Smart automated highway lighting system using IoT: a survey

Abstract

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified.

Introduction

Intelligent Transportation Systems (ITS) are undergoing a significant change in the way they operate. Intelligence, defined as the capacity of a system to aid, oversee, and make judgments to enhance important performance measures, is transitioning into a new realm. One of the causes is due to rapid advancements in the collecting of traffic data (Zhang et al. 2011). The growing abundance of accessible data enables more effective and conscientious use of resources. The source of this data is extensive. Overall, it can be said that the field of in-vehicle communication, which includes sensor devices and Road Side Units (RSU), as well as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X), is undergoing significant changes due to the advancements in IoT devices. IoT devices may be integrated into any system or device, enabling communication, information provision, and automation of tasks. Highway lighting plays a vital role in ensuring road safety by providing adequate visibility for drivers during nighttime and low-light conditions. However, traditional highway lighting systems are often inefficient, resulting in unnecessary energy consumption and high operational costs. These systems typically rely on timers or simple photosensors to turn lights on and off based on predetermined schedules or ambient light levels is shown in Fig. 1. This approach fails to account for dynamic traffic conditions, weather patterns, and other environmental factors that may necessitate adjustments in illumination levels.

Fig. 1

Architectural Overview of a Smart Automated Highway Lighting System

Based on statistics from the National Highway Traffic Safety Administration (Singh 2015), 33% of accidents may be attributed to poor driving judgments, with driver recognition being the leading cause at 41%. These values are anticipated to be substantially reduced with the appropriate processing of acquired data, along with the application of a sufficient level of system intelligence. Researchers have investigated many methods to achieve intelligent and autonomous operation of traffic lights at crossings. A variety of techniques have been employed to develop intelligent, self-governing control systems for signaled intersections. These include fuzzy logic ((Pappis and Mamdani 1977; Niittymäki and Pursula 2000)), reservation and market-based systems ((Dresner and Stone 2004; Li et al. 2013)), neural networks (Srinivasan et al. 2006), reinforcement learning (Genders and Razavi 2018), and swarm intelligence and evolutionary computation (Garcia-Nieto et al. 2013). The primary issue with the majority of these algorithms is in their intricate execution on IoT devices in real-time, necessitating the utilization of cloud computing resources (McKenney and White 2013). But with the rise of Smart Cities, autonomous cars, 5G, and ITS, using the advantages offered by IoT (integration, embedded, speed, simplicity, etc.) can be a definite advantage for the development of intelligent traffic light control systems. Figure 2 depict the percentage of total energy consumption used for lightening in various different sectors as shown below.

Fig. 2

Percentage of total energy consumption used for lighting, in six different sectors (Based onhttp:energiwiki.dk, index.php, Belysning. 2014)

Table 1. Summarizes the statistical data on the current energy consumption and inefficiencies of traditional highway lighting systems.

Table 1 Aspects of Traditional Highway Lighting Systems

Autonomous Vehicles (AVs) are becoming an integral part of our society due in part to recent advances in Artificial Intelligence (AI) and technology improvements, which enable safer, easier, and more reliable travel, along with a large reduction in the number of injuries and fatalities caused by human error. An approach to addressing the challenge of urban traffic is using Autonomous Intersection Management (AIM) (Hausknecht et al. 2011). An AIM is a centralized control system, located at an intersection and usually referred to as an Intersection Manager (IM), that coordinates the behavior, states, and actions of all AVs (speed, acceleration, steering, path, etc.) crossing the intersection to eliminate accidents due to human error, thus improving traffic flow. Figure 3 shows a representation of an AIM. Note that AIM refers to the control algorithms of the AVs and IM to the node in charge of the communication between the AVs and the AIM.

Fig. 3

Example of Autonomous Intersection Management (AIM) (Antonio and Maria-Dolores 2022)

The Internet of Things (IoT) has emerged as a transformative technology, enabling the interconnection of various devices and systems through the internet, facilitating data exchange and remote monitoring and control. By leveraging IoT technologies, smart automated highway lighting systems can be developed to dynamically adjust illumination levels based on real-time data from various sensors and inputs. These systems have the potential to significantly improve energy efficiency, reduce operational costs, and enhance road safety by providing adaptive and optimized lighting conditions.

Street lights are luminous fixtures that provide illumination for the road. The operation of a conventional street light is as follows: The street lights, often constructed using LED technology, are installed at the top. The lights are equipped with switches that need human operation by a designated individual to turn them on and off. Identifying malfunctioning lights and other associated issues may be challenging. Figure 4 illustrate the traditional street lights as shown below.

Fig. 4

Conventional street lights (Swathi et al. 2022)

There are many disadvantages of traditional street lights:

It must be turned on/off manually.

If the person does not turn it on for one day, the masses will have difficulty that night.

The lights are constantly on throughout the night irrespective of vehicles, people moving around. This is wastage of energy.

Despite the significant advancements and potential benefits of smart automated highway lighting systems using IoT, several limitations persist. One major challenge is the initial cost of deployment, including the expenses for IoT devices, sensors, and the necessary infrastructure upgrades, which can be substantial. Additionally, these systems rely heavily on stable and continuous internet connectivity, which may not be consistently available in all areas, especially in remote or underdeveloped regions. Another limitation is the potential vulnerability to cyber-attacks, as IoT devices can be targeted, leading to security breaches and unauthorized control of the lighting system. Furthermore, the maintenance and management of a large number of IoT devices require significant resources and technical expertise, which can be burdensome for local authorities. Lastly, the integration of various devices and systems from different manufacturers may result in compatibility issues, affecting the overall efficiency and effectiveness of the lighting system.

The objective of this survey is to explore and evaluate various smart automated highway lighting systems leveraging Internet of Things (IoT) technologies. This includes examining the design, implementation, and performance of these systems in improving energy efficiency, safety, and operational effectiveness. The survey aims to provide a comprehensive understanding of current trends, technological advancements, and practical applications of IoT in highway lighting. By analyzing different approaches and methodologies, this survey seeks to identify best practices and potential areas for future research and development in the field of smart highway lighting.

Key components of smart automated highway lighting systems

Smart automated highway lighting systems typically comprise several key components that work together to enable efficient and adaptive lighting control. These components include:

• 1. Lighting Infrastructure: This includes the luminaires (light fixtures) installed along the highway, which may be based on traditional technologies such as high-pressure sodium (HPS) or light-emitting diode (LED) lamps. LED luminaires are increasingly preferred due to their superior energy efficiency, long lifespan, and ability to dynamically adjust illumination levels (Beccali et al. 2017; Carli et al. 2018).

• 2. Sensor Network: A network of various sensors is deployed along the highway to collect real-time data relevant to lighting control. Common sensors include:

• ● Photosensors: Measure ambient light levels to determine when artificial lighting is required (Pandharipande and Caicedo 2015).

• ● Traffic Sensors: Detect the presence and density of vehicles to adjust lighting based on traffic conditions (Guan et al. 2015).

• ● Weather Sensors: Monitor parameters such as precipitation, temperature, and visibility to adapt lighting levels accordingly (Valentić et al. 2017).

• ● Occupancy Sensors: Detect the presence of pedestrians or other road users in specific areas, enabling localized lighting control (Zeng et al. 2015).

• 3. Communication Network: A reliable and robust communication network is essential for enabling data exchange between the sensor nodes, lighting controllers, and a central management system. Various communication technologies can be employed, including wired (e.g., Ethernet, power line communication) and wireless (e.g., cellular, Wi-Fi, LoRaWAN, ZigBee) options (Andón et al. 2018; Akshay et al. 2017).

• 4. Lighting Controllers: These devices are responsible for receiving data from the sensor network and executing lighting control algorithms to adjust the illumination levels of individual luminaires or groups of luminaires. They may be integrated into the luminaires themselves or deployed as separate units (Tsao et al. 2005; Balaji et al. 2017).

• 5. Central Management System: A central management system, typically a software platform hosted on a server or in the cloud, serves as the brain of the smart lighting system. It collects and processes data from the sensor network and lighting controllers, generates insights and analytics, and enables remote monitoring and control of the entire system (Kumar and Sanadhya 2015; Lau et al. 2016).

• 6. User Interface: A user-friendly interface, such as a web-based dashboard or mobile application, allows system operators and maintenance personnel to monitor the system's performance, configure settings, and receive alerts or notifications (Jing et al. March 2016).

• 7. Renewable Energy Sources and Energy Storage: To further enhance sustainability and reduce operational costs, smart automated highway lighting systems may incorporate renewable energy sources (e.g., solar panels, wind turbines) and energy storage systems (e.g., batteries, supercapacitors) to partially or fully power the lighting infrastructure (Catalina et al. 2018; Jiang et al. 2013).

Table 2 summarizes the key components of smart automated highway lighting systems and their respective functions.

Table 2 Key components of smart automated highway lighting systems

Advancements in lighting infrastructure, sensor networks, communication networks, lighting controllers, central management systems, and user interfaces can significantly enhance the efficiency and effectiveness of highway lighting systems. Modern lighting infrastructure, equipped with energy-efficient LEDs, provides superior illumination while reducing power consumption. Sensor networks can detect real-time traffic conditions, weather changes, and ambient light levels, allowing for dynamic adjustments in lighting to ensure optimal visibility and safety. Robust communication networks enable seamless data transmission between sensors, controllers, and central management systems, facilitating prompt responses to changing conditions. Advanced lighting controllers can automate lighting levels based on sensor inputs, optimizing energy usage without compromising safety. A central management system allows for centralized monitoring and control of the entire lighting network, making it easier to identify and address issues, schedule maintenance, and generate performance reports. User-friendly interfaces ensure that system operators can efficiently manage and customize the lighting system to meet specific needs. Additionally, integrating renewable energy sources such as solar or wind power with energy storage solutions can provide a sustainable and reliable power supply, further reducing the environmental impact and operational costs of highway lighting systems. Collectively, these advancements can lead to safer, more energy-efficient, and sustainable highway lighting solutions.

Communication protocols and technologies

Effective communication is crucial for the successful operation of smart automated highway lighting systems, as it enables the exchange of data between various components and the central management system. Several communication protocols and technologies can be employed, each with its own advantages and trade-offs in terms of data rates, range, power consumption, and scalability.

Wired communication technologies

Wired communication technologies provide reliable and high-speed data transfer but require physical infrastructure deployment, which can be challenging and costly in highway environments.

• 1. Ethernet: Ethernet is a widely adopted wired communication standard that offers high data rates and reliable communication. It can be used to connect lighting controllers and other components to the central management system, particularly in urban areas where existing infrastructure is available (Jiang et al. 2013).

• 2. Power Line Communication (PLC): PLC technology utilizes the existing power lines for data transmission, eliminating the need for dedicated communication infrastructure. This can be advantageous in remote highway locations where deploying new cabling is difficult or expensive. However, PLC may be susceptible to interference and have lower data rates compared to other wired technologies (Ouerhani et al. May 2010; Park et al. 2020).

Wireless communication technologies

Wireless communication technologies offer greater flexibility and ease of deployment, making them well-suited for highway environments where trenching or laying cables can be challenging and costly.

• 1. Cellular Networks (3G/4G/5G): Cellular networks provide wide coverage and high data rates, making them suitable for smart lighting systems that require real-time data transmission and remote monitoring and control. However, cellular connectivity may incur recurring subscription costs and be susceptible to network congestion or coverage gaps in remote areas (Lazaroiu et al. 2018; Shen et al. September 2006).

• 2. Wi-Fi (IEEE 802.11): Wi-Fi is a widely adopted wireless technology that offers high data rates and compatibility with a wide range of devices. It can be used for communication between lighting controllers and the central management system, particularly in urban areas with existing Wi-Fi infrastructure. However, Wi-Fi has limited range and may require multiple access points for highway deployments (Safari et al. 2015; Atzori et al. 2010).

• 3. LoRaWAN (Long Range Wide Area Network): LoRaWAN is a low-power wide-area network (LPWAN) technology specifically designed for IoT applications. It offers long-range communication (up to several kilometers in rural areas) and low power consumption, making it well-suited for sensor networks and lighting controllers in smart highway lighting systems. However, LoRaWAN has relatively low data rates and may not be suitable for applications requiring high-bandwidth data transmission (Löbbers et al. 2020; Saravanan et al. 2020).

• 4. ZigBee (IEEE 802.15.4): ZigBee is a low-power wireless mesh network protocol based on the IEEE 802.15.4 standard. It is designed for low-cost, low-power, and low-data-rate applications, making it suitable for sensor networks in smart lighting systems. ZigBee networks can operate in star, tree, or mesh topologies, providing flexibility and redundancy. However, ZigBee has a limited range and may require multiple hops or gateways for highway deployments (Saadi et al. 2021; Soma et al. 2016).

• 5. Bluetooth Low Energy (BLE): BLE is a low-power wireless communication standard designed for short-range applications. While not primarily intended for large-scale IoT deployments, BLE can be used for localized communication between lighting controllers and nearby sensors or user devices. Its low power consumption and compatibility with smartphones and other mobile devices make it a viable option for certain use cases (Martins et al. 2013; Narendra et al. 2016).

• 6. Narrowband IoT (NB-IoT): NB-IoT is a low-power wide-area network (LPWAN) technology standardized by 3GPP for IoT applications. It operates within licensed cellular frequency bands and offers improved coverage, low power consumption, and support for a large number of connected devices. NB-IoT can be a suitable choice for smart lighting systems that require long-range communication and high scalability, particularly in areas with good cellular network coverage (Avinash et al. 2020; Khan et al. 2020).

Table 3 provides a comparison of the various communication technologies and their key characteristics relevant to smart automated highway lighting systems.

Table 3 Comparison of communication technologies for smart automated highway lighting systems

The selection of the appropriate communication technology or a combination of technologies depends on factors such as the deployment environment, range requirements, data throughput needs, power constraints, and cost considerations. In many cases, a hybrid approach combining multiple technologies may be necessary to address the diverse requirements of smart automated highway lighting systems.

Communication faults detection and maintenance issues

Automated smart highway lighting systems leverage IoT technology to enhance road safety, reduce energy consumption, and provide adaptive lighting based on real-time conditions. However, the effectiveness of these systems hinges on reliable communication and robust maintenance protocols. This document addresses the detection of communication faults and the maintenance issues associated with these systems.

Communication faults detection

Types of Communication Faults:

Signal Interference: Interference from other wireless devices can disrupt communication.

Hardware Failures: Failures in sensors, controllers, or communication modules can lead to data loss.

Network Congestion: High traffic on the network can cause delays and data packet losses.

Power Issues: Inconsistent power supply can lead to intermittent communication failures.

Detection methods

Heartbeat Signals: Regular heartbeat signals from devices can indicate their operational status. A missing heartbeat indicates a potential fault.

Ping Tests: Periodic ping tests to devices can help ensure connectivity. Failure to receive a response signals a communication issue.

Error Logs Analysis: Analyzing error logs from communication modules can help identify recurring faults.

Data Integrity Checks: Regular checks on data packets for corruption or loss can help detect communication issues.

Redundancy Checks: Employing redundant communication paths and comparing their outputs can help in early detection of faults.

Maintenance issues

Predictive Maintenance.

Condition Monitoring: Regular monitoring of equipment condition can help predict failures before they occur.

Data Analytics: Using analytics to predict when a component is likely to fail based on historical data.

Scheduled Inspections: Regular inspections based on predictive data to maintain system integrity.

Challenges.

Environmental Factors: Weather conditions and natural disasters can cause unexpected damage to equipment.

Resource Allocation: Ensuring adequate resources (personnel and equipment) are available for maintenance tasks.

Component Lifespan: Accurately predicting the lifespan of various components to avoid premature failures.

Data processing and lighting control strategies

The effectiveness of smart automated highway lighting systems relies heavily on the ability to process the collected data from various sensors and implement intelligent lighting control strategies. This section discusses data processing techniques and lighting control algorithms employed in these systems.

Data processing techniques

The data collected from the sensor network comprising photosensors, traffic sensors, weather sensors, and occupancy sensors needs to be preprocessed, filtered, and analyzed to extract meaningful insights and support decision-making for lighting control. Common data processing techniques include:

• 1. Data Cleaning and Preprocessing: Raw sensor data may contain errors, noise, or missing values. Data cleaning and preprocessing techniques, such as outlier removal, interpolation, and normalization, are employed to ensure data quality and consistency (Karkudiren et al. 2019; Pei et al. 2020).

• 2. Data Fusion and Integration: Data from multiple sensor types and sources need to be fused and integrated to provide a comprehensive view of the environment and enable effective lighting control decisions. Techniques such as sensor data fusion, data correlation, and data alignment can be used for this purpose (Kowalski and Staworko 2022; Jayaraman et al. 2017).

• 3. Machine Learning and Predictive Analytics: Machine learning algorithms can be applied to the processed sensor data to identify patterns, make predictions, and support automated decision-making. Techniques such as regression, classification, clustering, and time-series forecasting can be employed to predict traffic patterns, weather conditions, and lighting requirements (Khaleghi et al. 2013; Moreno-Munoz et al. 2018).

• 4. Edge and Cloud Computing: Depending on the computational requirements and latency constraints, data processing can be distributed between edge devices (e.g., lighting controllers, gateways) and cloud-based systems. Edge computing enables low-latency local processing, while cloud computing provides scalable resources for more complex analytics and storage (Marino et al. 2021a; Shi et al. 2016).

• 5. Data Visualization and Reporting: Effective visualization and reporting tools are essential for system operators and stakeholders to monitor the performance of the smart lighting system, identify issues, and make informed decisions. Techniques such as dashboards, heat maps, and interactive charts can be employed to present relevant data and insights (Shahzad et al. 2019; Leccese et al. 2014).

Case study 1: smart highway lighting in the Netherlands

The Netherlands implemented a smart highway lighting system along the A44 highway to improve energy efficiency and road safety. This system uses data processing techniques to dynamically adjust lighting based on real-time traffic and environmental conditions.

Data processing techniques

Sensor Data Collection: Sensors installed along the highway gather data on traffic volume, vehicle speed, weather conditions, and ambient light levels.

Data Analysis: The collected data is transmitted to a central processing unit where algorithms analyze the information to determine the optimal lighting levels. For example, during heavy traffic or poor weather conditions, the system increases lighting to enhance visibility and safety.

Adaptive Lighting Control: The processed data triggers adjustments in the LED lighting fixtures. The lighting intensity can be reduced during low-traffic periods or when ambient light is sufficient, leading to significant energy savings.

Case study 2: intelligent lighting system in singapore

Singapore's Land Transport Authority (LTA) deployed an intelligent highway lighting system on the Pan Island Expressway (PIE) to enhance road safety and optimize energy use.

Data processing techniques

Integrated IoT Devices: IoT devices, including motion detectors, cameras, and weather sensors, are integrated along the highway to continuously monitor traffic and environmental conditions.

Real-Time Data Processing: A cloud-based platform processes the data in real-time, utilizing machine learning algorithms to predict traffic patterns and environmental changes.

Dynamic Lighting Adjustment: Based on the processed data, the system dynamically adjusts the brightness of the LED lights. For instance, the lights brighten when sensors detect increased vehicle movement or adverse weather conditions and dim during periods of low traffic.

Lighting control strategies

Based on the processed data and insights, various lighting control strategies can be implemented to optimize the illumination levels and energy efficiency of the highway lighting system. Some commonly employed strategies include:

• 1. Adaptive Dimming: Luminaires are dynamically dimmed or brightened based on real-time data from photosensors, traffic sensors, and occupancy sensors. This strategy ensures that illumination levels are adjusted according to actual needs, reducing energy consumption while maintaining safety and visibility (Leccese 2013; Wang et al. 2010).

• 2. Scheduled Dimming: Lighting levels are adjusted based on predefined schedules or profiles that take into account typical traffic patterns, weather conditions, and ambient light levels. This strategy can be effective in scenarios with relatively predictable patterns but may not adapt well to unexpected events or deviations (Cornel et al. 2015).

• 3. Occupancy-Based Lighting: Specific areas or zones along the highway are illuminated only when the presence of vehicles, pedestrians, or other road users is detected by occupancy sensors. This strategy enables localized and targeted lighting, resulting in significant energy savings in low-traffic areas (Coutinho et al. 2016; Llano et al. 2020).

• 4. Adaptive Traffic Lighting: Illumination levels are adjusted based on real-time traffic data, ensuring that high-traffic areas receive adequate lighting while low-traffic areas are dimmed or turned off. This strategy optimizes energy usage while maintaining safety in high-traffic zones (Marino et al. 2021b; Bruno et al. 2021).

• 5. Weather-Adaptive Lighting: Lighting levels are adjusted based on weather conditions, such as precipitation, fog, or snow, to ensure visibility and safety. For example, luminaires may be brightened during heavy rain or fog to improve visibility (Goodwin et al. 2018; Lee et al. 2012).

• 6. Predictive Maintenance: Machine learning and data analytics techniques can be employed to predict luminaire failures or degradation, enabling proactive maintenance and replacement, thereby reducing downtime and operational costs (Mamuta et al. 2017; Gomez and Paradells 2010).

These lighting control strategies can be implemented individually or combined in a hybrid approach to achieve optimal results based on the specific requirements and constraints of the highway lighting system.

Integration of renewable energy sources and energy storage

To further enhance the sustainability and environmental friendliness of smart automated highway lighting systems, the integration of renewable energy sources and energy storage systems can be explored. This section discusses the potential incorporation of these technologies into the overall system architecture is shown in Fig. 5.

Fig. 5

Comparison of Communication Technologies for Smart Lighting Systems

Renewable energy sources

Renewable energy sources, such as solar and wind power, can be leveraged to partially or fully power the lighting infrastructure, reducing reliance on traditional energy sources and minimizing the carbon footprint of the system.

• 1. Solar Photovoltaic (PV) Systems: Solar PV panels can be installed along the highway or in nearby locations to harness solar energy and generate electricity for the lighting system. The PV panels can be integrated with the luminaires themselves or installed as separate arrays. The generated electricity can be used directly to power the luminaires or stored in energy storage systems for later use (Velaga and Quddus 2010; Tao et al. 2017). Solar PV systems are particularly suitable for highway lighting applications due to the availability of open spaces and the potential for capturing sunlight throughout the day is shown in Fig. 6.

• 2. Wind Turbines: Small-scale wind turbines can be deployed along highways to harness wind energy and generate electricity. While wind energy may not be as reliable or consistent as solar energy, it can complement solar PV systems and provide additional renewable energy generation capacity (Bentzen et al. 2018; Kusakana and Vermaak 2014). The selection and placement of wind turbines should consider factors such as wind patterns, turbulence, and potential noise or visual impact.

• 3. Hybrid Renewable Energy Systems: Combining multiple renewable energy sources, such as solar PV and wind turbines, can provide a more stable and reliable supply of electricity for the lighting system. Hybrid systems can leverage the strengths of each technology and mitigate the intermittency and variability of individual sources (Essa et al. 2019; El-Sayed et al. 2020).

Fig. 6

Energy Savings and Benefits of Smart Automated Highway Lighting Systems

The integration of renewable energy sources requires careful planning and consideration of factors such as local climate conditions, available space, installation costs, and grid integration or energy storage requirements. Additionally, monitoring and control systems may be needed to optimize the performance and efficiency of the renewable energy systems.

Energy storage systems

Energy storage systems can be employed in conjunction with renewable energy sources to store excess energy generated during periods of high production and supply power to the lighting system during periods of low or no renewable energy generation.

• 1. Battery Energy Storage Systems (BESS): Lithium-ion batteries or other advanced battery technologies can be used to store electrical energy generated from solar PV or wind turbines. BESS systems can provide backup power, load shifting, and energy management capabilities, ensuring a reliable and uninterrupted supply of electricity for the lighting system (Mohanty et al. 2022; Vedikunnan et al. 2021).

• 2. Supercapacitors: Supercapacitors, also known as ultracapacitors, are energy storage devices with high power density and fast charging/discharging capabilities. They can be used in smart lighting systems for short-term energy storage and load leveling, complementing battery systems or serving as standalone storage solutions for certain applications (Singh et al. 2020; Harris et al. 2017).

• 3. Hybrid Energy Storage Systems: Combining different energy storage technologies, such as batteries and supercapacitors, can leverage the strengths of each technology and provide an optimized energy storage solution. Batteries can handle long-term energy storage, while supercapacitors can handle high-power demands and transient loads (Huang et al. 2020; Hu et al. 2015).

The selection and sizing of energy storage systems depend on factors such as the renewable energy generation capacity, lighting load profiles, desired autonomy (the ability to operate independently from the grid), and cost considerations. Effective energy management strategies, including charge/discharge control and demand-side management, are crucial for maximizing the benefits of energy storage systems.

Table 4 summarizes the potential renewable energy sources and energy storage systems that can be integrated into smart automated highway lighting systems.

Table 4 Renewable energy sources and energy storage systems for smart automated highway lighting systems

The integration of renewable energy sources and energy storage systems not only enhances the sustainability and environmental friendliness of smart automated highway lighting systems but also contributes to their resilience and reliability by providing backup power and energy management capabilities.

Table 5 presents the installation costs and economic evaluation results of the individual and central energy storage systems for solar road lighting systems. The road length was 1 km and the assumed project life was 20 years (Yoomak and Ngaopitakkul 2019).

Table 5 Comparison of installation cost and economic evaluation of different energy storage systems for individual and central installation systems

Table 6 presents the economic feasibility and return on investment for integrating renewable energy sources and energy storage in highway lighting systems.

• Initial Investment: Total cost required upfront for installation of renewable energy and storage systems.

• Operating Costs: Annual expenses for maintaining and monitoring the installed systems.

• Energy Savings: Yearly reduction in electricity costs due to using renewable energy.

• Maintenance Savings: Annual savings from reduced maintenance compared to traditional systems.

• Energy Storage Benefits: Additional financial benefits from energy storage operations.

• Return on Investment (ROI): Percentage return on the initial investment over a specified period.

• Payback Period: Time in years required for the project to break even and start generating positive cash flow.

• Environmental Benefits: Quantitative reduction in environmental impact, such as CO2 emissions.

• Risk Factors: Potential challenges or uncertainties affecting the economic feasibility.

• Regulatory and Incentive: Government policies or financial incentives that can affect project economics.

• Scalability and Future Growth: Potential for expanding the project or technology to other applications.

Table 6 Economic feasibility and return on investment for integrating renewable energy sources and energy storage in highway lighting systems

This structured approach provides a clear comparison of costs, benefits, and risks associated with integrating renewable energy and energy storage in highway lighting systems, facilitating decision-making based on economic feasibility and ROI considerations.

Data preprocessing and data storage

In a smart highway lighting system, data preprocessing and data storage are critical steps to ensure efficient and effective operation. Here’s a structured approach to both aspects:

Data preprocessing

Data collection

- Sensors: Collect data from various sensors such as light sensors, motion detectors, weather.

sensors, and traffic cameras.

- External Sources: Gather additional data from external sources like weather forecasts, traffic.

reports, and time schedules.

Data cleaning

- Noise Reduction: Remove noise and irrelevant data from sensor readings.

- Handling Missing Values: Implement methods like interpolation or use default values to handle.

missing data.

- Outlier Detection: Identify and handle outliers that could skew analysis.

Data transformation

- Normalization: Scale the data to a standard range to ensure uniformity.

- Aggregation: Summarize data into meaningful intervals (e.g., average light intensity per hour).

- Feature Engineering: Create new features from raw data that can provide better insights (e.g.,

combining temperature and humidity to assess foggy conditions).

Data integration

- Merge Data: Integrate data from different sensors and sources into a unified format.

- Time Alignment: Synchronize data from various sensors to ensure they are time-aligned for.

accurate analysis.

Data filtering

- Thresholding: Apply thresholds to filter out data points that do not meet certain criteria (e.g.,

ignoring light sensor data when there is no traffic).

Data storage

Database selection

- Relational Databases: Use SQL databases for structured data with complex queries (e.g.,

PostgreSQL, MySQL).

- NoSQL Databases: Use NoSQL databases for unstructured or semi-structured data and when.

scalability is a concern (e.g., MongoDB, Cassandra).

Data schema

- Structured Storage: Design tables and collections to store sensor data, processed data, and.

metadata.

- Indexing: Implement indexing on critical fields (e.g., timestamp, sensor ID) to speed up queries.

Data storage practices:

- Real-time Storage: Use stream processing tools (e.g., Apache Kafka) to handle real-time data.

ingestion and storage.

- Batch Storage: Store data in batches for non-real-time analysis and historical data storage.

Data retention and archiving

- Retention Policies: Define policies to retain only necessary data and archive or delete old data.

to save storage space.

- Archiving: Move historical data to cheaper storage solutions (e.g., cloud storage services like.

AWS S3) for long-term retention.

Backup and recovery

- Regular Backups: Schedule regular backups of the database to prevent data loss.

- Disaster Recovery Plans: Implement disaster recovery plans to restore data in case of system.

failures.

Security and privacy

- Data Encryption: Encrypt data at rest and in transit to protect against unauthorized access.

- Access Control: Implement strict access controls to ensure only authorized personnel can.

access or modify the data.

Practical implementation case studies

Several pilot projects and real-world implementations of smart automated highway lighting systems have been undertaken globally, demonstrating the feasibility and benefits of these systems. This section presents a few case studies to illustrate the practical aspects and challenges involved in deploying such systems.

Smart highway lighting in the Netherlands

The Netherlands has been at the forefront of implementing smart highway lighting solutions. In 2013, the Dutch Ministry of Infrastructure and the Environment launched a pilot project called "Smart Light on Smart Highway" on the A58 highway near Eindhoven (Yun and Bae 2019).

The system employed a combination of sensors, including photosensors, traffic sensors, and weather sensors, to collect real-time data. The data was processed and used to control the illumination levels of LED luminaires along the highway. The lighting control strategies included adaptive dimming based on traffic density, weather conditions, and ambient light levels.

The pilot project demonstrated significant energy savings of up to 35% compared to traditional highway lighting systems, while maintaining or improving road safety. Additionally, the system facilitated predictive maintenance by monitoring luminaire performance and identifying potential failures.

Building on the success of the pilot, the Netherlands has since expanded the implementation of smart highway lighting solutions to other highways and regions, leveraging the expertise and experience gained from the initial project.

Intelligent lighting system in Barcelona, Spain

The city of Barcelona, Spain, implemented an intelligent street lighting system called "Lumina" to improve energy efficiency and sustainability (Quintero et al. 2019). While not specifically focused on highways, the project demonstrates the potential of smart lighting solutions in urban environments.

The system employed a combination of LED luminaires, wireless communication networks, and a central management platform. Sensor data, including ambient light levels and traffic information, was collected and processed to dynamically adjust the illumination levels of the luminaires.

The project achieved energy savings of up to 30% compared to traditional lighting systems, along with reduced maintenance costs and improved quality of life for citizens. The success of the Lumina project has inspired other cities to explore similar intelligent lighting solutions.

Smart lighting pilot project in Singapore

In 2018, the Land Transport Authority (LTA) of Singapore launched a pilot project to test smart lighting solutions along a stretch of the Pan-Island Expressway (PIE) (Rinaldi et al. 2015). The project aimed to improve energy efficiency, reduce maintenance costs, and enhance road safety.

The system utilized LED luminaires, photosensors, and a wireless communication network based on LoRaWAN technology. The luminaires were capable of dimming and brightening based on real-time data from the sensors, enabling adaptive lighting control.

The pilot project demonstrated energy savings of up to 30% compared to conventional highway lighting systems. Additionally, the system provided real-time monitoring and diagnostics capabilities, enabling predictive maintenance and reducing downtime.

Based on the successful pilot, the LTA plans to expand the implementation of smart lighting solutions to other highways and expressways in Singapore.

Challenges and lessons learned

While the case studies demonstrate the potential benefits of smart automated highway lighting systems, several challenges and lessons learned can be identified:

• 1. Initial Investment Costs: The deployment of smart lighting systems often requires significant upfront investments in infrastructure, sensors, communication networks, and control systems. Careful cost–benefit analyses and long-term planning are necessary to justify the investments and achieve desired returns.

• 2. Interoperability and Integration: Ensuring interoperability and seamless integration among various components (e.g., sensors, luminaires, controllers) from different manufacturers can be challenging. Adopting open standards and protocols can facilitate better integration and future-proof the systems for upgrades and expansions (Tan et al. 2018).

• 3. Data Management and Cybersecurity: Smart lighting systems generate and process large volumes of data from multiple sources. Effective data management strategies, including data storage, processing, and analytics, are crucial. Additionally, cybersecurity measures must be implemented to protect the systems from potential threats and unauthorized access (Denardin et al. 2019).

• 4. Scalability and Flexibility: As smart lighting systems expand and evolve, scalability and flexibility become important considerations. The systems should be designed to accommodate future growth, technology upgrades, and changing requirements without significant disruptions or costly replacements (Haus et al. 2019).

• 5. Stakeholder Engagement and Public Acceptance: Effective communication and engagement with stakeholders, including local authorities, road operators, and the public, are essential for successful implementation and acceptance of smart lighting systems. Addressing concerns related to privacy, light pollution, and visual impact can contribute to wider adoption (Chalmers et al. 2019).

• 6. Maintenance and Lifecycle Management: While smart lighting systems can facilitate predictive maintenance and reduce downtime, proper maintenance strategies and lifecycle management plans should be in place to ensure long-term system reliability and performance (Andrade et al. 2022).

• 7. Regulatory and Policy Considerations: The deployment of smart lighting systems may be subject to regulatory requirements, standards, and policies related to energy efficiency, environmental impact, and public safety. Compliance with these regulations and proactive engagement with policymakers can facilitate smooth implementation (Ciocoiu et al. 2022).

Case study 1: adaptive lighting system on a busy urban highway

In a bustling urban area, the city's transportation department implemented a smart automated highway lighting system along a major highway using advanced data processing techniques and control strategies. The system was equipped with a network of sensors to collect real-time data on traffic flow, vehicle speed, and ambient light conditions. By processing this data, the system could dynamically adjust the brightness of the LED streetlights.

During peak traffic hours, when vehicle density was high, the lighting system automatically increased the brightness to enhance visibility and safety for drivers. Conversely, during late-night hours with minimal traffic, the lights were dimmed to conserve energy. The system also integrated weather sensors to detect fog or heavy rain, automatically boosting lighting levels to improve driving conditions during adverse weather.

This adaptive lighting approach resulted in a significant reduction in energy consumption, as the lights were only at full brightness when necessary. Additionally, the enhanced visibility during critical times reduced the number of traffic accidents on the highway, demonstrating the effectiveness of data-driven control strategies in improving both energy efficiency and road safety.

Case study 2: solar-powered smart lighting system in rural highway

In a rural region with limited access to the traditional power grid, a smart automated highway lighting system was deployed along a stretch of highway notorious for poor visibility and frequent accidents. The system utilized solar panels and energy storage solutions to provide a sustainable power source for the LED streetlights. A central management system collected data from motion sensors and ambient light detectors installed along the highway.

Using data processing techniques, the system analyzed patterns in vehicle movements and ambient light levels to develop optimal lighting schedules. During periods of low traffic, the lights operated at reduced brightness or turned off completely to save energy. When vehicles were detected approaching, the lights automatically increased in brightness to ensure safe passage.

The central management system also allowed for remote monitoring and control, enabling quick adjustments based on real-time data and periodic maintenance checks. The integration of renewable energy and smart control strategies not only provided reliable and efficient lighting but also contributed to the sustainability goals of the region.

This implementation showcased how smart automated lighting systems could be effectively utilized in areas with limited infrastructure, leveraging data processing and control strategies to enhance safety and sustainability.

By addressing these challenges and incorporating lessons learned from existing implementations, future smart automated highway lighting systems can be designed and deployed more effectively, maximizing their potential benefits and ensuring long-term sustainability.

Open research issues and future directions

While significant progress has been made in the development and implementation of smart automated highway lighting systems, several open research issues and future directions remain to be explored:

• 1. Integration of Renewable Energy Sources: Prioritizing renewable energy integration aligns with global sustainability goals and reduces operational costs.

• 2. IoT and Sensor Integration: Real-time monitoring enhances safety and efficiency, leveraging advancements in IoT and sensor technologies.

• 3. Advanced Energy Storage: Addressing energy storage improves reliability by mitigating renewable energy intermittency.

• 4. Data Analytics and AI: Predictive maintenance enhances system reliability and operational efficiency.

• 5. Adaptive Lighting Control: Optimizing lighting based on real-time conditions balances energy efficiency and safety.

• 6. Cybersecurity: Ensuring robust cybersecurity safeguards against potential threats to connected infrastructure.

• 7. Emerging Technologies: Exploring the potential of 5G and edge computing for enhanced performance and scalability.

• 8. Human-Centric Design: Designing systems that meet user needs and preferences promotes acceptance and usability.

• 9. Environmental Impact: Assessing environmental implications ensures sustainable deployment and operation.

• 10. Regulatory Frameworks: Supportive policies and regulations are essential for successful implementation and scalability.

Table 7 presents the open research issues based on current technological trends and their potential impact.

Table 7 Open research issues

Addressing these open research issues and future directions will drive the continuous improvement and evolution of smart automated highway lighting systems, unlocking new possibilities for energy efficiency, safety, and sustainability in transportation infrastructure.

Conclusion

Smart automated highway lighting systems leveraging IoT technologies have the potential to significantly enhance energy efficiency, reduce operational costs, and improve road safety by providing adaptive and optimized lighting conditions. This review paper has provided a comprehensive overview of the key components, communication protocols, data processing techniques, and lighting control strategies employed in these systems.

The integration of renewable energy sources and energy storage systems has been explored as a means to further enhance the sustainability and environmental friendliness of smart lighting systems. Practical implementation case studies have highlighted the benefits and challenges associated with deploying these systems in real-world scenarios.

While significant progress has been made, several open research issues and future directions remain to be explored, including advanced sensor technologies, artificial intelligence, cybersecurity, energy harvesting, integration with smart city initiatives, circular economy considerations, human-centric lighting, standardization, and impact assessments.

By addressing these challenges and continuing to drive innovation in this field, smart automated highway lighting systems can contribute to the realization of more sustainable, efficient, and safe transportation infrastructure, aligning with global efforts towards energy conservation, environmental protection, and enhanced quality of life.