Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain

Research questions

The primary objective of this study is to conduct an SLR that identifies, analyzes, and summarizes empirical evidence related to the integration of blockchain with UAVs in smart cities. The review focuses on using blockchain with UAVs in smart cities. It also focuses on the applications, components and types of UAVs. It also focusses on the challenges of UAVs in smart cities. Furthermore, it also focuses on UAVs applications. The article also focuses on the need for the integration of blockchain with UAVs in smart cities make secure transactions. The challenges with the integration of blockchain with UAVs are also discussed and the solutions are also provided in this article. The research questions and the motivation behind each question have been formulated to guide the review process to achieve this goal. Table 2 illustrates the research question, and Fig. 4 shows the proposed methodology.

Select data sources

Data sources are the libraries or repositories from where the research studies should be retrieved. Four digital libraries have been chosen to extract the primary analyses: IEEE Explore, Science Direct, ACM Digital Library, and Springer Link (Kitchenham, 2004). The full text of the documents is searched to identify the prior studies. There are various options available to search each digital library for pertinent information. To find the most relevant literature, the search strategy is modified to satisfy the needs of the respective data source. Selected data sources and the number of studies produced by search queries are illustrated in Table 3 and Fig. 5.

Formulate search string

A search string is a carefully crafted combination of keywords and search operators used to identify relevant studies that address the research question or topic of the review. This step focuses on specific keywords and their synonyms chosen from the identified research questions, as indicated in Table 2, to create the search string. These keywords are put together using the ‘AND’ ‘OR’ conditions in the order listed to complete the following search string: Fig. 6 and Table 4 illustrates the process of formulating a search string.

Define inclusion and exclusion criteria

• Inclusion criteria in an SLR refer to the predefined rules used to determine which studies will be included in the review. In this review, the following inclusion criteria will be considered:

  • Studies must have been published in the English language within the timeframe of 2016 to 2023. The subject of the study should be centered on UAVs utilization in the smart city’s domain. Selected studies must involve empirical research, conducting practical experiments on specific datasets. The investigations undertaken in the study should pertain to the applications, architecture, and components of UAVs and blockchain. Each chosen study must encompass a comprehensive evaluation of blockchain and integration of blockchain with UAVs in smart cities. The scope of selected articles should be confined to publications in reputable journals, conferences, or books.

• Exclusion criteria in an SLR refer to predesigned conditions to determine which studies will be excluded from the review.

  • The following categories of studies have been designated for exclusion:

  • Those published before 2016.

  • Those whose primary focus is not on UAVs and its applications in smart cities.

  • Studies that lack empirical analysis results.

  • Studies that fail to evaluate the performance of UAVs and blockchain.

Define quality assessment criteria

Quality assessment criteria in an SLR refer to the predefined standards or guidelines used to assess the included studies’ quality, reliability, and validity. Defining quality assessment criteria ensures that the selected primary studies offer sufficient details to analyze the identified research question effectively. In this step, a standard is defined against each research question. Each quality assessment criterion is denoted by C and its respective number, as shown in Table 5.

Primary study selection

Primary studies refer to the individual articles or book sections that directly address the research questions or topic of the review. This review has selected prior studies using the tollgate approach, a structured methodology of five phases (Keele, 2007). This approach was instrumental in carefully curating 49 primary studies, considering the specified quality criteria for prior studies. The primary study selection is illustrated in Table 6 and Fig. 7, and the overall process (prism diagram is shown in Fig. 8).

Applications of UAVs in smart cities

Recent advancements in drone technology and related fields have opened up various commercial opportunities. Different drones can be used depending on the task or the issue that must be resolved. Due to emerging scientific technologies, UAVs have become more creative in the public and private sectors (Euchi, 2021). UAVs are thought to have a significant advantage in several domains, particularly in public safety, rescue, and product delivery (Lagkas et al., 2018). UAVs may potentially be used to bring medical supplies to inaccessible places. UAVs can carry out multiple tasks using sensors, cams, and other payloads (Pathak et al., 2020). There are many applications of UAVs in smart cities shown in Fig. 9. UAVs’ communication network capacity has been placed to use in various situations. Drones can prove valuable in providing aid in scenarios when other communication channels fail by growing into self-sustaining infrastructure (Sharma et al., 2020). This section examines the work done in the deploying of UAV applications in smart cities.

UAV applications significantly deliver various smart city services by optimizing data collection and analysis, increasing infrastructure management, encouraging efficiency, and enhancing emergency service (Yigitcanlar et al., 2020).

Traffic management and monitoring

Traffic congestion is a significant issue that confronts numerous major cities. It arises from various factors, such as peak hours, major events, construction activities, or accidents, leading to sudden or periodic increases in the number of vehicles. These issues can arise at any time and in any city location. Consequently, transportation administrators must seek more efficient and effective approaches to alleviate traffic congestion. To identify an appropriate solution, it is crucial to know congestion-prone areas, their causes, the volume of vehicles, and the condition of the roads in those areas. Although static cameras installed on city streets can offer some insights into these factors, they often fail to provide comprehensive real-time information necessary for managing the problem (Chen et al., 2016). Figure 10 highlights diverse UAV applications for smart cities (Lucic et al., 2023).

Types of UAVs

UAVs drone, also known as a drone, is operated without a human pilot (Chaurasia & Mohindru, 2021). They use autonomous or remote-control systems to fly, making them suitable for a wide range of applications, including aerial photography, delivery of goods, an inspection of infrastructure, and military operations. A UAV drone typically consists of a flight control system, a propulsion system, navigation sensors (such as GPS), communication systems, and payload sensors or cameras (Alghamdi, Munir & La, 2021). The flight control system receives input from the remote operator or autonomous onboard systems and then controls the drone’s movement through its propulsion system. Navigation sensors help the drone determine its position and orientation, while communication systems allow it to receive commands and transmit data (Sandino et al., 2020). The payload sensors or cameras collect data or capture images and video. UAVs use various technologies to operate effectively and efficiently (Albeaino, Gheisari & Franz, 2019).

The flight mission of a UAV is typically predefined, but a pilot can manage its velocity and direction via remote robotic system commands from a base station (Radoglou-Grammatikis et al., 2020). UAVs are available in a variety of sizes, features, and models. UAVs of various combinations are employed for a variety of reasons. We distinguish and analyze multiple types of UAVs in this area according to their technical attributes and payload. Figure 11 shows the four major types of UAVs. Figure 12 (Partheepan, Sanati & Hassan, 2023) shows different classifications of UAVs.

Speed and security

UAV drones have the potential to revolutionize delivery services by offering faster and more efficient delivery times. However, drones’ speed can also threaten public safety if operated at high speeds in densely populated areas (Cherif et al., 2021). UAV drones also raise security concerns, particularly in surveillance and privacy. Drones equipped with cameras and other sensors can be used to collect sensitive data, and there is a risk of drones being hacked and used for malicious purposes (Nassi et al., 2019). It is essential to address speed and security concerns through effective regulations and best practices to ensure their safe and efficient integration into urban environments.

Secure communication channel

There is a need for a secure communication channel for UAVs in smart cities to protect against various security threats and ensure drones’ safe and efficient operation. A secure communication channel can help protect this data from unauthorized access and ensure it is transmitted and stored securely (Nikooghadam et al., 2021). UAVs must be operated safely to avoid accidents and other incidents that could risk public safety. Secure communication can help ensure that UAVs are managed, controlled, and safe by providing real-time communication and control between the drone and its operator (Khan et al., 2020a).

Inventory management system

Strict regulations surround the use of drones, especially regarding their usage in commercial operations. Most drones have limited flight time, which can challenge extended inventory management tasks (Reddy Maddikunta et al., 2021). Drones have limited storage capacity, making transporting and storing large items challenging for inventory management purposes. Operating and maintaining drones for inventory management requires technical expertise and training, which can be challenging for some organizations (Wawrla, Maghazei & Netland, 2019).

Global channel for connectivity and communication

Global communication channels are necessary for UAVs in smart cities to ensure coordination, safety, and efficiency in air traffic management. With the increasing use of drones for various purposes, it is essential to have a unified communication system that allows for real-time communication and exchange of information between different drone systems, ground control stations, and air traffic control (Vinogradov et al., 2019). This helps prevent potential collisions, ensure regulations comply, and allow for optimized flight paths. Global communication channels also facilitate communication with other air and ground-based vehicles and can provide critical information such as weather conditions, air traffic updates, and emergency alerts (Al-Mousa et al., 2019).

Inter-service operation capabilities

Inter-service operation capabilities in drones refer to the ability of drones to interact and coordinate with other technologies, systems, and services in smart cities. Different services may use different communication systems, leading to poor interoperability (Aloqaily et al., 2021). UAVs collect vast amounts of data, but sharing that data between various services can be challenging. Different services may have other operating procedures, leading to confusion and misunderstandings during UAV operations (Zhai et al., 2020). These challenges can limit the effectiveness and efficiency of UAV operations, making it essential for services to work together to overcome them.

Limited storage and processing power

The amount of storage and onboard processing power limits UAVs. These limitations can affect the performance and capabilities of the drone. Drones also have limited processing power, impacting their ability to process data in real time, especially for complex tasks such as object recognition, obstacle avoidance, and real-time decision-making (Fraga-Lamas et al., 2019). Drones’ limited storage and processing power can also impact their flight time, as these systems consume battery power, reducing the time the drone can remain in the air (Boukoberine, Zhou & Benbouzid, 2019).

Autonomous network security system

There are several challenges in implementing an autonomous network security system for UAVs in smart cities. UAVs operate in the same frequency bands as other communication systems, which can lead to interference and disruption of normal operations (Li et al., 2021). As UAV usage grows, the communication system must be able to handle increasing numbers of devices and provide adequate coverage to accommodate changing traffic patterns. UAVs require significant bandwidth, which can be challenging to allocate and manage efficiently (Khan et al., 2021a).

Securely sending data to end users

Data transmitted between UAVs and end users can be intercepted by unauthorized parties, compromising security. UAVs generate large amounts of data, leading to network congestion, particularly in densely populated urban environments (Alam et al., 2021). Other wireless communication systems in the urban environment can interfere with UAV communication, leading to errors and data loss. The privacy of end-user data must be protected, and UAV communication systems must ensure that data is transmitted securely and encrypted to prevent unauthorized access (Atoev et al., 2019).

Blocking of the line of sight

Blocking the line of sight is a common challenge in UAV communication in smart cities due to tall buildings, bridges, and other structures that can obstruct the communication between the UAV and the ground station or end-user (Yu et al., 2022). The UAV may lose contact with the ground station or end user, impacting real-time data transmission (Mohsan et al., 2022a). Obstructed line of sight (LoS) can increase latency and reduce the reliability of the UAV communication system. UAV communication systems may not provide adequate coverage in areas with obstructed LoS, leading to dead communication zones (Albanese, Sciancalepore & Costa-Pérez, 2021).

UAV surveillance network

UAVs must be able to cover a large area in real time, which requires a robust and scalable communication network. UAVs have limited energy resources, and the communication and data processing systems must be designed to minimize power consumption (Zhang et al., 2020). UAV surveillance systems must protect individuals’ privacy and comply with relevant privacy regulations. This surveillance system must be integrated with existing surveillance and security systems to ensure efficient and effective operations (Lykou, Moustakas & Gritzalis, 2020).

UAV networks for edge computing

UAV networks for edge computing can be used in various applications, including surveillance, environmental monitoring, agriculture, and emergency response (Xu, Ota & Dong, 2020). UAV networks for edge computing involve using a network of UAVs to collect and process data locally rather than relying on a centralized computing system (Liu et al., 2022). This enables real-time decision-making and reduces the amount of data that must be transmitted over long distances, thus reducing latency and increasing data processing speed. As the number of UAVs in a network grows, it becomes more challenging to manage and coordinate communication and data processing among the devices (Alzahrani et al., 2020).

Limited battery issue

Depending on factors such as its size, cargo, and battery life, a UAV’s flying time may be as little as 20 min or as long as several hours. A substantial quantity of energy is required to complete these duties. Additionally, not all drones have sufficient battery power to provide services for a lengthy duration of time (Islam et al., 2021). UAVs have a significant difficulty in the context of smart cities due to their limited battery life (Dai et al., 2021). As UAVs grow more commonplace in urban settings for purposes like surveillance, traffic monitoring, environmental evaluations, and emergency response, the problem of limited battery capacity becomes all the more pressing.

Blockchain integration with UAVs for better speed and security

The combination of UAVs and blockchain technology has the potential to improve efficiency and safety in many situations. Blockchain technology allows for decentralized and immutable data storage. Images, sensor readings, and flight logs from UAVs can all be recorded in a distributed ledger. The complete supply chain of UAV components could be tracked with blockchain technology. By eliminating the possibility of using fake or substandard components, this improves the UAVs’ dependability and security. In the study of Ch et al. (2020) the research introduces a blockchain technology (BCT)-based approach to protecting the confidentiality of information collected by devices. To test the effectiveness of the proposed design, an IoT based application has been integrated into a simulated vehicle monitoring system. Penta tope-based elliptic curve encryption and Secure Hash Algorithm (SHA) are used to secure privacy in data storage, and this includes the technical details of instructions to the vehicle (devices), authentication, integrity, and vehicle reactions. In order to facilitate BCT transactions, the information is later recorded in a public blockchain based on Ethereum. The system relies on the secure and private Ganache platform for BCT to store and process user information.

Blockchain integration with UAVs for secure communication channel

Blockchain technology can be utilized for the preservation of encryption keys. The blockchain can be used to encrypt messages transferred between UAVs, making it so that only approved UAVs can read them. With the use of blockchain technology, UAVs could safely authenticate each other’s identities before opening up communication lines. UAVs used for delivery services can benefit from blockchain’s ability to track the entire supply chain, which in turn assists in preventing counterfeiting. As the network grows larger, the use of blockchain technology could assist in guaranteeing its security and transparency. In order to ensure the safety of communications in a UAV network, the authors of the study (Ghribi et al., 2020) suggest a new consensus-building methodology that combines blockchain technology, the Elliptic Curve Diffie-Hellman public key cryptography technique, and the one-time pad encryption technique.

Blockchain integration with UAVs for inventory management system

UAVs equipped with sensors and GPS can monitor shipments in real time. With the use of the blockchain, all parties involved may track the whereabouts and status of stock in real time. Using low-cost mini-drones and single-board computers, The research of Cristiani et al. (2020) tested a prototype implementation of the data acquisition and management framework. Our goal is to propose a generic architecture for UAV-based inventory management inside large-scale warehouses, including the elements of UAV path planning, package recognition (via QR codes), data validation (via the blockchain), and wireless charging. Second, authors examine the system’s effectiveness, focusing on the compromise between inventory accuracy (the proportion of correctly identified packages) and total inventory processing time. Finally, using a small-scale testbed, the author validates the system’s functionality and configuration parameters by determining the ideal UAV mobility characteristics with regard to of speed and number of visits for each shelf unit.

Blockchain integration with UAVs for global channel for connectivity and communication

Using cryptographic techniques like public-key encryption and hash functions, blockchain ensures the integrity of the distributed ledger. It can be used to improve the accessibility and safety of aerial communication networks while also protecting the integrity of stored data. The research of Kumar et al. (2021) gives the results of an online survey on the topic of using blockchain technology with BAC. They begin by investigating the existing state of security in aerial communication networks, the benefits of blockchain technology, and the potential for its use in a solution. The next section provides an in-depth discussion of the author’s suggested options for implementing the blockchain in order to address the existing security vulnerability in aerial communication networks. Finally, the authors sort the solutions into categories and assess and contrast the benefits and drawbacks of each.

Blockchain integration with UAVs for inter-service operation capabilities

End-to-end encryption of UAV-based service-to-service communication channels is made possible by blockchain technology. The public and private keys for each service can be maintained in the distributed ledger to protect user data and maintain confidentiality. Cooperation between government bodies, technologists, and legal experts is necessary to integrate blockchain technology with UAVs to enable inter-service operating capabilities. When demand is too high for a single service provider to handle, blockchain can be used to coordinate the actions of multiple providers’ UAVs to meet that demand. UAVs’ operability in such a context is examined. In the study of Sharma, You & Kul (2017) a trust agreement between vendors is built on blockchain technology to facilitate inter-service activities, with each UAV operating as a node in the blockchain. Because the record of services given by each vendor is stored on the blockchain in an accessible and open way, this facilitates an environment of trust and confidence among all parties involved.

Blockchain integration with UAVs for limited storage and processing power

Blockchain technology can be successfully implemented in environments with limited resources such as storage and computing power. The architecture developed by Liang et al. (2017) ensures the authenticity of drone data by combining public blockchain technology with cloud storage. Performance evaluation results are acceptable after implementing a prototype of a drone system. The study of the author Zhu, Zheng & Wong (2019) proposed a blockchain-powered decentralized storage system in which UAVs acting as air sensors transmit data to ground sensors, which are then rewarded with incentives distributed via Blockchain for their capacity to store and interpret the data.

Blockchain integration with UAVs for autonomous network security system

The safety and efficacy of autonomous network systems can be greatly improved by incorporating blockchain technology with UAVs. In applications where UAVs operate independently and require secure communication with a centralized network, such as disaster response or surveillance, this integration can prove invaluable. A blockchain-based network in which each UAV has its own copy of the blockchain is proposed as a solution in the research (Alkadi et al., 2022). Data recorded on blockchains can be used by UAVs to figure out their flight paths and actions in the event that they lose contact with the control center or other network UAVs.

Blockchain integration with UAVs for securely sending data to end users

Data sent from UAVs to their ultimate recipients can be encrypted using blockchain technology. In order to protect the privacy and security of the information recorded on the blockchain, it might be encrypted before it is added. Micropayments for the data collected by UAVs are possible with blockchain-based solutions. An incentive for UAV operators to collect and communicate valuable data to end users in a secure manner is provided by smart contracts, which can automatically execute payments when data is delivered and validated. The author Aggarwal et al. (2019) used a public Ethereum-based blockchain to examine how to secure data collection and transmission in an Internet of Drones setting. It ensures the truthfulness, traceability, authorization, and confidentiality of the data.

Blockchain integration with UAVs for blocking of the line of sight

Blockchain technology was developed largely for use in managing and transacting data in a way that is both safe and transparent. It does not interfere with physical processes, such as obstructing the view of UAVs. However, when used in tandem with other technologies and protocols, blockchain can improve the safety and management of UAVs flying under limited or obstructed Line of Sight conditions. In the study of Chao et al. (2018) a safe and reliable data sharing amongst UAVs is being developed through the use of a Blockchain-based network. A double-block verification process can be created using Blockchain, making the system immune to cyberattacks and bottlenecks in the Los.

Blockchain integration with UAVs for UAV surveillance network

Integrating blockchain technology with UAVs for a surveillance network can boost the security, transparency, and efficiency of data handling. The security and trustworthiness of surveillance data can be guaranteed by using blockchain, which has various benefits in this context. Access control in a surveillance network can be managed with the help of smart contracts. Based on the conditions recorded in smart contracts, authorized users, such as law enforcement agencies or security staff, can gain access to specific surveillance data. A technique is represented in the research of García-Magariño et al. (2019). Security and personal privacy are both improved by this system of automatic access management. Through a distributed trust management technique, block-chain based security solutions may detect anomalies in surveillance data as well as fraudulent UAVs.

Blockchain integration with UAVs for UAV networks for edge computing

In the context of UAV networks for edge computing, incorporating blockchain technology with UAVs can produce novel and secure applications. Instead of depending only on remote cloud servers, edge computing moves data processing closer to its point of origin. In this way, data can be processed and stored independently using blockchain technology. In the research of Sharma et al. (2019) it is shown that UAVs can do data processing locally, allowing them to make decisions in real time at the network’s edge. UAVs are used as on-demand nodes in a distributed blockchain network powered by neural networks to achieve caching goals.

Blockchain integration with UAVs for limited battery issue

The low battery life of UAVs is not something that can be fixed by blockchain technology on its own. However, the limitations of battery life can be indirectly addressed by integrating blockchain into UAV systems in smart cities to optimize their operations. Blockchain-based smart contracts can run algorithms that optimize UAV routing, ensuring that drones travel the most efficient routes possible, and hence extending their runtime. A blockchain-based energy grid can store surplus energy generated from renewable sources, allowing for a decentralized energy management system (Zekiye & Özkasap, 2023).