In recent years, energy harvesting has gained significant attention as a solution for powering IoT devices in smart city environments. Various energy sources can be exploited to supply energy to IoT devices, including ambient, mechanical, human, organic, and hybrid sources. Ambient sources, such as solar, Radio Frequency (RF), thermal, wind, and hydro-based energies, are naturally available in the environment and can be harnessed with minimal operational costs. Additionally, vibrations and pressure are mechanical energy sources that can be explicitly harvested from the environment. Solar energy harvesting is one of the most abundant energy sources, providing a continuous power supply to IoT devices. It has been shown that approximately 173×10¹² kW of energy is continuously produced from solar sources, which is far more than the global energy demand. Photovoltaic (PV) cells, typically made from silicon, convert solar energy into electrical energy through the PV effect. Various types of PV cells, such as mono-crystalline, polycrystalline, and thin-film cells, offer varying efficiency levels. Mono-crystalline cells, with efficiency rates ranging from 15% to 24%, are the most efficient but also the most expensive. While outdoor applications of PV cells dominate, advancements have enabled the deployment of solar energy harvesting in indoor environments as well [5].

The increased use of wireless devices has caused spectrum scarcity issues in unlicensed bands, leading to the development of Cognitive Radio (CR) technology. CR enables dynamic access to licensed spectrum bands by allowing wireless devices to adapt their operating frequencies based on spectrum availability. CR can coexist with licensed users, such as primary users of the spectrum, by identifying vacant bands through techniques like spectrum sensing, spectrum decision, and spectrum handoff. CR-enabled wireless devices can detect unused spectrum bands and opportunistically access them, improving overall spectrum utilization while ensuring minimal interference with primary users. In the context of Cognitive Radio Sensor Networks (CRSNs), dynamic spectrum access allows sensor nodes to transmit data over underutilized licensed bands, significantly improving the efficiency and performance of Wireless Sensor Networks (WSNs).

The cognitive capability of sensor nodes enables spectrum-aware communication, reducing transmission errors and latency while also optimizing energy usage. The integration of energy harvesting techniques with CRSNs further enhances the sustainability of the network by powering CR-enabled sensor nodes autonomously, utilizing ambient energy sources such as solar or RF. This approach is particularly relevant in smart cities, where sensor nodes are deployed over large areas and require long-term, energy-efficient operation [6].

Smart cities rely on advanced sensing and communication es to optimize resource management, infrastructure operation, and service delivery. In such cities, sensors deployed across different systems, including transportation, waste management, and healthcare, require a reliable and continuous power supply. Energy harvesting techniques, particularly those leveraging ambient energy sources, provide a sustainable powering of these sensors. By utilizing energy harvesting, sensors can operate autonomously for extended periods without needing battery replacement or maintenance, which is critical in large-scale deployments.

The holistic planning and operation of smart city infrastructures involve the strategic management of various owes to enhance efficiency, sustainability, and resilience. In this context, energy harvesting plays a crucial role in ensuring the continuous operation of IoT devices that monitor and control city infrastructures. For example, waste management systems in smart cities can use electric trucks powered by energy harvested from the environmentalism overall efficiency by integrating energy, transportation, and waste collection systems. This interoperability between different infrastructures is essential for the seamless operation of smart city services, ensuring that rows are optimized and sustainable. Smart cities rely on advanced sensing and communication technologies to optimize resource management, infrastructure operation, and service delivery. In such cities, sensors deployed across different systems, including transportation, waste management, and healthcare, require a reliable and continuous power supply. Energy harvesting techniques, particularly those leveraging ambient energy sources, provide a sustainable solution for powering these sensors. By utilizing energy harvesting, sensors can operate autonomously for extended periods without needing battery replacement or maintenance, which is critical in large-scale deployments.

The holistic planning and operation of smart city infrastructures involve the strategic management of various resource flows to enhance efficiency, sustainability, and resilience. In this context, energy harvesting plays a crucial role in ensuring the continuous operation of IoT devices that monitor and control city infrastructures. For example, waste management systems in smart cities can use electric trucks powered by energy harvested from the environment, thus improving overall efficiency by integrating energy, transportation, and waste collection systems. This interoperability between different infrastructures is essential for the seamless operation of smart city services, ensuring that resource flows are optimized and sustainable [4].

The transportation sector is a significant consumer of energy, having consumed 40 million tonnes of oil-equivalent energy in 2020, with projections indicating a rise of 8% to 16% in congested traffic by 2050. This anticipated increase in traffic density is expected to lead to greater energy waste, particularly through thermal and mechanical forms, such as friction and vibration. Addressing this issue is crucial for promoting sustainability within the sector, primarily by focusing on strategies that minimize energy loss and enhance energy reclamation [7].

Among the promising technologies for energy reclamation are piezoelectric and electromagnetic road energy harvesters. These devices are designed to capture vibrations and compressive forces generated by moving vehicles. Additionally, alternative energy sources, such as thermoelectric systems and solar panels, can be utilized to harness unused solar and thermal energy from road surfaces. Various studies have concentrated on the development and optimization of piezoelectric road energy harvesters, employing both numerical simulations and laboratory tests to analyze their performance under dynamic loads that replicate real-world driving conditions. These harvesters are typically composed of interconnected piezoelectric elements arranged in various configurations, including cylinders, bridges, stacks, cantilever beams, and cymbals. Experimental testing has shown that certain designs of piezoelectric harvesters can produce voltage outputs ranging significantly based on their structure and the mechanical conditions they endure. While these devices demonstrate an increase in output power with higher applied stress and frequency, their energy yield often remains limited to milliwatt and microwatt ranges. The primary challenges associated with piezoelectric harvesters include their relatively low power output and high maintenance costs, which hinder their widespread applicability [7].

Electromagnetic road harvesters represent another avenue for energy collection. These devices, typically integrated into traffic-calming infrastructures like speed bumps and road rumble strips, convert surface displacements into electrical energy through mechanisms such as rack and pinion systems, hydraulics, and linear electromagnetic generators. Comprehensive evaluations through theoretical simulations, laboratory experiments, and field tests have indicated varying levels of power output, with some configurations achieving peak power outputs significantly higher than those of piezoelectric systems.

Solar energy also presents a viable source for road energy harvesting, encompassing technologies such as solar panel roads and solar collectors. Solar panel roads integrate engineered solar panels that meet the mechanical requirements of roadway surfaces. However, despite advancements in this area, these solar pavements have exhibited limitations, including brittleness and the necessity for frequent repairs, which significantly impact their durability and energy generation capacity. Conversely, solar collectors, which involve circulating water or air through pipelines embedded in the pavement, provide another means of harnessing solar energy, albeit with different operational dynamics [7].

Thermoelectric energy harvesters utilize the temperature gradient between the road surface and its subsurface to generate electricity. These systems capitalize on temperature differentials to produce electrical voltage, making them adaptable in various configurations. When comparing the power outputs of different road EHTs, it becomes evident that while piezoelectric and thermoelectric systems generate energy within the microwatt to milliwatt range, solar pavements and electromagnetic harvesters can yield watt-level power outputs. Nonetheless, the challenges faced by solar panel roads, particularly regarding cost, fragility, and weather dependence, remain a concern for their deployment in smart city infrastructure [7].

Many EHTs, such as piezoelectric and thermoelectric systems, typically produce low power outputs, often in the microwatt to milliwatt range. This limited energy generation may not be sufficient to meet the demands of high-performance IoT devices, particularly those requiring continuous power for real-time data processing and communication.

The efficiency of energy harvesting systems is highly dependent on environmental conditions. For example, solar energy harvesters are affected by factors such as weather, seasonal changes, and time of day. Similarly, piezoelectric harvesters rely on consistent mechanical stress, which can vary significantly based on traffic patterns or human activity.

Many energy harvesting devices face challenges related to durability and maintenance. For instance, solar panel roads have been reported to suffer from brittleness and require frequent repairs due to mechanical stress from traffic. This raises concerns about the long-term viability and cost-effectiveness of deploying such technologies on a large scale.

Integrating multiple EHTs into a cohesive system poses significant technical challenges. Each technology may require different infrastructure, control systems, and data management protocols, complicating the overall design and operation of smart city applications.

Certain energy harvesting solutions, particularly those that involve physical modifications to roadways (like speed bumps or other electromagnetic harvesters), can disrupt traffic flow if not designed carefully. This can lead to increased congestion and frustration among drivers.

The increasing number of wireless devices in urban areas has led to spectrum scarcity issues, complicating the communication needs of IoT networks. Efficiently managing spectrum access while ensuring minimal interference with licensed users remains a significant challenge.

The initial investment required for deploying EHTs can be substantial. Budget constraints in urban planning may limit the adoption of these solutions despite their potential long-term benefits.

As smart cities expand and the number of connected devices increases, ensuring that energy harvesting systems can scale effectively becomes critical. Solutions must be adaptable to accommodate growing energy demands without compromising performance.

The lack of standardized regulations for deploying EHTs can hinder their widespread adoption. Clear guidelines are needed to ensure safety, interoperability, and compatibility with existing infrastructure.

The integration of EHTs with IoT networks generates vast amounts of data that require efficient processing and management solutions. Developing robust data analytics frameworks that can handle this influx while ensuring timely decision-making is essential for optimizing smart city operations.

The first step in this proposed work involves designing a robust architecture for Energy-Harvesting Cognitive Radio Sensor Networks (EH-CRSNs). This architecture will incorporate CR capabilities, allowing sensor nodes to access underutilized licensed spectrum bands dynamically. By employing advanced spectrum sensing techniques, the network will identify available channels and adaptively switch frequencies to optimize communication while minimizing interference with primary users. The design will focus on enhancing the network's resilience against environmental challenges, ensuring reliable data transmission across various urban settings.

To address the power constraints of traditional sensor nodes, the proposed framework will integrate multiple EHTs, including solar, piezoelectric, and thermoelectric systems. Each node will be equipped with a hybrid energy harvesting module capable of capturing energy from various ambient sources such as sunlight, vibrations from traffic, and temperature gradients. This approach not only extends the operational lifetime of sensor nodes but also reduces dependency on conventional battery systems, thereby promoting sustainability.

A critical component of this work is the development of a dynamic spectrum access protocol tailored for EH-CRSNs. This protocol will facilitate efficient communication by enabling nodes to share spectrum sensing results while minimizing control overhead. The protocol will employ distributed decision-making mechanisms to allow nodes to collaboratively determine the best available channels for transmission based on real-time environmental conditions and traffic demands. By optimizing channel selection and reducing transmission errors, this protocol aims to enhance overall network performance and reliability.

To assess the effectiveness of the proposed EH-CRSN framework, a series of simulations and real-world experiments will be conducted. These evaluations will focus on key performance metrics such as energy efficiency, data throughput, latency, and reliability under varying traffic conditions. The results will provide insights into the operational dynamics of the network and inform further optimization strategies. Additionally, machine learning algorithms may be employed to predict traffic patterns and optimize resource allocation dynamically.

Scalability is essential for the success of any smart city application. Therefore, the proposed work will include an analysis of scalability challenges associated with deploying EH-CRSNs in urban environments. Strategies for scaling up the network while maintaining performance levels will be explored, including hierarchical network architectures and adaptive resource management techniques.

As with any IoT deployment, security and privacy are paramount considerations. The proposed work will incorporate security measures to protect data integrity and user privacy within EH-CRSNs. Techniques such as encryption, secure authentication protocols, and intrusion detection systems will be integrated into the network design to safeguard against potential threats.