Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottom–up governance approach, citizens play an active role in the process of policy making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this article, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.

Vehicles on the road, where an Intelligent Transportation System (ITS) is built, can share a lot of traffic information and drive more safely and efficiently through data sharing. Since incorrect information misleads vehicles and causes confusion in traffic, a vehicular trust model is needed to check the messages trustworthiness while considering the vehicles properties and protecting its privacy. In this paper, we proposed a two-layer blockchain-based vehicle reputation system, which consists of a local one-day message blockchain and a global vehicle reputation blockchain. It can administrate the reputation score securely and preserve the vehicles partial privacy. The proposed model efficiently manages local traffic information through the local one-day blockchain, reducing the memory overhead of vehicles. As the vehicles actual identity and activities in other areas are hidden by using one-time public keys, partial privacy of the vehicle is preserved. According to the activity of the vehicle, the vehicles reputation score is updated and stored permanently in the global reputation blockchain. We also suggested the location-based practical byzantine fault tolerance (LPBFT), a new consensus algorithm for fast block generating. The LPBFT lowers message propagation time through location-based primary node selection and is about 1.4 times faster than existing PBFT. The simulation results show the efficiency and the feasibility of LPBFT and our proposed protocol.

To create an environment for IoT devices, securely, it is necessary to establish a cryptographic key for those devices. Conventionally, this key has been stored on the actual device, but this leaves the key vulnerable to physical attacks in the IoT environment. To solve this problem, several research studies have been conducted on how best to conceal the cryptographic key. Recently, these studies have most often focused on generating the key dynamically from noisy data using a fuzzy extractor or providing secure storage using a fuzzy commitment. Thus far, all of these studies use only one type of noisy source data such as biometric data or PUF (Physical Unclonable Function). However, since most IoT devices are operated in unmanned environments where biometric data is unavailable, the method using biometric data cannot be utilized for unmanned IoT devices. Although the method using PUF is applied to these unmanned devices, these are still vulnerable against physical attacks including unintended move or theft. In this paper, we present a novel way to use the fuzzy commitment on such devices, called Two-Factor Fuzzy Commitment scheme. The proposed method utilizes two noisy factors from the inside and outside of the IoT device. Therefore, although an attacker acquiring the IoT device can access the internal noisy source, the attacker cannot extract the right key from that information only. We also give a prototype implementation for ensuring the feasibility of our two-factor fuzzy commitment concept by utilizing the image data and PUF data for two noisy factors.

Quantum modular multiplication circuit is one of the basic quantum computation circuits which are basic functions in quantum algorithms. However, since quantum-quantum modular multipliers require a high cost reversible modular inversion routine for modular multiplication, researchers have been unable to propose a feasible quantum-quantum modular multiplier. In this paper, we proposed efficient quantum-classical modular multipliers and the first quantum-quantum modular multipliers that do not require a reduction stage by transforming the partial product used in multiplication utilizing bit-shift operation. Then, we calculated quantum resource complexity and analyzed it compared to other quantum modular multipliers and utilized ETRI (Electronics and Telecommunications Research Institute) Qcrypton to analyze quantum resource complexity in the practical quantum computing situation. The proposed quantum modular multipliers show an improvement of 50% in terms of gates and circuit depth compared to the most recently proposed high-performance quantum modular multipliers.

An intelligent emergency system for hazard monitoring and building evacuation is a very important application area in Internet of Things (IoT) technology. Through the use of smart sensors, such a system can provide more vital and reliable information to first-responders and also reduce the incidents of false alarms. Several smart monitoring and warning systems do already exist, though they exhibit key weaknesses such as a limited monitoring coverage and security, which have not yet been sufficiently addressed. In this paper, we propose a monitoring and emergency response method for buildings by utilizing beacons and Unmanned Aerial Vehicles (UAVs) on an IoT security platform. In order to demonstrate the practicability of our method, we also implement a proof of concept prototype, which we call the UAV-EMOR (UAV-assisted Emergency Monitoring and Response) system. Our UAV-EMOR system provides the following novel features: (1) secure communications between UAVs, smart sensors, the control server and a smartphone app for security managers; (2) enhanced coordination between smart sensors and indoor/outdoor UAVs to expand real-time monitoring coverage; and (3) beacon-aided rescue and building evacuation.

In recent years, drones have been widely used in many areas such as farming, movie making, surveillance and delivery. So, there is a need to protect these drones against security attacks including hijacking, spying and theft of stored data through the utilization of security mechanisms. Cryptographic keys are needed to operate these security mechanisms, and they must be generated by using random number generators which create unpredictable and non-regenerable random numbers. However, existing random number generators used in drones are not tailored for drones specifically as they use random sources generated on a desktop, not a drone. Recently, random number generators utilizing sensors in mobile phones and IoT (Internet of Things) devices have been studied, but are not appropriate for drones. In this paper, considering that drone sensors must be applied to flight and stationary modes, we proposed a drone specific random number generator called DroneRNG and implemented it. Then, we showed that our DroneRNG passed all of the NIST randomization tests and possesses better statistical properties and unpredictability than random number generators that are currently used in drones.