Blockchain for Smart Cities

by Chun Sing Lai, Xuecong Li, Loi Lei Lai and Xi Lin

Blockchain (BC) technology is the distributed storage of information with high security. BC can store data on transactions such as from who it was received, to whom it was sent, and the amount of cryptocurrencies transferred. Currently, Blockchain has attempted to apply to manage smart cities, conduct energy trading, ‘connect’ Wireless Sensor Networks (WSN) and Internet of Things (IoT), create smart contracts and others. BC is completely protected from the substitution of information in existing blocks of the chain. This property makes the BC technology able to protect the information that is transmitted from various sensors and mobile devices.  

Smart Cities have developed considerably with the development of the Internet of Things. Smart cities are based on autonomous and distributed infrastructure that include intelligent information processing and control systems of systems infrastructure, and sensors involving millions of information sources. Due to the continued growth of data volume and number of connected IoT devices, concerns such as high latency, bandwidth bottlenecks, security and privacy, and scalability arise in the current smart city architecture. Designing an efficient, secure, and scalable distributed structure by bringing computational and storage resources closer to users is needed to address the limitations of present smart city network.

There are many possible applications of blockchain to smart cities. In this article a few of them will be discussed. Blockchain-powered products help smart cities manage their energy use, incentivize responsible practices and advance the supply chain for clean energy optimization.

Neighbors in the Brooklyn Microgrid project are empowered to produce, consume, and purchase power within their community with a blockchain enabled transactive energy platform [1]. It moves towards a distributed energy supply system that is highly based on renewable-based sources such as solar energy generation for a more resilient, low carbon and customer-driven economy.

Indeed, the deployment of microgrids sets the stage for an energy future consisting of networks of energy sources that Blockchain can support. Peer-to-peer business deals are very cost efficient and have great potential.

The Australian government has launched BC solutions for energy grid management [2]. The project is focused on smart cities and aims to use BC technology to create more efficient usage of water and electricity.

By combining data analytics with a decentralized platform for energy grids and water systems, Australia aims to create smart cities with a reduced carbon footprint. The project will have a large solar power installation and rainwater treatment plant connected to commercial buildings, electric vehicle charging stations, and residences using blockchain technology.

Turning to smart transportation and smart healthcare which are considered essential applications in a Smart City.  In 2015, the United Nations (UN) announced 17 Global Goals to achieve a better world. A sub-goal aims to reduce the number of road traffic injuries and deaths in 2020 by 50%. It is found that the total number of road traffic injuries and deaths amount to more than 50 million annually. The annual expenditure of the injuries is more than $500 billion [3]. Besides, traffic accidents are the top leading cause of death in the age group between 15 and 29. It is predicted that traffic accidents will become the seventh leading cause of death by 2030 if there is no prevention scheme.

Real-time monitoring of humans’ status to give an early alert could be considered as the most effective method on preventing traffic accidents. For example, if a candidate is identified as abnormal (such as the candidate is drunk) before driving, prohibiting an engine start can protect all other drivers and pedestrians.  The reviews on traffic accidents indicated that drowsy driving and drunk driving are the two major causes of traffic accidents. More than half of professional drivers feel sleepy and more than 30% of drivers fall asleep while driving. Nearly one person is killed by drunk driving every hour. Therefore, real-time detection scheme on both drowsy driving and drunk driving renders a signification reduction on traffic injuries and death. It is estimated that $50 billion can be saved from the traffic accidents.

The conventional detection schemes are divided into three types, namely, (1) image-based detection, (2) behavior-based detection and (3) biosignal-based detection. It is worth noting that image-based detection and behavior-based detection cannot achieve the purposes of providing pre-warning before driving and high measurement accuracy at the same time while biosignal-based detection does. Image-based detections identify the features of drivers’ head motion and eye blinking with the use of image processing. However, image-based approaches are usually unreliable in practical situations. Behavior-based approaches compare driving behaviors under normal conditions and abnormal conditions. The driving behaviors reflect on vehicle moving path such as lateral position, change in velocity, and turning angle. In other words, the approaches are not able to provide pre-warning to abnormal drivers before they drive. Recently, wireless and wearable healthcare sensors have been raised in the market. The emerging wireless technologies facilitates the network expansion by connected all various kinds of devices, sensors, algorithms, and applications together. Therefore, a huge number of wearable sensors such as smart watches, headbands, chest straps etc. have been developed recently. The integration of those wearable sensors is usually defined as Wireless Body Area Network (WBAN). WBANs are a new kind of personal area communication networks that consist of smart sensors placed inside, on, or around the human body, typically consists of a collection of low-power, miniaturized, and lightweight devices with wireless communication capabilities.

WBANs enable different applications and they provide real-time feedback to the user and medical personnel without causing any discomfort. WBANs are expected to cause a dramatic shift in how people manage and think about their health. WBAN facilitates real-time monitoring humans’ status in numerous applications such as worker safety and patient tracking. The wearable healthcare sensors measure biosignals. Among all the biosignals, a survey on the nonintrusive driver assistance system reported that Electrocardiogram (ECG) has the highest accuracy on real-time measurement.

The Huawei executive said that BC in healthcare is apparent for the secure exchange of value and information between medical specialists, or between patients and medical specialists, and between patients and the pharmacy. It is also between the MRI scanner and the application server that processes the medical images, or between the medical specialists that use the PC or computer and the medical imaging database.

In this article, few potential applications of blockchain to smart cities have been discussed. It looks that it can provide a very postive outocme in the near future.



[1] A microgrid grows in Brooklyn, (Visited on 1 January 2018)

[2] Australia funds blockchain project for smart cities, (Visited on 1 January 2018)

[3] Cheon Hoi Koo et al. “A humans’ status detection scheme for industrial safety,” The 27th IEEE International Symposium on Industrial Electronics (ISIE 2018), 13 – 15 June 2018, Carins, Australia.


Chun Sing Lai



Chun Sing Lai is an EPSRC (The Engineering and Physical Sciences Research Council, UK) Research Fellow at the School of Civil Engineering, University of Leeds, UK. He is also a Visiting Research Fellow at the School of Automation, Guangdong University of Technology, China. His research interests are in Energy Economics, Data Analytics, Smart Grid and Smart Cities. He is an IEEE Member.

Xuecong Li



Dr Xuecong Li is Deputy Head of the Department of Electrical Engineering, School of Automation, Guangdong University of Technology, China. His research interests include Pattern Recognition, Smart Building, Power Quality and Renewable Energy Generation. He is an IEEE Member.

Loi Lei Lai



Professor Loi Lei Lai is University Distinguished Professor at Guangdong University of Technology, China. His research interests are in Smart Grid and Smart Cities. He is an IEEE Fellow.

Xi Lin



Professor Xi Lin received his B.S. degree at Peking University in China and Ph.D. degree at Massachusetts Institute of Technology in the US.  Lin is currently the Chief Technology Officer at Zhejiang TTN Electric Co., Ltd.  His research interest is mainly in energy materials and distributed energy management.

Published 22 October 2018