likely to occur. The validity of this thesis can be confirmed once field tests have been carried out and preliminary results collected.

4.2. Selecting the System Operation Area

Given the design and nature of LoRaWAN systems, this solution will be scalable, meaning it can be deployed in specific, small areas with the option to scale up to a nationwide solution, in line with road traffic improvement policy. Therefore, it was decided that the minimum operation area for the system should be the basic local government unit, i.e., communes. This choice naturally aligns with the administrative division of Poland and enables an easy upscaling of the system following the district–province–country sequence. Communes have a number of responsibilities, including the broadly understood public order and citizen safety (Art. 7 (1) cl. 14) [60], which covers road traffic safety in addition to explicitly specified fire and flood safety. However, after being deprived of the right to conduct speed checks, communes have largely lost the ability to act directly in this regard. For this reason, they should support any new solutions aimed at improving road traffic safety and increasing operational effectiveness.
The following part of the paper focuses on the area of the Stare Babice commune, which is part of the Warsaw West (PL—Warszawa Zachodnia) district. The Stare Babice commune is located in the central part of the Mazowieckie province. It is situated in close proximity to Warsaw, at its western border, adjacent to the Kampinos Forest (PL—Puszcza Kampinoska), between two routes of international importance (Poznań and Gdańsk). Expressway S8 passes through the eastern part of the commune and is part of the Warsaw ring road junction. The commune borders the Ożarów Mazowiecki, Leszno, and Izabelin communes, as well as the Warsaw districts of Bemowo and Bielany. The commune covers an area of approximately 63 km². Excluding the section without roads and the portion within the Kampinos Forest, it can be fitted into a 14 km x 6 km rectangle (Figure 3).
A transport route consisting of provincial road No. 580 (leading from Warsaw towards Żelazowa Wola and Sochaczew) crosses the area of the commune as illustrated in Figure 3. Provincial roads No. 718 and 898 also connect with road No. 580. The proximity to Warsaw, high and constantly growing population density, extensive road network, and the vicinity of other communication routes (92, S7, S8) contribute to very high traffic volumes, which do not typically favor road traffic safety.

4.3. Estimating the Required Number of System Components

An operational assessment of the proposed solution will be possible after determining not only the types of its components but, above all, their quantity. While defining the number of network servers (NSs) and application servers (ASs) is not problematic (one NS and one AS are sufficient for a specific task), the high number of gateways (GWs) and end nodes (ENs) requires careful analysis and direct reference to the specified control area. The operating radius (transmission range) will have a significant impact in the case of gateways. In contrast, for end nodes, the determining factor is the road length.
The minimum number of gateways can be estimated using Relationship (2).
N G W = X 2 ∗ R ∗ [ Y 2 ∗ R ]  
where
NGW is the number of gateways;
R is the gateway transmission radius [km];
X, Y are the area dimensions [km].
The number of end nodes can be estimated from Relationship (3).
N E N = D d  
where
NEN is the number of end nodes;
D is the total road length within the system-covered area [km];
d is the distance between sensors [km].
For the adopted area dimensions (14 km × 6 km), the number of required gateways varies depending on the gateway transmission coverage radius, ranging from one gateway (for a radius R = 7 km or more) to 21 gateways (for a radius R = 1 km). The data are presented in Table 1.
The total length of district and provincial roads within the designated area is approximately 45 km. This value should be expanded to include local roads, the quality of which is sufficient to pose traffic safety hazards due to significant vehicle speeding. The length of these roads is estimated to be more than 15 km, which, after rounding up, gives a total road length within the system-covered area of D = 60 km. This allows for determining the required number of end nodes based on the distance between sensors d. The results are shown in Table 2.
Please note that an excessive distance between the sensors will undermine the system′s effectiveness and fail to provide a comprehensive picture of the traffic situation in terms of road safety. The best results will be obtained with a relatively short distance between the sensors. However, reducing this distance increases the number of sensors required, thereby increasing the deployment costs of the solution. For this reason, the distance d should be minimized while still maintaining the system′s ability to perform its intended task. Consequently, the distance d was set at 200 m, which translates to a required number of sensors NEN = 300 units. It should be emphasized that, relative to LoRaWAN′s capabilities, this number is small and may raise concerns only in relation to the costs of deploying the proposed solution, not its functional effectiveness.

5. Radio Coverage Planning

Radio coverage planning is the process of designing and configuring radio networks, such as cellular networks, radio transmitting systems, or Wi-Fi and LoRaWAN wireless networks, to ensure adequate radio signal range for a specific geographical area. It involves various activities and analyses primarily aimed at optimizing network performance and service quality for end users [62]. The most important aspects of radio coverage planning include the following:
  • Demand analysis: Defining the requirements related to network capacity and coverage based on predicted traffic and user numbers.
  • Base station location selection: Making decisions regarding the placement of base stations (in the case of cellular networks) or access points (in the case of a Wi-Fi network) to ensure optimal coverage.
  • Radio wave propagation modelling: Applying mathematical and computer models to predict radio wave propagation in different environments, taking into account factors such as land relief, buildings, vegetation, and other obstacles.
  • Network parameter optimization: Adjusting parameters such as transmitting power, frequency, antenna patterns, and others to minimize interference and ensure consistent coverage.
  • Testing and validation: Conducting field tests to verify the propagation model and implementing potential corrections in the coverage plan.
  • Redundancy planning: Ensuring that the network can function effectively even in the event of a failure of one or more network elements.
  • Compliance with regulations: Ensuring that radio network planning and deployment comply with local regulations regarding radio emissions, band sharing, and security.
  • Interference management: Considering existing networks in the area to avoid collisions and interference between systems.
Radio coverage planning is crucial for ensuring the high quality of telecommunications and wireless services, and its effectiveness directly impacts the end user experience [63,64]. Therefore, it is an area of intensive research and development, especially in light of the growing demand for data and the emergence of new technologies such as IoT, 5G, or LoRaWAN. It is particularly important in highly urbanized or densely populated environments [65].

Application of Radio Planning in LoRaWAN Networks

LoRaWAN elements and components typically need to operate on batteries and are often deployed over vast geographical areas, which aligns with the general concept of the Internet of Things (IoT) [66,67]. Therefore, given the aforementioned benefits of radio coverage planning, its application to LoRaWAN networks seems essential, particularly in the context of smart cities and related issues such as the following [68]:
  • Gateway location selection: Given the long range of LoRa, selecting optimal gateway locations is critical to ensure the best coverage for end nodes.
  • Signal propagation analysis: Due to the long-distance nature of LoRa technology, it is important to understand how signals will propagate in different environments, including urban, rural, and industrial settings.
  • Optimizing transmitting power and receiver sensitivity: To balance power consumption and range, coverage planning must consider gateway transmitting power and the sensitivity of receiving equipment.
  • Interference management: It is important to manage potential interference in environments with many LoRa or other devices operating on similar frequencies.
  • Redundancy planning: To ensure network reliability, coverage planning may involve placing additional gateways to guarantee service continuity in case one of them fails.
  • Compliance with regulations: Ensuring compliance with local regulations on radio emissions and band usage is crucial for LoRaWAN networks to meet legal requirements.
Ensuring optimal radio coverage is crucial for latency-critical IoT applications. In such cases, achieving the desired level of Quality of Service (QoS) is possible through the use of dedicated radio interface and network architecture solutions that primarily benefit from flexibility and service-oriented approaches [56].

6. Radio Coverage of the LoRaWAN Area Speed Control System and Traffic Monitoring

Meeting the requirements set out in Section 5, especially concerning the optimization of LoRa parameters, necessitates the proper arrangement of network equipment. “Proper” in this context means ensuring that the equipment maintains the required level of QoS services at a preset value and emission parameters. The placement of end nodes (speedometers) in the proposed solution is determined by the road system subject to monitoring. Therefore, it is crucial to deploy gateways in locations that meet specific requirements, such as the following:
  • placement in elevated positions;
  • the absence of terrain obstacles;
  • the availability of broadband, hard-wired Internet access, with emergency access via the GSM network;
  • permanent and emergency power supply sources.
Selecting gateway locations is not straightforward and cannot be based on intuition. It should be guided by specific procedures, and even algorithms, developed for this purpose, particularly when desired redundancy is considered [69,70].
As the topographic map in Figure 4 demonstrates, the selected area is a relatively flat terrain without significant hills or depressions, which favors good radio wave propagation. At the same time, it is a densely populated and built-up area due to its proximity to the capital city of Warsaw. However, the majority of buildings are single-family homes, typically two or three stories tall at most. For these reasons, and to meet the requirements outlined above, it is preferable to place gateways on or within buildings owned by the commune, such as the Communal Office, kindergartens, schools, health centers, sports centers, and so on. This approach also resolves several issues related to leasing space, connecting equipment, and ensuring access for maintenance and service.

Radio Coverage Simulation

Of course, the flat terrain and relatively small buildings largely facilitate meeting the previously established conditions and criteria. However, this does not mean that there is a complete absence of terrain obstacles and other risks to network functionality. This necessitates the analysis of several gateway arrangement scenarios, supported by specialist radio coverage planning software. To this end, the authors developed a model of an area-based vehicle speed control system using LoRaWAN technology and subjected it to several radio coverage simulation scenarios.
To calculate propagation, the authors selected the Deygout 94 diffraction model, which is further enhanced by the “Area” sub-path attenuation model. This model also assumes diffraction-type losses, even below the line of sight (LOS) [72]. To define the losses caused by lower obstacles, the area “obscured” by these obstacles, located in the first Fresnel zone, is identified. Sub-path attenuation is then calculated according to Equation (4).
L s p = 6.4 + 20 log ⁡   [ v + 1 + v 2 ]
where
v = H R ;
H R is the ratio between the obscured area and the area equal to half of the Fresnel zone (in Figure 5, H = H1 + H2).
Cartographic data, including a digital land map and land coverage classes, as well as specific vector data on the boundary of the area and the road system within it, were used to develop an environmental model that reflects the actual terrain conditions. The simulation was based on a terrain map with a maximum resolution of 1 m, allowing for an analysis of radio coverage relative to the immediate surroundings of the roads.
First, the impact of the hGW transmitting station installation height was tested. This also involved selecting the CompleTech ComAnt CAS+ antenna [73], the parameters of which are shown in Table 3, with its radiation characteristics illustrated in Figure 6. Next, the impact of the system’s transmitting station antenna installation height was tested at four values: 10, 15, 20, and 25 m. The obtained simulation results (Figure 7) confirm the general pattern that better propagation results (greater signal range and coverage) are achieved as the height of the transmitting antenna increases. This is, of course, associated with reduced attenuation caused by buildings, forests, land relief, and other obstacles.
However, when selecting the final transmitting station antenna height, it is important to consider a number of technical, structural, and economic constraints related to the need for constructing dedicated masts or placing antennas on existing buildings. For this reason, the subsequent analysis was based on realistic height values of 20–25 m, which included the height of existing buildings that could serve as bases for antenna mast installations.
HTZ Communication software (2024.9 version) [72], an advanced tool for radio network planning and design, was chosen as the simulation tool for this project. It is widely used across various sectors, including telecommunications, transport, military, aviation, and energy industries, to simulate, analyze, and optimize wireless networks. The software is capable of modeling a wide range of communication systems, such as radio networks, cellular networks (2G, 3G, 4G, and 5G), aeronautical and maritime communication systems, military systems, and LPWAN networks. It provides precise tools for assessing coverage, performance, and signal quality. The software focuses on signal propagation and uses advanced algorithms to predict coverage and signal quality in various environmental conditions, including urban, rural, forested, and mountainous areas. It allows users to model radio interference and assess how different sources of interference may affect network performance. Additionally, it enables the optimal placement of base stations, antennas, and other network elements to ensure maximum radio performance and coverage.
Variations in simulation parameters mainly included typical SF values and the influence of antenna radiation characteristics and placement height. The latter is particularly important as it directly affects the cost of the practical implementation of the proposed solution.
The impact of the hEN end node receiving antenna installation height was determined in a similar manner. The results (shown in Figure 8) confirmed the general pattern: increasing the end node antenna installation height leads to an increase in the received signal strength. Given the technical assumptions related to the proposed solution, which include utilizing existing infrastructure—specifically power and lighting poles—the practical installation height adopted was hEN = 5 m.
Of course, a single transmitting station will not cover the entire analyzed area, and the required minimum number of stations, as per Relationship (3), should be six. However, it is important to note that this number assumes a relatively even distribution of transmitting stations within the area and is applicable only under conditions of minimal variation in land relief and building height. An additional criterion for selecting the installation locations for transmitting stations was not only the use of existing buildings but, more importantly, buildings managed by the Commune (e.g., Commune Office, schools, health centers, and Fire Brigade stations). This approach aims to minimize system deployment costs and simplify installation procedures, particularly those associated with building antenna masts, ensuring electricity supply, and providing broadband Internet access. Following an analysis of available facilities, six installation locations for transmitting antennas were selected, five of which meet the aforementioned requirements. Their names, locations, and basic parameters are summarized in Table 4 and illustrated in Figure 9. The boundaries of the analyzed area and the main roads chosen for the installation of end nodes (ENs) are also marked in blue in this figure.
Next, a series of radio coverage simulations were conducted using the preset parameters for the transmitting stations (GWs) and their arrangement within the simulation area. Successive simulation scenarios involved adjusting the antenna angles to maximize radio coverage while minimizing the mutual interference between stations through a careful consideration of the antenna radiation characteristics. The radio coverage values and areas for the six transmitting stations (GWs) within the preset area are shown in Figure 10.
Figure 11 illustrates radio coverage by individual transmitting stations (GW1 to GW6).
The typical transmitting power for a LoRa transmitting station, as adopted in the model, is 14 dBm [74], while the limit receiver sensitivity is −137 dBm [75,76,77,78]. However, this sensitivity level is equivalent to the noise level, which in practice does not guarantee the system′s operation at the desired level of service quality and reliability. To ensure a safety margin for actual signal propagation conditions and potential interference, it is recommended to account for (and subtract) a value of at least 5 dB in the link budget.
The Received Signal Strength Indicator (RSSI) is the primary metric from the receiver′s perspective. It measures the power of the signal received by the receiver within wireless networks. RSSI is commonly used in wireless communication technologies, including Wi-Fi, Bluetooth, LoRaWAN, and GSM. General RSSI ranges that can be applied to assess the quality of a signal in LoRa networks are shown below on Table 5:
The values above, along with the corresponding signal quality assessments, are indicative and may vary in practice depending on numerous factors such as the environment, interference, or the specifications and parameters of the communication equipment. Nonetheless, an interesting aspect of analyzing the obtained results is the identification of areas covered by a signal with its adopted limit values within the preset area. The radio coverage of the area with signals above the thresholds of −100, −90, −80, and −70 dBm is shown in Figure 12. These figures indicate that full radio coverage of the preset area, with a signal ensuring reception even at a level defined as weak, was not achieved. However, simulation-based computations suggest that more than 80% of the roads within the preset area are within the range of the transmitting stations.

7. Conclusions

Vehicle traffic monitoring systems, particularly those related to traffic volume and speed control, will remain essential components of road traffic management, road safety improvement, and tools for reducing the risk of traffic accidents for a long time. Therefore, the search for new technological and system solutions that enhance the effectiveness of monitoring and controlling vehicle speed is both justified and desirable. One such technology is a low-energy LoRaWAN sensor network, which can facilitate the creation of a system for traffic volume monitoring and area-based speed control. The key to the smooth, reliable, and efficient operation of such a system is adequate radio coverage of the controlled area.
By analyzing the results obtained, it can be concluded that the practical implementation of an area-based vehicle speed control system using LoRaWAN technology is feasible. In a specific radio system, the primary factors influencing the achieved radio coverage are the height of the transmitting and receiving antennas, the radiation characteristics, and the gain of the transmitting antenna. However, it is important to remember that in practice, the heights of antenna installations will be limited by several constraints, both technical and economic. The power of the transmitted signal from the transmitting station is also subject to legal and technical limitations.
Regardless of the adopted limit value of the receiving signal, given the parameters and locations of the transmitting stations, full radio coverage of the given area and its main road system is not achieved in the analyzed model. Coverage gaps occur primarily in the northern and northwestern parts of the area, mainly due to the remoteness of the transmitting stations and significant forest cover in these regions. However, these areas are also characterized by roads with much lower traffic volumes and poorer quality road surfaces. Therefore, a road network coverage of more than 80% of the area can be considered satisfactory. Nevertheless, this coverage value is likely to change, and possibly decrease, as the diversity of the terrain and the height and density of buildings increase. Consequently, it should not be expected that the required number of transmitting stations can be determined solely from a basic relationship relating only to a one-dimensional space. Thus, radio coverage planning should become a fundamental element of traffic monitoring systems and the design of area-based vehicle speed control solutions.
The authors intend to continue research on the presented concept of monitoring and controlling vehicle speed. They plan to carry out field tests to compare the actual measurement results with the simulation test results. The next steps include deploying network devices in the presented area, with the ultimate goal of implementing the proposed solution in practice within that area.
As an additional measure, the plan is to extend LoRaWAN devices by integrating renewable energy technology. This is intended to simplify the deployment of terminal devices in the field. Additionally, the possibility of integrating the proposed solution with Police Management Systems will be explored. A change in the procedures for directing police patrols for speed control has been adopted as an organizational objective. The proposal is to modify the selection of speed control sites, focusing only on areas where there is currently the highest road safety risk.
The positive results of the research and the implementation of area-based vehicle speed control have broader implications. Not only will this contribute to increasing road safety, which is the primary objective of the ongoing research, but it will also provide real-time, comprehensive, and significantly more detailed traffic volume data compared to spot measurements. This will enhance the accuracy of tracking changes in vehicle traffic flows and improve the precision of traffic models. Additionally, it will support the use of machine learning and artificial intelligence in predicting road safety risks. Of course, implementing these goals will require addressing several constraints, including legal, economic, and even psychological factors. The authors hope that the data obtained from field research will help overcome these limitations. Importantly, they aim to shift the current negative attitude of drivers toward speed checks and foster a more positive public perception by clearly linking speed control measures to road safety risks. The results of this research will be detailed in a forthcoming publication, which will focus on the development of wireless sensor networks for area-based vehicle control and traffic monitoring.

Author Contributions

Conceptualization, M.R. and Z.K.; methodology, M.R.; formal analysis, M.R., M.P., and A.R.; investigation, M.R. and Z.K.; resources, M.R. and Z.K.; writing—original draft preparation, M.R., Z.K., M.P., and A.R.; writing—review and editing, Z.K. and A.R.; visualization, M.R. and Z.K.; supervision, M.R. and A.R.; project administration, M.R.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the research grant of the Warsaw University of Technology supporting scientific activity in the discipline of Civil Engineering, Geodesy and Transport, grant number 24/ILGiT/2023.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Typical LoRaWAN architecture. (Source: authors’ image based on [51,52,53]).
Figure 1. Typical LoRaWAN architecture. (Source: authors’ image based on [51,52,53]).
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Figure 2. Architecture of the proposed solution. (Source: authors’ own image based on [51,52,53]).
Figure 2. Architecture of the proposed solution. (Source: authors’ own image based on [51,52,53]).
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Figure 3. Road system in the Stare Babice commune (source: authors’ own image based on [61]).
Figure 3. Road system in the Stare Babice commune (source: authors’ own image based on [61]).
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Figure 4. Topographic map of the Stare Babice commune. (Source: authors’ own image based on [71]).
Figure 4. Topographic map of the Stare Babice commune. (Source: authors’ own image based on [71]).
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Figure 5. “Area” sub-path attenuation method. (Source: authors’ own image based on [72]).
Figure 5. “Area” sub-path attenuation method. (Source: authors’ own image based on [72]).
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Figure 6. CompleTech ComAnt CAS+ antenna radiation characteristics [73].
Figure 6. CompleTech ComAnt CAS+ antenna radiation characteristics [73].
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Figure 7. Impact of hGW transmitter station location height (10, 15, 20, and 25 m) on radio coverage.
Figure 7. Impact of hGW transmitter station location height (10, 15, 20, and 25 m) on radio coverage.
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Figure 8. Impact of hEN receiving antenna height-wise positioning (2, 4, 6, and 8 m).
Figure 8. Impact of hEN receiving antenna height-wise positioning (2, 4, 6, and 8 m).
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Figure 9. Locations of transmitting stations (GWs) and distribution of area boundaries and roads.
Figure 9. Locations of transmitting stations (GWs) and distribution of area boundaries and roads.
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Figure 10. Radio coverage areas and values for six transmitting stations (GWs) within the preset area.
Figure 10. Radio coverage areas and values for six transmitting stations (GWs) within the preset area.
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Figure 11. Areas of radio coverage by individual GW transmitting stations.
Figure 11. Areas of radio coverage by individual GW transmitting stations.
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Figure 12. Area radio coverage with a signal exceeding the preset value.
Figure 12. Area radio coverage with a signal exceeding the preset value.
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Table 1. Minimum number of gateways NGW depending on transmission radius R.
Table 1. Minimum number of gateways NGW depending on transmission radius R.
R = 1R = 2R = 3R = 4R = 5R = 6R = 7R = 8
218322211
(Source: authors’ own study).
Table 2. Minimum number of end nodes NEN depending on the distance between sensors d.
Table 2. Minimum number of end nodes NEN depending on the distance between sensors d.
d = 100d = 200d = 300d = 400d = 500d = 600d = 700d = 800d = 900
600300200150120100867567
(Source: authors’ own study).
Table 3. CompleTech ComAnt CAS+ antenna parameters.
Table 3. CompleTech ComAnt CAS+ antenna parameters.
Applsci 14 09243 i001Description: cross-polarized yagi, dual feed, physically phased a quarter wavelength along the boom
Frequency: 380–410 MHz, 405–440 MHz, 440–475 MHz, 703–803 MHz, 791–862 MHz, 830–890 MHz, 880–960 MHz
Impedance: 50 ohm
Gain: 8/8 dBi
H -3 dB: 69/68°
E -3 dB: 68/65°
F/B: 12/12 dB
Polarization: circular/slanted
Isolation: 30 dB
Connector: 2*N-/2*TNC-/2*7/16-female
VSWR: <1.5
Radome: UV resistant ABS/FG, RAL 7012, PU foam filling
Radiator: copper
Passive elements: coated aluminum
Attachment: Ø 35–60 mm, aluminum alloy bracket, stainless steel V-bolts and self-locking nuts
Lightning protection: DC short-circuited
Temperature: −40 °C–+80 °C
IP: 67
Table 4. Location data and technical parameters of GW transmitting stations.
Table 4. Location data and technical parameters of GW transmitting stations.
NameLongitudeLatitudeAntenna [m]Frequency [MHz]SFTx BW
[kHz]		Rx BW
[kHz]		Azimuth
[deg]
1GW120.79736052.26405025.00868.10012125.00125.00270
2GW220.84453052.25201025.00868.30012125.00125.000
3GW320.73378052.25285020.00868.30012125.00125.0090
4GW420.81821052.24853020.00868.50012125.00125.000
5GW520.87727052.24396020.00868.10012125.00125.000.00
6GW620.74258052.28003020.00868.50012125.00125.00180
Table 5. General RSSI ranges.
Table 5. General RSSI ranges.
RRSISignal Reception
−60 dBm and betterExcellent
−70 dBm to −60 dBmVery good
−80 dBm to −70 dBmGood
−90 dBm to −80 dBmSatisfactory
−100 dBm to −90 dBmWeak
−110 dBm to −100 dBmVery weak
−120 dBm to −110 dBmSignal absent or almost absent
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Rychlicki, M.; Kasprzyk, Z.; Pełka, M.; Rosiński, A. Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring. Appl. Sci. 2024, 14, 9243. https://doi.org/10.3390/app14209243

AMA Style

Rychlicki M, Kasprzyk Z, Pełka M, Rosiński A. Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring. Applied Sciences. 2024; 14(20):9243. https://doi.org/10.3390/app14209243

Chicago/Turabian Style

Rychlicki, Mariusz, Zbigniew Kasprzyk, Małgorzata Pełka, and Adam Rosiński. 2024. "Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring" Applied Sciences 14, no. 20: 9243. https://doi.org/10.3390/app14209243

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